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Geng K, Shen C, Wang X, Wang X, Shao W, Wang W, Chen T, Sun H, Xie H. A physiologically-based pharmacokinetic/pharmacodynamic modeling approach for drug-drug-gene interaction evaluation of S-warfarin with fluconazole. CPT Pharmacometrics Syst Pharmacol 2024; 13:853-869. [PMID: 38487942 PMCID: PMC11098157 DOI: 10.1002/psp4.13123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/17/2024] [Accepted: 02/07/2024] [Indexed: 05/18/2024] Open
Abstract
Warfarin is a widely used anticoagulant, and its S-enantiomer has higher potency compared to the R-enantiomer. S-warfarin is mainly metabolized by cytochrome P450 (CYP) 2C9, and its pharmacological target is vitamin K epoxide reductase complex subunit 1 (VKORC1). Both CYP2C9 and VKORC1 have genetic polymorphisms, leading to large variations in the pharmacokinetics (PKs) and pharmacodynamics (PDs) of warfarin in the population. This makes dosage management of warfarin difficult, especially in the case of drug-drug interactions (DDIs). This study provides a whole-body physiologically-based pharmacokinetic/PD (PBPK/PD) model of S-warfarin for predicting the effects of drug-drug-gene interactions on S-warfarin PKs and PDs. The PBPK/PD model of S-warfarin was developed in PK-Sim and MoBi. Drug-dependent parameters were obtained from the literature or optimized. Of the 34 S-warfarin plasma concentration-time profiles used, 96% predicted plasma concentrations within twofold range compared to observed data. For S-warfarin plasma concentration-time profiles with CYP2C9 genotype, 364 of 386 predicted plasma concentration values (~94%) fell within the twofold of the observed values. This model was tested in DDI predictions with fluconazole as CYP2C9 perpetrators, with all predicted DDI area under the plasma concentration-time curve to the last measurable timepoint (AUClast) ratio within twofold of the observed values. The anticoagulant effect of S-warfarin was described using an indirect response model, with all predicted international normalized ratio (INR) within twofold of the observed values. This model also incorporates a dose-adjustment method that can be used for dose adjustment and predict INR when warfarin is used in combination with CYP2C9 perpetrators.
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Affiliation(s)
- Kuo Geng
- Anhui Provincial Center for Drug Clinical EvaluationYijishan Hospital of Wannan Medical CollegeWuhuAnhuiChina
- Wannan Medical CollegeWuhuAnhuiChina
| | - Chaozhuang Shen
- Department of Clinical Pharmacy and Pharmacy Administration, West China College of PharmacySichuan UniversityChengduSichuanChina
| | - Xiaohu Wang
- Department of PharmaceuticsChina Pharmaceutical UniversityNanjingChina
| | - Xingwen Wang
- Anhui Provincial Center for Drug Clinical EvaluationYijishan Hospital of Wannan Medical CollegeWuhuAnhuiChina
- Wannan Medical CollegeWuhuAnhuiChina
| | - Wenxin Shao
- Anhui Provincial Center for Drug Clinical EvaluationYijishan Hospital of Wannan Medical CollegeWuhuAnhuiChina
- Wannan Medical CollegeWuhuAnhuiChina
| | - Wenhui Wang
- Anhui Provincial Center for Drug Clinical EvaluationYijishan Hospital of Wannan Medical CollegeWuhuAnhuiChina
- Wannan Medical CollegeWuhuAnhuiChina
| | - Tao Chen
- Anhui Provincial Center for Drug Clinical EvaluationYijishan Hospital of Wannan Medical CollegeWuhuAnhuiChina
- Wannan Medical CollegeWuhuAnhuiChina
| | - Hua Sun
- Anhui Provincial Center for Drug Clinical EvaluationYijishan Hospital of Wannan Medical CollegeWuhuAnhuiChina
| | - Haitang Xie
- Anhui Provincial Center for Drug Clinical EvaluationYijishan Hospital of Wannan Medical CollegeWuhuAnhuiChina
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Wang X, Wu J, Ye H, Zhao X, Zhu S. Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999-2023). Pharm Res 2024; 41:609-622. [PMID: 38383936 DOI: 10.1007/s11095-024-03676-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/05/2024] [Indexed: 02/23/2024]
Abstract
PURPOSE The physiologically based pharmacokinetic (PBPK) modeling has received increasing attention owing to its excellent predictive abilities. However, there has been no bibliometric analysis about PBPK modeling. This research aimed to summarize the research development and hot points in PBPK model utilization overall through bibliometric analysis. METHODS We searched for publications related to the PBPK modeling from 1999 to 2023 in the Web of Science Core Collection (WoSCC) database. The Microsoft Office Excel, CiteSpace and VOSviewers were used to perform the analyses. RESULTS A total of 4,649 records from 1999 to 2023 were identified, and the largest number of publications focused in the period 2018-2023. The United States was the leading country, and the Environmental Protection Agency (EPA) was the leading institution. The journal Drug Metabolism and Disposition published and co-cited the most articles. Drug-drug interactions, special populations, and new drug development are the main topics in this research field. CONCLUSION We first visualize the research landscape and hotspots of the PBPK modeling through bibliometric methods. Our study provides a better understanding for researchers, especially beginners about the dynamization of PBPK modeling and presents the relevant trend in the future.
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Affiliation(s)
- Xin Wang
- Department of Pharmacy, The First Affiliated Hospital of Chongqing Medical University, No. 1, Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Jiangfan Wu
- School of Pharmacy, Chongqing Medical University, Chongqing, China
| | - Hongjiang Ye
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Xiaofang Zhao
- School of Pharmacy, Chongqing Medical University, Chongqing, China
- Qiandongnan Miao and Dong Autonomous Prefecture People's Hospital, Guizhou, 556000, China
| | - Shenyin Zhu
- Department of Pharmacy, The First Affiliated Hospital of Chongqing Medical University, No. 1, Youyi Road, Yuzhong District, Chongqing, 400016, China.
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Zamir A, Alqahtani F, Rasool MF. Chronic kidney disease and physiologically based pharmacokinetic modeling: a critical review of existing models. Expert Opin Drug Metab Toxicol 2024; 20:95-105. [PMID: 38270999 DOI: 10.1080/17425255.2024.2311154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/24/2024] [Indexed: 01/27/2024]
Abstract
INTRODUCTION Physiologically based pharmacokinetic (PBPK) modeling is a paradigm shift in this era for determining the exposure of drugs in pediatrics, geriatrics, and patients with chronic diseases where clinical trials are difficult to conduct. AREAS COVERED This review has collated data regarding published PBPK models on chronic kidney disease (CKD), including the drug and system-specific input model parameters and model evaluation criteria. Four databases were used from 13th June 2023 to 10th July 2023 for identifying the relevant studies that met the inclusion/exclusion criteria. Alterations in plasma protein (albumin/alpha-1 acid glycoprotein), gastric emptying time, hematocrit, small intestinal transit time, the abundance of cytochrome (CYP) 450 enzymes, glomerular filtration rate, and physicochemical parameters for different drugs were explicitly elaborated from earlier reported studies. Moreover, model evaluation depicted that models in CKD for most of the included drugs were within the allowed two-fold error range. EXPERT OPINION This review will provide insights for researchers on applying PBPK models in managing patients with different levels of CKD to prevent undesirable side effects and increase the effectiveness of drug therapy.
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Affiliation(s)
- Ammara Zamir
- Department of Pharmacy Practice, Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Faleh Alqahtani
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud Universi-ty, Riyadh, Saudi Arabia
| | - Muhammad Fawad Rasool
- Department of Pharmacy Practice, Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
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Gong F, Hu H, Ouyang Y, Liao ZZ, Kong Y, Hu JF, He H, Zhou Y. Physiologically-based pharmacokinetic modeling-guided rational combination of tacrolimus and voriconazole in patients with different CYP3A5 and CYP2C19 alleles. Toxicol Appl Pharmacol 2023; 466:116475. [PMID: 36931438 DOI: 10.1016/j.taap.2023.116475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 03/11/2023] [Accepted: 03/14/2023] [Indexed: 03/17/2023]
Abstract
The drug-drug interactions (DDIs) between tacrolimus and voriconazole are highly variable among individuals. We aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the DDIs in people with different CYP3A5 and CYP2C19 alleles. First, pharmacokinetic data of humans receiving tacrolimus with or without voriconazole from the literature were used to construct and validate the PBPK model. Thereafter, we developed a model incorporating the metabolism of voriconazole mediated by CYP2C19 and the inhibitory effect of voriconazole on CYP3A4/5. Finally, the model was used to evaluate the dose adjustment of tacrolimus in people with different CYP3A5 and CYP2C19 alleles. When tacrolimus was administered alone (3 mg PO, single dose), the predicted AUC0-∞ of tacrolimus in CYP3A5 nonexpressers (19.22) was 3.5-fold higher than that in expressers (5.48). Following voriconazole (200 mg PO, bid) administration in human with different CYP2C19 genotypes, the AUC0-∞ of tacrolimus increased by 5.1- to 8.3-fold in CYP3A5 expressers and by 5.3- to 10.2-fold in CYP3A5 nonexpressers. The lower the gene expression level of CYP2C19 in the population, the higher the exposure to tacrolimus. When tacrolimus was combined with voriconazole (200 mg, bid; 400 mg, bid, on Day 1), the final model simulations suggested that the dose regimen of tacrolimus should be regulated to 0.15 mg/kg/day (qd) in CYP3A5 expressers with different CYP2C19 genotypes. For CYP3A5 nonexpressers, the dosing schedule of tacrolimus should be modified to 0.05 mg/kg/24 h for patients with 2C19 EM, 0.05 mg/kg/48 h for 2C19 IM and 0.05 mg/kg/72 h for 2C19 PM. In conclusion, a PBPK model with CYP3A5 and CYP2C19 polymorphisms was successfully established, providing more insights regarding the DDIs between tacrolimus and voriconazole to guide the clinical use of tacrolimus.
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Affiliation(s)
- Fei Gong
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China; Center for Molecular Diagnosis and Precision Medicine, Department of Clinical Laboratory, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China; School of Pharmacy, Nanchang University, Nanchang 330006, China
| | - Huihui Hu
- Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China
| | - Ying Ouyang
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China; School of Pharmacy, Nanchang University, Nanchang 330006, China
| | - Zheng-Zheng Liao
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Ying Kong
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Jin-Fang Hu
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Hua He
- Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China.
| | - Ying Zhou
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
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Mostafa S, Polasek TM, Bousman C, Rostami‐Hodjegan A, Sheffield LJ, Everall I, Pantelis C, Kirkpatrick CMJ. Delineating gene-environment effects using virtual twins of patients treated with clozapine. CPT Pharmacometrics Syst Pharmacol 2022; 12:168-179. [PMID: 36424701 PMCID: PMC9931435 DOI: 10.1002/psp4.12886] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 11/27/2022] Open
Abstract
Studies that focus on individual covariates, while ignoring their interactions, may not be adequate for model-informed precision dosing (MIPD) in any given patient. Genetic variations that influence protein synthesis should be studied in conjunction with environmental covariates, such as cigarette smoking. The aim of this study was to build virtual twins (VTs) of real patients receiving clozapine with interacting covariates related to genetics and environment and to delineate the impact of interacting covariates on predicted clozapine plasma concentrations. Clozapine-treated patients with schizophrenia (N = 42) with observed clozapine plasma concentrations, demographic, environmental, and genotype data were used to construct VTs in Simcyp. The effect of increased covariate virtualization was assessed by performing simulations under three conditions: "low" (demographic), "medium" (demographic and environmental interaction), and "high" (demographic and environmental/genotype interaction) covariate virtualization. Increasing covariate virtualization with interaction improved the coefficient of variation (R2 ) from 0.07 in the low model to 0.391 and 0.368 in the medium and high models, respectively. Whereas R2 was similar between the medium and high models, the high covariate virtualization model had improved accuracy, with systematic bias of predicted clozapine plasma concentration improving from -138.48 ng/ml to -74.65 ng/ml. A high level of covariate virtualization (demographic, environmental, and genotype) may be required for MIPD using VTs.
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Affiliation(s)
- Sam Mostafa
- Centre for Medicine Use and SafetyMonash UniversityVictoriaParkvilleAustralia,MyDNA LifeAustralia LimitedVictoriaSouth YarraAustralia
| | - Thomas M. Polasek
- Centre for Medicine Use and SafetyMonash UniversityVictoriaParkvilleAustralia,CertaraNew JerseyPrincetonUSA,Department of Clinical PharmacologyRoyal Adelaide HospitalSouth AustraliaAdelaideAustralia
| | - Chad Bousman
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of Melbourne & Melbourne HealthVictoriaMelbourneAustralia,The Cooperative Research Centre (CRC) for Mental HealthVictoriaMelbourneAustralia,Alberta Children's Hospital Research Institute, Cumming School of MedicineUniversity of CalgaryAlbertaCalgaryCanada,Hotchkiss Brain Institute, Cumming School of MedicineUniversity of CalgaryAlbertaCalgaryCanada,Departments of Medical Genetics, Psychiatry, and Physiology and PharmacologyUniversity of CalgaryAlbertaCalgaryCanada
| | - Amin Rostami‐Hodjegan
- Centre for Applied Pharmacokinetic Research (CAPKR), School of Health SciencesUniversity of ManchesterManchesterUK,Simcyp DivisionCertara UK LimitedSheffieldUK
| | | | - Ian Everall
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of Melbourne & Melbourne HealthVictoriaMelbourneAustralia,The Cooperative Research Centre (CRC) for Mental HealthVictoriaMelbourneAustralia,Western Australian Health Translation NetworkNedlandsWestern AustraliaAustralia,Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneVictoriaMelbourneAustralia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of Melbourne & Melbourne HealthVictoriaMelbourneAustralia,The Cooperative Research Centre (CRC) for Mental HealthVictoriaMelbourneAustralia,Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneVictoriaMelbourneAustralia
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Vijaywargi G, Kollipara S, Ahmed T, Chachad S. Predicting transporter mediated drug-drug interactions via static and dynamic physiologically based pharmacokinetic modeling: A comprehensive insight on where we are now and the way forward. Biopharm Drug Dispos 2022. [PMID: 36413625 DOI: 10.1002/bdd.2339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/07/2022] [Accepted: 11/04/2022] [Indexed: 11/23/2022]
Abstract
The greater utilization and acceptance of physiologically-based pharmacokinetic (PBPK) modeling to evaluate the potential metabolic drug-drug interactions is evident by the plethora of literature, guidance's, and regulatory dossiers available in the literature. In contrast, it is not widely used to predict transporter-mediated DDI (tDDI). This is attributed to the unavailability of accurate transporter tissue expression levels, the absence of accurate in vitro to in vivo extrapolations (IVIVE), enzyme-transporter interplay, and a lack of specific probe substrates. Additionally, poor understanding of the inhibition/induction mechanisms coupled with the inability to determine unbound concentrations at the interaction site made tDDI assessment challenging. Despite these challenges, continuous improvements in IVIVE approaches enabled accurate tDDI predictions. Furthermore, the necessity of extrapolating tDDI's to special (pediatrics, pregnant, geriatrics) and diseased (renal, hepatic impaired) populations is gaining impetus and is encouraged by regulatory authorities. This review aims to visit the current state-of-the-art and summarizes contemporary knowledge on tDDI predictions. The current understanding and ability of static and dynamic PBPK models to predict tDDI are portrayed in detail. Peer-reviewed transporter abundance data in special and diseased populations from recent publications were compiled, enabling direct input into modeling tools for accurate tDDI predictions. A compilation of regulatory guidance's for tDDI's assessment and success stories from regulatory submissions are presented. Future perspectives and challenges of predicting tDDI in terms of in vitro system considerations, endogenous biomarkers, the use of empirical scaling factors, enzyme-transporter interplay, and acceptance criteria for model validation to meet the regulatory expectations were discussed.
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Affiliation(s)
- Gautam Vijaywargi
- Biopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), Hyderabad, Telangana, India
| | - Sivacharan Kollipara
- Biopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), Hyderabad, Telangana, India
| | - Tausif Ahmed
- Biopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), Hyderabad, Telangana, India
| | - Siddharth Chachad
- Biopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), Hyderabad, Telangana, India
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van Hoogdalem MW, Wexelblatt SL, Akinbi HT, Vinks AA, Mizuno T. A review of pregnancy-induced changes in opioid pharmacokinetics, placental transfer, and fetal exposure: Towards fetomaternal physiologically-based pharmacokinetic modeling to improve the treatment of neonatal opioid withdrawal syndrome. Pharmacol Ther 2021; 234:108045. [PMID: 34813863 DOI: 10.1016/j.pharmthera.2021.108045] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/29/2021] [Accepted: 11/15/2021] [Indexed: 02/07/2023]
Abstract
Physiologically-based pharmacokinetic (PBPK) modeling has emerged as a useful tool to study pharmacokinetics (PK) in special populations, such as pregnant women, fetuses, and newborns, where practical hurdles severely limit the study of drug behavior. PK in pregnant women is variable and everchanging, differing greatly from that in their nonpregnant female and male counterparts typically enrolled in clinical trials. PBPK models can accommodate pregnancy-induced physiological and metabolic changes, thereby providing mechanistic insights into maternal drug disposition and fetal exposure. Fueled by the soaring opioid epidemic in the United States, opioid use during pregnancy continues to rise, leading to an increased incidence of neonatal opioid withdrawal syndrome (NOWS). The severity of NOWS is influenced by a complex interplay of extrinsic and intrinsic factors, and varies substantially between newborns, but the extent of prenatal opioid exposure is likely the primary driver. Fetomaternal PBPK modeling is an attractive approach to predict in utero opioid exposure. To facilitate the development of fetomaternal PBPK models of opioids, this review provides a detailed overview of pregnancy-induced changes affecting the PK of commonly used opioids during gestation. Moreover, the placental transfer of these opioids is described, along with their disposition in the fetus. Lastly, the implementation of these factors into PBPK models is discussed. Fetomaternal PBPK modeling of opioids is expected to provide improved insights in fetal opioid exposure, which allows for prediction of postnatal NOWS severity, thereby opening the way for precision postnatal treatment of these vulnerable infants.
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Affiliation(s)
- Matthijs W van Hoogdalem
- Division of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; James L. Winkle College of Pharmacy, University of Cincinnati, Cincinnati, OH, USA
| | - Scott L Wexelblatt
- Perinatal Institute, Division of Neonatology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA; Center for Addiction Research, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Henry T Akinbi
- Perinatal Institute, Division of Neonatology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Alexander A Vinks
- Division of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA; Center for Addiction Research, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Tomoyuki Mizuno
- Division of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA; Center for Addiction Research, College of Medicine, University of Cincinnati, Cincinnati, OH, USA.
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Physiologically based pharmacokinetic (PBPK) modeling for prediction of celecoxib pharmacokinetics according to CYP2C9 genetic polymorphism. Arch Pharm Res 2021; 44:713-724. [PMID: 34304363 DOI: 10.1007/s12272-021-01346-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 07/21/2021] [Indexed: 12/27/2022]
Abstract
Celecoxib is a non-steroidal anti-inflammatory drug (NSAID) and a representative selective cyclooxygenase (COX)-2 inhibitor, which is commonly prescribed for osteoarthritis, rheumatoid arthritis, ankylosing spondylitis, acute pain, and primary dysmenorrhea. It is mainly metabolized by CYP2C9 and partly by CYP3A4 after oral administration. Many studies reported that CYP2C9 genetic polymorphism has significant effects on the pharmacokinetics of celecoxib and the occurrence of adverse drug reactions. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model of celecoxib according to CYP2C9 genetic polymorphism for personalized pharmacotherapy. Initially, a clinical pharmacokinetic study was conducted where a single dose (200 mg) of celecoxib was administered to 39 healthy Korean subjects with CYP2C9*1/*1 or CYP2C9*1/*3 genotypes to obtain data for PBPK development. Based on the conducted pharmacokinetic study and a previous pharmacokinetic study involving subjects with CYP2C9*1/*13 and CYP2C9*3/*3 genotype, PBPK model for celecoxib was developed. A PBPK model for CYP2C9*1/*1 genotype group was developed and then scaled to other genotype groups (CYP2C9*1/*3, CYP2C9*1/*13 and CYP2C9*3/*3). After model development, model validation was performed with comparison of five pharmacokinetic studies. As a result, the developed PBPK model of celecoxib successfully described the pharmacokinetics of each CYP2C9 genotype group and its predicted values were within the acceptance criterion. Additionally, all the predicted values were within two-fold error range in comparison to the previous pharmacokinetic studies. This study demonstrates the possibility of determining the appropriate dosage of celecoxib for each individual through the PBPK modeling with CYP2C9 genomic information. This approach could contribute to the reduction of adverse drug reactions of celecoxib and enable precision medicine.
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Influence of CYP2D6 Phenotypes on the Pharmacokinetics of Aripiprazole and Dehydro-Aripiprazole Using a Physiologically Based Pharmacokinetic Approach. Clin Pharmacokinet 2021; 60:1569-1582. [PMID: 34125422 PMCID: PMC8613074 DOI: 10.1007/s40262-021-01041-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/22/2021] [Indexed: 01/26/2023]
Abstract
BACKGROUND AND OBJECTIVES Aripiprazole is an atypical antipsychotic drug that is metabolized by cytochrome P450 (CYP) 2D6 and CYP3A4, which mainly form its active metabolite dehydro-aripiprazole. Because of the genetic polymorphism of CYP2D6, plasma concentrations are highly variable between different phenotypes. In this study, phenotype-related physiologically based pharmacokinetic models were developed and evaluated to suggest phenotype-guided dose adjustments. METHODS Physiologically based pharmacokinetic models for single dose (oral and orodispersible formulation), multiple dose, and steady-state condition were built using trial data from genotyped healthy volunteers. Based on evaluated models, dose adjustments were simulated to compensate for genetically caused differences. RESULTS Physiologically based pharmacokinetic models were able to accurately predict the pharmacokinetics of aripiprazole and dehydro-aripiprazole according to CYP2D6 phenotypes, illustrated by a minimal bias and a good precision. For single-dose administration, 92.5% (oral formulation) and 79.3% (orodispersible formulation) of the plasma concentrations of aripiprazole were within the 1.25-fold error range. In addition, physiologically based pharmacokinetic steady-state simulations demonstrate that the daily dose for poor metabolizer should be adjusted, resulting in a maximum recommended dose of 10 mg, but no adjustment is necessary for intermediate and ultra-rapid metabolizers. CONCLUSIONS In clinical practice, CYP2D6 genotyping in combination with therapeutic drug monitoring should be considered to personalize aripiprazole dosing, especially in CYP2D6 poor metabolizers, to ensure therapy effectiveness and safety.
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Zhou K, Mi K, Ma W, Xu X, Huo M, Algharib SA, Pan Y, Xie S, Huang L. Application of physiologically based pharmacokinetic models to promote the development of veterinary drugs with high efficacy and safety. J Vet Pharmacol Ther 2021; 44:663-678. [PMID: 34009661 DOI: 10.1111/jvp.12976] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 10/27/2020] [Accepted: 04/18/2021] [Indexed: 12/12/2022]
Abstract
Physiologically based pharmacokinetic (PBPK) models have become important tools for the development of novel human drugs. Food-producing animals and pets comprise an important part of human life, and the development of veterinary drugs (VDs) has greatly impacted human health. Owing to increased affordability of and demand for drug development, VD manufacturing companies should have more PBPK models required to reduce drug production costs. So far, little attention has been paid on applying PBPK models for the development of VDs. This review begins with the development processes of VDs; then summarizes case studies of PBPK models in human or VD development; and analyzes the application, potential, and advantages of PBPK in VD development, including candidate screening, formulation optimization, food effects, target-species safety, and dosing optimization. Then, the challenges of applying the PBPK model to VD development are discussed. Finally, future opportunities of PBPK models in designing dosing regimens for intracellular pathogenic infections and for efficient oral absorption of VDs are further forecasted. This review will be relevant to readers who are interested in using a PBPK model to develop new VDs.
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Affiliation(s)
- Kaixiang Zhou
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues, Wuhan, China
| | - Kun Mi
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues, Wuhan, China
| | - Wenjin Ma
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues, Wuhan, China
| | - Xiangyue Xu
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues, Wuhan, China
| | - Meixia Huo
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues, Wuhan, China
| | - Samah Attia Algharib
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues, Wuhan, China.,Department of Clinical Pathology, Faculty of Veterinary Medicine, Benha University, Moshtohor, Toukh, Egypt
| | - Yuanhu Pan
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues, Wuhan, China.,MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan, China
| | - Shuyu Xie
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues, Wuhan, China.,MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan, China
| | - Lingli Huang
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues, Wuhan, China.,MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan, China
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Physiologically based pharmacokinetic (PBPK) modeling of RNAi therapeutics: Opportunities and challenges. Biochem Pharmacol 2021; 189:114468. [PMID: 33577889 DOI: 10.1016/j.bcp.2021.114468] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/03/2021] [Accepted: 02/04/2021] [Indexed: 02/06/2023]
Abstract
Physiologically based pharmacokinetic (PBPK) modeling is a powerful tool with many demonstrated applications in various phases of drug development and regulatory review. RNA interference (RNAi)-based therapeutics are a class of drugs that have unique pharmacokinetic properties and mechanisms of action. With an increasing number of RNAi therapeutics in the pipeline and reaching the market, there is a considerable amount of active research in this area requiring a multidisciplinary approach. The application of PBPK models for RNAi therapeutics is in its infancy and its utility to facilitate the development of this new class of drugs is yet to be fully evaluated. From this perspective, we briefly discuss some of the current computational modeling approaches used in support of efficient development and approval of RNAi therapeutics. Considerations for PBPK model development are highlighted both in a relative context between small molecules and large molecules such as monoclonal antibodies and as it applies to RNAi therapeutics. In addition, the prospects for drawing upon other recognized avenues of PBPK modeling and some of the foreseeable challenges in PBPK model development for these chemical modalities are briefly discussed. Finally, an exploration of the potential application of PBPK model development for RNAi therapeutics is provided. We hope these preliminary thoughts will help initiate a dialogue between scientists in the relevant sectors to examine the value of PBPK modeling for RNAi therapeutics. Such evaluations could help standardize the practice in the future and support appropriate guidance development for strengthening the RNAi therapeutics development program.
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12
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Abstract
Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.
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13
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Zheng L, Tang S, Tang R, Xu M, Jiang X, Wang L. Dose Adjustment of Quetiapine and Aripiprazole for Pregnant Women Using Physiologically Based Pharmacokinetic Modeling and Simulation. Clin Pharmacokinet 2020; 60:623-635. [DOI: 10.1007/s40262-020-00962-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/30/2020] [Indexed: 12/12/2022]
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14
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Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling to Predict the Impact of CYP2C9 Genetic Polymorphisms, Co-Medication and Formulation on the Pharmacokinetics and Pharmacodynamics of Flurbiprofen. Pharmaceutics 2020; 12:pharmaceutics12111049. [PMID: 33147873 PMCID: PMC7693160 DOI: 10.3390/pharmaceutics12111049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 10/23/2020] [Accepted: 10/26/2020] [Indexed: 02/01/2023] Open
Abstract
Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models can serve as a powerful framework for predicting the influence as well as the interaction of formulation, genetic polymorphism and co-medication on the pharmacokinetics and pharmacodynamics of drug substances. In this study, flurbiprofen, a potent non-steroid anti-inflammatory drug, was chosen as a model drug. Flurbiprofen has absolute bioavailability of ~95% and linear pharmacokinetics in the dose range of 50–300 mg. Its absorption is considered variable and complex, often associated with double peak phenomena, and its pharmacokinetics are characterized by high inter-subject variability, mainly due to its metabolism by the polymorphic CYP2C9 (fmCYP2C9 ≥ 0.71). In this study, by leveraging in vitro, in silico and in vivo data, an integrated PBPK/PD model with mechanistic absorption was developed and evaluated against clinical data from PK, PD, drug-drug and gene-drug interaction studies. The PBPK model successfully predicted (within 2-fold) 36 out of 38 observed concentration-time profiles of flurbiprofen as well as the CYP2C9 genetic effects after administration of different intravenous and oral dosage forms over a dose range of 40–300 mg in both Caucasian and Chinese healthy volunteers. All model predictions for Cmax, AUCinf and CL/F were within two-fold of their respective mean or geometric mean values, while 90% of the predictions of Cmax, 81% of the predictions of AUCinf and 74% of the predictions of Cl/F were within 1.25 fold. In addition, the drug-drug and drug-gene interactions were predicted within 1.5-fold of the observed interaction ratios (AUC, Cmax ratios). The validated PBPK model was further expanded by linking it to an inhibitory Emax model describing the analgesic efficacy of flurbiprofen and applying it to explore the effect of formulation and genetic polymorphisms on the onset and duration of pain relief. This comprehensive PBPK/PD analysis, along with a detailed translational biopharmaceutic framework including appropriately designed biorelevant in vitro experiments and in vitro-in vivo extrapolation, provided mechanistic insight on the impact of formulation and genetic variations, two major determinants of the population variability, on the PK/PD of flurbiprofen. Clinically relevant specifications and potential dose adjustments were also proposed. Overall, the present work highlights the value of a translational PBPK/PD approach, tailored to target populations and genotypes, as an approach towards achieving personalized medicine.
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15
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Zhu YT, Teng Z, Zhang YF, Li W, Guo LX, Liu YP, Qu XJ, Wang QR, Mao SY, Chen XY, Zhong DF. Effects of Apatinib on the Pharmacokinetics of Nifedipine and Warfarin in Patients with Advanced Solid Tumors. DRUG DESIGN DEVELOPMENT AND THERAPY 2020; 14:1963-1970. [PMID: 32546963 PMCID: PMC7246325 DOI: 10.2147/dddt.s237301] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 04/27/2020] [Indexed: 12/02/2022]
Abstract
Background and Purpose Apatinib is a small-molecule tyrosine kinase inhibitor for the treatment of recurrent or progressive advanced-stage gastric adenocarcinoma or gastroesophageal junction cancer. The in vitro inhibition studies suggested that apatinib exerted potent inhibition on CYP3A4 and CYP2C9. To evaluate the potential of apatinib as a perpetrator in CYP450-based drug–drug interactions in vivo, nifedipine and warfarin were, respectively, selected in the present study as the probe substrates of CYP3A4 and CYP2C9 for clinical drug–drug interaction studies. Since hypertension and thrombus are common adverse effects of vascular targeting anticancer agents, nifedipine and warfarin are usually coadministered with apatinib in clinical practice. Methods A single-center, open-label, single-arm, and self-controlled trial was conducted in patients with advanced solid tumors. The patients received a single dose of 30 mg nifedipine on Day 1/14 and a single dose of 3 mg warfarin on Day 3/16. On Day 9–21, the subjects received a daily dose of 750 mg apatinib, respectively. The pharmacokinetics of nifedipine and warfarin in the absence or presence of apatinib was, respectively, investigated. Results Compared with the single oral administration, coadministration with apatinib contributed to the significant increases of AUC0–48h and Cmax of nifedipine by 83% (90% confidence interval [CI] 1.46–2.31) and 64% (90% CI 1.34–2.01), respectively. Similarly, coadministration with apatinib contributed to the significant increases of AUC0-t and Cmax of S-warfarin by 92% (90% CI 1.68–2.18) and 24% (90% CI 1.10–1.39), respectively. Conclusion Concomitant apatinib administration resulted in significant increases in systemic exposure to nifedipine and S-warfarin. Owing to the risk of pharmacokinetic drug–drug interactions based on CYP3A4/CYP2C9 inhibition by apatinib, caution is advised in the concurrent use of apatinib with either CYP2C9 or CYP3A4 substrates.
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Affiliation(s)
- Yun-Ting Zhu
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Zan Teng
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, People's Republic of China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, People's Republic of China
| | - Yi-Fan Zhang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Wei Li
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Li-Xia Guo
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Yun-Peng Liu
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, People's Republic of China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, People's Republic of China
| | - Xiu-Juan Qu
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, People's Republic of China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, People's Republic of China
| | - Quan-Ren Wang
- Department of Clinical Research and Development, Jiangsu Hengrui Medicine Co., Ltd., Shanghai, People's Republic of China
| | - Si-Yuan Mao
- Department of Clinical Research and Development, Jiangsu Hengrui Medicine Co., Ltd., Shanghai, People's Republic of China
| | - Xiao-Yan Chen
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Da-Fang Zhong
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, People's Republic of China
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16
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Yadav J, Paragas E, Korzekwa K, Nagar S. Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions. Pharmacol Ther 2020; 206:107449. [PMID: 31836452 PMCID: PMC6995442 DOI: 10.1016/j.pharmthera.2019.107449] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Cytochrome P450 (CYP) enzyme kinetics often do not conform to Michaelis-Menten assumptions, and time-dependent inactivation (TDI) of CYPs displays complexities such as multiple substrate binding, partial inactivation, quasi-irreversible inactivation, and sequential metabolism. Additionally, in vitro experimental issues such as lipid partitioning, enzyme concentrations, and inactivator depletion can further complicate the parameterization of in vitro TDI. The traditional replot method used to analyze in vitro TDI datasets is unable to handle complexities in CYP kinetics, and numerical approaches using ordinary differential equations of the kinetic schemes offer several advantages. Improvement in the parameterization of CYP in vitro kinetics has the potential to improve prediction of clinical drug-drug interactions (DDIs). This manuscript discusses various complexities in TDI kinetics of CYPs, and numerical approaches to model these complexities. The extrapolation of CYP in vitro TDI parameters to predict in vivo DDIs with static and dynamic modeling is discussed, along with a discussion on current gaps in knowledge and future directions to improve the prediction of DDI with in vitro data for CYP catalyzed drug metabolism.
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Affiliation(s)
- Jaydeep Yadav
- Amgen Inc., 360 Binney Street, Cambridge, MA 02142, United States; Department of Pharmaceutical Sciences, Temple University, Philadelphia, PA 19140, United States
| | - Erickson Paragas
- Department of Pharmaceutical Sciences, Temple University, Philadelphia, PA 19140, United States
| | - Ken Korzekwa
- Department of Pharmaceutical Sciences, Temple University, Philadelphia, PA 19140, United States
| | - Swati Nagar
- Department of Pharmaceutical Sciences, Temple University, Philadelphia, PA 19140, United States.
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17
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Saeheng T, Na-Bangchang K, Siccardi M, Rajoli RKR, Karbwang J. Physiologically-Based Pharmacokinetic Modeling for Optimal Dosage Prediction of Quinine Coadministered With Ritonavir-Boosted Lopinavir. Clin Pharmacol Ther 2020; 107:1209-1220. [PMID: 31721171 DOI: 10.1002/cpt.1721] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 11/03/2019] [Indexed: 12/25/2022]
Abstract
The coformulated lopinavir/ritonavir significantly reduces quinine concentration in healthy volunteers due to potential drug-drug interactions (DDIs). However, DDI information in malaria and HIV coinfected patients are lacking. The objective of the study was to apply physiologically-based pharmacokinetic (PBPK) modeling to predict optimal dosage regimens of quinine when coadministered with lopinavir/ritonavir in malaria and HIV coinfected patients with different conditions. The developed model was validated against literature. Model verification was evaluated using the accepted method. The verified PBPK models successfully predicted unbound quinine disposition when coadministered with lopinavir/ritonavir in coinfected patients with different conditions. Suitable dose adjustments to counteract with the DDIs have identified in patients with various situations (i.e., a 7-day course at 1,800 mg t.i.d. in patients with malaria with HIV infection, 648 mg b.i.d. in chronic renal failure, 648 mg t.i.d. in hepatic insufficiency except for severe hepatic insufficiency (324 mg b.i.d.), and 648 mg t.i.d. in CYP3A4 polymorphism).
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Affiliation(s)
- Teerachat Saeheng
- Leading Program, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.,Department of Clinical Product Development, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan
| | - Kesara Na-Bangchang
- Center of Excellence in Pharmacology and Molecular Biology of Malaria and Cholangiocarcinoma, Chulabhorn International College, Thammasat University, Pathumthani, Thailand.,Drug Discovery and Development Center, Office of Advanced Science and Technology, Thammasat University, Klongluang, Thailand
| | - Marco Siccardi
- Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK
| | - Rajith K R Rajoli
- Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK
| | - Juntra Karbwang
- Department of Clinical Product Development, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan.,Center of Excellence in Pharmacology and Molecular Biology of Malaria and Cholangiocarcinoma, Chulabhorn International College, Thammasat University, Pathumthani, Thailand
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18
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Storelli F, Desmeules J, Daali Y. Physiologically-Based Pharmacokinetic Modeling for the Prediction of CYP2D6-Mediated Gene-Drug-Drug Interactions. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:567-576. [PMID: 31268632 PMCID: PMC6709421 DOI: 10.1002/psp4.12411] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 03/22/2019] [Indexed: 12/14/2022]
Abstract
The aim of this work was to predict the extent of Cytochrome P450 2D6 (CYP2D6)‐mediated drug–drug interactions (DDIs) in different CYP2D6 genotypes using physiologically‐based pharmacokinetic (PBPK) modeling. Following the development of a new duloxetine model and optimization of a paroxetine model, the effect of genetic polymorphisms on CYP2D6‐mediated intrinsic clearances of dextromethorphan, duloxetine, and paroxetine was estimated from rich pharmacokinetic profiles in activity score (AS)1 and AS2 subjects. We obtained good predictions for the dextromethorphan–duloxetine interaction (Ratio of predicted over observed area under the curve (AUC) ratio (Rpred/obs) 1.38–1.43). Similarly, the effect of genotype was well predicted, with an increase of area under the curve ratio of 28% in AS2 subjects when compared with AS1 (observed, 33%). Despite an approximately twofold underprediction of the dextromethorphan–paroxetine interaction, an Rpred/obs of 0.71 was obtained for the effect of genotype on the area under the curve ratio. Therefore, PBPK modeling can be successfully used to predict gene–drug–drug interactions (GDDIs). Based on these promising results, a workflow is suggested for the generic evaluation of GDDIs and DDIs that can be applied in other situations.
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Affiliation(s)
- Flavia Storelli
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals, Geneva, Switzerland.,Geneva-Lausanne School of Pharmacy, Geneva University, Geneva, Switzerland
| | - Jules Desmeules
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals, Geneva, Switzerland.,Geneva-Lausanne School of Pharmacy, Geneva University, Geneva, Switzerland.,Faculty of Medicine, Geneva University, Geneva, Switzerland.,Swiss Center of Applied Human Toxicology, Basel, Switzerland
| | - Youssef Daali
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals, Geneva, Switzerland.,Geneva-Lausanne School of Pharmacy, Geneva University, Geneva, Switzerland.,Faculty of Medicine, Geneva University, Geneva, Switzerland.,Swiss Center of Applied Human Toxicology, Basel, Switzerland
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19
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Dmitriev AV, Lagunin AA, Karasev DА, Rudik AV, Pogodin PV, Filimonov DA, Poroikov VV. Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes. Curr Top Med Chem 2019; 19:319-336. [PMID: 30674264 DOI: 10.2174/1568026619666190123160406] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 01/02/2019] [Accepted: 01/07/2019] [Indexed: 02/07/2023]
Abstract
Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.
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Affiliation(s)
| | - Alexey A Lagunin
- Institute of Biomedical Chemistry, Moscow, Russian Federation.,Pirogov Russian National Research Medical University, Moscow, RussiaN Federation
| | | | | | - Pavel V Pogodin
- Institute of Biomedical Chemistry, Moscow, Russian Federation
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20
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Lin J, Goosen TC, Tse S, Yamagami H, Malhotra B. Physiologically Based Pharmacokinetic Modeling Suggests Limited Drug-Drug Interaction for Fesoterodine When Coadministered With Mirabegron. J Clin Pharmacol 2019; 59:1505-1518. [PMID: 31090092 DOI: 10.1002/jcph.1438] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Accepted: 04/09/2019] [Indexed: 11/11/2022]
Abstract
5-Hydroxymethyl tolterodine (5-HMT; the active fesoterodine metabolite) is metabolized via the cytochrome P450 (CYP) 2D6 and CYP3A pathways. Mirabegron is a moderate CYP2D6 inhibitor and weak CYP3A inhibitor. Potential drug-drug interactions (DDIs) following coadministration of these 2 overactive bladder treatments were estimated using physiologically based pharmacokinetic models, developed and verified by comparing predicted and observed pharmacokinetic profiles from clinical studies. Models predicted and verified mirabegron and desipramine (CYP2D6 substrate) and 5-HMT and ketoconazole (strong CYP3A inhibitor) DDIs. Mirabegron model-predicted mean steady-state AUC and Cmax were within 11% of clinical observations. The predicted versus observed geometric mean ratio (GMR) of AUCinf for CYP2D6 substrates desipramine and metoprolol coadministered with mirabegron 100 or 160 mg once daily were 3.47 versus 3.41 and 2.97 versus 3.29, respectively, indicating that the mirabegron model can be used to predict clinical CYP2D6 inhibition. 5-HMT fractional clearance by CYP3A and CYP2D6 was verified from clinical DDI studies with a potent CYP3A4 inhibitor (ketoconazole) and inducer (rifampicin) in CYP2D6 extensive and poor metabolizers and with a moderate CYP3A inhibitor (fluconazole) in healthy volunteers. 5-HMT AUCinf and Cmax GMRs for fesoterodine DDIs were all predicted within 1.26-fold of clinical observation, providing verification for the fesoterodine substrate model. The predicted changes in 5-HMT AUCinf and Cmax ratios for 8 mg fesoterodine when coadministered with 50 mg mirabegron were 1.22-fold and 1.17-fold, respectively, relative to 8 mg fesoterodine given alone. This modest increase in 5-HMT exposures by approximately 20% is considered clinically insignificant and would not require fesoterodine dose adjustment when coadministered with mirabegron within approved daily-dose ranges.
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Affiliation(s)
- Jian Lin
- Medicine Design - Department of Pharmacokinetics, Dynamics, and Metabolism, Pfizer Inc., Groton, CT, USA
| | - Theunis C Goosen
- Medicine Design - Department of Pharmacokinetics, Dynamics, and Metabolism, Pfizer Inc., Groton, CT, USA
| | - Susanna Tse
- Medicine Design - Department of Pharmacokinetics, Dynamics, and Metabolism, Pfizer Inc., Groton, CT, USA
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21
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Tan ML, Zhao P, Zhang L, Ho YF, Varma MVS, Neuhoff S, Nolin TD, Galetin A, Huang SM. Use of Physiologically Based Pharmacokinetic Modeling to Evaluate the Effect of Chronic Kidney Disease on the Disposition of Hepatic CYP2C8 and OATP1B Drug Substrates. Clin Pharmacol Ther 2018; 105:719-729. [PMID: 30074626 PMCID: PMC8246729 DOI: 10.1002/cpt.1205] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 07/30/2018] [Indexed: 12/15/2022]
Abstract
Chronic kidney disease (CKD) differentially affects the pharmacokinetics (PK) of nonrenally cleared drugs via certain pathways (e.g., cytochrome P450 (CYP)2D6); however, the effect on CYP2C8‐mediated clearance is not well understood because of overlapping substrate specificity with hepatic organic anion‐transporting polypeptides (OATPs). This study used physiologically based pharmacokinetic (PBPK) modeling to delineate potential changes in CYP2C8 or OATP1B activity in patients with CKD. Drugs analyzed are predominantly substrates of CYP2C8 (rosiglitazone and pioglitazone), OATP1B (pitavastatin), or both (repaglinide). Following initial model verification, pharmacokinetics (PK) of these drugs were simulated in patients with severe CKD considering changes in glomerular filtration rate (GFR), plasma protein binding, and activity of either CYP2C8 and/or OATP1B in a stepwise manner. The PBPK analysis suggests that OATP1B activity could be decreased up to 60% in severe CKD, whereas changes to CYP2C8 are negligible. This improved understanding of CKD effect on clearance pathways could be important to inform the optimal use of nonrenally eliminated drugs in patients with CKD.
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Affiliation(s)
- Ming-Liang Tan
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ping Zhao
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.,Quantitative Sciences, Global Health-Integrated Development, Bill and Melinda Gates Foundation, Seattle, Washington, USA
| | - Lei Zhang
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.,Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Yunn-Fang Ho
- Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Manthena V S Varma
- Pharmacokinetics, Pharmacodynamics & Metabolism Department-New Chemical Entities, Pfizer Inc., Groton, Connecticut, USA
| | | | - Thomas D Nolin
- Center for Clinical Pharmaceutical Sciences, Department of Pharmacy and Therapeutics, and Department of Medicine Renal-Electrolyte Division, Schools of Pharmacy and Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research, School of Heath Sciences, University of Manchester, Manchester, UK
| | - Shiew-Mei Huang
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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22
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Storelli F, Samer C, Reny JL, Desmeules J, Daali Y. Complex Drug-Drug-Gene-Disease Interactions Involving Cytochromes P450: Systematic Review of Published Case Reports and Clinical Perspectives. Clin Pharmacokinet 2018; 57:1267-1293. [PMID: 29667038 DOI: 10.1007/s40262-018-0650-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Drug pharmacokinetics (PK) is influenced by multiple intrinsic and extrinsic factors, among which concomitant medications are responsible for drug-drug interactions (DDIs) that may have a clinical relevance, resulting in adverse drug reactions or reduced efficacy. The addition of intrinsic factors affecting cytochromes P450 (CYPs) activity and/or expression, such as genetic polymorphisms and diseases, may potentiate the impact and clinical relevance of DDIs. In addition, greater variability in drug levels and exposures has been observed when such intrinsic factors are present in addition to concomitant medications perpetrating DDIs. This variability results in poor predictability of DDIs and potentially dramatic clinical consequences. The present review illustrates the issue of complex DDIs using systematically searched published case reports of DDIs involving genetic polymorphisms, renal impairment, cirrhosis, and/or inflammation. Current knowledge on the impact of each of these factors on drug exposure and DDIs is summarized and future perspectives for the management of such complex DDIs in clinical practice are discussed, including the use of advanced Computerized Physician Order Entry (CPOE) systems, the development of model-based dose optimization strategies, and the education of healthcare professionals with respect to personalized medicine.
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Affiliation(s)
- Flavia Storelli
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals, University of Geneva, Geneva, Switzerland
- Geneva-Lausanne School of Pharmacy, University of Geneva, Geneva, Switzerland
| | - Caroline Samer
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals, University of Geneva, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Swiss Center for Applied Human Toxicology, Geneva, Switzerland
| | - Jean-Luc Reny
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Department of Internal Medicine, Rehabilitation and Geriatrics, Geneva University Hospitals, University of Geneva, Geneva, Switzerland
| | - Jules Desmeules
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals, University of Geneva, Geneva, Switzerland
- Geneva-Lausanne School of Pharmacy, University of Geneva, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Swiss Center for Applied Human Toxicology, Geneva, Switzerland
| | - Youssef Daali
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals, University of Geneva, Geneva, Switzerland.
- Geneva-Lausanne School of Pharmacy, University of Geneva, Geneva, Switzerland.
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.
- Swiss Center for Applied Human Toxicology, Geneva, Switzerland.
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23
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Adiwidjaja J, Boddy AV, McLachlan AJ. A Strategy to Refine the Phenotyping Approach and Its Implementation to Predict Drug Clearance: A Physiologically Based Pharmacokinetic Simulation Study. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:798-808. [PMID: 30260092 PMCID: PMC6310868 DOI: 10.1002/psp4.12355] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 09/10/2018] [Indexed: 12/13/2022]
Abstract
The phenotyping approach to predict drug metabolism activity is often hampered by a lack of correlation between the probe and the drug of interest. In this article, we present a strategy to refine the phenotyping approach based on a physiologically based pharmacokinetic simulation (implemented in Simcyp Simulator version 17) using previously published models. The apparent clearance (CL/F) of erlotinib was better predicted by the sum of caffeine and i.v. midazolam CL/F (r2 = 0.60) compared to that of either probe drug alone. The clearance of atorvastatin and repaglinide had a strong correlation (r2 = 0.70 and 0.63, respectively) with that of pitavastatin (a SLCO1B1 probe). Use of multiple probes for drugs that are predominantly metabolized by more than one cytochrome P450 (CYP) enzyme should be considered. In a case in which hepatic uptake transporters play a significant role in the disposition of a drug, the pharmacokinetic of a transporter probe will provide better predictions of the drug clearance.
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Affiliation(s)
- Jeffry Adiwidjaja
- Sydney Pharmacy School, The University of Sydney, Sydney, New South Wales, Australia
| | - Alan V Boddy
- Sydney Pharmacy School, The University of Sydney, Sydney, New South Wales, Australia.,School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia, Australia
| | - Andrew J McLachlan
- Sydney Pharmacy School, The University of Sydney, Sydney, New South Wales, Australia
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McSweeney MD, Wessler T, Price LSL, Ciociola EC, Herity LB, Piscitelli JA, Zamboni WC, Forest MG, Cao Y, Lai SK. A minimal physiologically based pharmacokinetic model that predicts anti-PEG IgG-mediated clearance of PEGylated drugs in human and mouse. J Control Release 2018; 284:171-178. [PMID: 29879519 DOI: 10.1016/j.jconrel.2018.06.002] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 05/18/2018] [Accepted: 06/02/2018] [Indexed: 10/14/2022]
Abstract
Circulating antibodies that specifically bind polyethylene glycol (PEG), a polymer routinely used in protein and nanoparticle therapeutics, have been associated with reduced efficacy and increased adverse reactions to some PEGylated therapeutics. In addition to acute induction of anti-PEG antibodies (APA) by PEGylated drugs, typically low but detectable levels of APA are also found in up to 70% of the general population. Despite the broad implications of APA, the dynamics of APA-mediated clearance of PEGylated drugs, and why many patients continue to respond to PEGylated drugs despite the presence of pre-existing APA, remains not well understood. Here, we developed a minimal physiologically based pharmacokinetic (mPBPK) model that incorporates various properties of APA and PEGylated drugs. Our mPBPK model reproduced clinical PK data of APA-mediated accelerated blood clearance of pegloticase, as well as APA-dependent elimination of PEGyated liposomes in mice. Our model predicts that the prolonged circulation of PEGylated drugs will be compromised only at APA concentrations greater than ~500 ng/mL, providing a quantitative explanation to why the effects of APA on PEGylated treatments appear to be limited in most patients. This mPBPK model is readily adaptable to other PEGylated drugs and particles to predict the precise levels of APA that could render them ineffective, providing a powerful tool to support the development and interpretation of preclinical and clinical studies of various PEGylated therapeutics.
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Affiliation(s)
- M D McSweeney
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
| | - T Wessler
- Department of Mathematics, University of North Carolina, Chapel Hill, NC, USA.
| | - L S L Price
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
| | - E C Ciociola
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
| | - L B Herity
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
| | - J A Piscitelli
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
| | - W C Zamboni
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
| | - M G Forest
- Department of Mathematics, University of North Carolina, Chapel Hill, NC, USA.
| | - Y Cao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
| | - S K Lai
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA; UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC, USA; Department of Microbiology and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
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25
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Lindmark B, Lundahl A, Kanebratt KP, Andersson TB, Isin EM. Human hepatocytes and cytochrome P450-selective inhibitors predict variability in human drug exposure more accurately than human recombinant P450s. Br J Pharmacol 2018; 175:2116-2129. [PMID: 29574682 PMCID: PMC5980217 DOI: 10.1111/bph.14203] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 02/27/2018] [Accepted: 03/02/2018] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND AND PURPOSE Drugs metabolically eliminated by several enzymes are less vulnerable to variable compound exposure in patients due to drug-drug interactions (DDI) or if a polymorphic enzyme is involved in their elimination. Therefore, it is vital in drug discovery to accurately and efficiently estimate and optimize the metabolic elimination profile. EXPERIMENTAL APPROACH CYP3A and/or CYP2D6 substrates with well described variability in vivo in humans due to CYP3A DDI and CYP2D6 polymorphism were selected for assessment of fraction metabolized by each enzyme (fmCYP ) in two in vitro systems: (i) human recombinant P450s (hrP450s) and (ii) human hepatocytes combined with selective P450 inhibitors. Increases in compound exposure in poor versus extensive CYP2D6 metabolizers and by the strong CYP3A inhibitor ketoconazole were mathematically modelled and predicted changes in exposure were compared with in vivo data. KEY RESULTS Predicted changes in exposure were within twofold of reported in vivo values using fmCYP estimated in human hepatocytes and there was a strong linear correlation between predicted and observed changes in exposure (r2 = 0.83 for CYP3A, r2 = 0.82 for CYP2D6). Predictions using fmCYP in hrP450s were not as accurate (r2 = 0.55 for CYP3A, r2 = 0.20 for CYP2D6). CONCLUSIONS AND IMPLICATIONS The results suggest that variability in human drug exposure due to DDI and enzyme polymorphism can be accurately predicted using fmCYP from human hepatocytes and CYP-selective inhibitors. This approach can be efficiently applied in drug discovery to aid optimization of candidate drugs with a favourable metabolic elimination profile and limited variability in patients.
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Affiliation(s)
- Bo Lindmark
- Cardiovascular, Renal and Metabolism, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Anna Lundahl
- Cardiovascular, Renal and Metabolism, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Kajsa P Kanebratt
- Cardiovascular, Renal and Metabolism, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Tommy B Andersson
- Cardiovascular, Renal and Metabolism, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Emre M Isin
- Cardiovascular, Renal and Metabolism, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Gothenburg, Sweden
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26
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Gong J, Iacono L, Iyer RA, Humphreys WG, Zheng M. Physiologically-based pharmacokinetic modelling of a CYP2C19 substrate, BMS-823778, utilizing pharmacogenetic data. Br J Clin Pharmacol 2018; 84:1335-1345. [PMID: 29469197 DOI: 10.1111/bcp.13565] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 02/05/2018] [Accepted: 02/07/2018] [Indexed: 12/17/2022] Open
Abstract
AIMS Previous studies demonstrated direct correlation between CYP2C19 genotype and BMS-823778 clearance in healthy volunteers. The objective of the present study was to develop a physiologically-based pharmacokinetic (PBPK) model for BMS-823778 and use the model to predict PK and drug-drug interaction (DDI) in virtual populations with multiple polymorphic genes. METHODS The PBPK model was built and verified using existing clinical data. The verified model was simulated to predict PK of BMS-823778 and significance of DDI with a strong CYP3A4 inhibitor in subjects with various CYP2C19 and UGT1A4 genotypes. RESULTS The verified PBPK model of BMS-823778 accurately recovered observed PK in different populations. In addition, the model was able to capture the exposure differences between subjects with different CYP2C19 genotypes. PK simulation indicated higher exposures of BMS-823778 in CYP2C19 poor metabolizers who were also devoid of UGT1A4 activity, compared to those with normal UGT1A4 functionality. Moderate DDI with itraconazole was predicted in subjects with wild-type CYP2C19 or UGT1A4. However, in subjects without CYP2C19 or UGT1A4 functionality, significant DDI was predicted when BMS-823778 was coadministered with itraconazole. CONCLUSIONS A PBPK model was developed using clinical data that accurately predicted human PK in different population with various CYP2C19 phenotypes. Simulations with the verified PBPK model indicated that UGT1A4 was probably an important clearance pathway in CYP2C19 poor metabolizers. DDI with itraconazole is likely to be dependent on the genotypes of CYP2C19 and UGT1A4.
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Affiliation(s)
- Jiachang Gong
- Pharmaceutical Candidate Optimization, Bristol-Myers Squibb, Princeton, NJ, 08543, USA
| | - Lisa Iacono
- Global Regulatory Safety & Biometrics, Bristol-Myers Squibb, Princeton, NJ, 08543, USA
| | - Ramaswamy A Iyer
- Pharmaceutical Candidate Optimization, Bristol-Myers Squibb, Princeton, NJ, 08543, USA
| | - William G Humphreys
- Pharmaceutical Candidate Optimization, Bristol-Myers Squibb, Princeton, NJ, 08543, USA
| | - Ming Zheng
- Clinical Pharmacology and Pharmacometrics, Bristol-Myers Squibb, Princeton, NJ, 08543, USA
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27
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Polasek TM, Tucker GT, Sorich MJ, Wiese MD, Mohan T, Rostami‐Hodjegan A, Korprasertthaworn P, Perera V, Rowland A. Prediction of olanzapine exposure in individual patients using physiologically based pharmacokinetic modelling and simulation. Br J Clin Pharmacol 2018; 84:462-476. [PMID: 29194718 PMCID: PMC5809347 DOI: 10.1111/bcp.13480] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Revised: 11/21/2017] [Accepted: 11/22/2017] [Indexed: 12/15/2022] Open
Abstract
AIM The aim of the present study was to predict olanzapine (OLZ) exposure in individual patients using physiologically based pharmacokinetic modelling and simulation (PBPK M&S). METHODS A 'bottom-up' PBPK model for OLZ was constructed in Simcyp® (V14.1) and validated against pharmacokinetic studies and data from therapeutic drug monitoring (TDM). The physiological, demographic and genetic attributes of the 'healthy volunteer population' file in Simcyp® were then individualized to create 'virtual twins' of 14 patients. The predicted systemic exposure of OLZ in virtual twins was compared with measured concentration in corresponding patients. Predicted exposures were used to calculate a hypothetical decrease in exposure variability after OLZ dose adjustment. RESULTS The pharmacokinetic parameters of OLZ from single-dose studies were accurately predicted in healthy Caucasians [mean-fold errors (MFEs) ranged from 0.68 to 1.14], healthy Chinese (MFEs 0.82 to 1.18) and geriatric Caucasians (MFEs 0.55 to 1.30). Cumulative frequency plots of trough OLZ concentration were comparable between the virtual population and patients in a TDM database. After creating virtual twins in Simcyp®, the R2 values for predicted vs. observed trough OLZ concentrations were 0.833 for the full cohort of 14 patients and 0.884 for the 7 patients who had additional cytochrome P450 2C8 genotyping. The variability in OLZ exposure following hypothetical dose adjustment guided by PBPK M&S was twofold lower compared with a fixed-dose regimen - coefficient of variation values were 0.18 and 0.37, respectively. CONCLUSIONS Olanzapine exposure in individual patients was predicted using PBPK M&S. Repurposing of available PBPK M&S platforms is an option for model-informed precision dosing and requires further study to examine clinical potential.
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Affiliation(s)
- Thomas M. Polasek
- Department of Clinical PharmacologyFlinders UniversityAdelaideSAAustralia
- d3 MedicineA Certara CompanyMelbourneVICAustralia
| | - Geoffrey T. Tucker
- Medicine and Biomedical Sciences (Emeritus)University of SheffieldSheffieldUK
| | - Michael J. Sorich
- Department of Clinical PharmacologyFlinders UniversityAdelaideSAAustralia
- Flinders Centre for Innovation in CancerFlinders UniversityAdelaideSAAustralia
| | - Michael D. Wiese
- School of Pharmacy and Medical SciencesUniversity of South AustraliaAdelaideSAAustralia
| | - Titus Mohan
- Department of PsychiatryFlinders Medical CentreAdelaideSAAustralia
| | - Amin Rostami‐Hodjegan
- Certara, Blades Enterprise CentreSheffieldUK
- Centre for Applied Pharmacokinetic ResearchUniversity of ManchesterManchesterUK
| | | | - Vidya Perera
- Clinical Pharmacology and Pharmacometrics, Early Clinical and Translational ResearchBristol Myers SquibbPrincetonNJUSA
| | - Andrew Rowland
- Department of Clinical PharmacologyFlinders UniversityAdelaideSAAustralia
- Flinders Centre for Innovation in CancerFlinders UniversityAdelaideSAAustralia
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28
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Predictive Performance of Physiologically-Based Pharmacokinetic Models in Predicting Drug–Drug Interactions Involving Enzyme Modulation. Clin Pharmacokinet 2018; 57:1337-1346. [DOI: 10.1007/s40262-018-0635-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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29
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Chitty KM, Chan B, Pulanco CL, Luu S, Egunsola O, Buckley NA. Discontinuities and disruptions in drug dosage guidelines for the paediatric population. Br J Clin Pharmacol 2018; 84:1029-1037. [PMID: 29411410 DOI: 10.1111/bcp.13511] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 12/21/2017] [Accepted: 12/23/2017] [Indexed: 12/30/2022] Open
Abstract
AIMS This study investigates paediatric drug dosage guidelines with the aim of investigating their agreement with body surface area (BSA) scaling principles. METHODS A total of 454 drug dosage guidelines listed in the AMH-CDC 2015 were examined. Data extracted included the administration, frequency and dose per age bracket from 0 to 18 years. Drug treatments were categorized as follows: (1) The same dose recommendation in milligrams per kilogram (mg kg-1 ) for all age/weights; (2) Change in the mg kg-1 dosing according to age/weight; (3) Change in dose in mg according to age/weight; (4) Change from mg kg-1 dosing to a dose in mg according to age/weight; (5) The same recommendation for all age/weight groups in mg; or (6) BSA dosing. Example drugs were selected to illustrate dose progression across ages. RESULTS Most drug treatments (63%) have the same mg kg-1 dose for all age/weight groups, 14% are dosed in mg kg-1 across all ages with dose changes according to age/weight, 13% were dosed in mg across all ages with dose changes, 10% switched from mg kg-1 to a set dose in mg, 4.2% have the same dose in mg for all age and weight groups and 2.2% are dosed according to BSA. CONCLUSIONS Paediatric dosage guidelines are based on weight-based formulas, available dosing formulations and prior patterns of use. Substantial variation from doses predicted by BSA scaling are common, as are large shifts in recommended doses at age thresholds. Further research is required to determine if better outcomes could be achieved by adopting biologically based scaling of paediatric doses.
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Affiliation(s)
- Kate M Chitty
- Discipline of Pharmacology, Sydney Medical School, Translational Australian Clinical Toxicology Program, The University of Sydney, NSW, Australia, 2006
| | - Bosco Chan
- Discipline of Pharmacology, Sydney Medical School, Translational Australian Clinical Toxicology Program, The University of Sydney, NSW, Australia, 2006
| | - Camille L Pulanco
- Discipline of Pharmacology, Sydney Medical School, Translational Australian Clinical Toxicology Program, The University of Sydney, NSW, Australia, 2006
| | - Sonya Luu
- Discipline of Pharmacology, Sydney Medical School, Translational Australian Clinical Toxicology Program, The University of Sydney, NSW, Australia, 2006
| | - Oluwaseun Egunsola
- Discipline of Pharmacology, Sydney Medical School, Translational Australian Clinical Toxicology Program, The University of Sydney, NSW, Australia, 2006
| | - Nicholas A Buckley
- Discipline of Pharmacology, Sydney Medical School, Translational Australian Clinical Toxicology Program, The University of Sydney, NSW, Australia, 2006
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30
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Storelli F, Matthey A, Lenglet S, Thomas A, Desmeules J, Daali Y. Impact of CYP2D6 Functional Allelic Variations on Phenoconversion and Drug-Drug Interactions. Clin Pharmacol Ther 2017; 104:148-157. [PMID: 28940476 DOI: 10.1002/cpt.889] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 09/11/2017] [Accepted: 09/20/2017] [Indexed: 12/17/2022]
Abstract
We investigated whether CYP2D6 extensive metabolizers carrying a nonfunctional allele are at higher risk of phenoconversion to poor metabolizers in the presence of CYP2D6 inhibitors. Seventeen homozygous carriers of two fully-functional alleles and 17 heterozygous carriers of one fully-functional and one nonfunctional allele participated in this trial. Dextromethorphan 5 mg and tramadol 10 mg were given at each of the three study sessions. CYP2D6 was inhibited by duloxetine 60 mg (session 2) and paroxetine 20 mg (session 3). A higher rate of phenoconversion to intermediate metabolizers with duloxetine (71% vs. 25%, P = 0.009) and to poor metabolizers with paroxetine (94% vs. 56%, P = 0.011) was observed in heterozygous than homozygous extensive metabolizers. The magnitude of drug-drug interaction between dextromethorphan and paroxetine was higher in homozygous than in heterozygous subjects (14.6 vs. 8.5, P < 0.028). Our study suggests that genetic extensive metabolizers may not represent a homogenous population and that available genetic data should be considered when addressing drug-drug interactions in clinical practice.
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Affiliation(s)
- Flavia Storelli
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals, Geneva, Switzerland.,Geneva-Lausanne School of Pharmacy, University of Geneva, Geneva, Switzerland
| | - Alain Matthey
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals, Geneva, Switzerland
| | | | - Aurélien Thomas
- Unit of Toxicology, CURML, Lausanne-Geneva, Switzerland.,Swiss Center for Applied Human Toxicology, Geneva, Switzerland.,Faculty of Biology and Medicine, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Jules Desmeules
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals, Geneva, Switzerland.,Geneva-Lausanne School of Pharmacy, University of Geneva, Geneva, Switzerland.,Swiss Center for Applied Human Toxicology, Geneva, Switzerland.,Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Youssef Daali
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals, Geneva, Switzerland.,Geneva-Lausanne School of Pharmacy, University of Geneva, Geneva, Switzerland.,Swiss Center for Applied Human Toxicology, Geneva, Switzerland.,Faculty of Medicine, University of Geneva, Geneva, Switzerland
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31
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Prediction of drug–drug interaction potential using physiologically based pharmacokinetic modeling. Arch Pharm Res 2017; 40:1356-1379. [DOI: 10.1007/s12272-017-0976-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2017] [Accepted: 10/19/2017] [Indexed: 12/22/2022]
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32
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Andrews KA, Wesche D, McCarthy J, Möhrle JJ, Tarning J, Phillips L, Kern S, Grasela T. Model-Informed Drug Development for Malaria Therapeutics. Annu Rev Pharmacol Toxicol 2017; 58:567-582. [PMID: 28992431 PMCID: PMC7198115 DOI: 10.1146/annurev-pharmtox-010715-103429] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Malaria is a critical public health problem resulting in substantial morbidity and
mortality, particularly in developing countries. Owing to the development of resistance
toward current therapies, novel approaches to accelerate the development efforts of new
malaria therapeutics are urgently needed. There have been significant advancements in the
development of in vitro and in vivo experiments that generate data used to inform
decisions about the potential merit of new compounds. A comprehensive disease-drug model
capable of integrating discrete data from different preclinical and clinical components
would be a valuable tool across all stages of drug development. This could have an
enormous impact on the otherwise slow and resource-intensive process of traditional
clinical drug development.
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Affiliation(s)
- Kayla Ann Andrews
- Cognigen Corporation, a subsidiary of Simulations Plus, Buffalo, New York 14221, USA; , , .,Department of Pharmaceutical Sciences, State University of New York, Buffalo, New York 14214, USA
| | - David Wesche
- Bill and Melinda Gates Foundation, Seattle, Washington 98109, USA; ,
| | - James McCarthy
- QIMR Berghofer Medical Research Institute, Brisbane, Australia.,School of Medicine, University of Queensland, Brisbane, Australia;
| | - Jörg J Möhrle
- Medicines for Malaria Venture, Geneva 1215, Switzerland;
| | - Joel Tarning
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; .,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, United Kingdom
| | - Luann Phillips
- Cognigen Corporation, a subsidiary of Simulations Plus, Buffalo, New York 14221, USA; , ,
| | - Steven Kern
- Bill and Melinda Gates Foundation, Seattle, Washington 98109, USA; ,
| | - Thaddeus Grasela
- Cognigen Corporation, a subsidiary of Simulations Plus, Buffalo, New York 14221, USA; , ,
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de Kanter R, Kohl C. Letter to the Editor, Physiologically based pharmacokinetic predictions of intestinal BCRP-mediated effect of telmisartan on the pharmacokinetics of rosuvastatin in humans. Biopharm Drug Dispos 2017; 38:443-444. [DOI: 10.1002/bdd.2079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Accepted: 04/20/2017] [Indexed: 11/10/2022]
Affiliation(s)
- Ruben de Kanter
- Preclinical Pharmacokinetics and Metabolism; Actelion Pharmaceuticals Ltd; Allschwil Switzerland
| | - Christopher Kohl
- Preclinical Pharmacokinetics and Metabolism; Actelion Pharmaceuticals Ltd; Allschwil Switzerland
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34
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Marsousi N, Desmeules JA, Rudaz S, Daali Y. Usefulness of PBPK Modeling in Incorporation of Clinical Conditions in Personalized Medicine. J Pharm Sci 2017; 106:2380-2391. [DOI: 10.1016/j.xphs.2017.04.035] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2017] [Revised: 04/06/2017] [Accepted: 04/07/2017] [Indexed: 12/14/2022]
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35
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Pan Y, Hsu V, Grimstein M, Zhang L, Arya V, Sinha V, Grillo JA, Zhao P. The Application of Physiologically Based Pharmacokinetic Modeling to Predict the Role of Drug Transporters: Scientific and Regulatory Perspectives. J Clin Pharmacol 2017; 56 Suppl 7:S122-31. [PMID: 27385170 DOI: 10.1002/jcph.740] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 03/21/2016] [Accepted: 03/22/2016] [Indexed: 01/24/2023]
Abstract
Transporters play an important role in drug absorption, disposition, and drug action. The evaluation of drug transporters requires a comprehensive understanding of transporter biology and pharmacology. Physiologically based pharmacokinetic (PBPK) models may offer an integrative platform to quantitatively evaluate the role of drug transporters and its interplay with other drug disposition processes such as passive drug diffusion and elimination by metabolizing enzymes. To date, PBPK modeling and simulations integrating drug transporters lag behind that for drug-metabolizing enzymes. In addition, predictive performance of PBPK has not been well established for predicting the role of drug transporters in the pharmacokinetics of a drug. To enhance overall predictive performance of transporter-based PBPK models, it is necessary to have a detailed understanding of transporter biology for proper representation in the models and to have a quantitative understanding of the contribution of transporters in the absorption and metabolism of a drug. This article summarizes PBPK-based submissions evaluating the role of drug transporters to the Office of Clinical Pharmacology of the US Food and Drug Administration.
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Affiliation(s)
- Yuzhuo Pan
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.,Current affiliation: Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Vicky Hsu
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Manuela Grimstein
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Lei Zhang
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Vikram Arya
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Vikram Sinha
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Joseph A Grillo
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Ping Zhao
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
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36
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Zhuang X, Lu C. PBPK modeling and simulation in drug research and development. Acta Pharm Sin B 2016; 6:430-440. [PMID: 27909650 PMCID: PMC5125732 DOI: 10.1016/j.apsb.2016.04.004] [Citation(s) in RCA: 227] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Revised: 04/25/2016] [Accepted: 04/26/2016] [Indexed: 01/13/2023] Open
Abstract
Physiologically based pharmacokinetic (PBPK) modeling and simulation can be used to predict the pharmacokinetic behavior of drugs in humans using preclinical data. It can also explore the effects of various physiologic parameters such as age, ethnicity, or disease status on human pharmacokinetics, as well as guide dose and dose regiment selection and aid drug-drug interaction risk assessment. PBPK modeling has developed rapidly in the last decade within both the field of academia and the pharmaceutical industry, and has become an integral tool in drug discovery and development. In this mini-review, the concept and methodology of PBPK modeling are briefly introduced. Several case studies were discussed on how PBPK modeling and simulation can be utilized through various stages of drug discovery and development. These case studies are from our own work and the literature for better understanding of the absorption, distribution, metabolism and excretion (ADME) of a drug candidate, and the applications to increase efficiency, reduce the need for animal studies, and perhaps to replace clinical trials. The regulatory acceptance and industrial practices around PBPK modeling and simulation is also discussed.
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Affiliation(s)
- Xiaomei Zhuang
- State Key Laboratory of Toxicology and Medical Countermeasures, Beijing
Institute of Pharmacology and Toxicology, Beijing 100850, China
| | - Chuang Lu
- Department of DMPK, Biogen, Inc., Cambridge, MA 02142, USA
- Corresponding author. Tel.: +1 6176793365.
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Nguyen HQ, Callegari E, Obach RS. The Use of In Vitro Data and Physiologically-Based Pharmacokinetic Modeling to Predict Drug Metabolite Exposure: Desipramine Exposure in Cytochrome P4502D6 Extensive and Poor Metabolizers Following Administration of Imipramine. ACTA ACUST UNITED AC 2016; 44:1569-78. [PMID: 27440861 DOI: 10.1124/dmd.116.071639] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Accepted: 07/18/2016] [Indexed: 02/06/2023]
Abstract
Major circulating drug metabolites can be as important as the drugs themselves in efficacy and safety, so establishing methods whereby exposure to major metabolites following administration of parent drug can be predicted is important. In this study, imipramine, a tricyclic antidepressant, and its major metabolite desipramine were selected as a model system to develop metabolite prediction methods. Imipramine undergoes N-demethylation to form the active metabolite desipramine, and both imipramine and desipramine are converted to hydroxylated metabolites by the polymorphic enzyme CYP2D6. The objective of the present study is to determine whether the human pharmacokinetics of desipramine following dosing of imipramine can be predicted using static and dynamic physiologically-based pharmacokinetic (PBPK) models from in vitro input data for CYP2D6 extensive metabolizer (EM) and poor metabolizer (PM) populations. The intrinsic metabolic clearances of parent drug and metabolite were estimated using human liver microsomes (CYP2D6 PM and EM) and hepatocytes. Passive diffusion clearance of desipramine, used in the estimation of availability of the metabolite, was predicted from passive permeability and hepatocyte surface area. The predicted area under the curve (AUCm/AUCp) of desipramine/imipramine was 12- to 20-fold higher in PM compared with EM subjects following i.v. or oral doses of imipramine using the static model. Moreover, the PBPK model was able to recover simultaneously plasma profiles of imipramine and desipramine in populations with different phenotypes of CYP2D6. This example suggested that mechanistic PBPK modeling combined with information obtained from in vitro studies can provide quantitative solutions to predict in vivo pharmacokinetics of drugs and major metabolites in a target human population.
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Affiliation(s)
- Hoa Q Nguyen
- Department of Pharmacokinetics, Dynamics, and Metabolism, Pfizer Global Research and Development, Groton, Connecticut
| | - Ernesto Callegari
- Department of Pharmacokinetics, Dynamics, and Metabolism, Pfizer Global Research and Development, Groton, Connecticut
| | - R Scott Obach
- Department of Pharmacokinetics, Dynamics, and Metabolism, Pfizer Global Research and Development, Groton, Connecticut
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Lagishetty CV, Deng J, Lesko LJ, Rogers H, Pacanowski M, Schmidt S. How Informative Are Drug-Drug Interactions of Gene-Drug Interactions? J Clin Pharmacol 2016; 56:1221-31. [PMID: 27040602 DOI: 10.1002/jcph.743] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 03/28/2016] [Indexed: 12/31/2022]
Abstract
FDA recommendations to manage polymorphic CYP-mediated drug-drug interactions (DDIs) and gene-drug interactions (GDIs) are typically similar. However, DDIs may not always reliably predict GDIs because the victim drug may have multiple metabolic pathways and the perpetrator drug may affect multiple enzymes or transporters. Consequently, it is of great interest to both the pharmaceutical industry and regulatory agencies to determine if DDI studies can be leveraged to inform GDIs or vice versa for dose adjustment and labeling. The objective of this study was to investigate under what circumstances DDIs can be used to predict GDIs for prototypical CYP2C9, CYP2C19, and CYP2D6 substrates. We investigated model substrates for CYP2D6 (metoprolol, dextromethorphan, atomoxetine, and vortioxetine), CYP2C9 (warfarin, flurbiprofen, and celecoxib), and CYP2C19 (omeprazole and clopidogrel). Data on drug exposure for poor metabolizers (GDI) and for DDIs mediated by strong/moderate inhibitors in extensive metabolizers were collected. The impact of DDIs and GDIs on drug exposure was compared using: (1) a descriptive and (2) a physiologically based pharmacokinetic convergence analysis. Results from both approaches indicate that information on DDIs can be used to reliably predict GDIs for CYP2D6 substrates. The situation is more complex for CYP2C9 and CYP2C19 substrates because dose of the inhibitor (CYP2C9) and potency of the inhibitor (CYP2C19) impact the extent to which perpetrator drugs phenotypically convert extensive metabolizers to poor(er) metabolizers.
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Affiliation(s)
- Chakradhar V Lagishetty
- Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida at Lake Nona, Orlando, FL, USA
| | - Jiexin Deng
- Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida at Lake Nona, Orlando, FL, USA
| | - Lawrence J Lesko
- Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida at Lake Nona, Orlando, FL, USA
| | - Hobart Rogers
- FDA Genomics and Targeted Therapy Group, Office of Clinical Pharmacology, Office of Translational Sciences, CDER, US FDA, Silver Spring, MD, USA
| | - Michael Pacanowski
- FDA Genomics and Targeted Therapy Group, Office of Clinical Pharmacology, Office of Translational Sciences, CDER, US FDA, Silver Spring, MD, USA
| | - Stephan Schmidt
- Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida at Lake Nona, Orlando, FL, USA.
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Brian W, Tremaine LM, Arefayene M, de Kanter R, Evers R, Guo Y, Kalabus J, Lin W, Loi CM, Xiao G. Assessment of drug metabolism enzyme and transporter pharmacogenetics in drug discovery and early development: perspectives of the I-PWG. Pharmacogenomics 2016; 17:615-31. [PMID: 27045656 DOI: 10.2217/pgs.16.9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Genetic variants of drug metabolism enzymes and transporters can result in high pharmacokinetic and pharmacodynamic variability, unwanted characteristics of efficacious and safe drugs. Ideally, the contributions of these enzymes and transporters to drug disposition can be predicted from in vitro experiments and in silico modeling in discovery or early development, and then be utilized during clinical development. Recently, regulatory agencies have provided guidance on the preclinical investigation of pharmacogenetics, for application to clinical drug development. This white paper summarizes the results of an industry survey conducted by the Industry Pharmacogenomics Working Group on current practice and challenges with using in vitro systems and in silico models to understand pharmacogenetic causes of variability in drug disposition.
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Affiliation(s)
- William Brian
- Sanofi, Translational Medicine and Early Development, 55 Corporate Drive, Bridgewater, NJ 08807, USA
| | - Larry M Tremaine
- Pfizer Inc., Worldwide Research and Development, Department of Pharmacokinetics, Dynamics and Metabolism, Eastern Point Road, Groton, CT 06340, USA
| | - Million Arefayene
- Biogen, Early Development Sciences, 14 Cambridge Center, Cambridge, MA 02142, USA
| | - Ruben de Kanter
- Preclinical Pharmacokinetics and Metabolism, Actelion Pharmaceuticals Ltd., Gewerbestrasse 16, CH-4123 Allschwil, Switzerland
| | - Raymond Evers
- Merck & Co, Pharmacodynamics, Pharmacokinetics and Drug Metabolism, 2000 Galloping Hill Road, Kenilworth, NJ07033, USA
| | - Yingying Guo
- Eli Lilly and Company, Drug Disposition, LillyCorporate Center, Indianapolis, IN 46285, USA
| | - James Kalabus
- Novartis Pharmaceuticals, 1 Health Plaza, EastHanover, NJ 07936, USA
| | - Wen Lin
- Novartis Institutes for Biomedical Research, Drug Metabolism and Pharmacokinetics, One Health Plaza, East Hanover, NJ07936-1080, USA
| | - Cho-Ming Loi
- Pfizer Inc., Worldwide Research and Development, Department of Pharmacokinetics, Dynamics and Metabolism,10646 Science Center Drive, San Diego, CA 92121, USA
| | - Guangqing Xiao
- Biogen, Preclinical PK and In vitro ADME, 14 Cambridge Center, Cambridge, MA 02142, USA
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Wagner C, Pan Y, Hsu V, Sinha V, Zhao P. Predicting the Effect of CYP3A Inducers on the Pharmacokinetics of Substrate Drugs Using Physiologically Based Pharmacokinetic (PBPK) Modeling: An Analysis of PBPK Submissions to the US FDA. Clin Pharmacokinet 2015; 55:475-83. [DOI: 10.1007/s40262-015-0330-y] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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41
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Jadhav PR, Cook J, Sinha V, Zhao P, Rostami-Hodjegan A, Sahasrabudhe V, Stockbridge N, Powell JR. A proposal for scientific framework enabling specific population drug dosing recommendations. J Clin Pharmacol 2015; 55:1073-8. [DOI: 10.1002/jcph.579] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2015] [Accepted: 06/23/2015] [Indexed: 11/12/2022]
Affiliation(s)
- Pravin R. Jadhav
- Quantitative Pharmacology and Pharmacometrics; Merck and Co.; Kenilworth NJ USA
| | - Jack Cook
- Clinical Pharmacology; Pfizer Inc.; Groton CT USA
| | - Vikram Sinha
- Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research; US Food and Drug Administration; Silver Spring MD USA
| | - Ping Zhao
- Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research; US Food and Drug Administration; Silver Spring MD USA
| | | | | | - Norman Stockbridge
- Office of New Drugs; Center for Drug Evaluation and Research; US Food and Drug Administration; Silver Spring MD USA
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Khalil F, Lüpken R, Läer S, Bernstein D. Innovative tools in the individualized medical therapy for children with heart muscle disease. PROGRESS IN PEDIATRIC CARDIOLOGY 2015. [DOI: 10.1016/j.ppedcard.2015.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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43
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Al Feteisi H, Achour B, Rostami-Hodjegan A, Barber J. Translational value of liquid chromatography coupled with tandem mass spectrometry-based quantitative proteomics forin vitro–in vivoextrapolation of drug metabolism and transport and considerations in selecting appropriate techniques. Expert Opin Drug Metab Toxicol 2015; 11:1357-69. [DOI: 10.1517/17425255.2015.1055245] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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44
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Heikkinen AT, Lignet F, Cutler P, Parrott N. The role of quantitative ADME proteomics to support construction of physiologically based pharmacokinetic models for use in small molecule drug development. Proteomics Clin Appl 2015; 9:732-44. [DOI: 10.1002/prca.201400147] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Revised: 01/16/2015] [Accepted: 02/05/2015] [Indexed: 01/26/2023]
Affiliation(s)
- Aki T. Heikkinen
- School of Pharmacy; Faculty of Health Sciences; University of Eastern Finland; Kuopio Finland
| | - Floriane Lignet
- Pharmaceutical Sciences; Pharmaceutical Research & Early Development; Roche Innovation Center Basel; Basel Switzerland
| | - Paul Cutler
- Pharmaceutical Sciences; Pharmaceutical Research & Early Development; Roche Innovation Center Basel; Basel Switzerland
| | - Neil Parrott
- Pharmaceutical Sciences; Pharmaceutical Research & Early Development; Roche Innovation Center Basel; Basel Switzerland
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Wagner C, Zhao P, Pan Y, Hsu V, Grillo J, Huang SM, Sinha V. Application of Physiologically Based Pharmacokinetic (PBPK) Modeling to Support Dose Selection: Report of an FDA Public Workshop on PBPK. CPT Pharmacometrics Syst Pharmacol 2015; 4:226-30. [PMID: 26225246 PMCID: PMC4429576 DOI: 10.1002/psp4.33] [Citation(s) in RCA: 180] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2015] [Accepted: 03/12/2015] [Indexed: 12/17/2022] Open
Abstract
The US Food and Drug Administration (FDA) public workshop, entitled "Application of Physiologically-based Pharmacokinetic (PBPK) Modeling to Support Dose Selection focused on the role of PBPK in drug development and regulation. Representatives from industry, academia, and regulatory agencies discussed the issues within plenary and panel discussions. This report summarizes the discussions and provides current perspectives on the application of PBPK in different areas, including its utility, predictive performance, and reporting for regulatory submissions.
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Affiliation(s)
- C Wagner
- Office of Clinical Pharmacology, Office of Translational Sciences, US Food and Drug Administration, Silver SpringMaryland, USA
| | - P Zhao
- Office of Clinical Pharmacology, Office of Translational Sciences, US Food and Drug Administration, Silver SpringMaryland, USA
| | - Y Pan
- Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver SpringMaryland, USA
| | - V Hsu
- Office of Clinical Pharmacology, Office of Translational Sciences, US Food and Drug Administration, Silver SpringMaryland, USA
| | - J Grillo
- Office of Clinical Pharmacology, Office of Translational Sciences, US Food and Drug Administration, Silver SpringMaryland, USA
| | - SM Huang
- Office of Clinical Pharmacology, Office of Translational Sciences, US Food and Drug Administration, Silver SpringMaryland, USA
| | - V Sinha
- Office of Clinical Pharmacology, Office of Translational Sciences, US Food and Drug Administration, Silver SpringMaryland, USA
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Varma MV, Pang KS, Isoherranen N, Zhao P. Dealing with the complex drug-drug interactions: Towards mechanistic models. Biopharm Drug Dispos 2015; 36:71-92. [DOI: 10.1002/bdd.1934] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2014] [Revised: 12/11/2014] [Accepted: 12/14/2014] [Indexed: 12/22/2022]
Affiliation(s)
- Manthena V. Varma
- Pharmacokinetics, Dynamics and Metabolism; Pfizer Inc; Groton Connecticut USA
| | - K. Sandy Pang
- Leslie Dan Faculty of Pharmacy; University of Toronto; M5S 3M2 Canada
| | - Nina Isoherranen
- Department of Pharmaceutics, School of Pharmacy; University of Washington; Seattle WA USA
| | - Ping Zhao
- Division of Pharmacometrics, Office of Clinical Pharmacology/Office of Translational Sciences; Center for Drug Evaluation and Research, US Food and Drug Administration; Silver Spring MD USA
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Chetty M, Rose RH, Abduljalil K, Patel N, Lu G, Cain T, Jamei M, Rostami-Hodjegan A. Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability. Front Pharmacol 2014; 5:258. [PMID: 25505415 PMCID: PMC4244809 DOI: 10.3389/fphar.2014.00258] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2014] [Accepted: 11/04/2014] [Indexed: 02/06/2023] Open
Abstract
This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.
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Affiliation(s)
| | - Rachel H Rose
- Simcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK
| | - Khaled Abduljalil
- Simcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK
| | - Nikunjkumar Patel
- Simcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK
| | - Gaohua Lu
- Simcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK
| | - Theresa Cain
- Simcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK
| | - Masoud Jamei
- Simcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK
| | - Amin Rostami-Hodjegan
- Simcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK ; Manchester Pharmacy School, University of Manchester Manchester, UK
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48
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Predicting the Effect of Cytochrome P450 Inhibitors on Substrate Drugs: Analysis of Physiologically Based Pharmacokinetic Modeling Submissions to the US Food and Drug Administration. Clin Pharmacokinet 2014; 54:117-27. [DOI: 10.1007/s40262-014-0188-4] [Citation(s) in RCA: 85] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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49
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Sinha V, Zhao P, Huang SM, Zineh I. Physiologically based pharmacokinetic modeling: from regulatory science to regulatory policy. Clin Pharmacol Ther 2014; 95:478-80. [PMID: 24747236 DOI: 10.1038/clpt.2014.46] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Assessment of controllable sources of intra- and interpatient variability in drug response is of critical importance in the regulatory evaluation of new drugs.(1) Although determinants of response variability would ideally be understood and accounted for before approval of a new pharmaceutical product, this is rarely the case for all; clinical trials in specific populations that definitively test optimal dosing in patient management strategies are not routinely performed prior to drug approval.
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Affiliation(s)
- V Sinha
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - P Zhao
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - S M Huang
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - I Zineh
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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