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Geci R, Gadaleta D, de Lomana MG, Ortega-Vallbona R, Colombo E, Serrano-Candelas E, Paini A, Kuepfer L, Schaller S. Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans. Arch Toxicol 2024; 98:2659-2676. [PMID: 38722347 PMCID: PMC11272695 DOI: 10.1007/s00204-024-03764-9] [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: 03/12/2024] [Accepted: 04/23/2024] [Indexed: 07/26/2024]
Abstract
Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.
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Affiliation(s)
- René Geci
- esqLABS GmbH, Saterland, Germany.
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, Germany.
| | | | - Marina García de Lomana
- Machine Learning Research, Research and Development, Pharmaceuticals, Bayer AG, Berlin, Germany
| | | | - Erika Colombo
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | | | | | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, Germany
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2
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Liu XI, Green DJ, van den Anker J, Calderon J, Ahmadzia H, Burckart GJ, Dallmann A. Labetalol Dosing in Pregnancy: PBPK/PD and CYP2C19 Polymorphisms. J Clin Pharmacol 2024. [PMID: 38973651 DOI: 10.1002/jcph.2496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 06/23/2024] [Indexed: 07/09/2024]
Abstract
As detailed information on the pharmacokinetics (PK) of labetalol in pregnant people are lacking, the aims of this study were: (1) to build a physiologically based PK (PBPK) model of labetalol in non-pregnant individuals that incorporates different CYP2C19 genotypes (specifically, *1/*1, *1/*2 or *3, *2/*2, and *17/*17); (2) to translate this model to the second and third trimester of pregnancy; and (3) to combine the model with a previously published direct pharmacodynamic (PD) model to predict the blood pressure lowering effect of labetalol in the third trimester. Clinical data for model evaluation was obtained from the scientific literature. In non-pregnant populations, the mean ratios of simulated versus observed peak concentration (Cmax), time to reach Cmax (Tmax), and exposure (area under the plasma concentration-time curve, AUC) were 0.94, 0.82, and 1.16, respectively. The pregnancy PBPK model captured the observed PK adequately, but clearance was slightly underestimated with mean ratios of simulated versus observed Cmax, Tmax, and AUC of 1.28, 1.30, and 1.39, respectively. The results suggested that pregnant people with CYP2C19 *2/*2 alleles have similar labetalol exposure and trough levels compared to non-pregnant controls, whereas those with other alleles were found to have increased exposure and trough concentrations. Importantly, the pregnancy PBPK/PD model predicted that, despite increased exposure in some genotypes, the blood pressure lowering effect was broadly comparable across all genotypes. In view of the large inter-individual variability and the potentially increasing blood pressure during pregnancy, patients may need to be closely monitored for achieving optimal therapeutic effects and avoiding adverse events.
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Affiliation(s)
- Xiaomei I Liu
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
| | - Dionna J Green
- Office of Pediatric Therapeutics, US Food and Drug Administration, Silver Spring, MD, USA
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
| | - Joaquin Calderon
- Division of Maternal-Fetal Medicine, Department of OB/Gyn, George Washington University, Washington, DC, USA
| | - Homa Ahmadzia
- Division of Maternal-Fetal Medicine, Department of OB/Gyn, George Washington University, Washington, DC, USA
| | - Gilbert J Burckart
- Office of Clinical Pharmacology, US Food and Drug Administration, Silver Spring, MD, USA
| | - André Dallmann
- Bayer HealthCare SAS, Loos, France, on behalf of: Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer, AG, Germany
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3
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Kovar C, Loer HLH, Rüdesheim S, Fuhr LM, Marok FZ, Selzer D, Schwab M, Lehr T. A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions. CPT Pharmacometrics Syst Pharmacol 2024; 13:1144-1159. [PMID: 38693610 PMCID: PMC11247110 DOI: 10.1002/psp4.13146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/28/2024] [Accepted: 04/02/2024] [Indexed: 05/03/2024] Open
Abstract
Dasatinib, a second-generation tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pH-sensitive solubility, dasatinib is susceptible to enzyme-mediated drug-drug interactions (DDIs) with CYP3A4 perpetrators and pH-dependent DDIs with acid-reducing agents. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model of dasatinib to describe and predict enzyme-mediated and pH-dependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentration-time curve from the first to the last timepoint of measurement (AUClast) and maximum plasma concentration (Cmax) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUClast and Cmax ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%-80% for strong and 0%-70% for moderate CYP3A4 inhibitors and a 2.3-3.1-fold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patient-related factors, such as elevated gastric pH.
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Affiliation(s)
- Christina Kovar
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
| | | | - Simeon Rüdesheim
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
| | | | | | - Dominik Selzer
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- Departments of Clinical Pharmacology, and Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180), Image-Guided and Functionally Instructed Tumor Therapies, University of Tübingen, Tübingen, Germany
| | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
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Marok FZ, Wojtyniak JG, Selzer D, Dallmann R, Swen JJ, Guchelaar HJ, Schwab M, Lehr T. Personalized Chronomodulated 5-Fluorouracil Treatment: A Physiologically-Based Pharmacokinetic Precision Dosing Approach for Optimizing Cancer Therapy. Clin Pharmacol Ther 2024; 115:1282-1292. [PMID: 38264789 DOI: 10.1002/cpt.3181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 01/03/2024] [Indexed: 01/25/2024]
Abstract
The discovery of circadian clock genes greatly amplified the study of diurnal variations impacting cancer therapy, transforming it into a rapidly growing field of research. Especially, use of chronomodulated treatment with 5-fluorouracil (5-FU) has gained significance. Studies indicate high interindividual variability (IIV) in diurnal variations in dihydropyrimidine dehydrogenase (DPD) activity - a key enzyme for 5-FU metabolism. However, the influence of individual DPD chronotypes on chronomodulated therapy remains unclear and warrants further investigation. To optimize precision dosing of chronomodulated 5-FU, this study aims to: (i) build physiologically-based pharmacokinetic (PBPK) models for 5-FU, uracil, and their metabolites, (ii) assess the impact of diurnal variation on DPD activity, (iii) estimate individual DPD chronotypes, and (iv) personalize chronomodulated 5-FU infusion rates based on a patient's DPD chronotype. Whole-body PBPK models were developed with PK-Sim(R) and MoBi(R). Sinusoidal functions were used to incorporate variations in enzyme activity and chronomodulated infusion rates as well as to estimate individual DPD chronotypes from DPYD mRNA expression or DPD enzymatic activity. Four whole-body PBPK models for 5-FU, uracil, and their metabolites were established utilizing data from 41 5-FU and 10 publicly available uracil studies. IIV in DPD chronotypes was assessed and personalized chronomodulated administrations were developed to achieve (i) comparable 5-FU peak plasma concentrations, (ii) comparable 5-FU exposure, and (iii) constant 5-FU plasma levels via "noise cancellation" chronomodulated infusion. The developed PBPK models capture the extent of diurnal variations in DPD activity and can help investigate individualized chronomodulated 5-FU therapy through testing alternative personalized dosing strategies.
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Affiliation(s)
| | - Jan-Georg Wojtyniak
- Clinical Pharmacy, Saarland University, Saarbruecken, Germany
- Dr. Margarete Fischer-Bosch-Institut of Clinical Pharmacology, Stuttgart, Germany
| | - Dominik Selzer
- Clinical Pharmacy, Saarland University, Saarbruecken, Germany
| | - Robert Dallmann
- Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Jesse J Swen
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, RC Leiden, The Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, RC Leiden, The Netherlands
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institut of Clinical Pharmacology, Stuttgart, Germany
- Departments of Clinical Pharmacology, and of Biochemistry and Pharmacy, University Tuebingen, Tuebingen, Germany
- Cluster of excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University Tuebingen, Tuebingen, Germany
| | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, Saarbruecken, Germany
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Gao W, Liu J, Shtylla B, Venkatakrishnan K, Yin D, Shah M, Nicholas T, Cao Y. Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development. CPT Pharmacometrics Syst Pharmacol 2024; 13:691-709. [PMID: 37969061 PMCID: PMC11098159 DOI: 10.1002/psp4.13079] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 10/20/2023] [Accepted: 10/30/2023] [Indexed: 11/17/2023] Open
Abstract
Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.
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Affiliation(s)
- Wei Gao
- Quantitative PharmacologyEMD Serono Research & Development Institute, Inc.BillericaMassachusettsUSA
| | - Jiang Liu
- Food and Drug AdministrationSilver SpringMarylandUSA
| | - Blerta Shtylla
- Quantitative Systems PharmacologyPfizerSan DiegoCaliforniaUSA
| | - Karthik Venkatakrishnan
- Quantitative PharmacologyEMD Serono Research & Development Institute, Inc.BillericaMassachusettsUSA
| | - Donghua Yin
- Clinical PharmacologyPfizerSan DiegoCaliforniaUSA
| | - Mirat Shah
- Food and Drug AdministrationSilver SpringMarylandUSA
| | | | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of PharmacyUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
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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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7
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Mehta K, Balazki P, van der Graaf PH, Guo T, van Hasselt JGC. Predictions of Bedaquiline Central Nervous System Exposure in Patients with Tuberculosis Meningitis Using Physiologically based Pharmacokinetic Modeling. Clin Pharmacokinet 2024; 63:657-668. [PMID: 38530588 PMCID: PMC11106169 DOI: 10.1007/s40262-024-01363-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND AND OBJECTIVE The use of bedaquiline as a treatment option for drug-resistant tuberculosis meningitis (TBM) is of interest to address the increased prevalence of resistance to first-line antibiotics. To this end, we describe a whole-body physiologically based pharmacokinetic (PBPK) model for bedaquiline to predict central nervous system (CNS) exposure. METHODS A whole-body PBPK model was developed for bedaquiline and its metabolite, M2. The model included compartments for brain and cerebrospinal fluid (CSF). Model predictions were evaluated by comparison to plasma PK time profiles following different dosing regimens and sparse CSF concentrations data from patients. Simulations were then conducted to compare CNS and lung exposures to plasma exposure at clinically relevant dosing schedules. RESULTS The model appropriately described the observed plasma and CSF bedaquiline and M2 concentrations from patients with pulmonary tuberculosis (TB). The model predicted a high impact of tissue binding on target site drug concentrations in CNS. Predicted unbound exposures within brain interstitial exposures were comparable with unbound vascular plasma and unbound lung exposures. However, unbound brain intracellular exposures were predicted to be 7% of unbound vascular plasma and unbound lung intracellular exposures. CONCLUSIONS The whole-body PBPK model for bedaquiline and M2 predicted unbound concentrations in brain to be significantly lower than the unbound concentrations in the lung at clinically relevant doses. Our findings suggest that bedaquiline may result in relatively inferior efficacy against drug-resistant TBM when compared with efficacy against drug-resistant pulmonary TB.
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Affiliation(s)
- Krina Mehta
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
| | | | - Piet H van der Graaf
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
- Certara, Canterbury, UK
| | - Tingjie Guo
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - J G Coen van Hasselt
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
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8
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Kanefendt F, Dallmann A, Chen H, Francke K, Liu T, Brase C, Frechen S, Schultze-Mosgau MH. Assessment of the CYP3A4 Induction Potential by Carbamazepine: Insights from Two Clinical DDI Studies and PBPK Modeling. Clin Pharmacol Ther 2024; 115:1025-1032. [PMID: 38105467 DOI: 10.1002/cpt.3151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/15/2023] [Indexed: 12/19/2023]
Abstract
In the past, rifampicin was well-established as strong index CYP3A inducer in clinical drug-drug interaction (DDI) studies. However, due to identified potentially genotoxic nitrosamine impurities, it should not any longer be used in healthy volunteer studies. Available clinical data suggest carbamazepine as an alternative to rifampicin as strong index CYP3A4 inducer in clinical DDI studies. Further, physiologically-based pharmacokinetic (PBPK) modeling is a tool with increasing importance to support the DDI risk assessment of drugs during drug development. CYP3A4 induction properties and the safety profile of carbamazepine were investigated in two open-label, fixed sequence, crossover clinical pharmacology studies in healthy volunteers using midazolam as a sensitive index CYP3A4 substrate. Carbamazepine was up-titrated from 100 mg twice daily (b.i.d.) to 200 mg b.i.d., and to a final dose of 300 mg b.i.d. for 10 consecutive days. Mean area under plasma concentration-time curve from zero to infinity (AUC(0-∞)) of midazolam consistently decreased by 71.8% (ratio: 0.282, 90% confidence interval (CI): 0.235-0.340) and 67.7% (ratio: 0.323, 90% CI: 0.256-0.407) in study 1 and study 2, respectively. The effect was adequately described by an internally developed PBPK model for carbamazepine which has been made freely available to the scientific community. Further, carbamazepine was safe and well-tolerated in the investigated dosing regimen in healthy participants. The results demonstrated that the presented design is appropriate for the use of carbamazepine as alternative inducer to rifampicin in DDI studies acknowledging its CYP3A4 inductive potency and safety profile.
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Affiliation(s)
| | - André Dallmann
- Bayer HealthCare SAS, Loos, France, on behalf of Bayer AG, Pharmacometrics/Modeling and Simulation, Systems Pharmacology & Medicine - PBPK, Germany
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9
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Reppas C, Kuentz M, Bauer-Brandl A, Carlert S, Dallmann A, Dietrich S, Dressman J, Ejskjaer L, Frechen S, Guidetti M, Holm R, Holzem FL, Karlsson Ε, Kostewicz E, Panbachi S, Paulus F, Senniksen MB, Stillhart C, Turner DB, Vertzoni M, Vrenken P, Zöller L, Griffin BT, O'Dwyer PJ. Leveraging the use of in vitro and computational methods to support the development of enabling oral drug products: An InPharma commentary. Eur J Pharm Sci 2023; 188:106505. [PMID: 37343604 DOI: 10.1016/j.ejps.2023.106505] [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: 03/13/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 06/23/2023]
Abstract
Due to the strong tendency towards poorly soluble drugs in modern development pipelines, enabling drug formulations such as amorphous solid dispersions, cyclodextrins, co-crystals and lipid-based formulations are frequently applied to solubilize or generate supersaturation in gastrointestinal fluids, thus enhancing oral drug absorption. Although many innovative in vitro and in silico tools have been introduced in recent years to aid development of enabling formulations, significant knowledge gaps still exist with respect to how best to implement them. As a result, the development strategy for enabling formulations varies considerably within the industry and many elements of empiricism remain. The InPharma network aims to advance a mechanistic, animal-free approach to the assessment of drug developability. This commentary focuses current status and next steps that will be taken in InPharma to identify and fully utilize 'best practice' in vitro and in silico tools for use in physiologically based biopharmaceutic models.
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Affiliation(s)
- Christos Reppas
- Department of Pharmacy, National and Kapodistrian University of Athens, Greece
| | - Martin Kuentz
- School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz CH 4132, Switzerland
| | - Annette Bauer-Brandl
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, Odense 5230, Denmark
| | | | - André Dallmann
- Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - Shirin Dietrich
- Department of Pharmacy, National and Kapodistrian University of Athens, Greece
| | - Jennifer Dressman
- Fraunhofer Institute of Translational Medicine and Pharmacology, Frankfurt am Main, Germany
| | - Lotte Ejskjaer
- School of Pharmacy, University College Cork, Cork, Ireland
| | - Sebastian Frechen
- Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - Matteo Guidetti
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, Odense 5230, Denmark; Solvias AG, Department for Solid-State Development, Römerpark 2, 4303 Kaiseraugst, Switzerland
| | - René Holm
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, Odense 5230, Denmark
| | - Florentin Lukas Holzem
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, Odense 5230, Denmark; Pharmaceutical R&D, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | | | - Edmund Kostewicz
- Fraunhofer Institute of Translational Medicine and Pharmacology, Frankfurt am Main, Germany
| | - Shaida Panbachi
- School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz CH 4132, Switzerland
| | - Felix Paulus
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, Odense 5230, Denmark
| | - Malte Bøgh Senniksen
- Fraunhofer Institute of Translational Medicine and Pharmacology, Frankfurt am Main, Germany; Pharmaceutical R&D, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | - Cordula Stillhart
- Pharmaceutical R&D, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | | | - Maria Vertzoni
- Department of Pharmacy, National and Kapodistrian University of Athens, Greece
| | - Paul Vrenken
- Department of Pharmacy, National and Kapodistrian University of Athens, Greece; Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - Laurin Zöller
- AstraZeneca R&D, Gothenburg, Sweden; Fraunhofer Institute of Translational Medicine and Pharmacology, Frankfurt am Main, Germany
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10
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Demeester C, Robins D, Edwina AE, Tournoy J, Augustijns P, Ince I, Lehmann A, Vertzoni M, Schlender JF. Physiologically based pharmacokinetic (PBPK) modelling of oral drug absorption in older adults - an AGePOP review. Eur J Pharm Sci 2023; 188:106496. [PMID: 37329924 DOI: 10.1016/j.ejps.2023.106496] [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: 03/28/2023] [Revised: 06/06/2023] [Accepted: 06/14/2023] [Indexed: 06/19/2023]
Abstract
The older population consisting of persons aged 65 years or older is the fastest-growing population group and also the major consumer of pharmaceutical products. Due to the heterogenous ageing process, this age group shows high interindividual variability in the dose-exposure-response relationship and, thus, a prediction of drug safety and efficacy is challenging. Although physiologically based pharmacokinetic (PBPK) modelling is a well-established tool to inform and confirm drug dosing strategies during drug development for special population groups, age-related changes in absorption are poorly accounted for in current PBPK models. The purpose of this review is to summarise the current state-of-knowledge in terms of physiological changes with increasing age that can influence the oral absorption of dosage forms. The capacity of common PBPK platforms to incorporate these changes and describe the older population is also discussed, as well as the implications of extrinsic factors such as drug-drug interactions associated with polypharmacy on the model development process. The future potential of this field will rely on addressing the gaps identified in this article, which can subsequently supplement in-vitro and in-vivo data for more robust decision-making on the adequacy of the formulation for use in older adults and inform pharmacotherapy.
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Affiliation(s)
- Cleo Demeester
- Systems Pharmacology & Medicine, Pharmaceuticals, Bayer AG, Leverkusen 51373, Germany; Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Gasthuisberg O&N II, Leuven, Belgium
| | - Donnia Robins
- Global CMC Development, Merck KGaA, Frankfurter Straße 250, Darmstadt, Germany; Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Zografou, Greece
| | - Angela Elma Edwina
- Gerontology and Geriatrics Unit, Department of Public Health and Primary care, KU Leuven - University of Leuven, Leuven, Belgium
| | - Jos Tournoy
- Gerontology and Geriatrics Unit, Department of Public Health and Primary care, KU Leuven - University of Leuven, Leuven, Belgium; Department of Geriatric Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Patrick Augustijns
- Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Gasthuisberg O&N II, Leuven, Belgium
| | - Ibrahim Ince
- Systems Pharmacology & Medicine, Pharmaceuticals, Bayer AG, Leverkusen 51373, Germany
| | - Andreas Lehmann
- Global CMC Development, Merck KGaA, Frankfurter Straße 250, Darmstadt, Germany
| | - Maria Vertzoni
- Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Zografou, Greece
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Morcos PN, Schlender J, Burghaus R, Moss J, Lloyd A, Childs BH, Macy ME, Reid JM, Chung J, Garmann D. Model-informed approach to support pediatric dosing for the pan-PI3K inhibitor copanlisib in children and adolescents with relapsed/refractory solid tumors. Clin Transl Sci 2023; 16:1197-1209. [PMID: 37042099 PMCID: PMC10339701 DOI: 10.1111/cts.13523] [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: 01/04/2023] [Revised: 02/21/2023] [Accepted: 03/17/2023] [Indexed: 04/13/2023] Open
Abstract
Copanlisib is an intravenously administered phosphatidylinositol 3-kinase (PI3K) inhibitor which was investigated in pediatric patients with relapsed/refractory solid tumors. A model-informed approach was undertaken to support and confirm an empirically selected starting dose of 28 mg/m2 for pediatric patients ≥1 year old, corresponding to 80% of the adult recommended dose adjusted for body surface area. An adult physiologically based pharmacokinetic (PBPK) model was initially established using copanlisib physicochemical and disposition properties and clinical pharmacokinetics (PK) data and was shown to adequately capture clinical PK across a range of copanlisib doses in adult cancer patients. The adult PBPK model was then extended to the pediatric population through incorporation of age-dependent anatomical and physiological changes and used to simulate copanlisib exposures in pediatric cancer patient age groups. The pediatric PBPK model predicted that the copanlisib 28 mg/m2 dose would achieve similar copanlisib exposures across pediatric ages when compared with historical adult exposures following the approved copanlisib 60 mg dose administered on Days 1, 8, and 15 of a 28-day cycle. Clinical PK were collected from a phase I study in pediatric patients with relapsed/refractory solid tumors (aged ≥4 years). An established adult population PK model was extended to incorporate an allometrically-scaled effect of body surface area and confirmed that the copanlisib maximum tolerated dose of 28 mg/m2 was appropriate to achieve uniform copanlisib exposures across the investigated pediatric age range and consistent exposures to historical data in adult cancer patients. The model-informed approach successfully supported and confirmed the copanlisib pediatric dose recommendation.
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Affiliation(s)
| | - Jan Schlender
- Pharmacometrics/Modeling & Simulation, Pharmaceuticals DivisionBayer AGWuppertalGermany
| | - Rolf Burghaus
- Pharmacometrics/Modeling & Simulation, Pharmaceuticals DivisionBayer AGWuppertalGermany
| | | | | | | | - Margaret E. Macy
- Department of Pediatrics, University of Colorado and Center for Cancer and Blood DisordersChildren's Hospital ColoradoAuroraColoradoUSA
| | - Joel M. Reid
- Department of PharmacologyMayo Clinic Graduate School of Biomedical SciencesRochesterMinnesotaUSA
| | - John Chung
- Bayer HealthCare Pharmaceuticals, Inc.WhippanyNew JerseyUSA
| | - Dirk Garmann
- Pharmacometrics/Modeling & Simulation, Pharmaceuticals DivisionBayer AGWuppertalGermany
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12
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Rajput AJ, Aldibani HKA, Rostami-Hodjegan A. In-depth analysis of patterns in selection of different physiologically based pharmacokinetic modeling tools: PartI - Applications and rationale behind the use of open source-code software. Biopharm Drug Dispos 2023. [PMID: 37083200 DOI: 10.1002/bdd.2357] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 03/29/2023] [Accepted: 04/04/2023] [Indexed: 04/22/2023]
Abstract
PBPK applications published in the literature support a greater adoption of non-open source-code (NOSC) software as opposed to open source-code (OSC) alternatives. However, a significant number of PBPK modelers are still using OSC software, understanding the rationale for the use of this modality is important and may help those embarking on PBPK modeling. No previous analysis of PBPK modeling trends has included the rationale of the modeler. An in-depth analysis of PBPK applications of OSC software is warranted to determine the true impact of OSC software on the rise of PBPK. Publications focussing on PBPK modeling applications, which used OSC software, were identified by systematically searching the scientific literature for original articles. A total of 171 articles were extracted from the narrowed subset. The rise in the use of OSC software for PBPK applications was greater than the general discipline of pharmacokinetics (9 vs. 4), but less than the overall growth of the PBPK area (9 vs. 43). Our report demonstrates conclusively that the surge in PBPK usage is primarily attributable to the availability and implementations of NOSC software. Modelers preferred not to share the reasons for their selection of certain modeling software and no 'explicit' rationale was given to support the use of OSC analysed by this study. As the preference for NOSC versus OSC software tools in the PBPK area continues to be divided, initiatives to add the rationale in using one form over another to every future PBPK modeling report will be a welcomed and informative addition.
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Affiliation(s)
- Arham Jamaal Rajput
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
| | | | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
- Certara UK Limited, Sheffield, UK
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13
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Khalid K, Rox K. All Roads Lead to Rome: Enhancing the Probability of Target Attainment with Different Pharmacokinetic/Pharmacodynamic Modelling Approaches. Antibiotics (Basel) 2023; 12:antibiotics12040690. [PMID: 37107052 PMCID: PMC10135278 DOI: 10.3390/antibiotics12040690] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023] Open
Abstract
In light of rising antimicrobial resistance and a decreasing number of antibiotics with novel modes of action, it is of utmost importance to accelerate development of novel treatment options. One aspect of acceleration is to understand pharmacokinetics (PK) and pharmacodynamics (PD) of drugs and to assess the probability of target attainment (PTA). Several in vitro and in vivo methods are deployed to determine these parameters, such as time-kill-curves, hollow-fiber infection models or animal models. However, to date the use of in silico methods to predict PK/PD and PTA is increasing. Since there is not just one way to perform the in silico analysis, we embarked on reviewing for which indications and how PK and PK/PD models as well as PTA analysis has been used to contribute to the understanding of the PK and PD of a drug. Therefore, we examined four recent examples in more detail, namely ceftazidime-avibactam, omadacycline, gepotidacin and zoliflodacin as well as cefiderocol. Whereas the first two compound classes mainly relied on the ‘classical’ development path and PK/PD was only deployed after approval, cefiderocol highly profited from in silico techniques that led to its approval. Finally, this review shall highlight current developments and possibilities to accelerate drug development, especially for anti-infectives.
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Affiliation(s)
- Kashaf Khalid
- Department of Chemical Biology, Helmholtz Centre for Infection Research (HZI), Inhoffenstraße 7, 38124 Braunschweig, Germany
| | - Katharina Rox
- Department of Chemical Biology, Helmholtz Centre for Infection Research (HZI), Inhoffenstraße 7, 38124 Braunschweig, Germany
- German Center for Infection Research (DZIF), Partner Site Hannover-Braunschweig, 38124 Braunschweig, Germany
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14
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Maass C, Schaller S, Dallmann A, Bothe K, Müller D. Considering developmental neurotoxicity in vitro data for human health risk assessment using physiologically-based kinetic modeling: deltamethrin case study. Toxicol Sci 2023; 192:59-70. [PMID: 36637193 PMCID: PMC10025876 DOI: 10.1093/toxsci/kfad007] [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] [Indexed: 01/14/2023] Open
Abstract
Developmental neurotoxicity (DNT) is a potential hazard of chemicals. Recently, an in vitro testing battery (DNT IVB) was established to complement existing rodent in vivo approaches. Deltamethrin (DLT), a pyrethroid with a well-characterized neurotoxic mode of action, has been selected as a reference chemical to evaluate the performance of the DNT IVB. The present study provides context for evaluating the relevance of these DNT IVB results for the human health risk assessment of DLT by estimating potential human fetal brain concentrations after maternal exposure to DLT. We developed a physiologically based kinetic (PBK) model for rats which was then translated to humans considering realistic in vivo exposure conditions (acceptable daily intake [ADI] for DLT). To address existing uncertainties, we designed case studies considering the most relevant drivers of DLT uptake and distribution. Calculated human fetal brain concentrations were then compared with the lowest benchmark concentration achieved in the DNT IVB. The developed rat PBK model was validated on in vivo rat toxicokinetic data of DLT over a broad range of doses. The uncertainty based case study evaluation confirmed that repeated exposure to DLT at an ADI level would likely result in human fetal brain concentrations far below the in vitro benchmark. The presented results indicate that DLT concentrations in the human fetal brain are highly unlikely to reach concentrations associated with in vitro findings under realistic exposure conditions. Therefore, the new in vitro DNT results are considered to have no impact on the current risk assessment approach.
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Affiliation(s)
| | | | - André Dallmann
- Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen 51373, Germany
| | - Kathrin Bothe
- Regulatory Toxicology, Research and Development, Bayer AG, CropScience, 40789 Monheim am Rhein, Germany
| | - Dennis Müller
- Regulatory Toxicology, Research and Development, Bayer AG, CropScience, 40789 Monheim am Rhein, Germany
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15
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Cordes H, Rapp H. Gene expression databases for physiologically based pharmacokinetic modeling of humans and animal species. CPT Pharmacometrics Syst Pharmacol 2023; 12:311-319. [PMID: 36715173 PMCID: PMC10014062 DOI: 10.1002/psp4.12904] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 12/05/2022] [Accepted: 12/08/2022] [Indexed: 01/31/2023] Open
Abstract
In drug research, developing a sound understanding of the key mechanistic drivers of pharmacokinetics (PK) for new molecular entities is essential for human PK and dose predictions. Here, characterizing the absorption, distribution, metabolism, and excretion (ADME) processes is crucial for a mechanistic understanding of the drug-target and drug-body interactions. Sufficient knowledge on ADME processes enables reliable interspecies and human PK estimations beyond allometric scaling. The physiologically based PK (PBPK) modeling framework allows the explicit consideration of organ-specific ADME processes. The sum of all passive and active ADME processes results in the observed plasma PK. Gene expression information can be used as surrogate for protein abundance and activity within PBPK models. The absolute and relative expression of ADME genes can differ between species and strains. This is affecting both, the PK and pharmacodynamics and is therefore posing a challenge for the extrapolation from preclinical findings to humans. We developed an automated workflow that generates whole-body gene expression databases for humans and other species relevant in drug development, animal health, nutritional sciences, and toxicology. Solely, bulk RNA-seq data curated and provided by the Swiss Institute of Bioinformatics from healthy, normal, and untreated primary tissue samples were considered as an unbiased reference of normal gene expression. The databases are interoperable with the Open Systems Pharmacology Suite (PK-Sim and MoBi) and enable seamless access to a central source of curated cross-species gene expression data. This will increase data transparency, increase reliability and reproducibility of PBPK model simulations, and accelerate mechanistic PBPK model development in the future.
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Affiliation(s)
- Henrik Cordes
- Drug Metabolism & Pharmacokinetics, Sanofi-Aventis Deutschland GmbH, Industriepark Höchst, Frankfurt am Main, Germany
| | - Hermann Rapp
- Research Drug Metabolism & Pharmacokinetics, Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
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16
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A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug-Drug Interaction Perpetrators. Pharmaceutics 2023; 15:pharmaceutics15020679. [PMID: 36840001 PMCID: PMC9965990 DOI: 10.3390/pharmaceutics15020679] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/06/2023] [Accepted: 02/13/2023] [Indexed: 02/19/2023] Open
Abstract
The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing's syndrome, is prone to drug-food interactions (DFIs) and is well known for its strong drug-drug interaction (DDI) potential. Some of ketoconazole's potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole's metabolites on its DDI potential. The parent-metabolites model was developed with PK-Sim® and MoBi® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUClast and DFI Cmax ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUClast and 21/21 DDI Cmax ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUClast and 18/21 DDI Cmax ratios were within the success limits.
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17
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Deepika D, Kumar V. The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3473. [PMID: 36834167 PMCID: PMC9966583 DOI: 10.3390/ijerph20043473] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.
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Affiliation(s)
- Deepika Deepika
- Environmental Engineering Laboratory, Departament d’Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Pere Virgili Health Research Institute (IISPV), Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, 43204 Reus, Catalonia, Spain
| | - Vikas Kumar
- Environmental Engineering Laboratory, Departament d’Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Pere Virgili Health Research Institute (IISPV), Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, 43204 Reus, Catalonia, Spain
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18
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Mehta K, Narayanan N, Heysell SK, Bisson GP, Subbian S, Kurepina N, Kreiswirth BN, Vinnard C. Pharmacogenetic variability and the probability of site of action target attainment during tuberculosis meningitis treatment: A physiologically based pharmacokinetic modeling and simulations study. Tuberculosis (Edinb) 2022; 137:102271. [PMID: 36375279 DOI: 10.1016/j.tube.2022.102271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 09/01/2022] [Accepted: 10/12/2022] [Indexed: 11/22/2022]
Abstract
OBJECTIVE AND METHODS Our objective was to investigate the role of patient pharmacogenetic variability in determining site of action target attainment during tuberculous meningitis (TBM) treatment. Rifampin and isoniazid PBPK model that included SLCO1B1 and NAT2 effects on exposures respectively were obtained from literature, modified, and validated using available cerebrospinal-fluid (CSF) concentrations. Population simulations of isoniazid and rifampin concentrations in brain interstitial fluid and probability of target attainment according to genotypes and M. tuberculosis MIC levels, under standard and intensified dosing, were conducted. RESULTS The rifampin and isoniazid model predicted steady-state drug concentration within brain interstitial fluid matched with the observed CSF concentrations. At MIC level of 0.25 mg/L, 57% and 23% of the patients with wild type and heterozygous SLCO1B1 genotype respectively attained the target in CNS with rifampin standard dosing, improving to 98% and 91% respectively with 35 mg/kg dosing. At MIC level of 0.25 mg/L, 33% of fast acetylators attained the target in CNS with isoniazid standard dosing, improving to 90% with 7.5 mg/kg dosing. CONCLUSION In this study, the combined effects of pharmacogenetic and M. tuberculosis MIC variability were potent determinants of target attainment in CNS. The potential for genotype-guided dosing during TBM treatment should be further explored in prospective clinical studies.
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Affiliation(s)
| | | | - Scott K Heysell
- University of Virginia, Division of Infectious Diseases and International Health, Charlottesville, VA, USA
| | - Gregory P Bisson
- University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Selvakumar Subbian
- Public Health Research Institute, New Jersey Medical School, Newark, NJ, USA
| | - Natalia Kurepina
- Center for Discovery & Innovation, Hackensack Meridian Health, Nutley, NJ, USA
| | - Barry N Kreiswirth
- Center for Discovery & Innovation, Hackensack Meridian Health, Nutley, NJ, USA
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Prediction of Drug-Drug-Gene Interaction Scenarios of ( E)-Clomiphene and Its Metabolites Using Physiologically Based Pharmacokinetic Modeling. Pharmaceutics 2022; 14:pharmaceutics14122604. [PMID: 36559098 PMCID: PMC9781104 DOI: 10.3390/pharmaceutics14122604] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/18/2022] [Accepted: 11/22/2022] [Indexed: 11/29/2022] Open
Abstract
Clomiphene, a selective estrogen receptor modulator (SERM), has been used for the treatment of anovulation for more than 50 years. However, since (E)-clomiphene ((E)-Clom) and its metabolites are eliminated primarily via Cytochrome P450 (CYP) 2D6 and CYP3A4, exposure can be affected by CYP2D6 polymorphisms and concomitant use with CYP inhibitors. Thus, clomiphene therapy may be susceptible to drug-gene interactions (DGIs), drug-drug interactions (DDIs) and drug-drug-gene interactions (DDGIs). Physiologically based pharmacokinetic (PBPK) modeling is a tool to quantify such DGI and DD(G)I scenarios. This study aimed to develop a whole-body PBPK model of (E)-Clom including three important metabolites to describe and predict DGI and DD(G)I effects. Model performance was evaluated both graphically and by calculating quantitative measures. Here, 90% of predicted Cmax and 80% of AUClast values were within two-fold of the corresponding observed value for DGIs and DD(G)Is with clarithromycin and paroxetine. The model also revealed quantitative contributions of different CYP enzymes to the involved metabolic pathways of (E)-Clom and its metabolites. The developed PBPK model can be employed to assess the exposure of (E)-Clom and its active metabolites in as-yet unexplored DD(G)I scenarios in future studies.
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Baier V, Paini A, Schaller S, Scanes C, Bone A, Ebeling M, Preuss T, Witt J, Heckmann D. P17-13 Development and application of a generic avian physiologically-based kinetic (PBK) model for three bird species. Toxicol Lett 2022. [DOI: 10.1016/j.toxlet.2022.07.623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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21
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Yang H, Ebeling C, Zobl W, Simetska N, Geci R, Paini A, Jacobs M, Escher S, Schaller S. P04-05 Machine-learning aided multi-scale modelling framework for toxicological endpoint predictions in the dog. Toxicol Lett 2022. [DOI: 10.1016/j.toxlet.2022.07.289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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22
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Frechen S, Rostami-Hodjegan A. Quality Assurance of PBPK Modeling Platforms and Guidance on Building, Evaluating, Verifying and Applying PBPK Models Prudently under the Umbrella of Qualification: Why, When, What, How and By Whom? Pharm Res 2022; 39:1733-1748. [PMID: 35445350 PMCID: PMC9314283 DOI: 10.1007/s11095-022-03250-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/31/2022] [Indexed: 12/19/2022]
Abstract
Modeling and simulation emerges as a fundamental asset of drug development. Mechanistic modeling builds upon its strength to integrate various data to represent a detailed structural knowledge of a physiological and biological system and is capable of informing numerous drug development and regulatory decisions via extrapolations outside clinically studied scenarios. Herein, physiologically based pharmacokinetic (PBPK) modeling is the fastest growing branch, and its use for particular applications is already expected or explicitly recommended by regulatory agencies. Therefore, appropriate applications of PBPK necessitates trust in the predictive capability of the tool, the underlying software platform, and related models. That has triggered a discussion on concepts of ensuring credibility of model-based derived conclusions. Questions like 'why', 'when', 'what', 'how' and 'by whom' remain open. We seek for harmonization of recent ideas, perceptions, and related terminology. First, we provide an overview on quality assurance of PBPK platforms with the two following concepts. Platform validation: ensuring software integrity, security, traceability, correctness of mathematical models and accuracy of algorithms. Platform qualification: demonstrating the predictive capability of a PBPK platform within a particular context of use. Second, we provide guidance on executing dedicated PBPK studies. A step-by-step framework focuses on the definition of the question of interest, the context of use, the assessment of impact and risk, the definition of the modeling strategy, the evaluation of the platform, performing model development including model building, evaluation and verification, the evaluation of applicability to address the question, and the model application under the umbrella of a qualified platform.
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Affiliation(s)
- Sebastian Frechen
- Bayer AG, Pharmaceuticals, Research & Development, Systems Pharmacology & Medicine, Leverkusen, 51368, Germany.
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
- Certara UK Limited (Simcyp Division), Sheffield, UK
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23
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Yang H, Yang L, Zhong X, Jiang X, Zheng L, Wang L. Physiologically based pharmacokinetic modeling of brivaracetam and its interactions with rifampin based on CYP2C19 phenotypes. Eur J Pharm Sci 2022; 177:106258. [PMID: 35840101 DOI: 10.1016/j.ejps.2022.106258] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/09/2022] [Accepted: 07/11/2022] [Indexed: 11/03/2022]
Abstract
Brivaracetam (BRV), a third-generation antiepileptic drug (AED), is primarily metabolized through amidase hydrolysis and CYP2C19-mediated hydroxylation in vivo. This study utilized physiologically based pharmacokinetic (PBPK) modeling to explore the pharmacokinetics of BRV and drug interactions between BRV and rifampin (RIF), a CYP2C19 inducer, based on CYP2C19 genetic polymorphisms. A PBPK model of BRV was developed in the general population and in individuals with different CYP2C19 phenotypes by adjusting catalytic rate constants (kcat), and the model was validated with observed clinical data. The model was then extrapolated to predict BRV steady-state plasma concentration in individuals with different CYP2C19 phenotypes, with or without coadministration of RIF. The developed model adequately described BRV exposure in the abovementioned populations. The predicted steady-state area under the curve (AUCτ-ss) increases by 20% in heterozygous extensive metabolizers (hEMs) and 55% in poor metabolizers (PMs), compared to homozygous extensive metabolizer (EMs). When coadministered with RIF, the model predicted the most significant magnitude of drug-drug interaction (DDI) in EMs, while the exposure change of BRV was minimal in PMs. Referencing the recommended concentration for therapeutic drug monitoring (TDM), we concluded that the current clinical maintenance dose of BRV is acceptable regardless of CYP2C19 polymorphisms and coadministration with RIF.
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Affiliation(s)
- Hongyi Yang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Leting Yang
- Chengdu Gencore Pharmaceutical Technology Co., Ltd., Chengdu, China
| | - Xiaofang Zhong
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Xuehua Jiang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Liang Zheng
- Department of Clinical Pharmacology, The Second Hospital of Anhui Medical University, Hefei, China.
| | - Ling Wang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China.
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Rüdesheim S, Selzer D, Fuhr U, Schwab M, Lehr T. Physiologically-based pharmacokinetic modeling of dextromethorphan to investigate interindividual variability within CYP2D6 activity score groups. CPT Pharmacometrics Syst Pharmacol 2022; 11:494-511. [PMID: 35257505 PMCID: PMC9007601 DOI: 10.1002/psp4.12776] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 02/01/2022] [Accepted: 02/09/2022] [Indexed: 01/17/2023] Open
Abstract
This study provides a whole‐body physiologically‐based pharmacokinetic (PBPK) model of dextromethorphan and its metabolites dextrorphan and dextrorphan O‐glucuronide for predicting the effects of cytochrome P450 2D6 (CYP2D6) drug‐gene interactions (DGIs) on dextromethorphan pharmacokinetics (PK). Moreover, the effect of interindividual variability (IIV) within CYP2D6 activity score groups on the PK of dextromethorphan and its metabolites was investigated. A parent‐metabolite‐metabolite PBPK model of dextromethorphan, dextrorphan, and dextrorphan O‐glucuronide was developed in PK‐Sim and MoBi. Drug‐dependent parameters were obtained from the literature or optimized. Plasma concentration‐time profiles of all three analytes were gathered from published studies and used for model development and model evaluation. The model was evaluated comparing simulated plasma concentration‐time profiles, area under the concentration‐time curve from the time of the first measurement to the time of the last measurement (AUClast) and maximum concentration (Cmax) values to observed study data. The final PBPK model accurately describes 28 population plasma concentration‐time profiles and plasma concentration‐time profiles of 72 individuals from four cocktail studies. Moreover, the model predicts CYP2D6 DGI scenarios with six of seven DGI AUClast and seven of seven DGI Cmax ratios within the acceptance criteria. The high IIV in plasma concentrations was analyzed by characterizing the distribution of individually optimized CYP2D6 kcat values stratified by activity score group. Population simulations with sampling from the resulting distributions with calculated log‐normal dispersion and mean parameters could explain a large extent of the observed IIV. The model is publicly available alongside comprehensive documentation of model building and model evaluation.
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Affiliation(s)
- Simeon Rüdesheim
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany.,Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tübingen, Stuttgart, Germany
| | - Dominik Selzer
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
| | - Uwe Fuhr
- Department I of Pharmacology, Center for Pharmacology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tübingen, Stuttgart, Germany.,Departments of Clinical Pharmacology, Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany.,Cluster of Excellence iFIT (EXC2180) "Image-guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
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Prediction of lung exposure to anti-tubercular drugs using plasma pharmacokinetic data: implications for dose selection. Eur J Pharm Sci 2022; 173:106163. [DOI: 10.1016/j.ejps.2022.106163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 12/28/2021] [Accepted: 03/02/2022] [Indexed: 01/08/2023]
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Shroff T, Aina K, Maass C, Cipriano M, Lambrecht J, Tacke F, Mosig A, Loskill P. Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics. Open Biol 2022; 12:210333. [PMID: 35232251 PMCID: PMC8889168 DOI: 10.1098/rsob.210333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Non-clinical models to study metabolism including animal models and cell assays are often limited in terms of species translatability and predictability of human biology. This field urgently requires a push towards more physiologically accurate recapitulations of drug interactions and disease progression in the body. Organ-on-chip systems, specifically multi-organ chips (MOCs), are an emerging technology that is well suited to providing a species-specific platform to study the various types of metabolism (glucose, lipid, protein and drug) by recreating organ-level function. This review provides a resource for scientists aiming to study human metabolism by providing an overview of MOCs recapitulating aspects of metabolism, by addressing the technical aspects of MOC development and by providing guidelines for correlation with in silico models. The current state and challenges are presented for two application areas: (i) disease modelling and (ii) pharmacokinetics/pharmacodynamics. Additionally, the guidelines to integrate the MOC data into in silico models could strengthen the predictive power of the technology. Finally, the translational aspects of metabolizing MOCs are addressed, including adoption for personalized medicine and prospects for the clinic. Predictive MOCs could enable a significantly reduced dependence on animal models and open doors towards economical non-clinical testing and understanding of disease mechanisms.
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Affiliation(s)
- Tanvi Shroff
- NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany,Department for Microphysiological Systems, Institute for Biomedical Engineering, Faculty of Medicine, Eberhard Karls University Tübingen, Österbergstraße 3, 72074 Tübingen, Germany
| | - Kehinde Aina
- Institute of Biochemistry II, Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
| | | | - Madalena Cipriano
- Department for Microphysiological Systems, Institute for Biomedical Engineering, Faculty of Medicine, Eberhard Karls University Tübingen, Österbergstraße 3, 72074 Tübingen, Germany
| | - Joeri Lambrecht
- Department of Hepatology and Gastroenterology, Charité University Medicine Berlin, Campus Virchow Klinikum and Campus Charité Mitte, Berlin, Germany
| | - Frank Tacke
- Department of Hepatology and Gastroenterology, Charité University Medicine Berlin, Campus Virchow Klinikum and Campus Charité Mitte, Berlin, Germany
| | - Alexander Mosig
- Institute of Biochemistry II, Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
| | - Peter Loskill
- NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany,Department for Microphysiological Systems, Institute for Biomedical Engineering, Faculty of Medicine, Eberhard Karls University Tübingen, Österbergstraße 3, 72074 Tübingen, Germany,3R-Center for In vitro Models and Alternatives to Animal Testing, Eberhard Karls University Tübingen, Tübingen, Germany
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Schaller S, Martins FS, Balazki P, Böhm S, Baumgart J, Hilger RA, Beelen DW, Hemmelmann C, Ring A. Evaluation of the drug-drug interaction potential of treosulfan using a physiologically-based pharmacokinetic modelling approach. Br J Clin Pharmacol 2022; 88:1722-1734. [PMID: 34519068 PMCID: PMC9291915 DOI: 10.1111/bcp.15081] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 08/22/2021] [Accepted: 09/04/2021] [Indexed: 11/30/2022] Open
Abstract
AIMS The aim of this work is the development of a mechanistic physiologically-based pharmacokinetic (PBPK) model using in vitro to in vivo extrapolation to conduct a drug-drug interaction (DDI) assessment of treosulfan against two cytochrome p450 (CYP) isoenzymes and P-glycoprotein (P-gp) substrates. METHODS A PBPK model for treosulfan was developed de novo based on literature and unpublished clinical data. The PBPK DDI analysis was conducted using the U.S. Food and Drug Administration (FDA) DDI index drugs (probe substrates) midazolam, omeprazole and digoxin for CYP3A4, CYP2C19 and P-gp, respectively. Qualified and documented PBPK models of the probe substrates have been adopted from an open-source online model database. RESULTS The PBPK model for treosulfan, based on both in vitro and in vivo data, was able to predict the plasma concentration-time profiles and exposure levels of treosulfan applied for a standard conditioning treatment. Medium and low potentials for DDI on CYP3A4 (maximum area under the concentration-time curve ratio (AUCRmax = 2.23) and CYP2C19 (AUCRmax = 1.6) were predicted, respectively, using probe substrates midazolam and omeprazole. Treosulfan was not predicted to cause a DDI on P-gp. CONCLUSION Medicinal products with a narrow therapeutic index (eg, digoxin) that are substrates for CYP3A4, CYP2C19 or P-gp should not be given during treatment with treosulfan. However, considering the comprehensive treosulfan-based conditioning treatment schedule and the respective pharmacokinetic properties of the concomitantly used drugs (eg, half-life), the potential for interaction on all evaluated mechanisms would be low (AUCR < 1.25), if concomitantly administered drugs are dosed either 2 hours before or 8 hours after the 2-hour intravenous infusion of treosulfan.
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Affiliation(s)
| | | | | | - Sonja Böhm
- medac Gesellschaft für klinische Spezialpräparate mbHWedelGermany
| | - Joachim Baumgart
- medac Gesellschaft für klinische Spezialpräparate mbHWedelGermany
| | - Ralf A. Hilger
- West German Cancer CentreUniversity Hospital EssenEssenGermany
| | | | | | - Arne Ring
- medac Gesellschaft für klinische Spezialpräparate mbHWedelGermany
- Department for Mathematical Statistics and Actuarial ScienceUniversity of the Free StateNelson Mandela DriveBloemfonteinSouth Africa
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28
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Zheng L, Yang H, Dallmann A, Jiang X, Wang L, Hu W. Physiologically Based Pharmacokinetic Modeling in Pregnant Women Suggests Minor Decrease in Maternal Exposure to Olanzapine. Front Pharmacol 2022; 12:793346. [PMID: 35126130 PMCID: PMC8807508 DOI: 10.3389/fphar.2021.793346] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/23/2021] [Indexed: 01/08/2023] Open
Abstract
Pregnancy is accompanied by significant physiological changes that might affect the in vivo drug disposition. Olanzapine is prescribed to pregnant women with schizophrenia, while its pharmacokinetics during pregnancy remains unclear. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of olanzapine in the pregnant population. With the contributions of each clearance pathway determined beforehand, a full PBPK model was developed and validated in the non-pregnant population. This model was then extrapolated to predict steady-state pharmacokinetics in the three trimesters of pregnancy by introducing gestation-related alterations. The model adequately simulated the reported time-concentration curves. The geometric mean fold error of Cmax and AUC was 1.14 and 1.09, respectively. The model predicted that under 10 mg daily dose, the systematic exposure of olanzapine had minor changes (less than 28%) throughout pregnancy. We proposed that the reduction in cytochrome P4501A2 activity is counteracted by the induction of other enzymes, especially glucuronyltransferase1A4. In conclusion, the PBPK model simulations suggest that, at least at the tested stages of pregnancy, dose adjustment of olanzapine can hardly be recommended for pregnant women if effective treatment was achieved before the onset of pregnancy and if fetal toxicity can be ruled out.
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Affiliation(s)
- Liang Zheng
- Department of Clinical Pharmacology, The Second Hospital of Anhui Medical University, Hefei, China
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Hongyi Yang
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - André Dallmann
- Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals Bayer AG, Leverkusen, Germany
| | - Xuehua Jiang
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Ling Wang
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
- *Correspondence: Ling Wang, ; Wei Hu,
| | - Wei Hu
- Department of Clinical Pharmacology, The Second Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Ling Wang, ; Wei Hu,
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Meid AD. Teaching reproducible research for medical students and postgraduate pharmaceutical scientists. BMC Res Notes 2021; 14:445. [PMID: 34886890 PMCID: PMC8656016 DOI: 10.1186/s13104-021-05862-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 11/26/2021] [Indexed: 11/10/2022] Open
Abstract
In medicine and other academic settings, (doctoral) students often work in interdisciplinary teams together with researchers of pharmaceutical sciences, natural sciences in general, or biostatistics. They should be fundamentally taught good research practices, especially in terms of statistical analysis. This includes reproducibility as a central aspect. Acknowledging that even experienced researchers and supervisors might be unfamiliar with necessary aspects of a perfectly reproducible workflow, a lecture series on reproducible research (RR) was developed for young scientists in clinical pharmacology. The pilot series highlighted definitions of RR, reasons for RR, potential merits of RR, and ways to work accordingly. In trying to actually reproduce a published analysis, several practical obstacles arose. In this article, reproduction of a working example is commented to emphasize the manifold facets of RR, to provide possible explanations for difficulties and solutions, and to argue that harmonized curricula for (quantitative) clinical researchers should include RR principles. These experiences should raise awareness among educators and students, supervisors and young scientists. RR working habits are not only beneficial for ourselves or our students, but also for other researchers within an institution, for scientific partners, for the scientific community, and eventually for the public profiting from research findings.
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Affiliation(s)
- Andreas D Meid
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
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30
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Hanke N, Gómez-Mantilla JD, Ishiguro N, Stopfer P, Nock V. Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions. Pharm Res 2021; 38:1645-1661. [PMID: 34664206 PMCID: PMC8602162 DOI: 10.1007/s11095-021-03109-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 09/10/2021] [Indexed: 12/29/2022]
Abstract
Purpose To build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its transporter-mediated drug-drug interactions (DDIs). Methods The Rosuvastatin model was developed using the open-source PBPK software PK-Sim®, following a middle-out approach. 42 clinical studies (dosing range 0.002–80.0 mg), providing rosuvastatin plasma, urine and feces data, positron emission tomography (PET) measurements of tissue concentrations and 7 different rosuvastatin DDI studies with rifampicin, gemfibrozil and probenecid as the perpetrator drugs, were included to build and qualify the model. Results The carefully developed and thoroughly evaluated model adequately describes the analyzed clinical data, including blood, liver, feces and urine measurements. The processes implemented to describe the rosuvastatin pharmacokinetics and DDIs are active uptake by OATP2B1, OATP1B1/OATP1B3 and OAT3, active efflux by BCRP and Pgp, metabolism by CYP2C9 and passive glomerular filtration. The available clinical rifampicin, gemfibrozil and probenecid DDI studies were modeled using in vitro inhibition constants without adjustments. The good prediction of DDIs was demonstrated by simulated rosuvastatin plasma profiles, DDI AUClast ratios (AUClast during DDI/AUClast without co-administration) and DDI Cmax ratios (Cmax during DDI/Cmax without co-administration), with all simulated DDI ratios within 1.6-fold of the observed values. Conclusions A whole-body PBPK model of rosuvastatin was built and qualified for the prediction of rosuvastatin pharmacokinetics and transporter-mediated DDIs. The model is freely available in the Open Systems Pharmacology model repository, to support future investigations of rosuvastatin pharmacokinetics, rosuvastatin therapy and DDI studies during model-informed drug discovery and development (MID3). Supplementary Information The online version contains supplementary material available at 10.1007/s11095-021-03109-6.
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Affiliation(s)
- Nina Hanke
- Translational Medicine & Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach, Germany.
| | - José David Gómez-Mantilla
- Translational Medicine & Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach, Germany
| | - Naoki Ishiguro
- Kobe Pharma Research Institute, Nippon Boehringer Ingelheim Co. Ltd, Kobe, Japan
| | - Peter Stopfer
- Translational Medicine & Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach, Germany
| | - Valerie Nock
- Translational Medicine & Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach, Germany
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31
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Liu XI, Green DJ, van den Anker JN, Rakhmanina NY, Ahmadzia HK, Momper JD, Park K, Burckart GJ, Dallmann A. Mechanistic Modeling of Placental Drug Transfer in Humans: How Do Differences in Maternal/Fetal Fraction of Unbound Drug and Placental Influx/Efflux Transfer Rates Affect Fetal Pharmacokinetics? Front Pediatr 2021; 9:723006. [PMID: 34733804 PMCID: PMC8559552 DOI: 10.3389/fped.2021.723006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 09/13/2021] [Indexed: 01/16/2023] Open
Abstract
Background: While physiologically based pharmacokinetic (PBPK) models generally predict pharmacokinetics in pregnant women successfully, the confidence in predicting fetal pharmacokinetics is limited because many parameters affecting placental drug transfer have not been mechanistically accounted for. Objectives: The objectives of this study were to implement different maternal and fetal unbound drug fractions in a PBPK framework; to predict fetal pharmacokinetics of eight drugs in the third trimester; and to quantitatively investigate how alterations in various model parameters affect predicted fetal pharmacokinetics. Methods: The ordinary differential equations of previously developed pregnancy PBPK models for eight drugs (acyclovir, cefuroxime, diazepam, dolutegravir, emtricitabine, metronidazole, ondansetron, and raltegravir) were amended to account for different unbound drug fractions in mother and fetus. Local sensitivity analyses were conducted for various parameters relevant to placental drug transfer, including influx/efflux transfer clearances across the apical and basolateral membrane of the trophoblasts. Results: For the highly-protein bound drugs diazepam, dolutegravir and ondansetron, the lower fraction unbound in the fetus vs. mother affected predicted pharmacokinetics in the umbilical vein by ≥10%. Metronidazole displayed blood flow-limited distribution across the placenta. For all drugs, umbilical vein concentrations were highly sensitive to changes in the apical influx/efflux transfer clearance ratio. Additionally, transfer clearance across the basolateral membrane was a critical parameter for cefuroxime and ondansetron. Conclusion: In healthy pregnancies, differential protein binding characteristics in mother and fetus give rise to minor differences in maternal-fetal drug exposure. Further studies are needed to differentiate passive and active transfer processes across the apical and basolateral trophoblast membrane.
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Affiliation(s)
- Xiaomei I. Liu
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, United States
| | - Dionna J. Green
- Office of Pediatric Therapeutics, Office of the Commissioner, US Food and Drug Administration, Silver Spring, MD, United States
| | - John N. van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, United States
| | - Natella Y. Rakhmanina
- Division of Infectious Diseases, Children's National Hospital, Washington, DC, United States
- Technical Strategies and Innovation, Elizabeth Glaser Pediatric AIDS Foundation, Washington, DC, United States
| | - Homa K. Ahmadzia
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
| | - Jeremiah D. Momper
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States
| | - Kyunghun Park
- Office of Clinical Pharmacology, US Food and Drug Administration, Silver Spring, MD, United States
| | - Gilbert J. Burckart
- Office of Clinical Pharmacology, US Food and Drug Administration, Silver Spring, MD, United States
| | - André Dallmann
- Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
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Rox K, Heyner M, Krull J, Harmrolfs K, Rinne V, Hokkanen J, Perez Vilaro G, Díez J, Müller R, Kröger A, Sugiyama Y, Brönstrup M. Physiologically Based Pharmacokinetic/Pharmacodynamic Model for the Treatment of Dengue Infections Applied to the Broad Spectrum Antiviral Soraphen A. ACS Pharmacol Transl Sci 2021; 4:1499-1513. [PMID: 34661071 DOI: 10.1021/acsptsci.1c00078] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Indexed: 12/22/2022]
Abstract
While a drug treatment is unavailable, the global incidence of Dengue virus (DENV) infections and its associated severe manifestations continues to rise. We report the construction of the first physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model that predicts viremia levels in relevant target organs based on preclinical data with the broad spectrum antiviral soraphen A (SorA), an inhibitor of the host cell target acetyl-CoA-carboxylase. SorA was highly effective against DENV in vitro (EC50 = 4.7 nM) and showed in vivo efficacy by inducing a significant reduction of viral load in the spleen and liver of IFNAR-/- mice infected with DENV-2. PBPK/PD predictions for SorA matched well with the experimental infection data. Transfer to a human PBPK/PD model for DENV to mimic a clinical scenario predicted a reduction in viremia by more than one log10 unit for an intravenous infusion regimen of SorA. The PBPK/PD model is applicable to any DENV drug lead and, thus, represents a valuable tool to accelerate and facilitate DENV drug discovery and development.
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Affiliation(s)
- Katharina Rox
- Department of Chemical Biology, Helmholtz Centre for Infection Research (HZI), Inhoffenstrasse 7, 38124 Braunschweig, Germany.,German Centre for Infection Research (DZIF), Partner-Site Hannover-Braunschweig, 38124 Braunschweig, Germany.,Sugiyama Laboratory, RIKEN Baton Zone Program, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Maxi Heyner
- Research Group Innate Immunity and Infection, Helmholtz Centre for Infection Research (HZI), Inhoffenstrasse 7, 38124 Braunschweig, Germany.,Institute for Medical Microbiology and Hospital Hygiene, Otto-von-Guericke University Magdeburg, Leipziger Strasse 44, 39120 Magdeburg, Germany
| | - Jana Krull
- Department of Chemical Biology, Helmholtz Centre for Infection Research (HZI), Inhoffenstrasse 7, 38124 Braunschweig, Germany
| | - Kirsten Harmrolfs
- Department of Microbial Natural Products, Helmholtz Institute for Pharmaceutical Research Saarland, Helmholtz Centre for Infection Research (HZI), Campus E 8.1, 66123 Saarbrücken, Germany
| | | | | | - Gemma Perez Vilaro
- Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Dr. Aiguader, 88, 08003 Barcelona, Spain
| | - Juana Díez
- Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Dr. Aiguader, 88, 08003 Barcelona, Spain
| | - Rolf Müller
- German Centre for Infection Research (DZIF), Partner-Site Hannover-Braunschweig, 38124 Braunschweig, Germany.,Department of Microbial Natural Products, Helmholtz Institute for Pharmaceutical Research Saarland, Helmholtz Centre for Infection Research (HZI), Campus E 8.1, 66123 Saarbrücken, Germany
| | - Andrea Kröger
- Research Group Innate Immunity and Infection, Helmholtz Centre for Infection Research (HZI), Inhoffenstrasse 7, 38124 Braunschweig, Germany.,Institute for Medical Microbiology and Hospital Hygiene, Otto-von-Guericke University Magdeburg, Leipziger Strasse 44, 39120 Magdeburg, Germany
| | - Yuichi Sugiyama
- Sugiyama Laboratory, RIKEN Baton Zone Program, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Mark Brönstrup
- Department of Chemical Biology, Helmholtz Centre for Infection Research (HZI), Inhoffenstrasse 7, 38124 Braunschweig, Germany.,German Centre for Infection Research (DZIF), Partner-Site Hannover-Braunschweig, 38124 Braunschweig, Germany
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Mian P, Nolan B, van den Anker JN, van Calsteren K, Allegaert K, Lakhi N, Dallmann A. Mechanistic Coupling of a Novel in silico Cotyledon Perfusion Model and a Physiologically Based Pharmacokinetic Model to Predict Fetal Acetaminophen Pharmacokinetics at Delivery. Front Pediatr 2021; 9:733520. [PMID: 34631628 PMCID: PMC8496351 DOI: 10.3389/fped.2021.733520] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/20/2021] [Indexed: 01/24/2023] Open
Abstract
Little is known about placental drug transfer and fetal pharmacokinetics despite increasing drug use in pregnant women. While physiologically based pharmacokinetic (PBPK) models can help in some cases to shed light on this knowledge gap, adequate parameterization of placental drug transfer remains challenging. A novel in silico model with seven compartments representing the ex vivo cotyledon perfusion assay was developed and used to describe placental transfer and fetal pharmacokinetics of acetaminophen. Unknown parameters were optimized using observed data. Thereafter, values of relevant model parameters were copied to a maternal-fetal PBPK model and acetaminophen pharmacokinetics were predicted at delivery after oral administration of 1,000 mg. Predictions in the umbilical vein were evaluated with data from two clinical studies. Simulations from the in silico cotyledon perfusion model indicated that acetaminophen accumulates in the trophoblasts; simulated steady state concentrations in the trophoblasts were 4.31-fold higher than those in the perfusate. The whole-body PBPK model predicted umbilical vein concentrations with a mean prediction error of 24.7%. Of the 62 concentration values reported in the clinical studies, 50 values (81%) were predicted within a 2-fold error range. In conclusion, this study presents a novel in silico cotyledon perfusion model that is structurally congruent with the placenta implemented in our maternal-fetal PBPK model. This allows transferring parameters from the former model into our PBPK model for mechanistically exploring whole-body pharmacokinetics and concentration-effect relationships in the placental tissue. Further studies should investigate acetaminophen accumulation and metabolism in the placenta as the former might potentially affect placental prostaglandin synthesis and subsequent fetal exposure.
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Affiliation(s)
- Paola Mian
- Department of Clinical Pharmacy, Medisch Spectrum Twente, Enschede, Netherlands
| | - Bridget Nolan
- Department of Obstetrics and Gynecology, Richmond University Medical Center, Staten Island, NY, United States
- Department of Obstetrics and Gynecology, New York Medical College, Valhalla, NY, United States
| | - John N. van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, United States
- Department of Pediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel, Basel, Switzerland
| | - Kristel van Calsteren
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Gynecology and Obstetrics, UZ Gasthuisberg, Leuven, Belgium
| | - Karel Allegaert
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
- Department of Hospital Pharmacy, Erasmus Medical Center Rotterdam, Rotterdam, Netherlands
| | - Nisha Lakhi
- Department of Obstetrics and Gynecology, Richmond University Medical Center, Staten Island, NY, United States
- Department of Obstetrics and Gynecology, New York Medical College, Valhalla, NY, United States
| | - André Dallmann
- Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
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Hanke N, Türk D, Selzer D, Ishiguro N, Ebner T, Wiebe S, Müller F, Stopfer P, Nock V, Lehr T. A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug-Drug-Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals. Clin Pharmacokinet 2021; 59:1419-1431. [PMID: 32449077 PMCID: PMC7658088 DOI: 10.1007/s40262-020-00896-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Background Metformin is a widely prescribed antidiabetic BCS Class III drug (low permeability) that depends on active transport for its absorption and disposition. It is recommended by the US Food and Drug Administration as a clinical substrate of organic cation transporter 2/multidrug and toxin extrusion protein for drug–drug interaction studies. Cimetidine is a potent organic cation transporter 2/multidrug and toxin extrusion protein inhibitor. Objective The objective of this study was to provide mechanistic whole-body physiologically based pharmacokinetic models of metformin and cimetidine, built and evaluated to describe the metformin-SLC22A2 808G>T drug–gene interaction, the cimetidine-metformin drug–drug interaction, and the impact of renal impairment on metformin exposure. Methods Physiologically based pharmacokinetic models were developed in PK-Sim® (version 8.0). Thirty-nine clinical studies (dosing range 0.001–2550 mg), providing metformin plasma and urine data, positron emission tomography measurements of tissue concentrations, studies in organic cation transporter 2 polymorphic volunteers, drug–drug interaction studies with cimetidine, and data from patients in different stages of chronic kidney disease, were used to develop the metformin model. Twenty-seven clinical studies (dosing range 100–800 mg), reporting cimetidine plasma and urine concentrations, were used for the cimetidine model development. Results The established physiologically based pharmacokinetic models adequately describe the available clinical data, including the investigated drug–gene interaction, drug–drug interaction, and drug–drug–gene interaction studies, as well as the metformin exposure during renal impairment. All modeled drug–drug interaction area under the curve and maximum concentration ratios are within 1.5-fold of the observed ratios. The clinical data of renally impaired patients shows the expected increase in metformin exposure with declining kidney function, but also indicates counter-regulatory mechanisms in severe renal disease; these mechanisms were implemented into the model based on findings in preclinical species. Conclusions Whole-body physiologically based pharmacokinetic models of metformin and cimetidine were built and qualified for the prediction of metformin pharmacokinetics during drug–gene interaction, drug–drug interaction, and different stages of renal disease. The model files will be freely available in the Open Systems Pharmacology model repository. Current guidelines for metformin treatment of renally impaired patients should be reviewed to avoid overdosing in CKD3 and to allow metformin therapy of CKD4 patients. Electronic supplementary material The online version of this article (10.1007/s40262-020-00896-w) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nina Hanke
- Clinical Pharmacy, Saarland University, Campus C2 2, 66123, Saarbrücken, Germany
| | - Denise Türk
- Clinical Pharmacy, Saarland University, Campus C2 2, 66123, Saarbrücken, Germany
| | - Dominik Selzer
- Clinical Pharmacy, Saarland University, Campus C2 2, 66123, Saarbrücken, Germany
| | - Naoki Ishiguro
- Kobe Pharma Research Institute, Nippon Boehringer Ingelheim Co. Ltd., Kobe, Japan
| | - Thomas Ebner
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Sabrina Wiebe
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.,Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Müller
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.,Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Peter Stopfer
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Valerie Nock
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, Campus C2 2, 66123, Saarbrücken, Germany.
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35
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Liu XI, van den Anker JN, Burckart GJ, Dallmann A. Evaluation of Physiologically Based Pharmacokinetic Models to Predict the Absorption of BCS Class I Drugs in Different Pediatric Age Groups. J Clin Pharmacol 2021; 61 Suppl 1:S94-S107. [PMID: 34185902 DOI: 10.1002/jcph.1845] [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: 01/03/2021] [Accepted: 02/17/2021] [Indexed: 11/06/2022]
Abstract
Age-related changes in many parameters affecting drug absorption remain poorly characterized. The objective of this study was to apply physiologically based pharmacokinetic (PBPK) models in pediatric patients to investigate the absorption and pharmacokinetics of 4 drugs belonging to the Biopharmaceutics Classification System (BCS) class I administered as oral liquid formulations. Pediatric PBPK models built with PK-Sim/MoBi were used to predict the pharmacokinetics of acetaminophen, emtricitabine, theophylline, and zolpidem in different pediatric populations. The model performance for predicting drug absorption and pharmacokinetics was assessed by comparing the predicted absorption profile with the deconvoluted dose fraction absorbed over time and predicted with observed plasma concentration-time profiles. Sensitivity analyses were performed to analyze the effects of changes in relevant input parameters on the model output. Overall, most pharmacokinetic parameters were predicted within a 2-fold error range. The absorption profiles were generally reasonably predicted, but relatively large differences were observed for acetaminophen. Sensitivity analyses showed that the predicted absorption profile was most sensitive to changes in the gastric emptying time (GET) and the specific intestinal permeability. The drug's solubility played only a minor role. These findings confirm that gastric emptying time, more than intestinal permeability or solubility, is a key factor affecting BCS class I drug absorption in children. As gastric emptying time is prolonged in the fed state, a better understanding of the interplay between food intake and gastric emptying time in children is needed, especially in the very young in whom the (semi)fed condition is the prevailing prandial state, and hence prolonged gastric emptying time seems more plausible than the fasting state.
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Affiliation(s)
- Xiaomei I Liu
- Division of Clinical Pharmacology, Children's National Hospital, Washington, District of Columbia, USA
| | - John N van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, District of Columbia, USA.,Division of Pediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland
| | - Gilbert J Burckart
- Office of Clinical Pharmacology, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - André Dallmann
- Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
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Fendt R, Hofmann U, Schneider ARP, Schaeffeler E, Burghaus R, Yilmaz A, Blank LM, Kerb R, Lippert J, Schlender JF, Schwab M, Kuepfer L. Data-driven personalization of a physiologically based pharmacokinetic model for caffeine: A systematic assessment. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:782-793. [PMID: 34053199 PMCID: PMC8302243 DOI: 10.1002/psp4.12646] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 03/17/2021] [Accepted: 04/29/2021] [Indexed: 12/18/2022]
Abstract
Physiologically based pharmacokinetic (PBPK) models have been proposed as a tool for more accurate individual pharmacokinetic (PK) predictions and model‐informed precision dosing, but their application in clinical practice is still rare. This study systematically assesses the benefit of using individual patient information to improve PK predictions. A PBPK model of caffeine was stepwise personalized by using individual data on (1) demography, (2) physiology, and (3) cytochrome P450 (CYP) 1A2 phenotype of 48 healthy volunteers participating in a single‐dose clinical study. Model performance was benchmarked against a caffeine base model simulated with parameters of an average individual. In the first step, virtual twins were generated based on the study subjects' demography (height, weight, age, sex), which implicated the rescaling of average organ volumes and blood flows. The accuracy of PK simulations improved compared with the base model. The percentage of predictions within 0.8‐fold to 1.25‐fold of the observed values increased from 45.8% (base model) to 57.8% (Step 1). However, setting physiological parameters (liver blood flow determined by magnetic resonance imaging, glomerular filtration rate, hematocrit) to measured values in the second step did not further improve the simulation result (59.1% in the 1.25‐fold range). In the third step, virtual twins matching individual demography, physiology, and CYP1A2 activity considerably improved the simulation results. The percentage of data within the 1.25‐fold range was 66.15%. This case study shows that individual PK profiles can be predicted more accurately by considering individual attributes and that personalized PBPK models could be a valuable tool for model‐informed precision dosing approaches in the future.
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Affiliation(s)
- Rebekka Fendt
- Systems Pharmacology & Medicine, Bayer AG, Leverkusen, Germany.,Institute of Applied Microbiology, Aachen Biology and Biotechnology, Rheinisch-Westfaelische Technische Hochschule Aachen University, Aachen, Germany
| | - Ute Hofmann
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Annika R P Schneider
- Systems Pharmacology & Medicine, Bayer AG, Leverkusen, Germany.,Institute of Applied Microbiology, Aachen Biology and Biotechnology, Rheinisch-Westfaelische Technische Hochschule Aachen University, Aachen, Germany
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Rolf Burghaus
- Systems Pharmacology & Medicine, Bayer AG, Leverkusen, Germany
| | - Ali Yilmaz
- Department of Cardiology I, University Hospital Muenster, Münster, Germany
| | - Lars Mathias Blank
- Institute of Applied Microbiology, Aachen Biology and Biotechnology, Rheinisch-Westfaelische Technische Hochschule Aachen University, Aachen, Germany
| | - Reinhold Kerb
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Jörg Lippert
- Systems Pharmacology & Medicine, Bayer AG, Leverkusen, Germany
| | | | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.,Departments of Clinical Pharmacology and Biochemistry and Pharmacy, University of Tuebingen, Tuebingen, Germany
| | - Lars Kuepfer
- Systems Pharmacology & Medicine, Bayer AG, Leverkusen, Germany.,Institute for Systems Medicine With Focus on Organ Interactions, University Hospital RWTH Aachen, Aachen, Germany
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Ince I, Dallmann A, Frechen S, Coboeken K, Niederalt C, Wendl T, Block M, Meyer M, Eissing T, Burghaus R, Lippert J, Willmann S, Schlender J. Predictive Performance of Physiology-Based Pharmacokinetic Dose Estimates for Pediatric Trials: Evaluation With 10 Bayer Small-Molecule Compounds in Children. J Clin Pharmacol 2021; 61 Suppl 1:S70-S82. [PMID: 34185905 PMCID: PMC8361729 DOI: 10.1002/jcph.1869] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 03/30/2021] [Indexed: 01/16/2023]
Abstract
Development and guidance of dosing schemes in children have been supported by physiology-based pharmacokinetic (PBPK) modeling for many years. PBPK models are built on a generic basis, where compound- and system-specific parameters are separated and can be exchanged, allowing the translation of these models from adults to children by accounting for physiological differences. Owing to these features, PBPK modeling is a valuable approach to support clinical decision making for dosing in children. In this analysis, we evaluate pediatric PBPK models for 10 small-molecule compounds that were applied to support clinical decision processes at Bayer for their predictive power in different age groups. Ratios of PBPK-predicted to observed PK parameters for the evaluated drugs in different pediatric age groups were estimated. Predictive performance was analyzed on the basis of a 2-fold error range and the bioequivalence range (ie, 0.8 ≤ predicted/observed ≤ 1.25). For all 10 compounds, all predicted-to-observed PK ratios were within a 2-fold error range (n = 27), with two-thirds of the ratios within the bioequivalence range (n = 18). The findings demonstrate that the pharmacokinetics of these compounds was successfully and adequately predicted in different pediatric age groups. This illustrates the applicability of PBPK for guiding dosing schemes in the pediatric population.
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Affiliation(s)
- Ibrahim Ince
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
| | - André Dallmann
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
| | - Sebastian Frechen
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
| | - Katrin Coboeken
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
| | - Christoph Niederalt
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
| | - Thomas Wendl
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
| | - Michael Block
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
| | - Michaela Meyer
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
| | - Thomas Eissing
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
| | - Rolf Burghaus
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
| | - Jörg Lippert
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
| | - Stefan Willmann
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
| | - Jan‐Frederik Schlender
- Pharmacometrics/Modeling and Simulation, Research and DevelopmentPharmaceuticalsBayerAGGermany
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38
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Frechen S, Solodenko J, Wendl T, Dallmann A, Ince I, Lehr T, Lippert J, Burghaus R. A generic framework for the physiologically-based pharmacokinetic platform qualification of PK-Sim and its application to predicting cytochrome P450 3A4-mediated drug-drug interactions. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:633-644. [PMID: 33946131 PMCID: PMC8213412 DOI: 10.1002/psp4.12636] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/08/2021] [Accepted: 04/01/2021] [Indexed: 01/05/2023]
Abstract
The success of applications of physiologically‐based pharmacokinetic (PBPK) modeling in drug development and drug labeling has triggered regulatory agencies to demand rigorous demonstration of the predictive capability of the specific PBPK platform for a particular intended application purpose. The effort needed to comply with such qualification requirements exceeds the costs for any individual PBPK application. Because changes or updates of a PBPK platform would require (re‐)qualification, a reliable and efficient generic qualification framework is needed. We describe the development and implementation of an agile and sustainable technical framework for automatic PBPK platform (re‐)qualification of PK‐Sim® embedded in the open source and open science GitHub landscape of Open Systems Pharmacology. The qualification approach enables the efficient assessment of all aspects relevant to the qualification of a particular purpose and provides transparency and traceability for all stakeholders. As a showcase example for the power and versatility of the qualification framework, we present the qualification of PK‐Sim® for the intended purpose of predicting cytochrome P450 3A4 (CYP3A4)–mediated drug–drug interactions (DDIs). Several perpetrator PBPK models featuring various degrees of CYP3A4 modulation and different types of mechanisms (competitive inhibition, mechanism‐based inactivation, and induction) were coupled with a set of PBPK models of sensitive CYP3A4 victim drugs. Simulations were compared to a comprehensive data set of 135 observations from published clinical DDI studies. The platform's overall predictive performance showed reasonable accuracy and precision (geometric mean fold error of 1.4 for both area under the plasma concentration‐time curve ratios and peak plasma concentration ratios with/without perpetrator) and suggests that PK‐Sim® can be applied to quantitatively assess CYP3A4‐mediated DDI in clinically untested scenarios.
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Affiliation(s)
- Sebastian Frechen
- Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - Juri Solodenko
- Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - Thomas Wendl
- Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - André Dallmann
- Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - Ibrahim Ince
- Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
| | - Jörg Lippert
- Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - Rolf Burghaus
- Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
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Rostami-Hodjegan A, Bois FY. Opening a debate on open-source modeling tools: Pouring fuel on fire versus extinguishing the flare of a healthy debate. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:420-427. [PMID: 33793084 PMCID: PMC8129708 DOI: 10.1002/psp4.12615] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 01/21/2021] [Accepted: 03/01/2021] [Indexed: 12/14/2022]
Abstract
As model‐informed drug development becomes an integral part of modern approaches to the discovery of new therapeutic entities and showing their safety and effectiveness, modalities of incorporating the paradigm into widespread practice require a revisit. Traditionally, modeling and simulation (M&S) have been performed by specialized teams who create bespoke models for each case and have reservations about letting modeling be done by the greater mass of scientists engaged in various stages of drug development. An analogy can be drawn between M&S and automobiles: typical drivers of ordinary cars use them for daily tasks, such as going from point A to B whereas specialized Formula 1 drivers using bespoke individually made cars to test the latest technologies. The reliability and robustness of ordinary cars for the first group requires elements related to quality and endurance that are very different from those applicable to any Formula 1 car supported by a large team of engineers. In this commentary, we frame and analyze the problems concerning the structure and setup of various M&S tools, and their pros and cons. We demonstrate that many misconceptions have precluded having an open discussion on what each modality of M&S tools strives to achieve, and we provide data and evidence that support the move of M&S to main stream use by many, as opposed to specialized usage by few. Parallels are drawn in many other areas involving laboratory instrumentation, statistical analyses, and so on.
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Affiliation(s)
- Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK.,Certara UK Limited (Simcyp Division), Sheffield, UK
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40
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Marok FZ, Fuhr LM, Hanke N, Selzer D, Lehr T. Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network. Pharmaceutics 2021; 13:331. [PMID: 33806634 PMCID: PMC8001859 DOI: 10.3390/pharmaceutics13030331] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/22/2021] [Accepted: 02/27/2021] [Indexed: 12/22/2022] Open
Abstract
The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6 and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drug-drug interaction (DDI) studies. The aim of this study was to build a whole-body physiologically based pharmacokinetic (PBPK) model of bupropion including its DDI-relevant metabolites, and to qualify the model using clinical drug-gene interaction (DGI) and DDI data. The model was built in PK-Sim® applying clinical data of 67 studies. It incorporates CYP2B6-mediated hydroxylation of bupropion, metabolism via CYP2C19 and 11β-HSD, as well as binding to pharmacological targets. The impact of CYP2B6 polymorphisms is described for normal, poor, intermediate, and rapid metabolizers, with various allele combinations of the genetic variants CYP2B6*1, *4, *5 and *6. DDI model performance was evaluated by prediction of clinical studies with rifampicin (CYP2B6 and CYP2C19 inducer), fluvoxamine (CYP2C19 inhibitor) and voriconazole (CYP2B6 and CYP2C19 inhibitor). Model performance quantification showed 20/20 DGI ratios of hydroxybupropion to bupropion AUC ratios (DGI AUCHBup/Bup ratios), 12/13 DDI AUCHBup/Bup ratios, and 7/7 DDGI AUCHBup/Bup ratios within 2-fold of observed values. The developed model is freely available in the Open Systems Pharmacology model repository.
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Affiliation(s)
| | | | | | | | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, 66123 Saarbrücken, Germany; (F.Z.M.); (L.M.F.); (N.H.); (D.S.)
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41
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Liu XI, Momper JD, Rakhmanina NY, Green DJ, Burckart GJ, Cressey TR, Mirochnick M, Best BM, van den Anker JN, Dallmann A. Physiologically Based Pharmacokinetic Modeling Framework to Predict Neonatal Pharmacokinetics of Transplacentally Acquired Emtricitabine, Dolutegravir, and Raltegravir. Clin Pharmacokinet 2021; 60:795-809. [PMID: 33527213 DOI: 10.1007/s40262-020-00977-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/12/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Little is understood about neonatal pharmacokinetics immediately after delivery and during the first days of life following intrauterine exposure to maternal medications. Our objective was to develop and evaluate a novel, physiologically based pharmacokinetic modeling workflow for predicting perinatal and postnatal disposition of commonly used antiretroviral drugs administered prenatally to pregnant women living with human immunodeficiency virus. METHODS Using previously published, maternal-fetal, physiologically based pharmacokinetic models for emtricitabine, dolutegravir, and raltegravir built with PK-Sim/MoBi®, placental drug transfer was predicted in late pregnancy. The total drug amount in fetal compartments at term delivery was estimated and subsequently integrated as initial conditions in different tissues of a whole-body, neonatal, physiologically based pharmacokinetic model to predict drug concentrations in the neonatal elimination phase after birth. Neonatal elimination processes were parameterized according to published data. Model performance was assessed by clinical data. RESULTS Neonatal physiologically based pharmacokinetic models generally captured the initial plasma concentrations after delivery but underestimated concentrations in the terminal phase. The mean percentage error for predicted plasma concentrations was - 71.5%, - 33.8%, and 76.7% for emtricitabine, dolutegravir, and raltegravir, respectively. A sensitivity analysis suggested that the activity of organic cation transporter 2 and uridine diphosphate glucuronosyltransferase 1A1 during the first postnatal days in term newborns is ~11% and ~30% of that in adults, respectively. CONCLUSIONS These findings demonstrate the general feasibility of applying physiologically based pharmacokinetic models to predict washout concentrations of transplacentally acquired drugs in newborns. These models can increase the understanding of pharmacokinetics during the first postnatal days and allow the prediction of drug exposure in this vulnerable population.
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Affiliation(s)
- Xiaomei I Liu
- Division of Clinical Pharmacology, Children's National Hospital, 10430 Owen Brown Road, Columbia, Maryland, 21044, USA. .,Division of Infectious Diseases, Children's National Hospital, Washington, DC, USA.
| | - Jeremiah D Momper
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, USA.,Pediatric Department, School of Medicine, Rady Children's Hospital San Diego, La Jolla, CA, USA
| | - Natella Y Rakhmanina
- Division of Infectious Diseases, Children's National Hospital, Washington, DC, USA.,Elizabeth Glaser Pediatric AIDS Foundation, Washington, DC, USA
| | - Dionna J Green
- Office of Pediatric Therapeutics, US Food and Drug Administration, Silver Spring, MD, USA
| | - Gilbert J Burckart
- Office of Clinical Pharmacology, US Food and Drug Administration, Silver Spring, MD, USA
| | - Tim R Cressey
- PHPT/IRD 174, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand.,Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK
| | | | - Brookie M Best
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, USA.,Pediatric Department, School of Medicine, Rady Children's Hospital San Diego, La Jolla, CA, USA
| | - John N van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, 10430 Owen Brown Road, Columbia, Maryland, 21044, USA.,Division of Pediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel, University of Basel, Basel, Switzerland
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Sjögren E, Tarning J, Barnes KI, Jonsson EN. A Physiologically-Based Pharmacokinetic Framework for Prediction of Drug Exposure in Malnourished Children. Pharmaceutics 2021; 13:pharmaceutics13020204. [PMID: 33540928 PMCID: PMC7913226 DOI: 10.3390/pharmaceutics13020204] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/21/2021] [Accepted: 01/28/2021] [Indexed: 02/06/2023] Open
Abstract
Malnutrition in children is a global health problem, particularly in developing countries. The effects of an insufficient supply of nutrients on body composition and physiological functions may have implications for drug disposition and ultimately affect the clinical outcome in this vulnerable population. Physiologically-based pharmacokinetic (PBPK) modeling can be used to predict the effect of malnutrition as it links physiological changes to pharmacokinetic (PK) consequences. However, the absence of detailed information on body composition and the limited availability of controlled clinical trials in malnourished children complicates the establishment and evaluation of a generic PBPK model in this population. In this manuscript we describe the creation of physiologically-based bridge to a malnourished pediatric population, by combining information on (a) the differences in body composition between healthy and malnourished adults and (b) the differences in physiology between healthy adults and children. Model performance was confirmed using clinical reference data. This study presents a physiologically-based translational framework for prediction of drug disposition in malnourished children. The model is readily applicable for dose recommendation strategies to address the urgent medicinal needs of this vulnerable population.
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Affiliation(s)
- Erik Sjögren
- Pharmetheus AB, 752 37 Uppsala, Sweden;
- Correspondence: ; Tel.: +46-737-750-545
| | - Joel Tarning
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand;
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LG, UK
| | - Karen I. Barnes
- Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town 7925, South Africa;
- WorldWide Antimalarial Resistance Network (WWARN) Pharmacology Scientific Working Group, University of Cape Town, Cape Town 7925, South Africa
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Salerno SN, Edginton A, Gerhart JG, Laughon MM, Ambalavanan N, Sokol GM, Hornik CD, Stewart D, Mills M, Martz K, Gonzalez D. Physiologically-Based Pharmacokinetic Modeling Characterizes the CYP3A-Mediated Drug-Drug Interaction Between Fluconazole and Sildenafil in Infants. Clin Pharmacol Ther 2021; 109:253-262. [PMID: 32691891 PMCID: PMC8138939 DOI: 10.1002/cpt.1990] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 07/05/2020] [Indexed: 12/12/2022]
Abstract
Physiologically-based pharmacokinetic (PBPK) modeling can potentially predict pediatric drug-drug interactions (DDIs) when clinical DDI data are limited. In infants for whom treatment of pulmonary hypertension and prevention or treatment of invasive candidiasis are indicated, sildenafil with fluconazole may be given concurrently. To account for developmental changes in cytochrome P450 (CYP) 3A, we determined and incorporated fluconazole inhibition constants (KI ) for CYP3A4, CYP3A5, and CYP3A7 into a PBPK model developed for sildenafil and its active metabolite, N-desmethylsildenafil. Pharmacokinetic (PK) data in preterm infants receiving sildenafil with and without fluconazole were used for model development and evaluation. The simulated PK parameters were comparable to observed values. Following fluconazole co-administration, differences in the fold change for simulated steady-state area under the plasma concentration vs. time curve from 0 to 24 hours (AUCss,0-24 ) were observed between virtual adults and infants (2.11-fold vs. 2.82-fold change). When given in combination with treatment doses of fluconazole (12 mg/kg i.v. daily), reducing the sildenafil dose by ~ 60% resulted in a geometric mean ratio of 1.01 for simulated AUCss,0-24 relative to virtual infants receiving sildenafil alone. This study highlights the feasibility of PBPK modeling to predict DDIs in infants and the need to include CYP3A7 parameters.
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Affiliation(s)
- Sara N. Salerno
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Andrea Edginton
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | - Jacqueline G. Gerhart
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Matthew M. Laughon
- Department of Pediatrics, UNC School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Namasivayam Ambalavanan
- Division of Neonatology, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Gregory M. Sokol
- Division of Neonatal-Perinatal Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Chi D. Hornik
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA
- Duke Clinical Research Institute, Durham, North Carolina, USA
- Department of Pharmacy, Duke University Medical Center, Durham, North Carolina, USA
| | - Dan Stewart
- University of Louisville Norton Children’s Hospital, Louisville, Kentucky, USA
| | - Mary Mills
- Duke Clinical Research Institute, Durham, North Carolina, USA
| | - Karen Martz
- The Emmes Company, LLC, Rockville, Maryland, USA
| | - Daniel Gonzalez
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Modeling and Simulation of Process Technology for Nanoparticulate Drug Formulations-A Particle Technology Perspective. Pharmaceutics 2020; 13:pharmaceutics13010022. [PMID: 33374375 PMCID: PMC7823784 DOI: 10.3390/pharmaceutics13010022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 12/09/2020] [Accepted: 12/14/2020] [Indexed: 11/17/2022] Open
Abstract
Crystalline organic nanoparticles and their amorphous equivalents (ONP) have the potential to become a next-generation formulation technology for dissolution-rate limited biopharmaceutical classification system (BCS) class IIa molecules if the following requisites are met: (i) a quantitative understanding of the bioavailability enhancement benefit versus established formulation technologies and a reliable track record of successful case studies are available; (ii) efficient experimentation workflows with a minimum amount of active ingredient and a high degree of digitalization via, e.g., automation and computer-based experimentation planning are implemented; (iii) the scalability of the nanoparticle-based oral delivery formulation technology from the lab to manufacturing is ensured. Modeling and simulation approaches informed by the pharmaceutical material science paradigm can help to meet these requisites, especially if the entire value chain from formulation to oral delivery is covered. Any comprehensive digitalization of drug formulation requires combining pharmaceutical materials science with the adequate formulation and process technologies on the one hand and quantitative pharmacokinetics and drug administration dynamics in the human body on the other hand. Models for the technical realization of the drug production and the distribution of the pharmaceutical compound in the human body are coupled via the central objective, namely bioavailability. The underlying challenges can only be addressed by hierarchical approaches for property and process design. The tools for multiscale modeling of the here-considered particle processes (e.g., by coupled computational fluid dynamics, population balance models, Noyes–Whitney dissolution kinetics) and physiologically based absorption modeling are available. Significant advances are being made in enhancing the bioavailability of hydrophobic compounds by applying innovative solutions. As examples, the predictive modeling of anti-solvent precipitation is presented, and options for the model development of comminution processes are discussed.
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Sinisi S, Alimguzhin V, Mancini T, Tronci E, Leeners B. Complete populations of virtual patients for in silico clinical trials. Bioinformatics 2020; 36:5465-5472. [PMID: 33325489 DOI: 10.1093/bioinformatics/btaa1026] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 10/30/2020] [Accepted: 11/30/2020] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION Model-based approaches to safety and efficacy assessment of pharmacological drugs, treatment strategies, or medical devices (In Silico Clinical Trial, ISCT) aim to decrease time and cost for the needed experimentations, reduce animal and human testing, and enable precision medicine. Unfortunately, in presence of non-identifiable models (e.g., reaction networks), parameter estimation is not enough to generate complete populations of Virtual Patient (VPs), i.e., populations guaranteed to show the entire spectrum of model behaviours (phenotypes), thus ensuring representativeness of the trial. RESULTS We present methods and software based on global search driven by statistical model checking that, starting from a (non-identifiable) quantitative model of the human physiology (plus drugs PK/PD) and suitable biological and medical knowledge elicited from experts, compute a population of VPs whose behaviours are representative of the whole spectrum of phenotypes entailed by the model (completeness) and pairwise distinguishable according to user-provided criteria. This enables full granularity control on the size of the population to employ in an ISCT, guaranteeing representativeness while avoiding over-representation of behaviours.We proved the effectiveness of our algorithm on a non-identifiable ODE-based model of the female Hypothalamic-Pituitary-Gonadal axis, by generating a population of 4 830 264 VPs stratified into 7 levels (at different granularity of behaviours), and assessed its representativeness against 86 retrospective health records from Pfizer, Hannover Medical School and University Hospital of Lausanne. The datasets are respectively covered by our VPs within Average Normalised Mean Absolute Error of 15%, 20%, and 35% (90% of the latter dataset is covered within 20% error).
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Affiliation(s)
- S Sinisi
- Computer Science Department, Sapienza University of Rome, Italy
| | - V Alimguzhin
- Computer Science Department, Sapienza University of Rome, Italy
| | - T Mancini
- Computer Science Department, Sapienza University of Rome, Italy
| | - E Tronci
- Computer Science Department, Sapienza University of Rome, Italy
| | - B Leeners
- Division of Reproductive Endocrinology, University Hospital Zurich, Switzerland
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Loisios-Konstantinidis I, Dressman J. Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling to Support Waivers of In Vivo Clinical Studies: Current Status, Challenges, and Opportunities. Mol Pharm 2020; 18:1-17. [PMID: 33320002 DOI: 10.1021/acs.molpharmaceut.0c00903] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling has been extensively applied to quantitatively translate in vitro data, predict the in vivo performance, and ultimately support waivers of in vivo clinical studies. In the area of biopharmaceutics and within the context of model-informed drug discovery and development (MID3), there is a rapidly growing interest in applying verified and validated mechanistic PBPK models to waive in vivo clinical studies. However, the regulatory acceptance of PBPK analyses for biopharmaceutics and oral drug absorption applications, which is also referred to variously as "PBPK absorption modeling" [Zhang et al. CPT: Pharmacometrics Syst. Pharmacol. 2017, 6, 492], "physiologically based absorption modeling", or "physiologically based biopharmaceutics modeling" (PBBM), remains rather low [Kesisoglou et al. J. Pharm. Sci. 2016, 105, 2723] [Heimbach et al. AAPS J. 2019, 21, 29]. Despite considerable progress in the understanding of gastrointestinal (GI) physiology, in vitro biopharmaceutic and in silico tools, PBPK models for oral absorption often suffer from an incomplete understanding of the physiology, overparameterization, and insufficient model validation and/or platform verification, all of which can represent limitations to their translatability and predictive performance. The complex interactions of drug substances and (bioenabling) formulations with the highly dynamic and heterogeneous environment of the GI tract in different age, ethnic, and genetic groups as well as disease states have not been yet fully elucidated, and they deserve further research. Along with advancements in the understanding of GI physiology and refinement of current or development of fully mechanistic in silico tools, we strongly believe that harmonization, interdisciplinary interaction, and enhancement of the translational link between in vitro, in silico, and in vivo will determine the future of PBBM. This Perspective provides an overview of the current status of PBBM, reflects on challenges and knowledge gaps, and discusses future opportunities around PBPK/PD models for oral absorption of small and large molecules to waive in vivo clinical studies.
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Affiliation(s)
| | - Jennifer Dressman
- Institute of Pharmaceutical Technology, Goethe University, Frankfurt am Main 60438, Germany.,Fraunhofer Institute of Translational Pharmacology and Medicine (ITMP), Carl-von-Noorden Platz 9, Frankfurt am Main 60438, Germany
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Physiologically Based Pharmacokinetic Modeling of Metoprolol Enantiomers and α-Hydroxymetoprolol to Describe CYP2D6 Drug-Gene Interactions. Pharmaceutics 2020; 12:pharmaceutics12121200. [PMID: 33322314 PMCID: PMC7763912 DOI: 10.3390/pharmaceutics12121200] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 12/02/2020] [Accepted: 12/05/2020] [Indexed: 01/13/2023] Open
Abstract
The beta-blocker metoprolol (the sixth most commonly prescribed drug in the USA in 2017) is subject to considerable drug–gene interaction (DGI) effects caused by genetic variations of the CYP2D6 gene. CYP2D6 poor metabolizers (5.7% of US population) show approximately five-fold higher metoprolol exposure compared to CYP2D6 normal metabolizers. This study aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model to predict CYP2D6 DGIs with metoprolol. The metoprolol (R)- and (S)-enantiomers as well as the active metabolite α-hydroxymetoprolol were implemented as model compounds, employing data of 48 different clinical studies (dosing range 5–200 mg). To mechanistically describe the effect of CYP2D6 polymorphisms, two separate metabolic CYP2D6 pathways (α-hydroxylation and O-demethylation) were incorporated for both metoprolol enantiomers. The good model performance is demonstrated in predicted plasma concentration–time profiles compared to observed data, goodness-of-fit plots, and low geometric mean fold errors of the predicted AUClast (1.27) and Cmax values (1.23) over all studies. For DGI predictions, 18 out of 18 DGI AUClast ratios and 18 out of 18 DGI Cmax ratios were within two-fold of the observed ratios. The newly developed and carefully validated model was applied to calculate dose recommendations for CYP2D6 polymorphic patients and will be freely available in the Open Systems Pharmacology repository.
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Wojtyniak J, Selzer D, Schwab M, Lehr T. Physiologically Based Precision Dosing Approach for Drug‐Drug‐Gene Interactions: A Simvastatin Network Analysis. Clin Pharmacol Ther 2020; 109:201-211. [DOI: 10.1002/cpt.2111] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 11/07/2020] [Indexed: 12/25/2022]
Affiliation(s)
- Jan‐Georg Wojtyniak
- Clinical Pharmacy Saarland University Saarbrücken Germany
- Dr. Margarete Fischer‐Bosch‐Institute of Clinical Pharmacology Stuttgart Germany
| | - Dominik Selzer
- Clinical Pharmacy Saarland University Saarbrücken Germany
| | - Matthias Schwab
- Dr. Margarete Fischer‐Bosch‐Institute of Clinical Pharmacology Stuttgart Germany
- Departments of Clinical Pharmacology and Pharmacy and Biochemistry University of Tübingen Tübingen Germany
- Cluster of Excellence iFIT (EXC2180) "Image‐guided and Functionally Instructed Tumor Therapies" University of Tübingen Tübingen Germany
| | - Thorsten Lehr
- Clinical Pharmacy Saarland University Saarbrücken Germany
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Britz H, Hanke N, Taub ME, Wang T, Prasad B, Fernandez É, Stopfer P, Nock V, Lehr T. Physiologically Based Pharmacokinetic Models of Probenecid and Furosemide to Predict Transporter Mediated Drug-Drug Interactions. Pharm Res 2020; 37:250. [PMID: 33237382 PMCID: PMC7688195 DOI: 10.1007/s11095-020-02964-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 10/26/2020] [Indexed: 12/14/2022]
Abstract
Purpose To provide whole-body physiologically based pharmacokinetic (PBPK) models of the potent clinical organic anion transporter (OAT) inhibitor probenecid and the clinical OAT victim drug furosemide for their application in transporter-based drug-drug interaction (DDI) modeling. Methods PBPK models of probenecid and furosemide were developed in PK-Sim®. Drug-dependent parameters and plasma concentration-time profiles following intravenous and oral probenecid and furosemide administration were gathered from literature and used for model development. For model evaluation, plasma concentration-time profiles, areas under the plasma concentration–time curve (AUC) and peak plasma concentrations (Cmax) were predicted and compared to observed data. In addition, the models were applied to predict the outcome of clinical DDI studies. Results The developed models accurately describe the reported plasma concentrations of 27 clinical probenecid studies and of 42 studies using furosemide. Furthermore, application of these models to predict the probenecid-furosemide and probenecid-rifampicin DDIs demonstrates their good performance, with 6/7 of the predicted DDI AUC ratios and 4/5 of the predicted DDI Cmax ratios within 1.25-fold of the observed values, and all predicted DDI AUC and Cmax ratios within 2.0-fold. Conclusions Whole-body PBPK models of probenecid and furosemide were built and evaluated, providing useful tools to support the investigation of transporter mediated DDIs. Supplementary Information The online version contains supplementary material available at 10.1007/s11095-020-02964-z.
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Affiliation(s)
- Hannah Britz
- Clinical Pharmacy, Saarland University, Campus C2 2, 66123, Saarbrücken, Germany
| | - Nina Hanke
- Clinical Pharmacy, Saarland University, Campus C2 2, 66123, Saarbrücken, Germany
| | - Mitchell E Taub
- Drug Metabolism and Pharmacokinetics, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut, USA
| | - Ting Wang
- Drug Metabolism and Pharmacokinetics, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut, USA
| | - Bhagwat Prasad
- Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington, USA
| | - Éric Fernandez
- Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Peter Stopfer
- Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Valerie Nock
- Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, Campus C2 2, 66123, Saarbrücken, Germany.
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A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug-Drug-Gene Interaction Predictions. Pharmaceutics 2020; 12:pharmaceutics12111074. [PMID: 33182761 PMCID: PMC7696733 DOI: 10.3390/pharmaceutics12111074] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 10/30/2020] [Accepted: 11/04/2020] [Indexed: 12/03/2022] Open
Abstract
Trimethoprim is a frequently-prescribed antibiotic and therefore likely to be co-administered with other medications, but it is also a potent inhibitor of multidrug and toxin extrusion protein (MATE) and a weak inhibitor of cytochrome P450 (CYP) 2C8. The aim of this work was to develop a physiologically-based pharmacokinetic (PBPK) model of trimethoprim to investigate and predict its drug–drug interactions (DDIs). The model was developed in PK-Sim®, using a large number of clinical studies (66 plasma concentration–time profiles with 36 corresponding fractions excreted in urine) to describe the trimethoprim pharmacokinetics over the entire published dosing range (40 to 960 mg). The key features of the model include intestinal efflux via P-glycoprotein (P-gp), metabolism by CYP3A4, an unspecific hepatic clearance process, and a renal clearance consisting of glomerular filtration and tubular secretion. The DDI performance of this new model was demonstrated by prediction of DDIs and drug–drug–gene interactions (DDGIs) of trimethoprim with metformin, repaglinide, pioglitazone, and rifampicin, with all predicted DDI and DDGI AUClast and Cmax ratios within 1.5-fold of the clinically-observed values. The model will be freely available in the Open Systems Pharmacology model repository, to support DDI studies during drug development.
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