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Pharmacokinetic and pharmacodynamic optimization of CFTR modulator therapy to mitigate potential drug interactions and adverse events in people with cystic fibrosis

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Pharmacokinetic and pharmacodynamic optimization of CFTR modulator therapy to mitigate potential drug interactions and adverse events in people with cystic fibrosis

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Content Pharmacokinetic and Pharmacodynamic Optimization of CFTR modulator Therapy to Mitigate Potential Drug Interactions and Adverse Events in People with Cystic Fibrosis By Eunjin Hong A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfilment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (CLINICAL AND EXPERIMENTAL THERAPEUTICS) May 2023 Copyright 2023 Eunjin Hong ii DEDICATION This dissertation is dedicated to my husband Eunjong Kim and my daughter Hayden without whom I could not have put a successful end to this journey. And to my parents Younghee Lee and Byeongchang Hong who made me who I am today. iii ACKNOWLEDGMENTS I want to first thank my advisor Prof. Paul Beringer for the endless support, encouragement, trust, and guidance you constantly showed me during the course of my PhD. No words can express how much I feel indebted to you. In particular, I appreciate for being open to trying out new things and supporting all of my research from the heart. From you, I learned to enjoy scientific and clinical research, not being overwhelmed by obstacles, and to be confident of my knowledge when dealing with projects. I owe a great debt of gratitude to Dr. Lisa Almond for her remarkably kind and limitless time, efforts, and guidance: I learned much through our conversations. Your advice and collaboration have been essential to my technical and professional development. I also owe a great thank you to Dr. Annie Wong-Beringer, who provided insightful comments during the lab meetings which were crucial to moving the research forward and coming up with new ideas for the projects. I also want to acknowledge my dissertation committee, Dr. Stan Louie and Dr. David D’Argenio for the generous dedication to my training. Thank you for your advice and inputs you made towards my successful completion of my PhD degree. I cannot help mentioning how I fortunate I was to learn from the most brilliant mentors, Dr. Weiqiang Chen and Dr. Suan Sin Foo from an early stage of my PhD. I owe you countless cups of coffee for the instruction of various experimental techniques and the academic mindsets. iv Also, I would like to express special gratitude to my colleague Dr. Jenny Ayoung Park who I spent the most time with in the lab. Beringer lab and Wong- Beringer lab collegues, Dr. Mansour Dughbaj, Brent Beadell, Dr. Michelle Kalu, Sun-woo Kim, Alan Shi, Alex Men, Vy Nguyen, I want to thank you guys for being nice friends and colleagues along the long journey to PhD. The USC Centers for Adult Cystic Fibrosis and Advanced Lung Disease (Dr. Adupa Rao, Dr. Peter Chung, Dr. Joshua Wang, Lynn Fukushima, Carese Lee) and their associated patients. The USC School of Pharmacy for their commitment to support student scientists. Last but certainly not least, I want to thank my family members for their unconditional encouragement and support. To my parents Younghee Lee and Byeongchang Hong, who should’ve been waiting for this moment more than I did from 6000 miles away full of heart. You’re not only great parents but also the greatest people I’ve ever known. Thank you for making me believe that I can do anything and everything in life. To my wonderful husband Eunjong Kim, thank you for always being there to encourage and support me through tough times in these past years. Your patient understanding, unshakable support, and reassuring belief in me have kept me moving forward on this adventure. I am so lucky to have a husband like you, and I love you with all of myself. To my daughter Hayden, thank you for coming into our lives. You do so much for us every day, your beautiful smile makes all the worries disappear, and you are making our life complete. I love you from the bottom of my heart. v TABLE OF CONTENTS DEDICATION ............................................................................................................................. ii ACKNOWLEDGMENTS ............................................................................................................ iii LIST OF TABLES ....................................................................................................................... vii LIST OF FIGURES ..................................................................................................................... viii ABSTRACT ............................................................................................................................... ix CHAPTER 1: Introduction .................................................................................................................... 1 1.1. Cystic Fibrosis Background .................................................................................................................... 1 1.2. Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) ................................................ 2 1.3. CFTR Modulator Therapy ........................................................................................................................ 5 1.4. Efficacy of CFTR Modulators ................................................................................................................. 8 1.5. Safety of CFTR Modulators .................................................................................................................... 9 1.6. Drug-drug interactions of CFTR Modulators ........................................................................................ 10 1.7. Physiologically based pharmacokinetic (PBPK) Modeling ................................................................. 20 CHAPTER 2: PBPK-led guidance for cystic fibrosis patients taking elexacaftor- tezacaftor-ivacaftor with nirmatrelvir-ritonavir for the treatment of COVID-19 ..................... 35 2.1. Aim.............................................................................................................................................................. 36 2.2. Introduction ................................................................................................................................................ 37 2.3. Methods and Materials ........................................................................................................................... 40 2.4. Results ....................................................................................................................................................... 51 2.5. Discussion ................................................................................................................................................. 68 CHAPTER 3: PBPK modeling to guide management of drug interactions between elexacaftor-tezacaftor-ivacaftor and antibiotics for the treatment of non- tuberculosis mycobacteria (NTM) ..................................................................................................... 72 3.1. Aim ............................................................................................................................................................. 73 3.2. Introduction ................................................................................................................................................ 74 3.3. Materials and Methods ........................................................................................................................... 77 3.4. Results ....................................................................................................................................................... 81 3.5. Discussion ................................................................................................................................................. 94 vi CHAPTER 4: Safety of elexacaftor/tezacaftor/ivacaftor dose reduction: mechanistic exploration through lung tissue PBPK modeling and a clinical case series ...................................................................................................................................................... 101 4.1. Aim .......................................................................................................................................................... 102 4.2. Introduction ............................................................................................................................................. 103 4.3. Methods and Materials ........................................................................................................................ 105 4.4. Results .................................................................................................................................................... 110 4.5. Discussion .............................................................................................................................................. 122 CHAPTER 5: Conclusion and Future work .................................................................................. 126 5.1. Conclusions .......................................................................................................................................... 126 5.2. Future Work ........................................................................................................................................... 129 REFERENCES ....................................................................................................................................... 134 APPENDICES ........................................................................................................................................ 150 APPENDIX A: List of Selected Publications............................................................................................... 150 APPENDIX B: List of Presentations ........................................................................................................... 151 viii LIST OF TABLES TABLE 1-1. Recommended dose adjustment of CFTR modulators when concomitantly used with CYP3A modulators ............................................................................................................... 15 TABLE 1-2. Drug ionization ................................................................................................................... 29 TABLE 2-1. Parameters used to develop the ivacaftor model ......................................................... 44 TABLE 2-2. Parameters used to develop the tezacaftor model ...................................................... 45 TABLE 2-3. Parameters used to develop the elexacaftor model .................................................... 47 TABLE 2-4. Comparison of PK parameters between simulated and observed data for model verification of ETI ......................................................................................................................... 54 TABLE 2-5. Summary of the simulated vs. observed geometric mean ratio of PK parameters in the presence and absence of CYP3A modulators .................................................... 55 TABLE 2-6. Summary of the predicted geometric mean ratio for standard doses of elexacaftor, tezacaftor, ivacaftor in the presence and absence of ritonavir ................................... 58 TABLE 2-7. Predicted mean Cmax and AUC of reduced dose of ETI (elexacaftor 200mg-tezacaftor 100mg-ivacaftor 150mg q96h) with ritonavir 150mg q12h administered day 1 through day 5 ................................................................................................................................. 62 TABLE 2-8. Predicted mean Cmax and AUC of reduced dose of ETI (elexacaftor 100mg-tezacaftor 50mg-ivacaftor 75mg q48h) with ritonavir 150mg q12h administered day 1 through day 5 ................................................................................................................................. 64 TABLE 2-9. Predicted mean Cmax and AUC of reduced dose of ivacaftor (150mg 5 days apart) with ritonavir (150mg q12h) administered day 1 through day 5 ................................... 66 TABLE 3-1. Summary of the predicted steady-state Cmax and AUC geometric mean ratio for standard dose ETI in the presence and absence of NTM treatments ............................... 84 TABLE 3-2. Predicted steady-state mean Cmax and AUC of adjusted regimen of ETI when co-administered with NTM treatments ....................................................................................... 85 TABLE 3-3. Suggested dosing schedule of ETI when co-administered with selected NTM treatments ........................................................................................................................................ 86 TABLE 3-4. Summary of predicted AUC ratios of ivacaftor from altered CYP3A4 modulating potentials of clofazimine ..................................................................................................... 98 TABLE 4-1. Baseline characteristics of the population included in the case series .................... 110 TABLE 4-2. Response to ETI dose reduction due to adverse events ............................................ 111 TABLE 4-3. Comparison of PK parameters between simulated and observed data for model verification of ETI ......................................................................................................................... 118 ix LIST OF FIGURES FIGURE 1-1. Schematic representation of CFTR protein ................................................................. 2 FIGURE 1-2. Classification of CFTR mutations ................................................................................. 4 FIGURE 1-3. Mechanisms of CFTR modulators ................................................................................ 7 FIGURE 1-4. PBPK model components .............................................................................................. 21 FIGURE 1-5. Minimal PBPK model (A) and full PBPK model (B) ................................................... 22 FIGURE 1-6. Processes of drug transport across a cell membrane ............................................... 24 FIGURE 1-7. Renal transporters .......................................................................................................... 33 FIGURE 1-8. Mech Kim nested in a full PBPK model ....................................................................... 34 FIGURE 2-1. PBPK modeling framework ........................................................................................... 40 FIGURE 2-2. Sensitivity analysis of the fmCYP3A4 of ivacaftor on the predicted AUC ratio with or without ketoconazole (A) or rifampin (B)......................................................................... 52 FIGURE 2-3. Observed and simulated plasma concentration-time profiles of ETI following a single oral dose of elexacaftor 200mg (A), tezacaftor 100mg (B), and ivacaftor 100mg (C) ................................................................................................................................. 53 FIGURE 2-4. Plasma concentration profile of elexacaftor (A), tezacaftor (B), ivacaftor (C), and ritonavir (D), and the % of active CYP3A4 enzyme (E) over time ................................... 59 FIGURE 2-5. Plasma concentration profile of ETI ............................................................................. 61 FIGURE 2-6. Plasma concentration profile of ETI ............................................................................. 63 FIGURE 2-7. Suggested dosing schedule of CFTR modulators co-administered with nirmatrelvir-ritonavir ................................................................................................................................. 67 FIGURE 3-1. Predicted plasma concentration profiles of ETI with or without NTM treatment .................................................................................................................................................. 81 FIGURE 3-2. Dose response curves obtained from induction assay ............................................ 88 FIGURE 3-3. Predicted plasma concentration profiles of ETI with or without NTM treatment ................................................................................................................................................... 91 FIGURE 3-4. Predicted plasma concentration profiles of ETI with or without NTM treatment ................................................................................................................................................... 93 FIGURE 4-1. Predicted steady-state lung concentration profiles of ETI at doses tested in phase 2 clinical trials ............................................................................................................................ 119 FIGURE 4-2. Predicted steady-state lung concentration profiles of standard dose of ETI (green) and reduced dose of ETI (red) with lower 10 th and 5 th percentile values of reduced dose (gray dotted lines) ........................................................................................................ 121 FIGURE 5-1. Structure of ivacaftor and deutivacaftor .................................................................... 132 ix ABSTRACT Cystic fibrosis (CF) is a chronic, hereditary, multi-organ disease caused by mutations in the gene that encodes for the cystic fibrosis transmembrane conductance regulator (CFTR) protein. Over the past decade, CF treatment has transitioned from therapies treating the secondary consequences of the disease to therapies directed towards restoration of CFTR function with the development of CFTR modulators. CFTR modulators are small molecules protein corrector/potentiator therapies that restore chloride channel function of CFTR, thereby resulting in substantial improvements in lung function and nutritional status in people with CF (pwCF). Ivacaftor was the first CFTR modulator approved and is also registered for clinical use in combination with other CFTR modulators as lumacaftor/ivacaftor, tezacaftor/ivacaftor, and elexacaftor/tezacaftor/ivacaftor (ETI), which have been the cornerstone of the treatment for many pwCF. As CFTR modulators are used chronically, learning more about the possible drug-drug interactions (DDIs) and adverse effects is of great importance in the era of highly effective CFTR modulators. The application of physiologically based pharmacokinetic (PBPK) modeling has significant advantages towards the x simulation of therapeutic scenarios regarding the use of CFTR modulators, which are not yet described by clinical studies. The prediction of pharmacokinetics (PK) and pharmacodynamics (PD) based on PBPK model enables finding the optimized dose of CFTR modulators in each clinical cases to improve treatment response and prevent adverse events. This dissertation addresses potential issues of CFTR modulator associated with CF treatment by incorporating PBPK modeling approach and explores the optimization of CFTR modulator therapies in clinical practice. One of the most clinically important issue of modulators is cytochrome P450 3A (CYP3A)-derived drug interaction, as ivacaftor, tezacaftor and elexacaftor are all extensively metabolized by CYP3A. In particular, treatment of COVID-19 (SARS-CoV-2) with nirmatrelvir-ritonavir (Paxlovid) possess significant therapeutic challenge. For treatment of COVID-19, ritonavir is co- administered with nirmatrelvir to boost nirmatrelvir concentrations to achieve its therapeutic levels by inhibiting CYP3A-derived metabolism. However, due to the potent inhibition effect of ritonavir, it may increase plasma concentrations of ETI causing potential adverse drug reactions. CF patients are more at risk of serious illness following COVID-19 infection and hence it is important to manage the DDI risk and provide treatment options. To investigate the magnitude of drug interactions and provide dosing recommendations of ETI to overcome the interaction with ritonavir, the treatment scenarios were simulated using PBPK approach. We demonstrated that administration of ritonavir 100mg twice daily for xi 5 days required a significant reduction in the ETI dosing frequency with delayed resumption of full dose due to the mechanism-based inhibition by ritonavir. Other notable therapeutic challenge is in the treatment of Nontuberculous mycobacteria (NTM). NTM are increasingly being isolated from the sputum of pwCF and are the pathogen of concern due to its association with deterioration of lung function. It requires the use of a multi-drug combination regimen of antibiotics over a prolonged period of up to 18 months, creating the potential for DDI with CFTR modulators. For example, concomitant use of strong CYP3A inducers including the rifamycins (e.g., rifampin and rifabutin) is not recommended with ETI, and this potentially compromises the treatment efficacy for NTM in pwCF by precluding the first-line antibiotics. Additionally, clarithromycin and clofazimine, also guideline recommended treatment for NTM, are CYP3A inhibitor which would cause significant DDI with ETI. We extended our PBPK model to evaluate the interactions with selected guideline recommended NTM therapies and to determine appropriate dose adjustments and transitions of ETI. This PBPK-guided ETI dosing allowed the use of key antibiotics for the treatment of NTM in pwCF. Lastly, chronic treatment with CFTR modulators can potentially cause safety issues (e.g., hepatic injury with elevated transaminases, rash, and creatinine phosphokinase elevations) that may warrant interruption on the treatment. One potential strategy is dose reduction with the goal of maintaining xii therapeutic efficacy while resolving adverse events. To determine optimal dose adjustment, we investigated our clinical experience of dose reduction in individuals who experienced adverse events following ETI therapy. Further, we utilized a full PBPK modeling approach to predict lung exposures of reduced dose ETI, which provided mechanistic support for dose reduction by exploring predicted lung exposures and underlying PK-PD relationships. Taken together, the results of these investigations provide proper guidance on the use of CFTR modulators in clinical practice to mitigate drug interactions or adverse reactions, which enables the safe and effective use of CFTR modulators. Further, this thesis demonstrates that PBPK modeling could be used to optimize CFTR modulator treatment in the case of complex and clinically important drug therapy problems. 1 CHAPTER 1 Introduction 1.1. Cystic Fibrosis Background Cystic fibrosis (CF) is a life-limiting genetic disorder that affects approximately 70,000 individuals worldwide. CF is caused by mutations in the gene that encodes for the cystic fibrosis transmembrane conductance regulator (CFTR) protein(1). It is characterized by defective hydration of exocrine secretions, resulting in mucus plugging in the affected organs that include airways of the lungs and the ducts of the pancreas(1). Other organ systems such as the sweat glands, biliary duct of the liver, male reproductive tract, and gastrointestinal tract are also affected(2). The viscous mucus causes obstructions that lead to chronic inflammation, tissue damage, and destruction of affected organs. While CF is a multi-organ disease, greater than 80% of the morbidity and mortality are secondary to the obstructive lung disease(3). Pathological hallmarks of CF lung disease include airway obstruction and a continuous cycle of chronic infection, which lead to severe bronchiectasis that ultimately results in pulmonary failure. In addition, loss of pancreatic exocrine function results in malnutrition and impaired growth, leading to death for most untreated patients(3). Indeed, replacement of pancreatic enzymes have resulted in progressive 2 improvements in survival(4). CF patients also experience clinical manifestations related to the reproductive systems, with male infertility presenting in 98% of CF cases(5). 1.2. Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) CFTR encodes an anion channel mainly expressed in the epithelial cells of various tissues such as lung, sweat glands, and gastrointestinal system(2). Its function includes 1) transportation of chloride across the apical membrane; 2) modulation of the activity of other ion channels, thereby regulating fluid and electrolyte balance in the mucosal membranes; and 3) secretion of bicarbonate, which is crucial for pH regulation and host defense(5). As an ATP-binding cassette (ABC) family protein, CFTR is comprised of two transmembrane domains (TMDs), two nucleotide-binding domains (NBDs), and a unique regulatory domain (RD) (Figure 1-1)(6). There are two TMD-NBD complexes Figure 1-1. Schematic representation of CFTR protein. (Hanssens, Laurence S., Jean Duchateau, and Georges J. Casimir. Cells 10.11 (2021): 2844.) 3 united by the RD, where TMD1 is linked to NBD1 and TMD2 is linked to NBD2. The TMDs form the chloride channel, while the NBDs regulate its opening and closure. As it is an ATP-dependent ion channel, its opening requires phosphorylation of RD by the protein kinase A (PKA) and ATP binding at the NBDs leading to their dimerization. Channel closure is triggered by ATP hydrolysis, which leads to the separation of the NBD dimer(7). The mutations of this CFTR gene cause CF disease, and more than 2000 gene variants of the CFTR gene have been identified(8). Disease causing variants were initially stratified into six groups, based on CFTR production, trafficking, function, and stability, which provided a useful framework for understanding the basic defect (Figure 1-2)(5). Class I mutants causes no protein synthesis due to the presence of premature stop codons or frameshifts that preclude translation of full-length CFTR. Class II mutants lead to impaired trafficking of protein due to the incomplete folding. Class I and II mutations result in severely reduced CFTR proteins at the cell surface, leading to more severe disease phenotypes. Class III mutants are defective channel gating due to the impaired ATP binding and hydrolysis. Class IV mutants produce less functional proteins with reduced chloride conductance. Class V mutants are less protein maturation caused by amino acid substitution or alternative splicing leading to reduced amount of CFTR proteins. Class VI mutants generate less stable protein, thereby causing lysosomal degradation of CFTR. Lack of CFTR expression at the membrane results in several biological effects including 4 reduced chloride and bicarbonate secretion, enhanced sodium absorption and mucin secretion, which leads to acidic microenvironments and increased liquid viscosity(9). The most prevalent CFTR mutation is the deletion of a phenylalanine at position 508 (F508del)(10). Approximately 90% of CF patients have at least one F508del allele, and about 50% of CF patients have two alleles of F508del(11). Figure 1-2. Classification of CFTR Mutations. (Lopes-Pacheco, Miquéias. Frontiers in pharmacology 7 (2016): 275.) Although the mutational classification above has provided a useful framework for understanding the basic defect, it has several limitations to predict effective treatment strategies for each variant. First, although CFTR mutations in the same group show similar characteristics, they may respond differently to the same treatment. This is because several mutations (e.g., F508del) present 5 pleiotropic defects. The mutation F508del causes misfolded CFTR, which is promptly degraded by the proteasome(12). This mutation also affects channel gating (class III) as well as cell membrane residence time (class VI)(13, 14). Given the complexity of CFTR variants, combination of treatments may be required to rectify their defects and thus achieve therapeutic efficacy in people with CF. Second, there are rare pathogenic variants where the limited studies have been performed due to the scarcity of patients. The poor functional characterization of those rare variants makes it challenging to identify available treatment option. For this reason, a reclassification of CFTR pathogenic variants in a wider number of mutational types is underway, incorporating a more specific multiclass functional description(15). The purpose of the new classification is to identify specific mutational types responding to specific therapy, an approach called “theratyping”(3). The theratyping approach would provide optimized treatment strategies that improve restoration of CFTR function, allowing the development of personalized therapy of CF that has been hindered by the limited number of mutational classes studied so far(16). 1.3. CFTR Modulator therapy Over the past decade, CF treatment has transitioned from therapies treating the secondary consequences of the disease to therapies directed towards restoration of CFTR function with the development of CFTR modulators(17). CFTR modulators are small molecules that target specific defects caused by mutations in the CFTR gene, thereby restoring the function of 6 chloride channel(18). The presence of CFTR modulators has affected positively the clinical outcomes of CF patients including improvements in forced expiratory volume in 1 second (FEV1), reduction in pulmonary exacerbations, reduction in sweat chloride and improvements in body mass index (BMI). Further, the treatments have increased the predicted lifespan of CF patients to 46 years of age or older(19). CFTR modulators include correctors that enhance cell-surface expression by rescuing the processing and trafficking of CFTR, and potentiators that increase the channel open probability improving CFTR channel activity(5). Correctors could benefit patients bearing class II mutations where CFTR fails to reach complete folding. Potentiators could benefit CF patients bearing class III and IV mutations where CFTR is present at the membrane, but exhibits reduced or no gating activity. Ivacaftor was the first CFTR modulator approved for use in pwCF. As a potentiator, it enhances anion transport by increasing CFTR channel open probability. In vitro study showed that it rescued CFTR channel gating in human bronchial epithelial cells bearing G551D mutation(20). Clinical trials also showed that ivacaftor improved lung function (FEV1) and reduced pulmonary exacerbations in CF patients who have G551D or other class III mutants (G178R, S549N, S549R, G551S, G1244E, S1251N, S1255P, and G1349D)(21). However, ivacaftor monotherapy is not effective in the majority of CF patients who have the F508del mutation. Due to the pleiotropic defects caused by F508del in CFTR, it requires combinations of drugs with different mechanism of actions for more 7 efficient rescue of the defect CFTR(22). Therefore, ivacaftor is also registered for clinical use in combination with CFTR correctors which target misprocessing of protein and enhance protein transport to the cell surface (Figure 1-3)(23, 24). Currently three correctors are available in the market: lumacaftor, tezacaftor, and elexacaftor. Lumacaftor is a first generation CFTR corrector, and acts directly on F508del-CFTR by binding to hydrophobic pocket in the first transmembrane domain (TMD1) to improve its cellular processing and trafficking, thereby increasing the amount of functional CFTR expressed at the cell surface(25). Tezacaftor is a second generation CFTR corrector that binds to TMD1 as well and has the same mechanism of action as lumacaftor(26). Thirdly, elexacaftor is a next-generation corrector that shares structural similarities with tezacaftor but binds to different sites (nucleotide binding domain 1) on the CFTR protein, leading to an additive effect in facilitating the trafficking of phe508del-CFTR when used in combination(27). The Figure 1-3. Mechanisms of CFTR modulators. (Modified from Bear, Christine E. Cell 180.2 (2020): 211.) 8 combination therapy of CFTR modulators includes dual combination of lumacaftor/ivacaftor (for patients homozygous for F508del mutation) and tezacaftor/ivacaftor (for patients homozygous for F508del mutation or F508del mutation and specific residual function mutations), and recently approved triple combination of elexacaftor/tezacaftor/ivacaftor (ETI, for patients with at least one F508del mutation). 1.4. Efficacy of CFTR modulators ETI is approved for use in pwCF aged 6 years and older with at least one F508del mutation, which includes 90% of the CF population. Recent trial showed an impressive clinical effect of ETI triple combination therapy, which led to a 10 percentage points higher percentage predicted FEV1 (ppFEV1) in patients who have homozygous F508del mutation, after 4 weeks of treatment with ETI compared to tezacaftor/ivacaftor combination therapy(28). Further, it was shown to be efficacious in pwCF with heterozygous F508del with minimal function mutations, in whom previous CFTR modulators were ineffective. For this genotype, it showed a 13.8 percentage points higher ppFEV1 after 4 weeks of treatment compared to placebo(29). ETI also exhibited a positive effect on growth and weight gain, showing improvement in mean BMI from 20.7 to 22.3 kg/m2 (p<0.0001) after 5 months of treatment in patients with ppFEV1 <40%(30). Recently, SIMPLIFY study 9 performed in pwCF receiving ETI also showed that discontinuing daily hypertonic saline or dornase alfa for 6 weeks did not result in significant differences in pulmonary function when compared with continuing treatment, which would potentially reduce the treatment burden in pwCF whose health has benefited from using modulator therapies(31). The results are promising and show the potential of life changing improvements for many pwCF, thereby making CFTR modulators be the cornerstone of CF treatment. 1.5. Safety of CFTR Modulators Although treatment was generally well tolerated in the clinical trials, several adverse events (AEs) were reported with greater frequency compared with placebo and also were experienced in the real-world setting(19, 28, 29). Especially, the rate of hepatic injury with elevated transaminases was increased in patients taking the modulators(19, 28, 29, 32), being the most notable safety concerns of the treatment. According to the National Institutes of Health’s database, elevated liver enzymes occurred in up to 25% of patients taking tezacaftor with ivacaftor, or ivacaftor alone(19). Specifically, ivacaftor has shown hepatotoxicity leading to drug discontinuation in 1-2% of patients studied(33). The study showed elevated alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels ≥ 8 times of the upper limit of normal (ULN) within 48 weeks and 5–8 times of the ULN within 144 weeks of the treatment. 10 Additionally, respiratory-related AEs are also commonly reported AEs that include upper respiratory tract infection, pulmonary exacerbation, and cough(33). This might be due to the destabilization of corrected CFTR and reduction of mature CFTR level with high ivacaftor concentrations observed from in vitro studies(34, 35). It tended to occur within the first few days after initiation, and improvement or resolution of AEs generally occurred over the 1–4 weeks following initiation of the treatment(32). AEs that occurred frequently also included rash and creatinine phosphokinase elevations. The incidence of rash was highest in females, particularly those on hormonal contraceptives(29). Cataracts have also been reported in children and adolescents receiving ivacaftor as monotherapy and when used in combination. Additionally, the real-world data have suggested mental health related AEs (e.g., insomnia, anxiety, and deterioration in mental health)(32). Therefore, close mental health monitoring needs to be established in CF patients who have baseline anxiety and depression. 1.6. Drug-drug interactions of CFTR Modulators One therapeutic challenge with CFTR modulators is the risk of drug-drug interactions with other concomitant medications used in pwCF. CFTR modulators are susceptible to drug interactions mainly via cytochrome enzymes. For example, ivacaftor, tezacaftor and elexacaftor are extensively metabolized by 11 cytochrome P450 3A (CYP3A), and therefore are subject to interactions involving commonly prescribed CF drugs that inhibit or induce the activity of CYP3A (e.g., azoles, rifamycin, macrolides), which would cause a potential lack of efficacy or an increased risk of adverse effects of CFTR modulators(17). Therefore, learning more about the possible interactions is of great importance in the era of highly effective CFTR modulators. 1.6.1 Medications affecting CFTR modulators Elexacaftor, tezacaftor, and ivacaftor, are all lipophilic and extensively metabolized in the human liver. Metabolism of ETI occurs principally by the cytochrome P450 (CYP) 3A subfamily, which represents the most abundant hepatic CYPs in human with broad substrate specificity(36). Though the contribution of intestinal metabolism of ETI has not been studied, CYP3A- mediated metabolism in small intestine is likely also involved, but appears to be less significant than hepatic metabolism considering the high bioavailability of ETI(37-39). Based on single oral and iv dose study, the absolute bioavailability of elexacaftor is reported to be approximately 80%. The radiolabeled tezacaftor study suggest that bioavailability of tezacaftor would also be higher than 80%, being less than 20% of the radioactivity detected within 24 hours in feces which may represent unabsorbed tezacaftor. For ivacaftor, 98.4% of the total radioactivity was detected in plasma at 0.75 hour post oral dose. 12 In vitro data suggests that elexacaftor, tezacaftor, and ivacaftor are metabolized by two CYP3A isoforms, CYP3A4 and CYP3A5(37-39). Ivacaftor is the most sensitive CYP3A substrate among ETI components with drug fraction metabolized by CYP3A greater than 95%, followed by tezacaftor and elexacaftor with fraction metabolized by CYP3A of 73% and 67%, respectively(38-40). Elexacaftor has one major metabolite, M23-elexacaftor which is pharmacologically active with similar potency to elexacaftor. The mean metabolite to parent AUC ratio at steady-state is 0.35 to 0.50(39). Tezacaftor has three major circulating metabolites, M1 that has the similar potency to that of tezacaftor, M2 and M5 that are much less active or not pharmacologically active. The M1 metabolite to parent AUC ratio at steady-state is approximately 1.5(38). For ivacaftor, the M1 metabolite has approximately one-sixth the potency of ivacaftor, while the M6 metabolite has less than one-fiftieth the potency of ivacaftor and is not considered pharmacologically active. The metabolite to parent ratio for M1 and M6 are 4.89 and 1.73, respectively(37). Following oral administration of elexacaftor, tezacaftor, and ivacaftor, the majority of the dose was recovered in the feces primarily as metabolites. For all three compounds, there was negligible urinary excretion of drugs as unchanged parent, indicating that renal excretion is not the major pathway of elimination in humans. Due to extensive metabolism following administration, ETI concentrations are significantly impacted when used in combination with other drugs that inhibit or induce the activity of CYP3A enzymes. The use of strong CYP3A inducers (e.g., 13 rifampin) will increase the metabolism of ETI resulting in reduced exposure and a potential lack of efficacy, while concomitant therapy with strong CYP3A inhibitors (e.g., itraconazole) will increase ETI levels placing the patient at increased risk of dose-related adverse effects. Therefore, the safe and effective use of CFTR modulators requires appropriate DDI management with concomitant CF medications. Results of several clinical trials confirm significant interactions between known inducers and inhibitors of CYP3A and elexacaftor, tezacaftor, and ivacaftor. Co-administration of ivacaftor monotherapy with ketoconazole, a strong CYP3A inhibitor, significantly increased ivacaftor AUC by 8.5-fold(37). Co- administration of the combination of tezacaftor and ivacaftor with itraconazole, also a strong CYP3A inhibitor, increased AUC of tezacaftor and ivacaftor by 4.0- and 15.6-fold, respectively. Co-administration of elexacaftor and tezacaftor with itraconazole increased AUC of elexacaftor and tezacaftor by 2.8- and 4.5-fold, respectively(38). Co-administration with fluconazole, a moderate inhibitor of CYP3A, increased ivacaftor exposure by 3.0-fold(37). PBPK simulations indicated that co-administration with fluconazole, a moderate CYP3A inhibitor, may increase elexacaftor and tezacaftor AUC by approximately 1.9 to 2.3-fold and 2.1-fold, respectively(38, 39). Therefore, the manufacturer recommends a reduced dose of elexacaftor, tezacaftor, and ivacaftor when co-administered with strong and moderate CYP3A inhibitors (Table 1-1). In patients 12 years and older, elexacaftor/tezacaftor/ivacaftor dose can be reduced to 200/100/150mg 14 taken approximately 3 to 4 days apart when co-administered with a strong inhibitor. This recommendation is consistent with the approved labeling for ivacaftor monotherapy and tezacaftor/ivacaftor combination. In contrast to the other CFTR combination therapies, DDIs between CYP inhibitor and lumacaftor- ivacaftor combination tends to be less significant, potentially due to the compensating CYP induction effect of lumacaftor. When lumacaftor and ivacaftor dual combination was co-administered with itraconazole, lumacaftor exposure was unaffected, while ivacaftor exposure increased by 4.3-fold, which was significantly less than with ivacaftor monotherapy co-administered with itraconazole(41). Since ivacaftor AUC is 3.66 mg x h/L in combination with lumacaftor, compared to 10.7 mg x h/L when ivacaftor administered alone, the increase in ivacaftor AUC when co-administered with itraconazole is less than 1.5-fold compared to the AUC of ivacaftor monotherapy(37, 41). Therefore, no dose adjustment is necessary when CYP3A inhibitors are initiated in patients currently taking lumacaftor-ivacaftor(42). However, dose adjustment for lumacaftor/ivacaftor is required when initiating therapy in patients who have been taking strong CYP3A inhibitors, for the first week of treatment to allow for the steady-state induction effect of lumacaftor. The detailed dosing recommendations of CFTR modulators based on different ages and body weights are described in Table 1-1. Co-administration of CYP3A inducer is expected to increase metabolism of elexacaftor, tezacaftor, and ivacaftor, which may reduce the effectiveness of 15 CFTR modulators. Co-administration of ivacaftor with rifampin, a strong CYP3A inducer, significantly decreased the ivacaftor AUC by 89% (AUC ratio of 0.11)(37). Elexacaftor and tezacaftor exposures are also expected to decrease significantly during co-administration with strong CYP3A inducers. While co- administration of lumacaftor and ivacaftor with rifampin had minimal effect on the exposure of lumacaftor, it decreased ivacaftor AUC by 57% (AUC ratio of 0.43)(43). Therefore, co-administration of strong CYP3A inducers with elexacaftor, tezacaftor, and ivacaftor, including the ivacaftor and lumacaftor combination, are not recommended. The dosing guidance for CFTR modulators when co-administered moderate CYP3A inducers is currently lacking. Table 1-1. Recommended dose adjustment of CFTR modulators when concomitantly used with CYP3A modulators. CFTR Modulators Dose adjustment Kalydeco (ivacaftor) Standard dose 1) patients 4 months-6 years with body weight 5-7kg : ivacaftor 25mg twice daily 2) patients 6 months-6 years with body weight 7-14kg : ivacaftor 50mg twice daily 3) patients 6 months-6years with body weight 14kg : ivacaftor 75mg twice daily 4) patients  6 years : ivacaftor 150mg twice daily With strong inhibitor* one dose twice a week 16 With moderate inhibitor* one dose once daily Symdeko (tezacaftor/ ivacaftor) Standard dose 1) patients 6-12 years with body weight <30kg : tezacaftor 50mg once daily, ivacaftor 75mg twice daily 2) patients 6-12 years with body weight 30kg or patients 12 years : tezacaftor 100mg once daily, ivacaftor 150mg twice daily With strong inhibitor tezacaftor/ivacaftor one dose twice a week With moderate inhibitor tezacaftor one dose every other day ivacaftor one dose once daily Trikafta (elexacaftor/ tezacaftor/ ivacaftor) Standard dose 1) patients 6-12 years with body weight <30kg : elexacaftor 100mg once daily, tezacaftor 50mg once daily, ivacaftor 75mg twice daily 2) patients 6-12 years with body weight 30kg or patients 12 years : elexacaftor 200mg once daily, tezacaftor 100mg once daily, ivacaftor 150mg twice daily With strong inhibitor elexacaftor/tezacaftor/ivacaftor one dose twice a week With moderate inhibitor elexacaftor one dose every other day tezacaftor one dose every other day ivacaftor one dose once daily Orkambi (ivacaftor/ lumacaftor ) Standard dose 1) patients 1-2 years with body weight 7-9kg : lumacaftor 75mg/ivacaftor 94mg twice daily 2) patients 1-2 years with body weight 9-14kg or patients 2-5 years with body weight <14kg : lumacaftor 100mg/ivacaftor 125 mg twice daily 3) patients 1-5 years with body weight 14kg : lumacaftor 150mg/ivacaftor 188mg twice daily 4) patients 6-11 years 17 : lumacaftor 200mg/ivacaftor 250mg twice daily 5) patients  12 years : lumacaftor 400mg/ivacaftor 250mg twice daily With strong inhibitor** When initiating orkambi in patients taking strong CYP3A inhibitors, reduce the dose for the first week of treatment as follows: 1) patients 1-5 years: lumacaftor/ivacaftor one dose every other day 2) patients  6 years: lumacaftor/ivacaftor half dose once daily With moderate inhibitor No dose adjustment needed * Patients age <6 months: Concomitant use with strong/moderate CYP3A inhibitors is not recommended. **No dose adjustment is recommended when CYP3A inhibitors are initiated in patients already taking orkambi. 18 1.6.2 Effect of CFTR modulators on other medications Effect of lumacaftor on other medications Unlike ETI, lumacaftor is not extensively metabolized in humans with the majority of lumacaftor excreted unchanged in the feces. However, lumacaftor can cause significant drug interactions since it is a strong CYP3A inducer. The induction of cytochrome P450 enzymes occurs mainly via nuclear receptor- mediated increase in gene transcription. Based on in vitro studies, lumacaftor was found to be a pregnane X receptor (PXR) activator, which could potentially induce CYP3A and CYP2C subfamilies, as well as p-gp(41). Additional in vitro studies to evaluate the effect of lumacaftor on mRNA levels and isozyme- selective CYP activities in cultured human hepatocytes confirmed lumacaftor to be a potential inducer of CYP3A(41, 44). Indeed, co-administration of lumacaftor with ivacaftor, a sensitive CYP3A substrate, decreased ivacaftor exposure by 78%(41). This data suggests that lumacaftor is a clinically significant CYP3A inducer. Therefore, administration of lumacaftor may decrease systemic exposure of drugs that are CYP3A substrates, which may reduce the therapeutic effect. In vitro studies suggest that lumacaftor also has the potential to induce CYP2B6, CYP2C8, CYP2C9, and CYP2C19, and inhibition of CYP2C8 and CYP2C9 has also been observed in vitro(41). Therefore, lumacaftor has the potential to simultaneously induce and inhibit CYP2C8 and 2C9, but the net effect of lumacaftor on CYP2C8 and 2C9 substrates is not clear. The clinical drug 19 interaction data with the substrates of CYP2B6 and CYP2C8/9/19 do not exist, so it is not clear whether concomitant use of lumacaftor would alter the exposure of these substrates. Effect of ivacaftor on other medications In vitro studies demonstrate that ivacaftor may inhibit CYP2C8 and CYP2C9(37). However, clinical studies of ivacaftor with rosiglitazone and desipramine led to the AUC ratio of 0.98 and 1.04 for rosiglitazone and despiramine, respectively, indicating that ivacaftor is not an inhibitor of CYP2C8 or CYP2D6(37). Drug interaction data with CYP2C9 do not exist, so the clinical effect of ivacaftor on CYP2C9 substrates is not known. Therefore, caution is required when CYP2C9 substrates are co-administered with ivacaftor, especially for the drugs with a narrow therapeutic index (e.g., warfarin). There are conflicting data regarding ivacaftor’s inhibition potential on CYP3A. Based on in vitro results, ivacaftor has the potential to inhibit CYP3A(37). Co- administration with oral midazolam, a sensitive CYP3A substrate, increased midazolam exposure 1.54-fold, consistent with in vitro inhibition of CYP3A by ivacaftor(37). However, no drug interaction of ivacaftor was observed with oral contraceptives (norethindrone and ethinyl estradiol), which are also metabolized by CYP3A. Further, clinical studies of the combination regimen of tezacaftor/ivacaftor with midazolam showed no drug interaction, with an AUC ratio of midazolam of 1.12(38). Therefore, currently no dose adjustment is 20 required for CYP3A substrates when co-administered with ivacaftor. While in vitro studies have suggested that elexacaftor and tezacaftor have a low potential to inhibit CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6 and CYP3A4, none of these has been proven clinically, except CYP3A4 where no significant DDI was observed with midazolam and oral contraceptives(38, 39). 1.7. Physiologically based pharmacokinetic (PBPK) modeling The physiologically based pharmacokinetic (PBPK) modeling has emerged as an important technique in prediction of pharmacokinetic behavior of drugs in human, including the effect of intrinsic (e.g., age, race, disease) and extrinsic factors (e.g., smoking, alcohol, drug-drug interactions) on drug absorption, distribution, metabolism, and excretion. Such predictions can help decision making in relation to development progression, dose selection, and clinical study strategies. The components of PBPK model include system components as well as drug-dependent components (Figure 1-4)(45). The system components are based on anatomical, physiological and pathophysiological information such as body fluid dynamics (e.g., blood flow), tissue size and composition, drug- metabolizing enzymes, and membrane transporters in various organ and tissue compartments. The drug-dependent component includes physicochemical properties of drug, protein binding, drug permeability and metabolic properties 21 which could be obtained from in vitro biochemical, preclinical and clinical studies. Consequently, the established PBPK model enables the simulation of time course drug exposure. This strategy is increasingly included during regulatory review by the FDA as an alternative for exploring PK estimates to provide dosing recommendations in product labeling(46). Figure 1-4. PBPK model components. In PBPK modeling, different organs of the body are represented by smaller compartments. PBPK models are considered to be minimal if the model includes no more than five compartments, including the gastrointestinal tract, blood, and liver, and up to two additional compartments(47). The full PBPK 22 models mean that all distribution organs are represented as independent perfused chambers. The full PBPK modeling enables simulation of the exposure of drugs in specific tissues that are not available for clinical sampling, but too many parameters may lead to overfitting(48). Different processes affecting drug concentrations in the body (e.g., release of drug molecule from its delivery system into the systemic circulation) are modeled in mathematical expressions to quantify the drug exposure in the particular tissues. Figure 1-5 describes the structure of a minimal model and a typical full PBPK model. Figure 1-5. Minimal PBPK model (A) and full PBPK model (B) 23 1.7.1. Dissolution process Pharmacokinetic processes of drug disposition and elimination can be mechanistically described with several mathematical models. Specifically, mathematical model for dissolution was developed by Brunner and Nernst (49, 50) who expanded the Noyes and Whitney’s equation(51). Dissolution is the process whereby the active pharmaceutical ingredients (API) is dissolved in the medium of the gastrointestinal tract. The dissolution rate is affected by manufacturing processes, water solubility, particle size and the excipients in the formulation. Brunner and Nernst described the model of dissolution by incorporating drug-specific properties and surface area (𝐴 ) of the drug particle as in the equation (1): 𝑑𝐶 𝑑𝑡 = 𝑘𝐴 (𝐶 𝑠 − 𝐶 ) (1) where 𝐴 is surface area, 𝑘 is the dissolution rate constant, 𝐶 𝑠 is solubility concentration of the drug, and 𝐶 is the concentation of the drug in bulk media. Brunner and Nernst also incorporated the diffusion coefficient (𝐷 ) of drug, volume of the media (𝑉 𝑚 ) wherein the drug is to be dissolved, and the thickness (ℎ) of the diffusion layer into the equation by relating these variables to 𝑘 using Fick’s second law as shown in equation (2): 𝐷 𝑉 𝑚 ℎ = 𝑘 (2) 24 Therefore, the dissolution model equation is as follows: 𝑑𝐶 𝑑𝑡 = 𝐷𝐴 𝑉 𝑚 ℎ (𝐶 𝑠 − 𝐶 ) (3) where 𝐴 : surface area, 𝐶 : concentration; 𝐶 𝑠 : Solubility concentration, 𝐷 : diffusion coefficient, ℎ: thickness of the diffusion layer, 𝑡 :time, 𝑉 𝑚 : volume of the media, respectively. 1.7.2. Permeability of drug Following dissolution, the released drug molecule permeates through the cell membrane of the gastrointestinal tract. Absorption through the cell membrane typically occurs either by passive or active transport in the small intestine (Figure 1-6). Figure 1-6. Processes of drug transport across a cell membrane. 25 Molecules with low molecular weight are usually absorbed by passive diffusion, where drug molecules from a region of higher concentration to a region of lower concentration across the epithelial cells membrane until equilibrium is reached. For lipophilic drug, the main route of passive diffusion is transcellular, while paracellular diffusion occurs for hydrophilic drug(52). The epithelial cells in the small intestine express several metabolizing enzymes capable of oxidative, conjugative or hydrolytic metabolic reactions(53). Therefore, drug passing through the luminal membrane will be exposed to these enzymes leading to the extra-hepatic metabolism of the drugs before the drug reaches the hepatocyte. The portion of drug that escapes the intestinal metabolism is expressed as Fg(54). In active transport, drug moves through the membrane with the aid of transporter proteins. Key transporter categories include the adenosine triphosphate (ATP) binding cassette (ABC) as well as the solute carrier (SLC) transporters, which are present at the apical and basolateral membranes. Active transport through ABC transporters uses the energy released from the hydrolysis of ATP, while transport through SLC utilizes gradients of H+, Na+ and Ca2+ generated by Na+/K+-ATPase or Na+/H+- ATPase(55, 56). The flux (𝐽 ) of drug molecules that are passively moving across a membrane can be expressed by equation (4). This incorporates the in vitro permeability 26 (𝑃 𝑒𝑓𝑓 ), intestinal surface area (S), and the change in concentration across the membrane (∆𝐶 ): 𝐽 = 𝑃 𝑒𝑓𝑓 × 𝑆 × ∆𝐶 (4) As the 𝑃 𝑒𝑓𝑓 value may not be obtained easily, it is generally estimated from an apprent permeability (𝑃 𝑎𝑝𝑝 ). 𝑃 𝑎𝑝𝑝 can be obtained by in vitro experiments using cell culture-based permeability studies with either human epithelial colorectal adenocarcinoma cells (Caco-2) or Madin-Darby canine kidney (MDCK- II) cells. The regression relationships were established to correlate 𝑃 𝑒𝑓𝑓 values to 𝑃 𝑎𝑝𝑝 , which can be used for the estimation of 𝑃 𝑒𝑓𝑓 (57). For example, the relationship between 𝑃 𝑎𝑝𝑝 obtained from Caco-2 (in a pH 6.5 condition) is as follow: 𝐿𝑜𝑔 (𝑃 𝑒𝑓𝑓 ) = 0.6532 𝐿𝑜𝑔 (𝑃 𝑎𝑝𝑝 ) – 0.3036 (5) If active transport is involved in the flux of drug across the membrane, such compounds is subject to further studies to determine the kind of transporters that affect the drug transport. The determination of drug-transporter interactions using the Michaelis-Menten equation is needed to determine the saturation rate of transporter binding sites. The flux of substrates of the active transporter can be described by the following equation: 27 𝐽 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡𝑒𝑟 = 𝐽 𝑚𝑎𝑥 𝐶 𝑙𝑢𝑚𝑒𝑛 𝐾 𝑚 + 𝐶 𝑙𝑢𝑚𝑒𝑛 (6) Where is 𝐽 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡𝑒𝑟 is the flux of transporter; 𝐽 𝑚𝑎𝑥 is maximum velocity of flux, 𝐶 𝑙𝑢𝑚𝑒𝑛 is the concentration at the lumen, and 𝐾 𝑚 is the Michaelis-Menten constant. 1.7.3. Drug distribution and tissue partitioning To elicit the desired therapeutic efficacy, drugs need to be distributed to the primary site of action. During distribution, unbound fraction of drug molecules distributes into the tissues and organs. The factors affecting drug distribution include protein binding of the drug, tissue perfusion, and tissue partitioning. As only unbound drug would distribute to the tissue, drug binding to plasma protein is an important factor for distribution of drug. The plasma proteins to which most drugs are bound are human serum albumin (HSA) and alpha-1 acidic glycoprotein (AAG)(58). The blood flow to different tissues would also determine the extent of drug distribution into that tissue. Rapidly perfused organs (e.g., liver, brain and kidney) generally receive the rich supply of drugs, while slowly perfused tissues (e.g., adipose tissue) have limits of drug supply leading to a delayed clinical effect(59). The rate and extent of drug partitioning into a tissue could be affected by tissue composition as well as the lipophilicity of drug molecule. To estimate drug 28 partitioning into the tissue, the ratio of the tissue concentration of drug to its plasma concentration at steady-state needs to be determined. This ratio is termed the partition coefficient (𝐾𝑝 ) and calculated as follows: 𝐾𝑝 = 𝐶 𝑡𝑖𝑠𝑠𝑢𝑒 𝐶 𝑝𝑙𝑎𝑠𝑚𝑎 (7) This equation can be expanded as (8) by accounting unbound tissue-to- plasma concentration ratio (𝐾𝑝 𝑢 ) and unbound fraction in the tissue (𝑓 𝑢𝑡 ). 𝐾𝑝 = 𝐶 𝑡𝑖𝑠𝑠𝑢𝑒 𝐶 𝑝𝑙𝑎𝑠𝑚𝑎 = 𝑓 𝑢𝑡 𝐾𝑝 𝑢 (8) As it is generally difficult to experimentally determine these values, Rodgers and Rowland developed a model to predict the 𝐾𝑝 𝑢 (60). This model accounts for the ionization of the drugs as well as the composition of the tissue(61, 62). Based on the model, tissue partitioning coefficient for moderate-to-strong basic drugs can be estimated as follows: 𝐾𝑝 𝑢 = ( 1+𝑋 𝑓 𝐼𝑊 1+𝑌 ) + 𝑓 𝐸𝑊 + ( 𝐾 𝑎 [𝐴 𝑃 ] 𝑇 𝑋 1+𝑌 ) + ( 𝑃𝑓 𝑁𝐿 +(0.3𝑃 +0.7)𝑓 𝑁𝑃 1+𝑌 ) (9) Additionally, 𝐾𝑝 𝑢 for drugs which are not moderate-to-strong basic can be estimated as follows: 29 𝐾𝑝 𝑢 = ( 1+𝑋 𝑓 𝐼𝑊 1+𝑌 ) + 𝑓 𝐸𝑊 + (𝐾 𝑎 [𝑃𝑅 ] 𝑇 ) + ( 𝑃𝑓 𝑁𝐿 +(0.3𝑃 +0.7)𝑓 𝑁𝑃 1+𝑌 ) (10) where 𝑓 refers to fractional tissue volume (with ∑ 𝑓 =1), and subscripts IW, EW, NL, and NP refer to intracellular water, extracellular water, neutral lipid, and neutral phospholipid, respectively. Additionally, 𝑃 : n-octanol:water partition coefficient for unionised compound for all tissues besides the adipose; [𝐴 𝑃 ] 𝑇 : tissue concentrations of acidic phospholipids, [𝑃𝑅 ] 𝑇 : tissue concentrations of lipoprotein, 𝐾 𝑎 : affinity constant of the drug for acidic phospholipids or lipoprotein. X and Y are terms describing ionization of the compound, calculated using the Hendersen-Hasselbalch relationship with the drug pKa and pH of the intracellular water and plasma, respectively(60), as shown in Table 1-2. Table 1-2. Drug Ionization. X Y Monoprotic base 10 𝑝𝐾𝑎 −𝑝𝐻𝑖𝑤 10 𝑝𝐾𝑎 −𝑝𝐻𝑝 Diprotic base 10 𝑝𝐾𝑎 2−𝑝𝐻 𝑖𝑤 + 10 𝑝𝐾𝑎 1+𝑝𝐾𝑎 2−2𝑝𝐻 𝑖𝑤 10 𝑝𝐾𝑎 2−𝑝𝐻 𝑝 + 10 𝑝𝐾𝑎 1+𝑝𝐾𝑎 2−2𝑝𝐻 𝑝 Monoprotic acid 10 𝑝𝐻 𝑖𝑤 −𝑝𝐾𝑎 10 𝑝𝐻 𝑝 −𝑝𝐾𝑎 Diprotic acid 10 𝑝𝐻 𝑖𝑤 −𝑝𝐾𝑎 + 10 2𝑝𝐻 𝑖𝑤 −𝑝𝐾𝑎 1−𝑝𝐾𝑎 2 10 𝑝𝐻 𝑝 −𝑝𝐾𝑎 1 + 10 2𝑝𝐻 𝑝 −𝑝𝐾𝑎 1−𝑝𝐾𝑎 2 Zwitterion 10 𝑝𝐾𝑎 𝐵𝐴𝑆𝐸 −𝑝𝐻 𝐼𝑊 + 10 𝑝𝐻 𝑖𝑤 −𝑝𝐾𝑎 𝐴𝐶𝐼𝐷 10 𝑝𝐾𝑎 𝐵𝐴𝑆𝐸 −𝑝𝐻 𝑝 + 10 𝑝𝐻 𝑝 −𝑝𝐾𝑎 𝐴𝐶𝐼𝐷 Neutral 0 0 30 1.7.4. Drug metabolism Drug metabolism is the process where the chemical structure of a drug is altered to enhance its elimination. The primary site of metabolism is liver, and it occurs in two phases (phase I and phase II). Phase I metabolism is typically mediated by cytochrome P450 (CYP) enzymes which are located within the mitochondria or the endoplasmic reticulum. Of all the human CYP which have been characterized, CYP1, CYP2, CYP3 are largely responsible for the metabolism of approximately 75% of drugs, and CYP3A4 is most abundant and has relatively greater number of drug substrates compared to the other CYP enzymes. Phase II metabolism is typically mediated by non-CYP enzymatic pathway, where the products are conjugated through glucuronidation, sulphation, acetylation, and methylation, or conjugated with glutathione. Uridine 5’- diphospho-glucuronosyltransferase (UGT) is a common phase II enzyme which mediates glucuronidation with hydroxyl, carboxylic acid and amine groups. Other enzymes that mediate phase II metabolism include sulfotransferases (SULTS), N-acetyl transferases (NATs), glutathione S-transferase (GST), and methyltransferase. Modeling of drug metabolism involves estimation of in vivo human clearance. This could be estimated from human clinical studies or in vitro experiments. In vitro hepatic intrinsic clearance (𝐶𝐿𝑖𝑛𝑡 , 𝑖𝑛 𝑣𝑖𝑡𝑟𝑜 ) can be converted to the in vivo intrinsic clearance (𝐶𝐿𝑖𝑛𝑡 , 𝑖𝑛 𝑣𝑖𝑣𝑜 ) by accounting for microsomal 31 recovery or hepatocyte cell number, and liver weight. The in vivo intrinsic clearance (𝐶𝐿𝑖𝑛𝑡 , 𝑖𝑛 𝑣𝑖𝑣𝑜 ) can be also scaled up to hepatic clearance ( 𝐶𝐿 𝐻 ) based on the well-stirred liver model established by Rowland et al(63). This model assumes that the drug is distributed instantly and homogenously throughout liver water and eliminated by liver metabolism. In this model, hepatic clearance (𝐶𝐿 𝐻 ) is derived as a function of hepatic blood flow (𝑄 𝐻 ) and the unbund intrinsic clearance, which can be obtained by multiplication of the unbound fraction of drug (𝑓 𝑢 ) and the intrinsic clearance (𝐶𝐿𝑖𝑛𝑡 , 𝑖𝑛 𝑣𝑖𝑣𝑜 ). 𝐶𝐿𝑖𝑛𝑡 , 𝑖𝑛 𝑣𝑖𝑣𝑜 = 𝐶𝐿𝑖𝑛𝑡 , 𝑖𝑛 𝑣𝑖𝑡𝑟𝑜 × 𝑚𝑖𝑙𝑙𝑖𝑔𝑟𝑎𝑚 𝑜𝑓 𝑚𝑖𝑐𝑟𝑜𝑠𝑜𝑚𝑎𝑙 𝑝𝑟𝑜𝑡𝑒𝑖𝑛 (𝑚𝑔 ) 𝑔𝑟𝑎𝑚 𝑜𝑓 𝑙𝑖𝑣𝑒𝑟 (𝑔 ) × 𝑙𝑖𝑣𝑒𝑟 𝑤𝑒𝑖𝑔 ℎ𝑡 (𝑔 ) 𝑏𝑜𝑑𝑦 𝑤𝑒𝑖𝑔 ℎ𝑡 (𝑘𝑔 ) (11) 𝐶𝐿 𝐻 = 𝑄 𝐻 × 𝑓 𝑢 ×𝐶𝐿𝑖𝑛𝑡 ,𝑖𝑛 𝑣𝑖𝑣𝑜 𝑄 𝐻 + 𝑓 𝑢 ×𝐶𝐿𝑖𝑛𝑡 ,𝑖𝑛 𝑣𝑖𝑣𝑜 (12) 1.7.5. Renal excretion Excretion is the process whereby drug molecule or its metabolites are irreversibly removed through kidney or through biliary excretion into the faces. Kidney is the primary route of drug excretion which involves filtration, secretion and reabsorption. Therefore, the estimation of renal clearance incorporates three processes as follows: 𝐶𝐿𝑟𝑒𝑛𝑎𝑙 = 𝐶𝐿𝑓𝑖𝑙𝑡𝑟𝑎𝑡𝑖𝑜𝑛 + 𝐶𝐿𝑠𝑒𝑐𝑟𝑒𝑡𝑖𝑜𝑛 − 𝐶𝐿𝑟𝑒𝑎𝑏𝑠𝑜𝑟𝑝𝑡𝑖𝑜𝑛 (13) 32 Where 𝐶𝐿𝑟𝑒𝑛𝑎𝑙 : overall renal excretion, 𝐶𝐿𝑓𝑖𝑙𝑡𝑟𝑎𝑡𝑖𝑜𝑛 : filtration clearace, 𝐶𝐿𝑠𝑒𝑐𝑟𝑒𝑡𝑖𝑜𝑛 : secretion clearance, 𝐶𝐿𝑟𝑒𝑎𝑏𝑠𝑜𝑟𝑝𝑡𝑖𝑜𝑛 : reabsorption clearance. Blood flow to the kidney undergoes glomerular filtration driven by hydrostatic pressure in the glomerular capillaries. The rate of filtration is expressed as glomerular filtration rate (GFR), which is an indicator of the elimination of drug which does not undergo secretion or reabsorption. The factors of drug molecule which determine whether it would be filtrated are size and charge of the molecule(64); molecules with size less than 500 g/mol are easily filtered by the kidney, while molecules with negative charges are not easily filtered. Tubular secretion is an active transport process for the drug molecules that occurs primarily at the proximal tubules. As it is through the transporter proteins, it is saturable and may cause drug-drug interactions. Renal transporters include organic anion transporting polypeptide (OATP), organic cation transporter (OCT), organic anion transporter (OAT), multidrug and toxin extrusion protein (MATE), permeability-limiting glycoprotein (p-gp), and multidrug resistant-associated protein (MRP)(65) (Figure 1-7). 33 Figure 1-7. Renal transporters (from Neuhoff, Sibylle, et al. Transporters in drug development. Springer, New York, NY, 2013. 155-177.) Tubular reabsorption involves transportation of drugs from the kidney back into the blood, occurring at the proximal tubules. It could occur through both passive and active transport. The factors affecting reabsorption process include the size of the molecule, hydrophilicity, and rate of urine flow(66). Large molecules and ionized or hydrophilic molecules are not likely to be reabsorbed. Also, a high urine flow rate reduces reabsorption by reducing the contact time and concentration gradient. Modeling the renal clearance has not been extensively studied. Generally, in vivo renal clearance obtained from animal studies can be extrapolated to 34 determine human clearance. Additionally, human clearance can be estimated from glomerular filtration rates as follows: 𝐶𝐿𝑟𝑒𝑛𝑎𝑙 = [ 𝑓 𝑢 × 𝐺𝐹𝑅 + 𝐶𝐿 𝑠𝑒𝑐𝑟𝑒𝑡𝑖𝑜𝑛 ] × (1 − 𝐶𝐿𝑟𝑒𝑎𝑏𝑠𝑜𝑟𝑝𝑡𝑖𝑜𝑛 𝐶𝐿𝑓𝑖𝑙𝑡𝑟𝑎𝑡𝑖𝑜𝑛 +𝐶𝐿𝑠𝑒𝑐𝑟𝑒𝑡𝑖𝑜𝑛 ) (14) From the mechanistic viewpoint, Neuhoff et al developed a mechanistic kidney model (Mech KiM) that was able to account for the effect of both active and passive transport to predict renal elimination process(65). The model incorporates passive permeability across the basal and apical membranes of each cell compartment, uptake and efflux transport of each proximal tubular cell compartment, and metabolic clearance occurring in kidney. It is nested within the kidney compartment of the full PBPK model as described in Figure 1-8. Figure 1-8. Mech KiM nested in a full PBPK model. (from Neuhoff, Sibylle et al. Transporters in drug development. Springer, New York, NY, 2013. 155-177.) 35 CHAPTER 2 PBPK-led guidance for cystic fibrosis patients taking elexacaftor-tezacaftor-ivacaftor with nirmatrelvir-ritonavir for the treatment of COVID-19 Elements of this chapter have been published as follows: Eunjin Hong, Lisa Almond, Peter Chung, Adupa Rao, Paul Beringer. "Physiologically‐Based Pharmacokinetic‐Led Guidance for Patients With Cystic Fibrosis Taking Elexacaftor‐Tezacaftor‐Ivacaftor With Nirmatrelvir‐Ritonavir for the Treatment of COVID‐19." Clinical Pharmacology & Therapeutics, 2022;111(6):1324-1333. 36 2.1. Aim The aim of this chapter was to demonstrate the application of PBPK modeling in the prediction of CYP3A-mediated DDI of ETI. Specifically, the potential for a DDI between the ritonavir (the CYP3A inhibiting component of nirmatrelvir-ritonavir) and ETI was assessed for the treatment of COVID-19 in patients with CF. Further, a subsequent dosing optimization strategy of ETI in the presence of nirmatrelvir-ritonavir was proposed. The study of this chapter ultimately contributes to improved treatment for CF, by providing PBPK tools to evaluate and potentially overcome clinically important drug interactions involving highly active CFTR modulator therapy. 37 2.2. Introduction As described in Chapter 1, the introduction of the Cystic Fibrosis Transmembrane Conductance Regular (CFTR) modulator, a triple combination of elexacaftor, tezacaftor, and ivacaftor (ETI) has resulted in significant improvements in lung function and nutritional status in people with cystic fibrosis(28). While ETI is indicated in up to 90% of the CF population(28), all 3 components are eliminated mainly through cytochrome P450 (CYP) 3A-mediated hepatic metabolism(67), and therefore present a therapeutic challenge in pwCF due to the potential for significant drug-drug interactions (DDI). The use of strong CYP3A inducers will increase the metabolism of ETI resulting in reduced exposure and a potential lack of efficacy, while concomitant therapy with agents that inhibit CYP3A will increase ETI levels placing the patient at increased risk of dose-related adverse effects. Therefore, the safe and effective use of CFTR modulators requires appropriate DDI management with concomitant CF medications. One notable therapeutic challenge is in the treatment of COVID-19 (SARS- CoV-2). In pwCF, viral respiratory tract infections can lead to acute pulmonary exacerbations with a negative impact on lung function(68). COVID-19 infection triggers a cytokine storm which can lead to the life-threatening respiratory distress syndrome, potentially putting CF population infected with COVID-19 be at high risk of serious illness(69). The U.S. Food and Drug Administration (FDA) has recently issued an emergency use authorization (EUA) for the use of the 38 nirmatrelvir-ritonavir for the treatment of mild-to-moderate COVID-19. Nirmatrelvir-ritonavir treatment significantly reduces hospital admissions and deaths among people with COVID-19 who are at high risk of severe illness(70). Nirmatrelvir is co-administered with ritonavir, a CYP3A inhibitor, to boost nirmatrelvir concentrations to achieve therapeutic levels(70). However, due to the potent inhibition effect of ritonavir, it may increase plasma concentrations of drugs that are primarily metabolized by CYP3A. Therefore, co-administration of nirmatrelvir-ritonavir is contraindicated with drugs highly dependent on CYP3A for clearance and for which elevated concentrations are associated with serious and/or life-threatening reactions. Since all 3 components of ETI are eliminated mainly through CYP3A, nirmatrelvir-ritonavir is expected to exhibit a significant drug interaction with ETI. Thus, the use of nirmatrelvir-ritonavir in pwCF would require an adjusted dosing regimen of ETI to prevent increased plasma concentrations and potential adverse drug reactions. However, there was no clinical data available regarding the interactions of ETI with nirmatrelvir-ritonavir and no specific dosing guidelines was established prior to this study. Therefore, there was an urgent need for the proper guidance regarding the use of nirmatrelvir-ritonavir for pwCF to prevent progression of COVID-19 to severe disease. This study investigated the magnitude of the drug interactions of ritonavir- ETI, to simulate possible treatment scenarios and provide dosing recommendations to overcome the interaction. The CYP3A inhibition-mediated 39 drug interaction of ETI was evaluated using a PBPK simulation-based approach. The predictive performance of PBPK simulations for CYP enzyme-based DDIs has been well established(45, 71), and this strategy is increasingly included during regulatory review by the FDA as an alternative for exploring DDI potential to provide dosing recommendations in product labeling(46). This chapter contributes to improved treatment for COVID-19 in pwCF, by providing PBPK tools to evaluate and potentially overcome clinically important drug interactions involving highly active CFTR modulator therapy. 40 2.3. Methods and Materials The workflow adopted for PBPK model development, verification, and application are illustrated in Figure 2-1. The models were implemented within the Simcyp Simulator (version 19; Certara, Sheffield, UK). Figure 2-1. PBPK modeling framework detailing the processes of model development and verification that were performed in this study. Successful model verification must precede the application of the PBPK model of ETI for predictions of drug interactions with nirmatrelvir-ritonavir. 41 2.3.1. Model Development PBPK Models of ETI. In the default healthy population library file (Sim-Healthy volunteers) provided in Simcyp ® , the distribution of ages and proportion of female were corrected to reflect the demographics of CF population based on patient registry 2020 annual report published by cystic fibrosis foundation(72). Specifically, the frequency of population aged 18-21 years, was adjusted from 4.5% in healthy population to 13.1% in CF. Also, the proportion of females was adjusted from 0.32 in healthy population to 0.48 in CF. The mean body mass index (BMI) of healthy population (23.5 kg/m2) was similar to that observed in CF (21.2 kg/m2 in 2005 to 23.1 kg/m2 in 2020) and no further adjustment was needed, reflecting how BMI in pwCF has been increased over the years with continued improvements in CF care (72, 73). All other system parameters were kept as the default healthy, and this assumption is in line with the PK parameters of ETI not differing between healthy adults and patients with CF(39, 74, 75). This population library file was used for all simulations. For trial design, we used a total size of 100 population (10 trials and 10 subjects in each trial). The PBPK model input parameters for ETI are summarized in Table 2-1, 2-2, and 2-3. The ivacaftor model consists of the advanced dissolution, absorption, and metabolism (ADAM) model and minimal PBPK model. The ADAM model divides the gastrointestinal tract into nine segments, where the drug absorption from each segment is predicted by a function of drug dissolution, luminal degradation, 42 metabolism, transport, etc(76). The minimal PBPK model consists of no more than five compartments, that include the gastrointestinal tract, blood, liver, and up to two additional compartments, reducing the complexity of the model while allowing for mechanistic simulations in the compartment of interest(47). The ivacaftor model was constructed based on available physicochemical properties and clinical data from published PK studies(74, 77-80). Ivacaftor is a diprotic acid with an acid dissociation constant (pKa) of 9.4 and 11.6. Ivacaftor has a logP of 5.68, and predominantly binds to albumin with fraction of unbound drug in plasma of 0.001. The blood/plasma partition ratio was 0.55. The absorption parameter Peff,man was predicted by the Simcyp calculator based on its physicochemical properties. The distribution parameters including Vsac and Vss were obtained from published data. The in vitro studies and clinical DDI data suggest that ivacaftor is predominantly eliminated through CYP3A4 mediated hepatic metabolism(39). Therefore, the excretion was set to enzyme kinetics to quantify its metabolism by CYP3A. The intrinsic clearance by CYP3A4 was back calculated from the oral clearance observed in healthy subjects (19.0 L/h)(74). The fraction of ivacaftor being metabolized by CYP3A4 (fmCYP3A4) was set to 98% in order to capture the observed drug interactions of ivacaftor with the strong CYP3A4 modulators, ketoconazole or rifampin. The Fg was also optimized to 0.50 using observed DDI data. The additional human liver microsomes (HLM) clearance representing non-CYP3A pathways was also estimated using the retrograde calculator and entered into the model to capture the observed pharmacokinetics profiles. 43 The tezacaftor and elexacaftor PBPK models were constructed based on the data from the PBPK review section within the NDA documents and the publication of Tsai et al(39, 75, 78). Briefly, the elexacaftor and tezacaftor models consist of the first-order absorption and a minimal PBPK model. In the NDA document, it was described that the absorption and distribution parameters were obtained from the observed PK profiles following clinical phase 1-3 studies, and the fmCYP3A4 of elexacaftor and tezacaftor were set to 67% and 73.2% based on human ADME studies. Using the retrograde model, the rCYP3A4 CLint values of elexacaftor and tezacaftor attributed to CYP3A4 were calculated to be 0.233 and 0.175 µL/min per picomole of isoform. The M1-tezacaftor, active metabolite of tezacaftor, was also incorporated into the tezacaftor PBPK model based on data in the NDA document(75). The other active metabolites, M23-elexacaftor and M1-ivacaftor, were not incorporated into the PBPK analyses due to insufficient information to build the model. PBPK Models of CYP3A Modulators. Rifampin, ketoconazole, fluconazole, itraconazole, and its primary metabolite, hydroxy itraconazole, are prototypical CYP3A modulators that have been implicated in clinical drug interaction studies with elexacaftor, tezacaftor, or ivacaftor. Ritonavir is also a CYP3A modulator that we aimed to predict interactions with ETI. For simulation of DDIs, the validated compound files of these CYP3A modulators provided in Simcyp® (version 19) were used. 44 Table 2-1. Parameters Used to Develop the Ivacaftor Model Parameter Value Physiochemical properties Molecular weight (g/mol) 392.49 Log Po:w 5.68 Compound type Diprotic acid pKa 1 9.4 pKa 2 11.6 B/P 0.55 fup 0.001 Absorption Absorption model ADAM (Advanced Dissolution, Absorption, and Metabolism) Caco-2 permeability (10 −06 cm/sec) 119 fugut 0.279 ka (h −1 ) 5.188 Fg 0.5 Peff,man (× 10 −4 cm/s) 11.9 Distribution Distribution model Minimal PBPK model Q (l/h) 3.752 Vsac (l/kg) 6.70×10 −01 Vss (l/kg) 1.89 Elimination CLpo (l/h) 19.0 rCYP3A4 CLint (µL/min/pmol of isoform) 19.9 Additional HLM clearance (µL/min/mg protein) 78.8 45 B/P, blood-to-plasma ratio; CLint, intrinsic clearance; CLpo, in vivo oral clearance; Fg, fraction escaping gut-wall elimination; fugut, fraction unbound in the enterocyte; fup, fraction unbound in plasma; HLM, human liver microsome; ka, absorption rate constant; Log Po:w, logarithmic partition coefficient octonal:water; Peff,man, effective permeability in man; pKa, logarithm of acid dissociation constant; Q, inter-compartment clearance; Vsac, single adjusted compartment volume; Vss, volume of distribution at steady state. Table 2-2. Parameters Used to Develop the Tezacaftor Model Parameter Tezacaftor M1-tezacaftor Physiochemical properties Molecular weight (g/mol) 520.5 518.48 Log Po:w 3.6 4.397 Compound type Neutral Neutral B/P 0.658 0.573 fup 0.009 0.004 Absorption Absorption model First order fugut 1 1 fa 0.82 Fg 0.95 PCaco-2(10 −6 cm/s) 4.70 Reference Compound Multiple Scalar 2.997 Distribution Distribution model Minimal PBPK model Q (l/h) 0.873 Vsac (l/kg) 0.113 Vss (l/kg) 0.332 0.232 Elimination 46 CLiv (l/h) 0.387 CLpo (l/h) 1.39 rCYP3A4 CLint (µL/min/pmol of isoform) 0.175 0.141 Additional HLM clearance (µL/min/mg protein) 7.359 4.819 Interaction CYP3A4 Ki (µM) 12.5 11.65 fumic 0.876 0.730 CYP2C9 Ki (µM) 13.5 7.0 fumic 0.876 0.730 CYP2C19 Ki (µM) 22.5 19.55 fumic 0.876 0.730 CYP2C8 Ki (µM) 13 6.05 fumic 0.738 0.52 CYP2CB6 Ki (µM) 21.2 fumic 0.52 CYP2D6 Ki (µM) 24.4 fumic 0.730 B/P, blood-to-plasma ratio; CLint, intrinsic clearance; CLiv, in vivo intravenous clearance; CLpo, in vivo oral clearance; fa, fraction absorbed; Fg, fraction escaping gut-wall elimination; fugut, fraction unbound in the enterocyte; fumic, fraction unbound in the in vitro microsomal incubation; fup, fraction unbound in plasma; HLM, human liver microsome; Ki, concentration of inhibitor that supports half maximal inhibition; Log Po:w, logarithmic partition coefficient octonal:water; Q, inter-compartment clearance; Vsac, single adjusted compartment volume; Vss, volume of distribution at steady state. 47 Table 2-3. Parameters Used to Develop the Elexacaftor Model Parameter Value Physiochemical properties Molecular weight (g/mol) 597.66 Log Po:w 6.00 Compound type Monoprotic acid pKa 5.04 B/P 0.55 fup 0.00704 Absorption Absorption model First order fugut 7.75e-4 fa 0.82 Fg 1 ka (h −1 ) 0.59 Tlag (h) 2.17 PCaco-2(10 −6 cm/s) 3.08 Distribution Distribution model Minimal PBPK model Q (l/h) 10.05 Vsac (l/kg) 0.27 Vss (l/kg) 0.62 Elimination CLiv (l/h) 1.25 rCYP3A4 CLint (µL/min/pmol of isoform) 0.233 Biliary CLint (Hep) (µL/min/10 6 ) 4.56 Interaction CYP2C8 Ki (µM) 8.35 fumic 0.399 CYP2C9 Ki (µM) 5.45 48 fumic 0.399 B/P, blood-to-plasma ratio; CLint, intrinsic clearance; CLiv, in vivo intravenous clearance; fa, fraction absorbed; Fg, fraction escaping gut-wall elimination; fugut, fraction unbound in the enterocyte; fumic, fraction unbound in the in vitro microsomal incubation; fup, fraction unbound in plasma; ka, absorption rate constant; Ki, concentration of inhibitor that supports half maximal inhibition; Log Po:w, logarithmic partition coefficient octonal:water; pKa, logarithm of acid dissociation constant; Q, inter-compartment clearance; Tlag, lag time; Vsac, single adjusted compartment volume; Vss, volume of distribution at steady state. *additional simulations were run modifying the fugut in published model to assess a value equal to 1 but it was unable to capture the clinically observed data so no modifications were made. 49 2.3.2. Model Verification: PK Simulations The PK profiles of ETI following a single oral dose administration and multiple administrations of clinically relevant doses (elexacaftor 200mg qd, tezacaftor 100mg qd, and ivacaftor 150mg q12h) were first simulated to verify the performance of the PBPK models. ETI was orally administered under fed conditions to mimic the clinical setting, where the fat-containing food is required for optimal absorption of ETI. The simulated data were qualified using the observed PK data in a CF population aged older than 17 years old. The prediction accuracy for the area under the curve (AUC) and maximum plasma concentration (Cmax) values were calculated as a ratio of mean observed values over mean predicted values. Successful model performance was defined by mean ratios of AUC and Cmax within a two-fold range as previously described(81, 82). 2.3.3. Model Verification: DDI Simulations Upon accurate recapitulation of ETI’s PK, the models were further assessed against the clinical DDI data to verify fmCYP3A4 and establish if the models were adequate for the assessment of victim DDI liability. For verification simulations, the dose and schedule of drugs were matched to the design of the corresponding clinical DDI study in healthy subjects. For elexacaftor, itraconazole solution (200 mg) was administered daily from Day 1 to Day 10 and a single 20 mg dose of elexacaftor was administered orally on Day 5(39). For tezacaftor, the itraconazole solution (200 mg) was administered BID on Day 1 and QD from Day 50 2 to Day 14 and tezacaftor 25 mg was administered daily from Day 1 to Day 14(75). For ivacaftor, four clinical DDI studies were conducted with ritonavir, ketoconazole, fluconazole, or rifampin(74, 83). For ritonavir, 50mg was administered daily from Day 1 to Day 18 and a single 150 mg dose of ivacaftor was administered on Day 15. The ketoconazole 400 mg was administered daily from Day 1 to Day 10 and a single 150 mg dose of ivacaftor was administered on Day 4. For fluconazole, 400mg was administered on Day 1 and 200mg was administered daily from Day 2 to Day 8, and ivacaftor 150 mg was administered BID from Day 1 to Day 8. For rifampin, 600mg was administered daily from Day 1 to Day 10 and a single 150 mg dose of ivacaftor was administered on Day 6. To quantify the DDIs, the geometric mean ratios of AUC or Cmax with or without the presence of CYP3A4 modulators were calculated. The assessment of DDI prediction success was based on whether predictions fall within a two-fold range of the observed data. 2.3.4. Model Application: DDI Predictions of ETI with Ritonavir Although ritonavir-nirmatrelvir (PAXLOVID) is a fixed dose combination of 2 drugs, nirmatrelvir has not been included in simulations as there is no clinical evidence that it modulates CYP3A4 activity, and we have focused on interactions with ritonavir which is a potent CYP3A4 inhibitor. The verified PBPK-DDI model was applied to (1) predict the effect of ritonavir on the PK of ETI and (2) determine a potential dose alteration of ETI to overcome the CYP3A inhibition mediated by ritonavir. We first simulated the steady-state PK of standard dose 51 ETI alone and when co-administered with 100mg ritonavir twice daily for 5 days based on the instruction for dosage and administration in the FDA-approved fact sheet of nirmatrelvir-ritonavir (70). In addition, since ritonavir acts as a mechanism-based CYP3A4 inhibitor by covalently binding to the CYP3A4(84), simulations were run until CYP3A4 and the PK of ETI had returned to baseline (10 days after ritonavir discontinuation). We then simulated several adjusted dosing regimens of ETI when co-administered with ritonavir to find the regimen that could provide the closest PK profiles of standard dosing of ETI alone. 2.4. Results 2.4.1. Development and Verification of the Models of ETI Parameter Sensitivity Analysis of the Fractional Metabolism of Ivacaftor by CYP3A4 on the Predicted AUC Ratio with CYP3A Modulators For the PBPK model of ivacaftor, in the absence of an in vitro estimate, clinical interaction data with strong modulators of CYP3A4 were used to assign fmCYP3A4. First, we predicted DDI with ketoconazole by varying the fmCYP3A4 value of ivacaftor from 95% to 100% (Figure 2-2A), since it has been reported that the fractional metabolism of ivacaftor assigned to CYP3A4 is greater than 95%(78). An fmCYP3A4 of 98 % predicted the AUC ratio (GMR 6.95) of ivacaftor within the bioequivalence limit (80-125%) of the observed AUC ratio (GMR 8.45). Repeating this analysis for rifampicin, indicated the magnitude of DDI was well captured with fmCYP3A4 between 98 and 100% (simulated GMR 0.11 vs. observed 0.11) (Figure 2-2B). Taken together, the fmCYP3A4 value of 98% was 52 chosen as it describes observed DDIs between ivacaftor and ketoconazole or rifampin within the bioequivalence limit. Figure 2-2. Sensitivity analysis of the fmCYP3A4 of ivacaftor on the predicted AUC ratio with or without ketoconazole (A) or rifampin (B). The grey-colored area shows the 90% confidence interval of predicted AUC ratio. PBPK Models of ETI Recapitulated Clinically Observed PK Profiles. Model predictive performance of ETI was assessed using observed pharmacokinetic data sets from clinical trials(39, 75). The observed and simulated plasma concentration-time profiles of ETI following a single oral dose administration of elexacaftor 200mg, tezacaftor 100mg, and ivacaftor 100mg in healthy subjects are shown in Figure 2-3. For elexacaftor and tezacaftor, the mean plasma concentrations were used in the graph while the median plasma concentrations were used for ivacaftor as median value was reported from ivacaftor single dose PK study. The pharmacokinetic profile of ETI after single oral dose administration was captured by the PBPK model. 53 Figure 2-3. Observed and simulated plasma concentration-time profiles of ETI following a single oral dose of elexacaftor 200mg (A), tezacaftor 100mg (B), and ivacaftor 100mg (C). The grey-colored area shows the range of predicted concentrations from 95th to 5th percentiles. Further verification of the model was performed by simulating the steady-state PK of ETI when administered with multiple oral doses of elexacaftor 200mg qd, tezacaftor 100mg qd, and ivacaftor 150mg q12h. The predicted steady-state AUC and Cmax of ETI were in the range of 0.9 to 1.2 of the observed values demonstrating the excellent performance of the model. The observed and simulated PK parameters of ETI are summarized in Table 2-4. 54 Table 2-4. Comparison of PK parameters between simulated and observed data for model verification of ETI PK study Steady-state PK parameters Drug Regimen Simulated Observed Cmax (mg/L) AUC* (mg∙h/L) Cmax (mg/L) AUC* (mg∙h/L) elexacaftor 200mg qd Mean 8.1 158.0 8.8 167.0 CV(%) 40.0 45.7 24.6 30.2 Simulated/ observed 0.9 0.9 tezacaftor 100mg qd Mean 8.3 114.0 6.7 92.4 CV(%) 38.8 49.9 20.8 25.8 Simulated/ observed 1.2 1.2 ivacaftor 150 mg q12h Mean 1.6 13.4 1.3 12.1 CV(%) 51.4 61.2 27.8 34.5 Simulated/ observed 1.2 1.1 * AUC(0-24h) for elexacaftor and tezacaftor, and AUC(0-12h) for ivacaftor. 55 PBPK-DDI Models of ETI Recapitulated Clinically Observed Drug Interactions Although preliminary PK simulations verified the predicted PK ETI, given that the PBPK models of ETI are intended to be applied for the characterization of DDIs involving CYP3A modulation, it is essential to verify the victim properties defined in the models by simulating independent clinical DDI studies with a range of perpetrator drugs. The robustness of the model was assessed by comparing the magnitude of simulated DDIs of ETI with that observed from the clinical trials. The PBPK-DDI models accurately recapitulated the observed DDI magnitude (Table 2-5). Table 2-5. Summary of the simulated vs. observed Geometric Mean Ratio (GMR) of PK parameters in the presence and absence of CYP3A modulators DDI study PK Parameters Simulated GMR (90% CI) Observed GMR (90% CI) Ratio (Simulated/ Observed) Ivacaftor +/- Ritonavir Cmax Ratio 2.61 (2.47, 2.77) 2.28 (1.84, 2.83) 1.14 AUC Ratio 3.64 (3.37, 3.94) 3.06 (2.36, 3.97) 1.19 Ivacaftor +/- Ketoconazole Cmax Ratio 2.04 (1.96, 2.12) 2.65 (2.21, 3.18) 0.77 AUC Ratio 6.95 (6.44, 7.49) 8.45 (7.14, 10.01) 0.82 Ivacaftor +/- Fluconazole Cmax Ratio 2.72 (2.64, 2.81) 2.47 (1.93, 3.17) 1.10 56 AUC Ratio 3.33 (3.21, 3.46) 2.95 (2.27, 3.82) 1.13 Ivacaftor +/- Rifampin Cmax Ratio 0.29 (0.27, 0.31) 0.20 (0.17, 0.24) 1.45 AUC Ratio 0.11 (0.10, 0.13) 0.11 (0.10, 0.14) 1.00 Tezacaftor +/- Itraconazole Tezacaftor Cmax Ratio 2.62 (2.53, 2.72) 2.83 (2.62, 3.07) 0.93 AUC Ratio 3.85 (3.65, 4.07) 4.02 (3.71, 4.63) 0.96 M1- tezacaftor Cmax Ratio 0.59 (0.53, 0.65) 0.60 (0.54, 0.66) 0.98 AUC Ratio 0.60 (0.54, 0.66) 0.60 (0.55, 0.66) 1.00 Elexacaftor +/- Itraconazole Cmax Ratio 1.08 (1.08, 1.09) 1.05 (0.98, 1.13) 1.03 AUC Ratio 2.00 (1.94, 2.06) 2.83 (2.59, 3.10) 0.71 2.4.2. DDI Simulation of ETI with Ritonavir Simulated DDI of ETI and Ritonavir Suggests Significant DDI with ETI The verified PBPK-DDI models of ETI were used to simulate the standard dose of ETI when co-administered with nirmatrelvir/ritonavir to determine the magnitude of the DDI for its intended use for treatment of COVID-19. To mimic the clinical setting of nirmatrelvir/ritonavir administration, we simulated steady state PK of ETI, then ritonavir q12h for 5 days while continuing ETI standard dosing during and after ritonavir administrations. We calculated the Cmax and 57 AUC ratio of ETI in the presence and absence of ritonavir on day 6 of co- administration (Table 2-6). The magnitude of DDI achieves its maximum level on day 6, since ETI has not achieved a new steady-state after ritonavir administration. (Figure 2-4. A, B, and C). In addition, the maximum CYP3A4 inhibition effect is maintained through day 6 (Figure 2-4. E). The simulated geometric mean AUC ratio was highest for ivacaftor (9.31, 90% CI: 8.28, 10.47), followed by tezacaftor (3.11, 90% CI: 2.96, 3.27) and elexacaftor (2.31, 90% CI: 2.20, 2.42). Plasma concentrations of ETI in the presence and absence of ritonavir is shown in Figure 2-4. Although ritonavir itself is eliminated the day after discontinuation (Figure 2-4. D), the CYP3A4 inhibition is time-dependent(85), so the inhibition is prolonged and the recovery to baseline time is reliant on the turnover of the CYP3A4 itself (Figure 2-4. E). Thus, baseline steady state of all ETI drugs is predicted to be re-established on day 15 with AUC ratios within 1.13 to 1.17 for all 3 components. Crucially, this indicates that dose adjustment of ETI in case of co-administration with nirmatrelvir/ritonavir would be required to extend beyond the 5 days of co-administration. 58 Table 2-6. Summary of the predicted Geometric Mean Ratio (90% CIs) for standard doses of elexacaftor (200mg q24h), tezacaftor (100mg q24h) and ivacaftor (150mg q12h) in the presence and absence of ritonavir (150mg q12h administered on day 1 through day 5) Drugs Predicted GMR (90% CI) of ETI PK parameters in the presence and absence of ritonavir on day 6 Substrate Modulator Cmax AUC Elexacaftor Ritonavir 2.02 (1.95, 2.10) 2.31 (2.20, 2.42) Tezacaftor 2.18 (2.11, 2.25) 3.11 (2.96, 3.27) Ivacaftor 6.84 (6.28, 7.45) 9.31 (8.28, 10.47) 59 Figure 2-4. Plasma Concentration Profile of Elexacaftor (A), Tezacaftor (B), Ivacaftor (C), and Ritonavir (D), and the % of Active CYP3A4 Enzyme (E) Over Time. Green: without ritonavir, Red: with ritonavir administered day 1 through day 5. 60 Altered Dose of ETI to Recapitulate the PK profile of Standard Dose ETI Alone We next utilized the models to simulate ETI dose adjustments when these agents are co-administered with ritonavir and determine how long the adjusted dosage needed to be maintained, to overcome the enzyme inhibition effect mediated by ritonavir. Based on the simulated effects of ritonavir, elexacaftor 200mg, tezacaftor 100mg, ivacaftor 150mg in the morning (2 orange tablets) every 4 days (administered on day 1 and day 5 and resumed full dose on day 9) provided similar steady-state PK profile of the conventional regimen of ETI alone (Figure 2-5). The trough concentrations of ETI were all above the EC50 targets, which are 0.99 mg/L, 0.5 mg/L, and 0.048 mg/L for elexacaftor, tezacaftor, and ivacaftor, respectively(39, 74, 75). Since CYP3A4 inhibition dynamics mediated by ritonavir changes over time, we measured the mean Cmax and AUC of reduced dosing of ETI regarding the first dose on day 1 and the second dose on day 5 and calculated the percentage of ETI standard regimen alone (Table 2-7). The AUC(0-96h) of reduced dosing regimen ranged from 83.0-142.5% of ETI alone. Resumption of the full dose of ETI on day 9 is based on simulations to optimize the concentration profiles of all components of ETI, where the level of elexacaftor and tezacaftor do not become lower than 80% of the standard regimen before resuming the full dose, while striving to maintain levels of ivacaftor below 125% of the standard regimen after resuming the full dose. At day 9, the CYP3A4 enzyme activities were recovered to 60% of the steady-state values. 61 Figure 2-5. Plasma Concentration Profile of ETI. Green: standard dose without ritonavir, Red: reduced dose with ritonavir 150mg q12h administered day 1 through day 5. (EC50 for tezacaftor and ivacaftor: obtained from exposure-response analysis in clinical trials regarding the reduction of sweat chloride, EC50 for elexacaftor: obtained from in vitro study of chloride transport in phe508del/phe508del human bronchial epithelial cells as no in vivo data are available.) 62 Table 2-7. Predicted mean Cmax and AUC of reduced dose of ETI (elexacaftor 200mg- tezacaftor 100mg-ivacaftor 150mg q96h) with ritonavir 150mg q12h administered day 1 through day 5 Drug regimen with ritonavir administered day 1 through 5 Cmax and % of standard dose ETI alone AUC and % of standard dose ETI alone Drug Regimen Days Cmax (mg/L) % of ETI alone AUC* (mg∙h/L) % of ETI alone Elexacaftor 200mg on day 1 Day 1-2 8.7 107.4 185.9 117.7 Day 1-5 605.3 95.8 200mg on day 5 Day 5-6 7.9 97.5 168.9 106.9 Day 5-9 524.4 83.0 Tezacaftor 100mg on day 1 Day 1-2 8.7 104.8 158.2 138.8 Day 1-5 451.0 98.9 100mg on day 5 Day 5-6 9.2 110.8 165.6 145.3 Day 5-9 426.8 93.6 Ivacaftor 150mg on day 1 Day 1-2 1.8 112.5 35.9 134.0 Day 1-5 125.2 116.8 150mg on day 5 Day 5-6 2.7 168.8 54.4 203.0 Day 5-9 152.8 142.5 *AUC(0-24h) for day 1-2 and day 5-6, AUC(0-96h) for day 1-5 and day 5-9. 63 In addition, we simulated an alternate dosing regimen, which is elexacaftor 100mg, tezacaftor 50mg, ivacaftor 75mg in the morning (1 orange tablet) administered every 2 days. This regimen provided concentration profiles closer to the standard regimen of ETI alone with less fluctuations of peak/trough concentrations (Figure 2-6 and Table 2-8). Especially for ivacaftor on day 5 which showed higher Cmax (2.7 mg/L, 168.8% of standard regimen) in case of 150mg q96h, the Cmax of ivacaftor was 2.1 mg/L (131.3 % of standard regimen) with the 75mg q48h. However, the dosing regimen of two orange tablets every 3- 4 days is consistent with recommendations for other strong CYP3A inhibitors and the Cmax and AUC values between the two regimens were not demonstrably different. Figure 2-6. Plasma Concentration Profile of ETI. Green: standard dose without ritonavir, Red: reduced dose (elexacaftor 100mg q48h, tezacaftor 50mg q48h, ivacaftor 75mg q48h) with ritonavir 150mg q12h administered day 1 through day 5 64 Table 2-8. Predicted mean Cmax and AUC of reduced dose of ETI (elexacaftor 100mg- tezacaftor 50mg-ivacaftor 75mg q48h) with ritonavir 150 mg q12h administered day 1 through day 5. Drug regimen with ritonavir administered day 1 through 5 Cmax and % of standard dose ETI alone AUC and % of standard dose ETI alone Drug Regimen Days Cmax (mg/L) % of ETI alone AUC(0-48h) (mg∙h/L) % of ETI alone Elexacaftor 100mg q48h With ritonavir Day 1-3 6.9 85.2 282.8 89.5 Day 3-5 6.6 81.5 272.8 86.3 After ritonavir Day 5-7 6.5 80.2 265.9 84.1 Day 7-9 6.1 75.3 231.3 73.2 Tezacaftor 50mg q48h With ritonavir Day 1-3 5.8 69.9 198.9 87.2 Day 3-5 6.3 75.9 213.9 93.8 After ritonavir Day 5-7 6.5 78.3 219.7 96.3 Day 7-9 6.3 75.9 180.9 79.4 Ivacaftor 75mg q48h With ritonavir Day 1-3 1.2 75.0 49.9 93.0 Day 3-5 1.7 106.3 68.8 128.3 After ritonavir Day 5-7 2.1 131.3 81.9 152.8 Day 7-9 1.9 118.8 57.8 107.8 65 For the dosing recommendation of tezacaftor/ivacaftor (SYMDEKO), since it is provided as a fixed dose yellow tablet consisting of tezacaftor 100mg and ivacaftor 150mg, the same dosing recommendation above (tezacaftor-ivacaftor 100-150mg q96h) can be applied. Also, for ivacaftor 150mg tablet (KALYDECO), the same dosing recommendation (one tablet q96h) can be applied, but alternatively, the dosing interval of ivacaftor could be further increased to 5 days rather than 4 days, to recapitulate similar PK profile of standard regimen. When ivacaftor 150mg was administered on day 6 instead of day 5, the AUC(0-24h) was decreased to 48.31 mg∙h/L (180.2% of standard regimen) from 54.4 mg∙h/L (203.0% of standard regimen) (Table 2-9). Taken together, the suggested dosing schedule of CFTR modulators co-administered with nirmatrelvir/ritonavir is described in Figure 2-7. 66 Table 2-9. Predicted mean Cmax and AUC of reduced dosing of ivacaftor (150mg 5 days apart) with ritonavir (150 mg q12h) administered day 1 through day 5. Ivacaftor 150mg 5 days apart Cmax and % of standard dose ivacaftor alone AUC and % of standard dose ivacaftor alone Ivacaftor dose Days Cmax (mg/L) % of ivacaftor alone AUC* (mg∙h/L) % of ivacaftor alone 150mg on day 1 day 1-2 1.8 111.9 35.9 134.0 day 1-6 151.5 113.0 150mg on day 6 day 6-7 2.5 156.9 48.3 180.2 day 6-9 101.4 126.1 *AUC(0-24h) for day 1-2 and day 6-7, AUC(0-120h) for day 1-6, and AUC(0-72h) for 6-9. 67 Figure 2-7. Suggested Dosing Schedule of CFTR modulators co-administered with nirmatrelvir-ritonavir 68 2.5. Discussion All three components of ETI are eliminated predominantly through hepatic metabolism along with limited renal excretion. The clinical DDI study with strong CYP3A4 inhibitors (Ketoconazole and Itraconazole) or inducer (Rifampin) showed that ETI are the sensitive CYP3A4 substrates. Therefore, the safe and effective use of CFTR modulators is complicated by DDI management with concomitant CF medications, as CYP3A4 modulation by inducers or inhibitors can lead to altered systemic exposure, resulting in variability in drug response. CF patients often take multiple antibiotics including rifamycins, macrolides, and azole antifungals, which potentially inhibit or induce CYP3A4-mediated metabolism of ETI. Recently Tsai et. al. published a ETI PBPK model to evaluate exposures during the transition from mono or dual combination of CFTR modulators to ETI(78). We extended the models by refining the ivacaftor model and further validating the ETI PBPK-DDI model with published clinical DDI data. Since the ETI PBPK-DDI model we employed could robustly predict PK parameters and the observed drug interactions of ETI, it can provide an approach to the evaluation and management of other potential DDIs involving CFTR modulators. In particular, we aimed to provide guidance for ETI dose adjustment with ritonavir, the CYP3A inhibitor and the component of nirmatrelvir/ritonavir for the treatment of COVID-19. From the ETI-ritonavir DDI simulations, we found that when ritonavir 100mg q12h was administered for 5 days, it led to the AUC ratio of 69 ivacaftor as 9.31 (90% CI: 8.28, 10.47) which far exceeded the observed and simulated AUC ratio (3.06) when ivacaftor was administered with ritonavir 50 mg q24h(83). The increase in interaction with the therapeutic regimen shows that dose adjustments should not be estimated purely on the basis of the available clinical study. Further, through the simulations we found that the elevated concentrations of ETI were continued even after ritonavir is eliminated due to the irreversible CYP3A4 inhibition effect. The suggested reduced dosing regimen that is maintained until 4 days after ritonavir is discontinued provided a similar PK profile of the standard regimen of ETI alone. However, dose-dependent adverse reactions of ivacaftor should be more closely monitored due to its high peak concentrations on day 5. This study has important clinical implications, bridging the gap between the available clinical DDI study and that of the therapeutic regimen and providing proper dosing guidelines for treatment of COVID-19 with nirmatrelvir/ritonavir in pwCF receiving concomitant CFTR modulator therapy. A limitation of this study is that in the absence of data, population system parameters, such as plasma protein levels in pwCF was not incorporated into the modeling. However, changes in demography reflecting CF population were incorporated. Furthermore, a prior study evaluating the hepatic clearance of drugs showing that CYP3A enzyme activity is unaffected in pwCF(86) and the current weight of evidence based on comparisons of ETI PK in healthy volunteers compared to patients suggests they are comparable(39, 74, 75). Previous studies indicate that differences in pharmacokinetics of drugs in CF is 70 attributed to differences in body composition and plasma protein concentrations secondary to nutritional deficiencies(87). However, the BMI of pwCF has increased over the years with continued improvements in CF-care, including highly effective CF modulators and nutritional support(72), to the extent it is now similar to that of healthy volunteers(73). A potential limitation of this work is that we did not include models of all active metabolites of ETI. M1-tezacaftor is an important metabolite due to its similar potency with parent drug as well as its high metabolite to parent AUC ratio (157.8%)(75). Since M1-tezacaftor is also metabolized by CYP3A4(75), its formation and elimination may be altered with ritonavir co-administration. Using the PBPK model of M1-tezacaftor, we were able to determine that the reduced dose of ETI provided mean AUC(0-96h) for M1-tezacaftor during the co- administration period with ritonavir that was 80.9% of the standard regimen of ETI alone. We did not include models of active metabolites of ivacaftor (M1- ivacaftor) and elexacaftor (M23-elexacaftor), since there was insufficient information to build the models incorporating them. While it is currently unknown whether ritonavir would alter the levels of M23-elexacaftor, the exposure of this metabolite is significantly reduced when compared with the parent compound and is not considered to contribute significantly to the overall efficacy(39). For M1-ivacaftor, there is evidence that its plasma concentration of is decreased by 35% in the presence of rifampin, indicating that M1-ivacaftor may also metabolized by CYP3A4(88). This perhaps suggests that the reduced dose of 71 ivacaftor may not significantly reduce M1-ivacaftor exposure when co- administered with ritonavir, since ritonavir will inhibit the metabolism of both ivacaftor and M1-ivacaftor. In conclusion, using a PBPK modeling approach, we demonstrated that nirmatrelvir/ritonavir can be administered concomitantly with ETI in pwCF with proper dose adjustment. The outcome of this study ensures the use of nirmatrelvir/ritonavir for the treatment of COVID-19 in patients with CF while continuing to receive highly active CFTR modulators. In addition, this work provides tools to evaluate and potentially overcome clinically important DDIs involving highly active CFTR modulator therapy. 72 CHAPTER 3 PBPK modeling to guide management of drug interactions between elexacaftor-tezacaftor-ivacaftor and antibiotics for the treatment of non-tuberculosis mycobacteria (NTM) Elements of this chapter have been published as follows: Eunjin Hong, Lisa Almond, Peter Chung, Adupa Rao, Paul Beringer. "Physiologically-Based Pharmacokinetic (PBPK) modeling to guide management of drug interactions between elexacaftor-tezacaftor-ivacaftor and antibiotics for the treatment of non-tuberculosis mycobacteria (NTM)." Antimicrobial Agents and Chemotherapy, 2022;66(11): e01104-22. 73 3.1. Aim In addition to COVID-19, another significant therapeutic challenge regarding CFTR modulators is in the treatment of Nontuberculous mycobacteria (NTM). Therefore, the aim of this chapter was to assess the DDI risk for ETI when co- administered with NTM treatment. Further, we also investigated appropriate dose adjustment and transitions of ETI when concomitantly used with NTM therapies to provide a proper guidance on dosing. We extended PBPK model developed in Chapter 1, to further evaluate the interactions with selected guideline recommended NTM treatments that include rifabutin, clofazimine, and clarithromycin. The study of this chapter ultimately contributes to improved treatment for CF, by providing evidence in support of the use of NTM treatments in pwCF receiving concomitant ETI therapy. 74 3.2. Introduction Nontuberculous mycobacteria (NTM) are increasingly being isolated from the sputum of people with CF (pwCF) with estimates of prevalence increasing from 1.3 percent in 1984 to 12 percent in 2012(89). The prevalence of NTM increases with age from 10% in children aged 10 years, to over 30% in adults over the age of 40 years(89). NTM are the pathogen of concern in CF due to its association with deterioration of lung function, due to progressive inflammatory lung damage. A significant barrier to effective treatment is the limited number of safe and effective antibiotics for treatment of NTM. Further, it requires the use of a multi- drug combination regimen over a prolonged period of up to 18 months, creating the potential for drug-drug interactions (DDIs) with other chronic medications for CF including the Cystic Fibrosis Transmembrane Conductance Regular (CFTR) modulators(90). TRIKAFTA®, a triple combination of elexacaftor, tezacaftor, and ivacaftor (ETI), has resulted in significant improvements in lung function and nutritional status in pwCF and is indicated in 90% of patients age 6 or greater(28). However, all 3 components of ETI are eliminated primarily through cytochrome P450 (CYP) 3A-mediated hepatic metabolism(67), and therefore present a therapeutic challenge to the treatment of NTM in pwCF. Exclusion of antibiotics due to drug-drug interactions further reduces the available treatment options for the NTM which already exhibits relatively poor outcomes with available treatments. Rifamycins are the first line treatment of Mycobacterium Avium Complex, 75 the most commonly identified NTM species(90, 91). Rifamycins are a key component of the treatment regimen since they reduce the emergence of macrolide resistance which is associated with poor clinical outcomes(92, 93). However, concomitant use of strong inducers including the rifamycins (e.g. rifampin and rifabutin) is not recommended with ETI as a pharmacokinetic (PK) study has shown that the co-administration of rifampin decreased the area- under-the concentration-time curve (AUC) of ivacaftor by 89% (AUC ratio of 0.11)(94). This DDI potentially compromises the treatment efficacy for NTM in pwCF, by precluding the first-line antibiotics for patients receiving ETI therapy. While rifabutin induces CYP3A activity, its effect appears to be moderate when compared with rifampin. For example, a study with lersivirine (a CYP3A substrate) demonstrated an 85% reduction in lersivirine AUC with rifampin but only a 34% reduction with rifabutin(95). Thus, we hypothesized that use of rifabutin with an adjusted dosing regimen of ETI could offer a key alternative to rifampin in patients receiving ETI. Additional potential drug interactions include the use of clofazimine and clarithromycin. Clofazimine is a guideline recommended therapy for Mycobacterium abscessus complex (MABSC), which is difficult to treat due to its intrinsic resistance to antibiotics and is associated with more rapid decline in lung function in pwCF(90, 96). There is conflicting information regarding the CYP3A modulating activity of clofazimine (e.g. inhibitor, inducer) leaving a question about whether concomitant use with ETI without dose adjustment is appropriate. 76 Clarithromycin is also a guideline recommended treatment for NTM(90). Clarithromycin acts as both a competitive and mechanism-based CYP3A inhibitor(97), thereby potentially exerting a significant and prolonged inhibitory effect requiring dose adjustment of ETI. Currently there are no clinical trial data available regarding the interactions of ETI with rifabutin, clofazimine or clarithromycin. Therefore, there is an urgent need for the DDI predictions and development of dosing guidelines for these NTM treatments for pwCF. In the present study we evaluated the CYP3A modulation mediated drug interactions of ETI with selected NTM therapies using a physiologically based pharmacokinetic (PBPK) simulation-based approach with the goal of enabling the use of additional NTM therapies with appropriate dosage adjustment. The predictive performance of PBPK simulations for CYP enzyme-based DDIs has been well established(45, 71), and this strategy is increasingly included during regulatory review by the FDA as an alternative for exploring DDI potential to provide dosing recommendations in product labeling(98). We applied the verified physiologically based pharmacokinetic-drug-drug interaction (PBPK-DDI) models of ETI that we constructed in a recent investigation to determine appropriate dosing of ETI when co-administered with nirmatrelvir-ritonavir (Paxlovid)(40). In the current study, we extended our PBPK model to evaluate the interaction of ETI with selected guideline recommended NTM therapies and determine appropriate dosage adjustments for coadministration. 77 3.3. Methods and Materials 3.3.1. PBPK population model and trial design The models were implemented within the Simcyp Simulator (version 21; Certara, Sheffield, UK). In the default adult healthy population library file (Sim- Healthy volunteers) provided in Simcyp ® , the distribution of ages and proportion of female were corrected to reflect the demographics of CF population based on patient registry 2020 annual report published by cystic fibrosis foundation(40, 99). The corrected healthy population library file was used for all simulations since PK parameters of ETI were not found to differ between healthy adults and pwCF(40, 100-102). It should be noted that the simulation results apply to adults with CF only, as the simulated population include ages between 18 and 65. For the trial design, a total of ten trials with ten subjects per trial were simulated (10 x 10 design). To predict the effect of CYP3A modulators on ETI pharmacokinetics, published PBPK models of ETI that we previously verified using known DDI liability data were employed(40). Briefly, the ETI models were developed based on available physicochemical properties and clinical data from published PK studies(40). Since ETI is predominantly eliminated through CYP3A mediated hepatic metabolism, the excretion was set to enzyme kinetics to quantify the metabolism by CYP3A. The fraction of ETI being metabolized by CYP3A (fmCYP3A) was set to 98%, 73.2%, and 67% for ivacaftor, tezacaftor, and elexacaftor, respectively, with ivacaftor being the most sensitive CYP3A 78 substrate among ETI components. The models were validated against PK profiles of ETI following a single and multiple administrations of clinically relevant doses. Upon accurate recapitulation of the PK of ETI, the models were further assessed against the clinical DDI data with strong CYP3A modulators, which indicated that the models were adequate for the assessment of DDI. For clofazimine, we incorporated the induction potential acquired from an in vitro induction assay (described below) into a published model that contains the inhibition potential(103). For rifabutin and clarithromycin, the validated compound files provided in Simcyp® (version 21) were used. Clofazimine induction assay While clofazimine’s inhibition potential (Ki) on CYP3A is known and has been incorporated into a published PBPK model(103), no quantitative measurement on its induction potential has been performed. Therefore, for completeness of the model, we quantified its induction potential by assessing the changes in CYP3A4 mRNA upon treating cryopreserved primary human hepatocytes with clofazimine. Cells were obtained from GIBCO/Life Technologies and were cultured according to manufacturers’ instructions(104). After the cell attachment period, cells were treated daily for 2 consecutive days, with fresh culture medium containing either vehicle control (0.1% DMSO), clofazimine, or rifampin at concentrations ranging from 0.01 to 50 mM. Quantification of CYP3A4 mRNA induction was performed using SYBR Green-based quantitative PCR. The maximal fold-induction (Emax) 79 and the concentration resulting in half-maximal induction ((EC50) of CYP3A4 for each compound were determined after curve fitting using GraphPad Prism (version 5). Clofazimine PBPK model development The clofazimine model was constructed based on a published PBPK model, with the addition of the induction potential (EC50, Emax) determined by the induction assay. The in vitro EC50 and Emax data for clofazimine was calibrated against in vitro data for rifampin, for which the CYP3A induction potential is well characterized. The fraction of unbound drug in the in vitro hepatocyte incubation (fuinc) was predicted using a quantitative model previously established by Austin et al(105, 106). The developed model of clofazimine was further assessed against the observed DDI data with bedaquiline (CYP3A substrate with fmCYP3A of 16%(107) to verify that the model was adequate for the assessment of DDIs. The published model of bedaquiline that has been verified against its victim drugs was used(108). 3.3.2 DDI predictions with selected NTM antibiotics We first simulated the steady-state PK of standard dose ETI alone and when co-administered with rifabutin 300mg daily, clofazimine 100 mg daily, and clarithromycin 500mg every 12 hours following the guideline for treatment of NTM in CF(90). The duration of coadministrations with rifabutin, clarithromycin, and clofazimine were set to 20, 50, and 130 days, based on the timelines 80 required to reach to the steady-state. Additional simulations were run to evaluate the transitions after the discontinuation of the antibiotics due to the time- dependent CYP3A modulating activities of rifabutin, the prolonged elimination half-life of clofazimine (25 days)(103), and the prolonged mechanism based CYP3A inhibition effects of clarithromycin. To quantify the DDIs, the geometric mean ratios of AUC or Cmax with or without the presence of CYP3A modulators were calculated. We further simulated adjusted dosing regimens of ETI when co- administered with the NTM antibiotics to determine the optimal dosing regimen targeting an AUC of ETI within the bioequivalence limit (0.80 to 1.25) relative to the standard regimen. 81 3.4. Results 3.4.1 DDI simulation of ETI with rifabutin Simulated DDI suggests rifabutin can be co-administered with dose adjusted ETI To mimic the clinical setting where rifabutin is initiated in patients receiving chronic ETI therapy, we simulated steady-state PK of ETI, with the addition of rifabutin 300mg daily for 20 days while continuing ETI standard dosing during and after rifabutin administration (Figure 3-1. A, B, C). The results of the simulated effect of rifabutin on PK of ETI at the steady state are summarized in Table 3-1. The simulated geometric mean AUC ratio was lowest for ivacaftor (0.31, 90% CI: 0.29, 0.34), followed by tezacaftor (0.60, 90% CI: 0.58, 0.62) and elexacaftor (0.67, 90% CI: 0.66, 0.69). This result corresponds to the magnitude of the drug fraction metabolized by CYP3A (fmCYP3A), with ivacaftor being the most sensitive CYP3A substrate among ETI components with fmCYP3A of 98%. Importantly, the predicted magnitude of interaction for rifabutin-ivacaftor was lower than that observed when ivacaftor was co-administered with lumacaftor (0.22), which is combined with an increased dose of ivacaftor(43). 82 Figure 3-1. Predicted plasma concentration profiles of ETI without NTM treatment (green) and with NTM treatment (red). A, B, C: standard dose of ETI with or without NTM treatment. D, E, F: standard dose of ETI without NTM treatment and adjusted dose of ETI with NTM treatment 83 Altered dose of ETI when co-administered with rifabutin recapitulates the PK profile of standard dose ETI alone We next utilized the PBPK models to simulate steady-state ETI dose adjustments when these agents are co-administered with rifabutin, and to determine dose transitions when initiating and discontinuing rifabutin. Based on the simulated effects of rifabutin, elexacaftor 200mg, tezacaftor 100mg, ivacaftor 450mg in the morning (2 orange and 2 blue tablets), and elexacaftor 100mg, tezacaftor 50mg, ivacaftor 375mg in the evening (1 orange and 2 blue tablets) provided Cmax and AUC (80.6-102.2%) values approximately equivalent to the conventional regimen of ETI alone at the steady-state (Figure 3-1. D, E, F and Table 3-2). Since ivacaftor showed the most significant DDI among the 3 components, an additional dose adjustment step for ivacaftor was needed, by initiating increased dose of ivacaftor (ivacaftor 300mg q12h) on day 2 of rifabutin. On day 5, the increased dose for all components of ETI needed to be initiated. The standard dose of ETI could be resumed 8 days after rifabutin discontinuation (Table 3-3). In addition, using the PBPK model of M1-tezacaftor we were able to determine that the adjusted dose of ETI in combination with rifabutin resulted in a steady- state M1-tezacaftor AUC of 101.9% of the standard regimen. 84 Table 3-1. Summary of the predicted steady-state Cmax and AUC Geometric Mean Ratio for standard dose ETI in the presence and absence of NTM treatments Drugs Predicted GMR (90% CI) of ETI PK parameters in the presence and absence of NTM treatments NTM treatments ETI Cmax Ratio AUC Ratio Rifabutin 300mg daily elexacaftor 200mg daily 0.74 (0.73, 0.76) 0.67 (0.66, 0.69) tezacaftor 100mg daily 0.74 (0.72, 0.76) 0.60 (0.58, 0.62) ivacaftor 150mg q12h 0.37 (0.34, 0.39) 0.31 (0.29, 0.34) Clofazimine 100mg daily elexacaftor 200mg daily 1.60 (1.57, 1.64) 1.75 (1.71, 1.80) tezacaftor 100mg daily 1.51 (1.48, 1.55) 1.87 (1.82, 1.91) ivacaftor 150mg q12h 2.48 (2.39, 2.57) 2.98 (2.88, 3.08) Clarithromycin 500mg q12h elexacaftor 200mg daily 2.14 (2.02, 2.27) 2.43 (2.27, 2.59) tezacaftor 2.11 2.92 85 100mg daily (1.99, 2.24) (2.73, 3.12) ivacaftor 150mg q12h 7.28 (6.31, 8.41) 9.64 (8.33, 11.16) Table 3-2. Predicted steady-state mean Cmax and AUC of adjusted regimen of ETI when co-administered with NTM treatments Adjusted regimen of ETI with NTM treatments Cmax and % of standard dose ETI alone AUC and % of standard dose ETI alone NTM treatments ETI Regimen Cmax (mg/L) % of ETI alone AUC a (mg∙h/L) % of ETI alone Rifabutin 300mg daily elexacaftor 200mg in AM 100mg in PM 8.1 100.0 161.4 102.2 tezacaftor 100mg in AM 50mg in PM 7.3 88.0 104.7 91.8 ivacaftor 450mg in AM 375mg in PM 1.6 100.0 21.6 80.6 Clofazimine 100mg daily elexacaftor 200mg q48h 7.5 92.6 274.8 87.0 tezacaftor 100mg q48h 8.1 97.6 213.3 93.6 ivacaftor 150mg daily 2.3 143.8 74.0 138.1 Clarithromycin 500mg q12h elexacaftor 200mg q72h 7.3 90.1 392.6 82.8 tezacaftor 100mg q72h 9.1 109.6 348.8 102.0 ivacaftor 150mg q72h 3.1 193.8 147.7 183.7 86 a AUC(0-24h) for co-administration with rifabutin, AUC(0-48h) for co-administration with clofazimine, AUC(0-72h) for co-administration with clarithromycin Table 3-3. Suggested dosing schedule of ETI when co-administered with select NTM treatments NTM treatment Suggested ETI dose adjustments and transitions Rifabutin, 300mg daily a DOT 2-4 DOT 5 – DPT 1 DPT 2-7 DPT 8 AM elexacaftor/ tezacaftor/ ivacaftor 200/100/ 150mg 200/100/150 mg 200/100/ 150mg Resumption of standard dose b ivacaftor 150mg 300mg 150mg PM elexacaftor/ tezacaftor/ ivacaftor - 100/50/75mg - ivacaftor 300mg 300mg 300mg Clofazimine, 100mg daily DOT 3-23 DOT 24 – DPT 9 DPT 10-41 DPT 42 elexacaftor/ tezacaftor/ 200/100/ 150mg 200/100/150 mg q48h c 200/100/ 150mg Resumption of standard dose b 87 ivacaftor daily daily Ivacaftor - 150mg q48h c - Clarithromy cin, 500mg q12h DOT 2 – DPT 3 DPT 4 elexacaftor/ tezacaftor/ ivacaftor 200/100/150mg q72h Resumption of standard dose b a DOT: Day of Treatment, DPT: Day Post Treatment b Standard dose = Elexacaftor 200mg/Tezacaftor 100mg/Ivacaftor 150mg AM & Ivacaftor 150mg PM c Elexacaftor 200mg/Tezacaftor 100mg/Ivacaftor 150mg alternating with Ivacaftor 150mg every other day. 3.4.2. DDI simulation of ETI with clofazimine PBPK model of clofazimine recapitulates observed drug interaction with bedaquiline Based on the in vitro CYP3A induction experiments, the Emax and EC50 of CFZ were 6.8 and 6.7 mM (95% CI: 5.4, 8.4), respectively (Figure 3-2). Rifampin exhibited significantly higher induction of CYP3A with an Emax of 40.3 and EC50 of 1.4 mM (95% CI: 0.8, 2.4). The fuinc of clofazimine was predicted to be 0.05 based on the model established by Austin et al(105) and was incorporated into 88 the PBPK model. To verify its perpetrator property defined in the model we simulated the interaction between clofazimine and bedaquiline (a CYP3A substrate). A published clinical study evaluating the potential DDI between clofazimine and bedaquiline found no statistically significant DDI between the two drugs (109). In our PBPK simulation, clofazimine did not show a significant interaction with bedaquiline, with Cmax and AUC ratios of 1.14 (1.13, 1.15) and 1.17 (1.15, 1.18), respectively, which agrees with the data from the published clinical trial. Figure 3-2. Dose response curves obtained from induction assay upon treatment of rifampin (a) and clofazimine (b) 89 Simulated clofazimine-ETI DDI suggests moderate accumulation as a net effect of concurrent moderate CYP3A4 inhibition and weak induction activities The simulation of steady-state PK of ETI with or without clofazimine administered 100mg daily resulted in AUC ratio of 1.75 (1.71, 1.80), 1.87 (1.82, 1.91), and 2.98 (2.88, 3.08) for elexacaftor, tezacaftor, and ivacaftor respectively (Table 3-1). The AUC ratio of ivacaftor derived by clofazimine’s inhibition potential (3.11, 90% CI 3.00, 3.23) was not significantly different from that derived by CFZ’s inhibition and induction potential (2.98, 90% CI: 2.88, 3.08), suggesting that CFZ’s induction effect is very mild, and its inhibition activities outweighs the induction activities on CYP3A. Since CYP3A inhibition effect increases over time as clofazimine accumulates toward its steady-state, the AUC ratio of ETI gradually increases until the steady- state of clofazimine (130 days). Similarly, re-establishment of steady-state ETI after discontinuation of clofazimine, requires a prolonged period due to the long elimination half-life of clofazimine (Figure 3-3. A, B, C). Crucially, this indicates that dose reduction of ETI in the case of co-administration with clofazimine would require delayed initiation and would need to be extended beyond the period of co-administration after clofazimine discontinuation. 90 Reduced dose of ETI decreases the impact of the drug interaction with clofazimine We simulated steady-state ETI dose adjustments when these agents are co- administered with clofazimine. Based on the simulated effects of clofazimine, elexacaftor/tezacaftor 200/100mg every 2 days and ivacaftor 150mg once a day (2 orange tablets and 1 blue tablet on alternate days) achieves AUC ratios between 87.0-138% when compared with the standard regimen of ETI alone at steady-state (Figure 3-3. D, E, F). We could not meet the FDA bioequivalence limit (0.8-1.25), since ETI is provided as a fixed-dose combination tablet which limited the dosing regimens that could be simulated. Since ivacaftor showed the most significant DDI among the 3 components, the reduced dose of ivacaftor (ivacaftor 150mg daily) needs to be initiated earlier on day 3, followed by the reduced dose of all components of ETI on days 24. The standard dose of ETI could be resumed on 42 days after clofazimine discontinuation (Table 3-3). The simulation of the active metabolite M1-tezacaftor showed that with the reduced dose of tezacaftor when co-administered with clofazimine, resulted in an AUC ratio of 70.3% compared with tezacaftor alone. 91 Figure 3-3. Predicted plasma concentration profiles of ETI without NTM treatment (green) and with NTM treatment (red). A, B, C: standard dose of ETI with or without NTM treatment. D, E, F: standard dose of ETI without NTM treatment and adjusted dose of ETI with NTM treatment 92 3.4.3. DDI simulation of ETI with clarithromycin Simulated clarithromycin-ETI DDI indicates significant drug accumulation The simulated geometric mean AUC ratio at the steady state was highest for ivacaftor (9.64, 90% CI: 8.33, 11.16), followed by tezacaftor (2.92, 90% CI: 2.73, 3.12) and elexacaftor (2.43, 90% CI: 2.27, 2.59) (Table 3-1). Plasma concentrations of ETI in the presence and absence of clarithromycin are shown in Figure 3-4. A, B, C. Although clarithromycin itself is eliminated the day after discontinuation, it is mechanism-based CYP3A inhibitor, so the inhibition is prolonged and baseline steady-state of ETI is not predicted to be re-established until 10 days after discontinuation of clarithromycin. Reduced dose of ETI decreases the impact of the drug interaction with clarithromycin We next utilized the models to simulate ETI dose adjustments when co- administered with clarithromycin. Based on the simulations, elexacaftor 200mg, tezacaftor 100mg, ivacaftor 150mg in the morning (2 orange tablets) every 3 days provides a steady-state PK profile closest to that of the standard regimen of ETI, with the AUC ranging from 82.8-183.7% of standard regimen (Figure 3-4. D, E, F and Table 3-2). The reduced dose ETI was initiated on day 2 of co- administration and the standard dose ETI was resumed on 4 days after clarithromycin discontinuation (Table 3-3). We could not meet the FDA bioequivalence limit (0.8-1.25) for ivacaftor, as targeting 1.25-fold of ivacaftor exposure would cause significant subtherapeutic levels of elexacaftor and 93 tezacaftor due to the fixed-dose combination tablet. The simulation of the M1- tezacaftor showed that with reduced dose ETI and clarithromycin, its exposure achieved 36.5 % of standard regimen of tezacaftor alone. 94 Figure 3-4. Predicted plasma concentration profiles of ETI without NTM treatment (green) and with NTM treatment (red). A, B, C: standard dose of ETI with or without NTM treatment. D, E, F: standard dose of ETI without NTM treatment and adjusted dose of ETI with NTM treatment 3.5. Discussion NTM pulmonary disease remains a significant therapeutic challenge in pwCF requiring the use of multi-drug combination regimens over a prolonged period, creating the potential for drug interactions with ETI. Our PBPK simulations indicate that co-administration of either rifabutin, clofazimine, or clarithromycin leads to clinically significant DDIs with ETI, necessitating dose adjustment. In addition, we found that the altered concentrations of ETI were sustained after the discontinuation of NTM treatments, requiring delayed resumption of the standard dose of ETI. The optimal dosing transitions determined by simulations depend on the characteristics of the perpetrator drugs including the mechanism of CYP3A4 modulation as well as the elimination half- lives. Rifabutin and clarithromycin require 8 and 4 additional days of ETI dose adjustment after drug discontinuation due to their residual enzyme induction or inhibition effects. On the other hand, clofazimine required 42 additional days of adjusted dose after drug discontinuation due to its prolonged elimination half-life. Rifamycins are first-line therapy for the treatment of MAC, the most frequently identified NTM species in pwCF; however, the CYP3A induction effect creates a therapeutic challenge to coadministration with ETI. Although both 95 rifampin and lumacaftor reduce exposure of ivacaftor significantly, a reduction in AUC of ivacaftor was greater with rifampin than with lumacaftor (the AUC ratio 0.11 and 0.22, respectively (43, 100)). The concomitant use of ETI with rifampin is not recommended; however, lumacaftor, a first-generation CFTR corrector therapy, is combined with an increased dose of ivacaftor (250 mg q12h) from the standard dose (150mg q12h) to partially compensate for the induction effect of lumacaftor. The clinical efficacy of lumacaftor/ivacaftor demonstrates the feasibility of dose adjustment of ETI to overcome the CYP3A induction effect of rifabutin. From the ETI-rifabutin DDI simulations, we found that rifabutin led to an ivacaftor AUC ratio of 0.31 indicating a smaller reduction than that seen when ivacaftor was co-administered with either rifampin or lumacaftor. A clinical trial to assess the impact of rifabutin on ETI is ongoing (NCT04840862). Once these data become available, we will reassess the model giving us the opportunity to further learn and confirm; however, in the meantime we believe the modeling is sufficiently verified to guide therapy options and dose adjustments for pwCF. Use of these modified dosing regimens will enable the use of first-line therapy for the treatment of MAC while maintaining the efficacy of highly active CFTR modulators. While the adjusted dosing regimen of ETI for co-administration with rifabutin was designed to match the plasma exposure of ETI with that of standard dose, we recognize that this would result in a substantial increase in cost. Alternatively, the reduced plasma exposure of ETI might still be effective if its 96 exposure at the site of action (lung) is sufficient to achieve effective concentration, thereby potentially avoiding the cost increase associated with the increased dose. We simulated lung concentrations of ETI derived from full PBPK models upon rifabutin co-administration (model data are not shown). The predicted lung trough concentrations of ETI at the steady-state (1.30mg/L, 1.05mg/L, and 0.58mg/L for elexacaftor, tezacaftor, and ivacaftor, respectively) exceed the half-maximal effective concentration (EC50) for chloride transport in phe508del human bronchial epithelial cells (0.05mg/L, 0.31mg/L, and 0.09mg/L for elexacaftor, tezacaftor, and ivacaftor, respectively) (102, 110). This result suggests that the standard dose ETI might maintain therapeutic efficacy despite its decreased plasma exposure upon rifabutin administration. These results require further clinical evaluation. Lung infections with MABSC represent a significant therapeutic challenge in pwCF due to bacterial resistance and poor clinical outcomes with current therapies(111, 112). Clofazimine is a guideline recommended therapy for the treatment of MABSC lung infections. There has been conflicting information regarding the CYP3A modulating activity of clofazimine. Several in vitro studies have suggested that clofazimine is a CYP3A inhibitor (103, 113), but there is also a report of its potential CYP3A4 induction activity in vitro (114). Currently, specific guidance on dosing of ETI when coadministered with clofazimine is not provided due to the insufficient data available to classify the degree of CYP3A modulation 97 by clofazimine(115). Our PBPK simulation results suggest that clofazimine moderately inhibits ivacaftor metabolism leading to drug accumulation. This data is in contrast to a recent case report of a 16-year-old person with CF which found no significant DDI between clofazimine and tezacaftor and ivacaftor (116). The observed data from this report shows ivacaftor AUC ratios of 1.46 and 1.09 on days 8 and 115, respectively demonstrating a weak interaction at day 8 (prior to steady state of clofazimine), but no DDI at day 115. We performed sensitivity analyses in an attempt to address the disparity between these data and our predictions, and to explore the impact of variability in interaction parameters. By altering the parameters for CYP3A modulating potential (Ki, EC50, and Emax) within our model, we were unable to recapitulate the observed date from this case report (Table 3-4). To recapitulate no DDI at the steady state would require potent induction activity such that it could offset the inhibition activity at the steady state; however, potent CYP3A modulating activities are not consistent with the moderate inhibition observed on day 8 of co-administration in this case report. No data on medication adherence or plasma concentrations of clofazimine were provided, and as indicated in the case report, food intake was not controlled although ivacaftor is advised to take with fat containing food to maximize absorption as described in the prescribing information(94). A limitation of the clofazimine PBPK model is that it was only partially validated with a single drug (bedaquiline) that is not classified as a sensitive index substrate. Therefore, clinical DDI study of clofazimine with ETI or another sensitive CYP3A substrate is necessary to validate the model and provide definite guidance on dose 98 adjustment with ETI. Table 3-4. Summary of predicted AUC ratios of ivacaftor from altered CYP3A4 modulating potentials of clofazimine. CYP3A4 modulating potentials of clofazimine Predicted AUC ratio of ivacaftor* Induction potential Inhibition potential EC50 Emax Ki AUC ratio Day 8 AUC ratio Day 115 Parameters of the current model 1.55 3.19 0.000786 1.58 2.97 Parameters altered to match observed AUC ratio on day 8 a 1.05 15.5 0.000786 1.46 2.18 0.18 3.60 0.000786 1.46 2.24 1.55 3.19 0.001 1.46 2.54 Parameters altered to match observed AUC ratio on day 115 a 0.09 7.6 0.000786 1.10 1.09 0.011 3.19 0.000786 0.88 1.09 1.55 3.19 0.012 1.03 1.09 a The observed AUC ratios from case report: 1.46 and 1.09 for day 8 and day 115, respectively. 99 Clarithromycin is guideline recommended treatment for NTM, and it strongly inhibits CYP3A activity by an irreversible mechanism-based inhibition. While azithromycin is more widely used due to its once daily administration and fewer DDIs, there may be instances where clarithromycin is indicated, considering its higher potency against NTM species when compared with azithromycin (117-119). The DDI between clarithromycin and ivacaftor in pwCF has been reported(120). Applying our PBPK model with the same dose and schedule of drugs (e.g. clarithromycin 500mg q12h for 2.5 days with single dose of ivacaftor) demonstrated very close prediction of the observed DDI (3.75 and 3.20 for predicted and observed median AUC ratio of ivacaftor, respectively), which further verifies that the model was adequate for the assessment of this DDI. Following the sponsor recommended dosing regimens of ETI when concomitantly used with a strong CYP3A inhibitor, elexacaftor 200mg/tezacaftor 100mg/ivacaftor 150mg q72h provided the closest PK profiles of standard dosing of ETI alone. A limitation of this study is that no controlled clinical DDI study has been conducted for ETI with rifabutin or clofazimine, or elexacaftor/tezacaftor with clarithromycin. However, the predictions of drug interactions were preceded by the thorough validation of the ETI-DDI models with a range of CYP3A4 modulators conducted in a previous publication(40). The PBPK simulation is a practical tool to investigate drug interactions where clinical investigation is limited, especially for the case of clofazimine which exhibits prolonged half-life. In 100 the absence of clinical data, we used the PBPK modeling approach to provide guidance for the treatment of NTM in pwCF, bridging the gap with urgent need for the proper dosing guidelines. A possible future work may involve simulations of ETI when co- administered with the combination of NTM antibiotics. In particular, when the inducer (rifabutin) and inhibitor (clofazimine or clarithromycin) are co- administered, the CYP3A modulating activities may be balanced out, potentially leading to less DDI thereby decreasing a burden of dose adjustment of ETI. One thing that should be considered is that rifabutin is also metabolized by CYP3A, so the exposure of rifabutin and 25-O-desacetyl rifabutin, its active metabolite which exerts almost equivalent potency to that of rifabutin, can be affected by concomitant use with CYP3A inhibitor, requiring further research to determine the optimal regimen of ETI, clofazimine, and rifabutin. In conclusion, using a PBPK modeling approach, we determined adjusted dosing regimens of ETI when administered concomitantly with NTM therapies that will likely decrease the impact of DDIs. The outcome of this study provides preliminary guidance on dosing of NTM treatments in patients with CF while continuing to receive highly active CFTR modulators. In addition, this work provides tools to address new drug interactions and suggest timely guidelines for dose adjustments where clinical trial data do not yet exist. 101 CHAPTER 4 Safety of elexacaftor/tezacaftor/ivacaftor dose reduction: mechanistic exploration through lung tissue PBPK modeling and a clinical case series Elements of this chapter have been submitted and under review: Eunjin Hong, Regina Li, Alan Shi, Lisa Almond, Joshua Wang, Amin Khudari, Soumar Haddad, Sarkis Sislyan, Marissa Angelich, Peter Chung, Adupa Rao, Paul Beringer. "Safety of elexacaftor/tezacaftor/ivacaftor dose reductions: mechanistic exploration through physiologically based pharmacokinetic modeling and a clinical case series." Pharmacotherapy, 2023;00:1-9. 102 4.1. Aim In addition to drug interactions that were discussed in chapter 2 and 3, CFTR modulators could potentially cause safety issues that include hepatic injury with elevated transaminases, rash, and creatinine phosphokinase elevations. This may warrant interruption on the treatment, and one potential strategy is dose reduction with the goal of maintaining therapeutic efficacy while resolving adverse events. Given the extraordinary clinical response to elexacaftor/tezacaftor/ivacaftor in people with CF, dose reduction to alleviate adverse events may be preferable to abrupt discontinuation which has been associated with precipitation of acute pulmonary exacerbations. Therefore, the aim of this chapter was to assess the clinical response to elexacaftor/tezacaftor/ivacaftor dose reduction in patients experiencing adverse effects to determine optimal dose adjustment. Further, we utilized a full PBPK modeling approach to predict lung exposures of reduced dose ETI, which provided mechanistic support for dose reduction by exploring predicted lung exposures and underlying PK-PD relationships. The study of this chapter contributes to improved treatment for CF, by providing proper guidance on the use of CFTR modulators in clinical practice to mitigate adverse reactions, which enables the safe and effective use of CFTR modulators. Furthermore, in the light of the fact that data on lung tissue exposures of ETI are currently not available, the development of full PBPK model is a useful tool to predict lung concentrations of CFTR modulators and explore clinical response to treatment. 103 4.2. Introduction Although ETI treatment was generally well tolerated in the clinical trials, several adverse events (AEs) were reported with greater frequency compared with placebo and also were experienced in the real-world setting. The rate of hepatic injury with elevated transaminases was increased in patients taking the modulator treatments(19, 28, 29, 32). According to the National Institutes of Health’s database, evidence of elevated liver enzymes in up to 25% of patients taking tezacaftor with ivacaftor, or ivacaftor alone (19). Specifically, ivacaftor has shown hepatotoxicity leading to drug discontinuation in 1-2% of patients studied(33). AEs that occurred frequently also included rash and creatinine phosphokinase elevations. The incidence of rash was highest in females, particularly those on hormonal contraceptives(29). The real-world data have suggested mental health related AEs (e.g., insomnia, anxiety, and deterioration in mental health)(32). These findings demonstrate that chronic treatment with ETI can potentially cause safety issues that may warrant interruption on the treatment. Since ETI significantly improves pulmonary functions and quality of life in pwCF, discontinuation of ETI treatment is often considered a last option. Alternatively, appropriate dose adjustments of ETI to continue effective modulator treatment while resolving or minimizing AEs for the safety of pwCF may be another option. In this chapter, we reported our experience of dose reduction of ETI in 104 individuals who experienced AEs following ETI therapy. Dose adjustment of ETI was associated with recovery of AEs without significant clinical deterioration. The investigation of the clinical cases provides insight on optimized dose for the safe use of ETI while maintaining therapeutic efficacy of modulator therapies. Further, we utilized a whole-body physiologically based pharmacokinetic (PBPK) modeling approach to predict lung exposures of ETI and investigate the hypothesis that drug concentrations at the target site remained adequate. We performed pharmacokinetic-pharmacodynamic (PK/PD) correlations by comparing simulated lung concentrations with the reported half-maximal effective concentrations (EC50) determined in vitro by measurement of chloride transport in human bronchial epithelial cells from a CF donor (121, 122). Taken together, this chapter evaluated the clinical response to elexacaftor/tezacaftor/ivacaftor dose reduction in patients experiencing adverse effects and perform mechanistic explorations of the responses using PBPK modeling. 105 4.3. Methods and Materials 4.3.1. Case Presentation Adults (n=15) prescribed ETI and underwent dose reduction due to the adverse events were identified from the electronic medical records. Cases of ETI dose reductions due to the CF-related liver disease (CFLD) and drug-drug interactions were excluded. Types and extent of AEs, time to onset, dose of ETI administered, and time to resolution of AEs were collected. To track patients’ pulmonary response, the FEV1 and self-reported respiratory symptoms were collected. The percent predicted FEV1 (ppFEV1) was calculated based on global lung function 2012 equations(123). For ppFEV1 (%), the values measured prior to ETI therapy, on standard dose, and on reduced dose were collected. The ppFEV1 of baseline, standard dose, and reduced dose were defined by the ppFEV1 value proximal to the start of ETI, preceding the start of reduced dose ETI, and in a range of 3-6 months after the start of reduced dose, respectively. 4.3.2. Development of PBPK Model of ETI Population model The PBPK models were implemented within the Simcyp Simulator (version 19; Certara, Sheffield, UK). To mimic the CF population, the distribution of ages and proportion of female were corrected to reflect the demographics of CF population in the default healthy population library file (Sim-Healthy volunteers) provided in Simcyp ® (72). For trial design, we used a population size of 100 (10 trials with 10 subjects in each trial). 106 Perfusion-limited full PBPK model Although plasma concentrations are a frequently used indicator to predict therapeutic efficacy, the lung tissue concentrations would ideally be obtained to correlate with the treatment effect of ETI on the CFTR protein located within the lung. As lung tissue concentrations is not easily accessible, we developed a full PBPK model of elexacaftor, tezacaftor, and ivacaftor to predict their tissue concentrations. In particular, a perfusion-limited full PBPK distribution model was implemented, where it is assumed that drugs passively diffuse into tissue water and reach equilibrium instantaneously and distribute homogeneously into the available space. PBPK models with small molecule therapeutics, especially lipophilic compounds like ETI, typically assume perfusion rate-limited kinetics as blood flow becomes the limiting process for tissue distribution in these cases(124). Our initial models utilized minimal PBPK models of ETI that we constructed in a recent investigation to determine drug-drug interactions of ETI when co- administered with nirmatrelvir-ritonavir (Paxlovid)(40). In the current study, we extended the models to full PBPK by incorporating the predicted tissue:plasma partition coefficients (Kp) values. Kp values are generally predicted using one of the three mechanistic methods as Kp values are not readily available, particularly for human tissues(125). For elexacaftor and ivacaftor, the Kp values were predicted by using method 2 (the Rodgers and Rowland method) implemented 107 within Simcyp(60). Method 2 accounts for binding of the ionized fraction of the drug, thereby improving predictions for both acids and bases. Since elexacaftor and ivacaftor are monoprotic (pKa of 5.04) and diprotic acids (pKa 1 of 9.4 and pKa 2 of 11.6), respectively, method 2 was the most appropriate to predict their Kp values. For tezacaftor, Kps were predicted by method 3, which is derived from a combination of the Fick-Nernst-Planck equation with the Rodgers and Rowland’s method(126). The lung Kp values predicted for elexacaftor, tezacaftor, and ivacaftor were 0.36, 0.49, and 2.7, respectively. 4.3.3. PBPK Model Verification Plasma pharmacokinetic simulations The PK profiles of ETI following multiple administrations of clinically relevant doses (elexacaftor 200mg qd, tezacaftor 100mg qd, and ivacaftor 150mg q12h) were simulated to verify the performance of the PBPK models. ETI was orally administered under fed conditions to mimic the clinical setting, where a fat- containing food is required for optimal absorption of ETI. The simulated data were qualified using the observed PK data in a CF population with age greater than 17 years. The prediction accuracy for the area under the curve (AUC) and maximum plasma concentration (Cmax) values were calculated as a ratio of mean observed values over mean predicted values. Successful model performance was defined by mean ratios of AUC and Cmax within a two-fold range as previously described(127, 128). 108 Assessing pharmacodynamic efficacy based on phase 2 clinical trial data The validated PBPK model was further assessed using dose-response relationship data from phase 2 clinical trials. The lung PK following administration of the range of doses tested in the published phase 2 trials were simulated and plotted relative to the reported in vitro EC50. The simulations were performed to verify that the current PBPK model is able to recapitulate the observed clinical efficacy of ETI by determining whether ETI reaches sufficient therapeutic concentrations in the lung tissue. Here, we compared the total tissue concentrations to the in vitro EC50 assuming that the unbound fraction of drug would be similar in both cases, since unbound tissue concentrations and protein binding adjusted EC50 values were not available. The dose-response data of ETI were obtained from NDA documents(37, 38, 121). For ivacaftor, the effect of multiple doses of monotherapy (25mg q12h to 250mg q12h) was tested. For tezacaftor, a range of tezacaftor doses (10mg qd to 150mg qd) when administered in combination with 150mg q12h of ivacaftor was evaluated. For elexacaftor, the efficacy of triple combination therapy with elexacaftor (50mg qd to 200mg qd) in combination with tezacaftor 100mg qd and ivacaftor 150mg q12h was evaluated. All doses tested were efficacious except for the lowest dose (tezacaftor 10mg daily) where suboptimal changes in lung function occurred. Sweat chloride concentrations were chosen as a measure of efficacy for ETI, with the exception of tezacaftor where FEV1 was used as sweat chloride data was not available in the NDA. Sweat chloride may serve as the 109 most relevant translational correlate to the EC50 measured on chloride transport in vitro. Further, sweat chloride has been suggested as a surrogate marker of CFTR function that would potentially guide dose adjustments of CFTR modulators(129). The EC50 values were obtained from in vitro studies of chloride transport in phe508del human bronchial epithelial cells isolated from CF donor lung tissue (121, 122). For ivacaftor, the EC50 value measured independently was used, as a phase 2 trial has been conducted for ivacaftor monotherapy. For elexacaftor and tezacaftor, the EC50 values measured in triple and double combinations were used respectively, to match the condition of phase 2 trials that were conducted to evaluate the effect of combination therapy. The reported EC50 values are 0.05 mg/L, 0.31 mg/L, and 0.09 mg/L for elexacaftor, tezacaftor, and ivacaftor, respectively. 4.3.4. Model Application: Prediction of lung concentrations and PK/PD correlations The PBPK models were applied to predict the efficacy of ETI, when its dose was reduced to alternate-day dosing regimen. We simulated the steady-state lung PK of reduced dose ETI and compared the concentrations with reported EC50 values, to elucidate the potential rationale for the maintained therapeutic efficacy after dose reduction. 110 4.4. Results 4.4.1. Case presentation Fifteen individuals who experienced one or more clinically significant adverse events requiring ETI dose adjustment were included in this case series. Table 1 summarizes the baseline characteristics of the 15 individuals. The median age [range] of the study population was 34 [21-56], and the number of females were 2 (13.3%). Five patients had received previous modulator treatment prior to starting ETI; 4 (26.7%) patients received prior dual combination therapy of tezacaftor and ivacaftor and 1 patient (6.7%) received a combination of lumacaftor and ivacaftor. Table 4-1. Baseline characteristics of the population included in the case series. N=15 Median age [range], years 34 [21-56] Female Sex, n (%) 2 (13.3%) Median BMI [range] 25.99 [20.34-33.23] Genotype F508del/F508del 7 (46.7%) F508del/MF 7 (46.7%) F508del/novel variant 1 (6.7%) Previous modulator tezacaftor/ivacaftor 4 (26.7%) ivacaftor/lumacaftor 1 (6.7%) None 10 (66.7%) 111 Adverse events, dose adjustment, ppFEV1, and the self-reported respiratory symptom data are summarized in Table 4-2. Pulmonary function was measured at three different times for each case, except in one case where baseline ppFEV1 was not available due to the COVID restrictions. The actual mean time intervals of ppFEV1 collected were 1.4 months before the start of ETI, 2.7 months before the start of reduced dose, and 4.2 months after the start of reduced dose for baseline, standard dose, and reduced dose ppFEV1, respectively. To confirm that ppFEV1 was stable while patients were on standard dose ETI, we compared ppFEV1 obtained 1-3 month after the start of ETI with that obtained immediately preceding the start of reduced dose, and the ppFEV1 measured at two different times were not significantly different (mean ppFEV1 of 71.1% vs 70.5%, p=0.3253). Table 4-2. Response to ETI dose reduction due to adverse events Case †Adverse Events (time to onset after ETI start) Dose reduction ‡Response to dose reduction §ppFEV1 (%) Self-reported respiratory symptoms Baseline Standard dose Adjusted dose 1 ¶ ALT category 2 (2 months) QOD Improved within 1 month after reduction, and wnl after 6 months 57.4 64.1 66.0 No change in respiratory signs or symptoms reported since dose reduction 2 GI symptoms Hypoglycemia 5x/week Improved within 1 month after reduction, 54.0 52.3 53.8 No change in respiratory signs 112 ALT category 1 (18 months) and wnl after 3 months or symptoms reported since dose reduction 3 ALT category 1 (5 months) 5x/week, QOD Wnl a week after QOD 34.6 40.3 40.0 A slight increase in productive cough since reducing the ETI dose 4 ALT category 1 (12 months) 5x/week, QOD Slightly elevated baseline ALT, and remained elevated after reduced dose 73.0 82.6 84.4 No change in respiratory signs or symptoms reported since dose reduction 5 ALT category 1 (12 months) ELX/TEZ/ IVA x 1 morning + IVA night Improved within 2 months after reduction, and wnl after 7 months 96.0 115.7 116.1 No change in respiratory signs or symptoms reported since dose reduction 6 ALT category 2 (2 months) QOD Improved within 1 month after reduction, and wnl after 2 months - 77.5 72.2 A slight increase in congestion and cough 7 ALT category 1 (6 months) QOD Improved within 2 months after reduction, and wnl after 9 months 67.0 68.8 66.3 No change in respiratory signs or symptoms reported since 113 dose reduction 8 ALT category 2 (3 months) QOD Temporarily discontinued ETI due to the persistent transaminitis. While not on ETI, and with resumed reduced dose ETI, LFT levels remained wnl. 72.7 83.6 86.6 Significant increase in cough, sputum, and chest tightness after stopping ETI. Symptoms significantly improved after resumption of reduced dose ETI 9 ALT category 1 (16 months) QOD Improved within 1 month after reduction, but remained slightly elevated. 54.2 73.8 78.7 No change in respiratory signs or symptoms reported since dose reduction 10 ALT category 2 (2 years) QOD No improvement within 1 month of dose reduction, improved but still elevated at 8 months after dose reduction 39.0 48.7 45 No change in respiratory signs or symptoms reported since dose reduction 11 High blood glucose level QOD Improved blood glucose control within 2 months 78.2 88.2 87.1 No change in respiratory signs 114 (1 year) after reduction, wnl after 3 months or symptoms reported since dose reduction 12 ALT category 2 (20 months) QOD Wnl 2 weeks after reduction 46.5 58.0 60.1 No change in respiratory signs or symptoms reported since dose reduction 13 GI symptoms ALT category 1 (8 months) ELX/TEZ/ IVA x 1 morning + IVA night Wnl 2 weeks after reduction 53.7 72.7 68.3 No change in respiratory signs or symptoms reported since dose reduction 14 ALT category 2 (2 years) 3x/week Improved within 1 month after reduction, and wnl after 8 months 54.8 65.8 66.0 Reported increased cough and mucus production, but is still able to walk for exercise 15 ALT category 2 (18 months) 3x/week Wnl 3 weeks after reduction 60.5 64.7 62.9 No change in respiratory signs or symptoms reported since dose reduction 115 †For ALT increase, category 1 is ALT increase more than 1 x ULN and less than 3 x ULN, and category 2 is ALT increase more than 3 x ULN. ‡wnl: within normal limits (ALT < 55 IU/L) §The ppFEV1(%) of baseline, standard dose, and reduced dose are defined by the ppFEV1 value measured preceding the start of ETI, preceding the start of reduced dose ETI, and within 3-6 months after the start of reduced dose, respectively. (The actual mean time intervals of data collected were 1.4 months, 2.7 months, and 4.2 months for baseline, standard dose, and reduced dose, respectively.) ¶ Cases of ALT increase more than 5 x ULN. 116 Overall, there was no significant change in lung function after ETI dose reduction. The ppFEV1 remained stable, above pre-ETI treatment in all individuals. For cases (n=14) where baseline data were available, the mean baseline, standard dose, and reduced dose ppFEV1 were 60.1%, 70.0%, and 70.1%, respectively, and the median values were 56.1%, 67.3%, and 66.2%, respectively. While both standard and reduced dose showed significant improvement in ppFEV1 compared with baseline (p value < 0.001), no significant difference in ppFEV1 occurred after reducing the dose (p value of 0.8424). Out of 15 cases, 12 cases reported no change in respiratory symptoms after dose reduction, while 3 cases reported a slight increase in symptoms including cough and sputum production. Of the patients who experienced changes in respiratory symptoms, one had a 5.3-point reduction in ppFEV1 (from 77.5% to 72.2%) while the remaining two did not experience any change in ppFEV1 post dose reduction. Out of 15 patients, 14 cases had elevated transaminases, including 2 patients who also reported GI symptoms (e.g. abdominal pain). In one case dose reduction was based on patient’s decision due to uncontrolled glucose levels. Most (12, 80%) received half dose (alternate-day dose or 3 times a week administration). The dose reduction in the remaining 3 cases was less than half dose, due to less severe adverse events. A resolution or improvement in toxicities occurred in 13/15 (86.7%) cases after dose reduction. Two cases did not show improvement in their ALT levels after dose reduction: one case had a slightly elevated baseline ALT. After start of ETI, ALT increased by 60% and was 117 remained elevated with reduced dose (5x/week then alternate-day dose). In the remaining case, temporary discontinuation of ETI occurred due to the persistent transaminitis. With discontinuation, LFTs returned to the normal range; however, the patient experienced a significant increase in cough and chest tightness, so reduced dose ETI was resumed and LFTs remained in the normal range. 4.4.2. Verification of the full PBPK models of ETI PBPK models of ETI recapitulated clinically observed PK profiles. Model predictive performance was assessed using observed plasma pharmacokinetic data sets from clinical trials(39, 75). The observed and simulated steady-state PK of ETI following a standard dose administration of elexacaftor 200mg qd, tezacaftor 100mg qd, and ivacaftor 150mg q12h are summarized in Table 3-3. The predicted steady-state AUC and Cmax of ETI were in the range of 0.8 to 1.2 of the observed values demonstrating the excellent performance of the model. 118 Table 4-3. Comparison of PK parameters between simulated and observed data for model verification of ETI PK study Steady-state PK parameters Drug Regime n Simulated Observed Cmax (mg/L) †AUC (mg∙h/L) Cmax (mg/L) †AUC (mg∙h/L) elexacaftor 200mg qd Mean 7.2 149.0 8.8 167.0 CV(%) 36.3 40.3 24.6 30.2 Simulated/ observed 0.8 0.9 tezacaftor 100mg qd Mean 5.9 109.7 6.7 92.4 CV(%) 36.5 44.5 20.8 25.8 Simulated/ observed 0.9 1.2 ivacaftor 150 mg q12h Mean 1.1 10.0 1.3 12.1 CV(%) 37.0 42.8 27.8 34.5 Simulated/ observed 0.9 0.8 † AUC(0-24h) for elexacaftor and tezacaftor, and AUC(0-12h) for ivacaftor. 119 Assessing efficacy of ETI at a range of doses in clinical trials We next assessed the full PBPK models with dose-response data obtained from phase 2 clinical trials. From clinical trials of ivacaftor monotherapy, the statistically significant mean change from baseline in sweat chloride was observed in all ivacaftor doses tested (25, 75, and 150mg groups)(37). In our PBPK simulations of ivacaftor with the same range of doses, the predicted mean lung concentrations of ivacaftor exceeded the reported EC50 at all dosing levels (Figure 4-1). The trough concentration of ivacaftor at the lowest dose (25mg q12h) was 0.31 mg/L, which exceeded the EC50 by 3.4-fold. For tezacaftor, the double combination regimen of tezacaftor (10, 30, 100, and 150mg groups) with ivacaftor demonstrated a significant improvement in FEV1 from baseline for all groups except for 10mg (p value of 0.2368)(38). The predicted mean lung concentrations of tezacaftor all exceeded the EC50 with the exception of the 10mg daily regimen (Figure 4-1). Figure 4-1. Predicted steady-state lung concentration profiles of ETI at doses tested in phase 2 clinical trials: standard dose (green), 2 nd lower dose (red), and the lowest dose tested (blue). The predicted lung concentrations of ETI were all above the EC50 except the tezacaftor 10mg qd, which was in agreement with phase 2 clinical trials result. 120 Treatment of ETI triple combination with the range of elexacaftor doses (50, 100, and 200mg groups) all resulted in significant improvements in sweat chloride concentrations compared to baseline(121). The PBPK simulation of elexacaftor for all dose groups also resulted in lung concentrations which exceeded the EC50 (Figure 4-1). The trough concentration of elexacaftor at the lowest dose (50mg daily) was 0.49 mg/L, which was 9.8-fold higher than the reported EC50. Simulations indicated lung concentrations were above the EC50 in all cases where significant improvements in lung function occurred. In one case (tezacaftor 10mg daily) where suboptimal changes in the FEV1 were observed, simulations indicated lung concentrations below the EC50 which corresponds to the clinical observations. 4.4.3. Prediction of efficacy of ETI at a reduced dose The simulated lung concentration profiles of ETI with a reduced dose (alternate-day dose) at the steady-state are shown in Figure 4-2. The predicted mean trough concentrations are 0.89, 0.83, and 0.79 mg/L for elexacaftor, tezacaftor, and ivacaftor, respectively. The reduced doses of elexacaftor and ivacaftor are expected to provide lung trough concentrations above the reported EC50 in the majority (>95%) of population. For tezacaftor, the lower 5 th percentile’s trough concentration was 0.26 mg/L, which was slightly below the reported EC50 (0.31 mg/L), but it was able to provide trough concentrations above EC50 in more than 90% of population. This data suggests that the efficacy 121 of ETI would likely be maintained despite the decreased concentrations of ETI from the standard dose. The simulation results also correspond to our clinical experience where the patients maintained clinical stability upon dose reductions of ETI. Especially for ivacaftor, the EC50 value measured in combination with tezacaftor was much lower than that measured independently (0.002 mg/L and 0.09 mg/L, respectively), suggesting that the ivacaftor dose could potentially be reduced further. Figure 4-2. Predicted steady-state lung concentration profiles of standard dose of ETI (green) and reduced dose of ETI (red) with lower 10 th and 5 th percentile values of reduced dose (gray dotted lines). The reduced dose was expected to provide lung trough concentrations above the reported EC50 in the majority (>90%) of population. 122 4.5. Discussion We present here a case series with data extracted from clinical notes and resulting from a structured clinical assessment. In this case series, ALT increase was the major AE driving dose adjustments, and none of the patients had concurrent bilirubin elevation. Having reduced dose of ETI resulted in maintained efficacy and resolved AEs in most cases. For cases (n=14) where baseline ppFEV1 were available, there were 9.8 points absolute increase in mean standard dose ppFEV1 compared with baseline, which was less efficacious than what reported from the phase 3 clinical trial which showed 13.8 points higher ppFEV1 after 4 weeks of ETI treatment (29). This might be due to the difference in study population, as the study above was exclusively performed in patients who have a single phe508del allele. Further, 5/15 (33.3%) of patients in our case series had previous modulator treatment. The reduced dose maintained the significant benefit in lung function, with an average improvement of 10.0 ppFEV1 when compared to baseline. The maintained efficacy of ETI after dose reduction is consistent with prior findings in a case series documenting ETI dose reduction due to mental health AEs(130). They reported stable lung function after dose reduction, where mean ppFEV1 at standard dose was 71% and 69% after dose reduction (either one tablet of elexacaftor/tezacaftor/ivacaftor in the morning or one tablet of elexacaftor/tezacaftor/ivacaftor in the morning and one tablet of ivacaftor in the evening). Although liver toxicities accounted for most cases in our study, we had 123 one case of dose reduction due to the elevated blood glucose. ETI treatment is generally associated with either no change in glycemic control or improvement in cystic fibrosis-related diabetes due to increase in insulin secretion and sensitivity, however, there have also been some documentations of worsened glycemic control(131). This is potentially due to increased nutritional absorption and appetite, although more research is needed to determine if there is a relationship between hyperglycemia and ETI therapy. As patients who underwent dose reduction of ETI were clinically stable, we hypothesized that reduced dose of ETI would achieve sufficient therapeutic concentrations and retain efficacy. The dose selection of drugs should consider drug exposure at the target tissue, which for the CFTR modulators the lung is the primary site of action of action. However, in vivo measurements of lung concentrations are difficult to obtain from people with CF due to the invasive nature of the procedure. Therefore, full PBPK modeling approach is a valuable tool to obtain such information. The comparison of the predicted lung concentrations with published EC50 values indicate that patients undergoing dose reduction due to adverse effects will not likely undergo clinical deterioration. An example where dose adjustment is applied to CF therapy is the drug combination of lumacaftor-ivacaftor (ORKAMBI®). Lumacaftor is a strong CYP3A inducer, resulting in an 80% reduction in the plasma AUC of ivacaftor(132). In order to overcome the induction effect, the dose of ivacaftor in the combination 124 product is increased from 150 mg to 250 mg q12h. The mean steady state AUC of ivacaftor when administered alone following 150 mg q12h is 10.7 compared to 3.66 mg x h/L following 250 mg q12h in combination with lumacaftor(37, 132). Although the increase in ivacaftor dose with lumacaftor only partially compensated for the induction effect on PK, it resulted in retained therapeutic efficacy. The main limitations of the study include the lack of standardized assessment of the respiratory symptoms using a validated questionnaire as these data are based self-reported symptoms. In addition, our full PBPK model was not verified against the tissue concentrations of ETI as only plasma PK data were available. Instead, we assessed the model with dose-response data from phase 2 clinical trials, but there were limited data available to fully validate PK/PD correlations as nearly all doses tested were efficacious. Also, we did not develop an integrated PK/PD model to quantitatively predict FEV1 responses, as triple combination therapy of ETI did not demonstrate a dose response relationship based on phase 2 data (22). Despite these limitations, this study provides increased confidence in the ability to manage ETI dose adjustments for people with CF experiencing AEs. In conclusion, the ETI treatment can result in drug toxicities including liver function test elevations, which could be ameliorated by dose reduction without significant clinical deterioration. PBPK modeling approach provides a 125 mechanistic basis for the retained clinical response with reduced dose ETI that has been empirically used, by providing further understanding of predicted drug exposure in the target tissues. Since access to CFTR modulators has been gradually increasing for people with CF, we believe that this timely report will help to guide treatment strategy of CFTR modulators which are essential treatment for people with CF. In addition, our work provides the ETI model for the utility of modeling to investigate tissue concentrations that can be correlated to in vitro efficacy data. This increases our confidence and understanding around dose adjusting when AEs are observed, maintaining the most effective therapeutic options for these patients. Although extremely challenging, determination of lung concentrations in patients is required to further verify the model and increase confidence for prospective dose adjustment. 126 CHAPTER 5 Conclusion and Future work 5.1. Conclusions The results from this thesis have provided proper guidance on the use of CFTR modulator therapies to mitigate drug interactions or adverse events which enables the safe and effective use of modulators. Further, this thesis has demonstrated that the PBPK modeling approach is a powerful and evolving technique that could be used to understand the pharmacokinetic of CFTR modulators and address several drug-related therapeutic challenges which are clinically important. In Chapter 2, the CYP3A-derived interactions of elexacaftor, tezacaftor, and ivacaftor (ETI) with nirmatrelvir-ritonavir was investigated, which was one of the most clinically important issues for the treatment of COVID-19 in CF. For this, PBPK models of ETI were successfully developed by incorporating drug-specific parameters. The model performed well in the prediction of the PK of ETI. Further, the models were verified against the prediction of DDIs with CYP3A inhibitors and inducer. The satisfactory performance of the model led to a further prediction of the DDI between ETI and nirmatrelvir-ritonavir, and we demonstrated that administration of ritonavir 100mg twice daily for 5 days required a significant 127 reduction in ETI dosing frequency due to the mechanism-based inhibition of ritonavir. This result could serve as a guide for the optimized treatment of COVID-19 in CF patients who are receiving CFTR modulators. In Chapter 3, the PBPK model was further used to predict the DDI of ETI with selected NTM therapies. NTM requires the use of a multi-drug combination regimen over a prolonged period of up to 18 months, creating potential for DDI with CFTR modulators which are also chronically administered. The DDIs of ETI with rifamycins (e.g., rifampin and rifabutin) potentially compromise the treatment efficacy for NTM in pwCF by precluding the first-line antibiotics. Additionally, clarithromycin and clofazimine are CYP3A inhibitor which would cause significant DDI with ETI. We evaluated these interactions and determined appropriate dose adjustments and transitions of ETI using established PBPK model. This contributes to improved treatment for CF by providing evidence in support of the use of key NTM treatments in CF patients receiving concomitant ETI therapy. In Chapter 4, the safety of dose reduced ETI was explored. Chronic treatment with CFTR modulators can potentially cause safety issues including hepatic injury that may warrant interruption on the treatment. One potential strategy is dose reduction with the goal of maintaining therapeutic efficacy while resolving adverse events. To determine optimal dose adjustment, we investigated our clinical experience of dose reduction in individuals who experienced adverse events following ETI therapy. Further, we utilized a full 128 PBPK modeling approach to predict lung exposures of reduced dose ETI, which provided mechanistic support for dose reduction by exploring predicted lung exposures and underlying PK-PD relationships. This serves as a guide for pwCF who are experiencing adverse events requiring dose reduced ETI. Overall, this thesis provides guidance on optimized use of CFTR modulators based on advanced understandings on their drug interactions and adverse effects, which is of great importance in the era of modulators. The PBPK modeling has significantly contributed to the prediction of PK and PD of CFTR modulators, enabling the establishment of proper guidance in each clinical cases to improve treatment response and prevent adverse events. 129 5.2. Future work While the enclosed results provided guidance on the proper use of CFTR modulators in various therapeutic scenarios not yet described by clinical studies, as well as the PBPK models that could be further applied to predict therapeutic responses of ETI, there were several limitations requiring future studies for further insight and advancement. In this Chapter, we will discuss potential studies that could be carried out to answer the remaining questions. 5.2.1. Development of CF specific population model Disease may alter the physiological and biochemical characteristics, that could influence pharmacokinetic properties of drugs which may contribute to variation in drug exposure. Similarly, CF disease is also known to change several physiological parameters(86, 133). Indeed, multiple studies which investigated the impact of CF on the PK of drugs (e.g., anti-infective drugs) have shown that the PK of some drugs could be altered, although the PK of CFTR modulators has been reported to be similar with healthy controls. Therefore, CF specific population model would be useful for the prediction of PK in CF, especially for the cases where PK obtained from healthy volunteer is not representative enough to guide dosing in PwCF. For the development of CF population model and application of the model to PBPK simulation, detailed knowledge of CF pathophysiology as well as the mechanistic understanding of factors driving PK alterations in CF would be 130 essential. For this, a comprehensive literature search is required to identify clinical studies investigating PK or physiological variables (e.g., intestinal pH, serum albumin concentration, body fat %, GFR, etc) in CF population. In general, slower absorption rates, higher clearance, and equal or increased volume of distribution have been observed in PwCF when compared to healthy volunteers(86). In addition, a recent meta-analysis investigated significantly lower serum albumin levels in PwCF compared to controls(134). These data need to be quantitatively summarized, and the estimates of the physiological parameters could be used to construct a virtual CF population model. Additionally, the effect of CFTR modulators on the physiological variables is also needed to be considered. Restoring CFTR function in CF, the use of CFTR modulators could potentially reduce the differences between CF and healthy controls observed in previous publications. As CFTR modulators are becoming more widely available, it would be critical for the development of CF model in the post modulator era. 5.2.2. Application of organoid system to theratyping Theratyping is to group CFTR variants according to their responses to CFTR modulator compounds, which would be an important approach to provide personalized treatment strategies for CF(3). The goals of theratyping are to assess modulator responsiveness of unique CFTR variants and compare several modulator responses of various variants. This data would not only guide 131 selection of modulators for patients for whom multiple options exist, but also expand patient access to CFTR modulators(135). Therefore, advancement and validation of theratyping technology will be necessary. Patient-derived organoids have emerged as an effective in vitro model to study disease and predict in vivo therapeutic responses(136). Organoids are long-term culture system which allow cells to be grown in 3D culture conditions mimicking tissue architecture found in vivo(137, 138). Therefore, the CF patient derived organoid system could be applied toward theratyping, assessing CFTR modulator effects in vivo(139-142). Dekkers et al. previously described the assay to measure CFTR modulator activity using intestinal organoids, where they observed a dose-dependent increase of forskolin-induced organoid swelling for ivacaftor(141). More recently, the organoid models generated from patient- derived nasal cultures were developed, in order to mimic the CF respiratory tissue and better characterize drug response of new mutations(16, 143, 144). These patient specific organoid models are attractive systems for testing modulator responses against various genotypes, especially for the rare pathogenic variants where the clinical trials have been challenging due to the scarcity of patients. To validate and apply the organoid model towards clinical translation and theratyping, we may need to perform co-clinical trials to compare drug response in organoids with clinical responses in the corresponding patients. This is to confirm the predictive performance of the model and establish 132 correlation of in vitro responses and in vivo parameters (e.g., FEV1 and sweat chloride level). If organoid model is verified and applied to theratyping, this would enable more rapid modulator testing for CF patients with specific variants and provide better understanding of the clinically relevant drug responses. Further, through the collaboration with PBPK simulation, the model predicted PK/PD would facilitate therapeutic decision making for effective CF treatment. 5.2.3. Next generation CFTR modulator combination The next generation CFTR modulator combination therapy, VX121/tezacaftor/deutivacaftor, is currently being investigated in a phase 3 clinical study (NCT05033080). It has shown the clinical benefit in pwCF with one F508del mutation and one minimal function mutation or with two F508del mutations in a previous phase 2 study(NCT03912233)(145). Deutivacaftor (VX561) is a deuterated analogue form of ivacaftor, where one of the tert-butyl groups of ivacaftor is replaced by a per-deuterated one (Figure 5-1). This compound has shown similar pharmacologic potency with that of ivacaftor, but its metabolic stability is significantly increased compared to ivacaftor(146). In a phase 1 study, deutivacaftor showed better pharmacokinetics profile with longer Figure 5-1. Structure of ivacaftor and deutivacaftor. 133 half-life and greater plasma concentrations at 24h, which enabled once daily dosing in CF(146, 147). Further, the combination regimen that includes VX121 showed increased efficacy restoring CFTR function compared to ETI in human bronchial epithelial cells in vitro. 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State of the Art on Approved Cystic Fibrosis Transmembrane Conductance Regulator ( CFTR) Modulators and Triple-Combination Therapy. Pharmaceuticals (Basel, Switzerland) 14, (2021). 150 Appendices A. List of Selected Publications 1. Eunjin Hong, Lisa Almond, Peter Chung, Adupa Rao, Paul Beringer. "Physiologically‐Based Pharmacokinetic‐Led Guidance for Patients With Cystic Fibrosis Taking Elexacaftor‐Tezacaftor‐Ivacaftor With Nirmatrelvir‐Ritonavir for the Treatment of COVID‐19." Clinical Pharmacology & Therapeutics, 2022;111(6):1324-1333. 2. Eunjin Hong, Lisa Almond, Peter Chung, Adupa Rao, Paul Beringer. "Physiologically-Based Pharmacokinetic (PBPK) modeling to guide management of drug interactions between elexacaftor-tezacaftor-ivacaftor and antibiotics for the treatment of non-tuberculosis mycobacteria (NTM)." Antimicrobial Agents and Chemotherapy, 2022;66(11): e01104-22. 3. Eunjin Hong, Regina Li, Alan Shi, Lisa Almond, Joshua Wang, Amin Khudari, Soumar Haddad, Sarkis Sislyan, Marissa Angelich, Peter Chung, Adupa Rao, Paul Beringer. "Safety of elexacaftor/tezacaftor/ivacaftor dose reductions: mechanistic exploration through physiologically based pharmacokinetic modeling and a clinical case series." Pharmacotherapy, 2023;00:1-9. 4. Eunjin Hong, Alan Shi, Paul Beringer. “CFTR modulator drug interactions: a review of the evidence and clinical implications.” Expert Rev Drug Metab Toxicol 2023. Manuscript submitted. 151 B. List of Presentations 1. Eunjin Hong, Adupa Rao, Paul Beringer. "Evaluation of the drug-drug interaction potential of rifabutin and elexacaftor/tezacaftor/ivacaftor using a physiologically based pharmacokinetic simulation approach.” Pediatric Pulmonology, vol. 55 (2020): pp. S244 - S244. *Chosen as a Top 3 Abstract and selected for oral presentation. 2. Eunjin Hong, Adupa Rao, Paul Beringer. “Evaluation of the drug-drug interaction potential of clofazimine and ivacaftor using a physiologically based pharmacokinetic simulation approach.” Journal of Cystic Fibrosis, Vol. 20 (2021): pp. S117 - S118 *Chosen as a Top 3 Abstract and selected for oral presentation. 3. Eunjin Hong, Sarah Parsons, Paul Beringer. “PBPK modeling of lung tissue predicts efficacy of elexacaftor/tezacaftor/ivacaftor with concomitant rifabutin administration.” Submitted to ASCPT 2023 annual meeting, March 22-24, Atlanta, GA. *Chosen as a recipient of a Top Poster Ribbon and selected for oral presentation.

Abstract (if available)

Abstract Cystic fibrosis (CF) is a chronic, hereditary, multi-organ disease caused by mutations in the gene that encodes for the cystic fibrosis transmembrane conductance regulator (CFTR) protein. Over the past decade, CF treatment has transitioned from therapies treating the secondary consequences of the disease to therapies directed towards restoration of CFTR function with the development of CFTR modulators. CFTR modulators are small molecules protein corrector/potentiator therapies that restore chloride channel function of CFTR, thereby resulting in substantial improvements in lung function and nutritional status in people with CF (pwCF). Ivacaftor was the first CFTR modulator approved and is also registered for clinical use in combination with other CFTR modulators as lumacaftor/ivacaftor, tezacaftor/ivacaftor, and elexacaftor/tezacaftor/ivacaftor (ETI), which have been the cornerstone of the treatment for many pwCF.

As CFTR modulators are used chronically, learning more about the possible drug-drug interactions (DDIs) and adverse effects is of great importance in the era of highly effective CFTR modulators. The application of physiologically based pharmacokinetic (PBPK) modeling has significant advantages towards the simulation of therapeutic scenarios regarding the use of CFTR modulators, which are not yet described by clinical studies. The prediction of pharmacokinetics (PK) and pharmacodynamics (PD) based on PBPK model enables finding the optimized dose of CFTR modulators in each clinical cases to improve treatment response and prevent adverse events. This dissertation addresses potential issues of CFTR modulator associated with CF treatment by incorporating PBPK modeling approach and explores the optimization of CFTR modulator therapies in clinical practice.

One of the most clinically important issue of modulators is cytochrome P450 3A (CYP3A)-derived drug interaction, as ivacaftor, tezacaftor and elexacaftor are all extensively metabolized by CYP3A. In particular, treatment of COVID-19 (SARS-CoV-2) with nirmatrelvir-ritonavir (Paxlovid) possess significant therapeutic challenge. For treatment of COVID-19, ritonavir is co-administered with nirmatrelvir to boost nirmatrelvir concentrations to achieve its therapeutic levels by inhibiting CYP3A-derived metabolism. However, due to the potent inhibition effect of ritonavir, it may increase plasma concentrations of ETI causing potential adverse drug reactions. CF patients are more at risk of serious illness following COVID-19 infection and hence it is important to manage the DDI risk and provide treatment options. To investigate the magnitude of drug interactions and provide dosing recommendations of ETI to overcome the interaction with ritonavir, the treatment scenarios were simulated using PBPK approach. We demonstrated that administration of ritonavir 100mg twice daily for 5 days required a significant reduction in the ETI dosing frequency with delayed resumption of full dose due to the mechanism-based inhibition by ritonavir.

Other notable therapeutic challenge is in the treatment of Nontuberculous mycobacteria (NTM). NTM are increasingly being isolated from the sputum of pwCF and are the pathogen of concern due to its association with deterioration of lung function. It requires the use of a multi-drug combination regimen of antibiotics over a prolonged period of up to 18 months, creating the potential for DDI with CFTR modulators. For example, concomitant use of strong CYP3A inducers including the rifamycins (e.g., rifampin and rifabutin) is not recommended with ETI, and this potentially compromises the treatment efficacy for NTM in pwCF by precluding the first-line antibiotics. Additionally, clarithromycin and clofazimine, also guideline recommended treatment for NTM, are CYP3A inhibitor which would cause significant DDI with ETI. We extended our PBPK model to evaluate the interactions with selected guideline recommended NTM therapies and to determine appropriate dose adjustments and transitions of ETI. This PBPK-guided ETI dosing allowed the use of key antibiotics for the treatment of NTM in pwCF.

Lastly, chronic treatment with CFTR modulators can potentially cause safety issues (e.g., hepatic injury with elevated transaminases, rash, and creatinine phosphokinase elevations) that may warrant interruption on the treatment. One potential strategy is dose reduction with the goal of maintaining therapeutic efficacy while resolving adverse events. To determine optimal dose adjustment, we investigated our clinical experience of dose reduction in individuals who experienced adverse events following ETI therapy. Further, we utilized a full PBPK modeling approach to predict lung exposures of reduced dose ETI, which provided mechanistic support for dose reduction by exploring predicted lung exposures and underlying PK-PD relationships.

Taken together, the results of these investigations provide proper guidance on the use of CFTR modulators in clinical practice to mitigate drug interactions or adverse reactions, which enables the safe and effective use of CFTR modulators. Further, this thesis demonstrates that PBPK modeling could be used to optimize CFTR modulator treatment in the case of complex and clinically important drug therapy problems.

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Asset Metadata

Creator Hong, Eunjin (author)

Core Title Pharmacokinetic and pharmacodynamic optimization of CFTR modulator therapy to mitigate potential drug interactions and adverse events in people with cystic fibrosis

School School of Pharmacy

Degree Doctor of Philosophy

Degree Program Clinical and Experimental Therapeutics

Degree Conferral Date 2023-05

Publication Date 03/30/2023

Defense Date 02/17/2023

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag adverse events,CFTR modulator,cystic fibrosis,drug interactions,OAI-PMH Harvest,pharmacodynamics,pharmacokinetics

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Beringer, Paul (committee chair), D'Argenio, David (committee member), Louie, Stan (committee member)

Creator Email eunjinho@usc.edu,eunjinhong430@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC112936960

Unique identifier UC112936960

Identifier etd-HongEunjin-11543.pdf (filename)

Legacy Identifier etd-HongEunjin-11543

Document Type Dissertation

Format theses (aat)

Rights Hong, Eunjin

Internet Media Type application/pdf

Type texts

Source 20230331-usctheses-batch-1014 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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