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Goerdeler C, Engelmann B, Aldehoff AS, Schaffert A, Blüher M, Heiker JT, Wabitsch M, Schubert K, Rolle-Kampczyk U, von Bergen M. Metabolomics in human SGBS cells as new approach method for studying adipogenic effects: Analysis of the effects of DINCH and MINCH on central carbon metabolism. ENVIRONMENTAL RESEARCH 2024; 252:118847. [PMID: 38582427 DOI: 10.1016/j.envres.2024.118847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/20/2024] [Accepted: 03/31/2024] [Indexed: 04/08/2024]
Abstract
Growing evidence suggests that exposure to certain metabolism-disrupting chemicals (MDCs), such as the phthalate plasticizer DEHP, might promote obesity in humans, contributing to the spread of this global health problem. Due to the restriction on the use of phthalates, there has been a shift to safer declared substitutes, including the plasticizer diisononyl-cyclohexane-1,2-dicarboxylate (DINCH). Notwithstanding, recent studies suggest that the primary metabolite monoisononyl-cyclohexane-1,2-dicarboxylic acid ester (MINCH), induces differentiation of human adipocytes and affects enzyme levels of key metabolic pathways. Given the lack of methods for assessing metabolism-disrupting effects of chemicals on adipose tissue, we used metabolomics to analyze human SGSB cells exposed to DINCH or MINCH. Concentration analysis of DINCH and MINCH revealed that uptake of MINCH in preadipocytes was associated with increased lipid accumulation during adipogenesis. Although we also observed intracellular uptake for DINCH, the solubility of DINCH in cell culture medium was limited, hampering the analysis of possible effects in the μM concentration range. Metabolomics revealed that MINCH induces lipid accumulation similar to peroxisome proliferator-activated receptor gamma (PPARG)-agonist rosiglitazone through upregulation of the pyruvate cycle, which was recently identified as a key driver of de novo lipogenesis. Analysis of the metabolome in the presence of the PPARG-inhibitor GW9662 indicated that the effect of MINCH on metabolism was mediated at least partly by a PPARG-independent mechanism. However, all effects of MINCH were only observed at high concentrations of 10 μM, which are three orders of magnitudes higher than the current concentrations of plasticizers in human serum. Overall, the assessment of the effects of DINCH and MINCH on SGBS cells by metabolomics revealed no adipogenic potential at physiologically relevant concentrations. This finding aligns with previous in vivo studies and supports the potential of our method as a New Approach Method (NAM) for the assessment of adipogenic effects of environmental chemicals.
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Affiliation(s)
- Cornelius Goerdeler
- Department of Molecular Toxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany.
| | - Beatrice Engelmann
- Department of Molecular Toxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany.
| | - Alix Sarah Aldehoff
- Department of Molecular Toxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany.
| | - Alexandra Schaffert
- Department of Molecular Toxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany.
| | - Matthias Blüher
- Department of Endocrinology, Nephrology and Rheumatology, Faculty of Medicine, University of Leipzig, Leipzig, Germany; Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany.
| | - John T Heiker
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany.
| | - Martin Wabitsch
- Division of Pediatric Endocrinology and Diabetes, Ulm University Medical Center, Ulm, Germany.
| | - Kristin Schubert
- Department of Molecular Toxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany.
| | - Ulrike Rolle-Kampczyk
- Department of Molecular Toxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany.
| | - Martin von Bergen
- Department of Molecular Toxicology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany; Institute of Biochemistry, Leipzig University, Leipzig, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
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Pumkathin S, Hanlumyuang Y, Wattanathana W, Laomettachit T, Liangruksa M. Investigating pharmacokinetic profiles of Centella asiatica using machine learning and PBPK modelling. J Biopharm Stat 2024:1-16. [PMID: 38860461 DOI: 10.1080/10543406.2024.2358797] [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: 01/09/2024] [Accepted: 05/12/2024] [Indexed: 06/12/2024]
Abstract
Physiologically based pharmacokinetic (PBPK) modeling serves as a valuable tool for determining the distribution and disposition of substances in the body of an organism. It involves a mathematical representation of the interrelationships among crucial physiological, biochemical, and physicochemical parameters. A lack of the values of pharmacokinetic parameters can be challenging in constructing a PBPK model. Herein, we propose an artificial intelligence framework to evaluate a key pharmacokinetic parameter, the intestinal effective permeability (Peff). The publicly available Peff dataset was utilized to develop regression machine learning models. The XGBoost model demonstrates the best test accuracy of R-squared (R2, coefficient of determination) of 0.68. The model is then applied to compute the Peff of asiaticoside and madecassoside, the parent compounds found in Centella asiatica. Subsequently, PBPK modeling was conducted to evaluate the biodistribution of the herbal substances following oral administration in a rat model. The simulation results were evaluated and validated, which agreed with the existing in vivo studies in rats. This in silico pipeline presents a potential approach for investigating the pharmacokinetic parameters and profiles of drugs or herbal substances, which can be used independently or integrated into other modeling systems.
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Affiliation(s)
- Siriwan Pumkathin
- Department of Sustainable Energy and Resources Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand
| | - Yuranan Hanlumyuang
- Department of Materials Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand
| | - Worawat Wattanathana
- Department of Materials Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand
| | - Teeraphan Laomettachit
- Theoretical and Computational Physics Group, Center of Excellence in Theoretical and Computational Science (TaCS-CoE), King Mongkut's University of Technology Thonburi (KMUTT), Bangkok, Thailand
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
| | - Monrudee Liangruksa
- National Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani, Thailand
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Kang DW, Kim JH, Choi GW, Cho SJ, Cho HY. Physiologically-based pharmacokinetic model for evaluating gender-specific exposures of N-nitrosodimethylamine (NDMA). Arch Toxicol 2024; 98:821-835. [PMID: 38127128 DOI: 10.1007/s00204-023-03652-8] [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: 10/04/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023]
Abstract
N-nitrosodimethylamine (NDMA) is classified as a human carcinogen and could be produced by both natural and industrial processes. Although its toxicity and histopathology have been well-studied in animal species, there is insufficient data on the blood and tissue exposures that can be correlated with the toxicity of NDMA. The purpose of this study was to evaluate gender-specific pharmacokinetics/toxicokinetics (PKs/TKs), tissue distribution, and excretion after the oral administration of three different doses of NDMA in rats using a physiologically-based pharmacokinetic (PBPK) model. The major target tissues for developing the PBPK model and evaluating dose metrics of NDMA included blood, gastrointestinal (GI) tract, liver, kidney, lung, heart, and brain. The predictive performance of the model was validated using sensitivity analysis, (average) fold error, and visual inspection of observations versus predictions. Then, a Monte Carlo simulation was performed to describe the magnitudes of inter-individual variability and uncertainty of the single model predictions. The developed PBPK model was applied for the exposure simulation of daily oral NDMA to estimate blood concentration ranges affecting health effects following acute-duration (≤ 14 days), intermediate-duration (15-364 days), and chronic-duration (≥ 365 days) intakes. The results of the study could be used as a scientific basis for interpreting the correlation between in vivo exposures and toxicological effects of NDMA.
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Affiliation(s)
- Dong Wook Kang
- College of Pharmacy, CHA University, 335 Pangyo-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13488, Republic of Korea
| | - Ju Hee Kim
- College of Pharmacy, CHA University, 335 Pangyo-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13488, Republic of Korea
| | - Go-Wun Choi
- College of Pharmacy, CHA University, 335 Pangyo-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13488, Republic of Korea
| | - Seok-Jin Cho
- College of Pharmacy, CHA University, 335 Pangyo-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13488, Republic of Korea
| | - Hea-Young Cho
- College of Pharmacy, CHA University, 335 Pangyo-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13488, Republic of Korea.
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Lancheros Porras KD, Alves IA, Novoa DMA. PBPK Modeling as an Alternative Method of Interspecies Extrapolation that Reduces the Use of Animals: A Systematic Review. Curr Med Chem 2024; 31:102-126. [PMID: 37031391 DOI: 10.2174/0929867330666230408201849] [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: 10/27/2022] [Revised: 01/03/2023] [Accepted: 02/03/2023] [Indexed: 04/10/2023]
Abstract
INTRODUCTION Physiologically based pharmacokinetic (PBPK) modeling is a computational approach that simulates the anatomical structure of the studied species and presents the organs and tissues as compartments interconnected by arterial and venous blood flows. AIM The aim of this systematic review was to analyze the published articles focused on the development of PBPK models for interspecies extrapolation in the disposition of drugs and health risk assessment, presenting to this modeling an alternative to reduce the use of animals. METHODS For this purpose, a systematic search was performed in PubMed using the following search terms: "PBPK" and "Interspecies extrapolation". The revision was performed according to PRISMA guidelines. RESULTS In the analysis of the articles, it was found that rats and mice are the most commonly used animal models in the PBPK models; however, most of the physiological and physicochemical information used in the reviewed studies were obtained from previous publications. Additionally, most of the PBPK models were developed to extrapolate pharmacokinetic parameters to humans and the main application of the models was for toxicity testing. CONCLUSION PBPK modeling is an alternative that allows the integration of in vitro and in silico data as well as parameters reported in the literature to predict the pharmacokinetics of chemical substances, reducing in large quantity the use of animals that are required in traditional studies.
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Jeong YS, Jusko WJ. A Complete Extension of Classical Hepatic Clearance Models Using Fractional Distribution Parameter f d in Physiologically Based Pharmacokinetics. J Pharm Sci 2024; 113:95-117. [PMID: 37279835 PMCID: PMC10902797 DOI: 10.1016/j.xphs.2023.05.019] [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: 04/11/2023] [Revised: 05/30/2023] [Accepted: 05/30/2023] [Indexed: 06/08/2023]
Abstract
The classical organ clearance models have been proposed to relate the plasma clearance CLp to probable mechanism(s) of hepatic clearance. However, the classical models assume the intrinsic capability of drug elimination (CLu,int) that is physically segregated from the vascular blood but directly acts upon the unbound drug concentration in the blood (fubCavg), and do not handle the transit-time delay between the inlet/outlet concentrations in their closed-form clearance equations. Therefore, we propose unified model structures that can address the internal blood concentration patterns of clearance organs in a more mechanistic/physiological manner, based on the fractional distribution parameter fd operative in PBPK. The basic partial/ordinary differential equations for four classical models are revisited/modified to yield a more complete set of extended clearance models, i.e., the Rattle, Sieve, Tube, and Jar models, which are the counterparts of the dispersion, series-compartment, parallel-tube, and well-stirred models. We demonstrate the feasibility of applying the resulting extended models to isolated perfused rat liver data for 11 compounds and an example dataset for in vitro-in vivo extrapolation of the intrinsic to the systemic clearances. Based on their feasibilities to handle such real data, these models may serve as an improved basis for applying clearance models in the future.
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Affiliation(s)
- Yoo-Seong Jeong
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY, 14214, USA
| | - William J Jusko
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY, 14214, USA.
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Pei L, Li R, Zhou H, Du W, Gu Y, Jiang Y, Wang Y, Chen X, Sun J, Zhu J. A Physiologically Based Pharmacokinetic Approach to Recommend an Individual Dose of Tacrolimus in Adult Heart Transplant Recipients. Pharmaceutics 2023; 15:2580. [PMID: 38004558 PMCID: PMC10675244 DOI: 10.3390/pharmaceutics15112580] [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: 08/03/2023] [Revised: 09/07/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
Tacrolimus is the principal immunosuppressive drug which is administered after heart transplantation. Managing tacrolimus therapy is challenging due to a narrow therapeutic index and wide pharmacokinetic (PK) variability. We aimed to establish a physiologically based pharmacokinetic (PBPK) model of tacrolimus in adult heart transplant recipients to optimize dose regimens in clinical practice. A 15-compartment full-PBPK model (Simbiology® Simulator, version 5.8.2) was developed using clinical observations from 115 heart transplant recipients. This study detected 20 genotypes associated with tacrolimus metabolism. CYP3A5*3 (rs776746), CYP3A4*18B (rs2242480), and IL-10 G-1082A (rs1800896) were identified as significant genetic covariates in tacrolimus pharmacokinetics. The PBPK model was evaluated using goodness-of-fit (GOF) and external evaluation. The predicted peak blood concentration (Cmax) and area under the drug concentration-time curve (AUC) were all within a two-fold value of the observations (fold error of 0.68-1.22 for Cmax and 0.72-1.16 for AUC). The patients with the CYP3A5*3/*3 genotype had a 1.60-fold increase in predicted AUC compared to the patients with the CYP3A5*1 allele, and the ratio of the AUC with voriconazole to alone was 5.80 when using the PBPK model. Based on the simulation results, the tacrolimus dosing regimen after heart transplantation was optimized. This is the first PBPK model used to predict the PK of tacrolimus in adult heart transplant recipients, and it can serve as a starting point for research on immunosuppressive drug therapy in heart transplant patients.
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Affiliation(s)
- Ling Pei
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing 210006, China
- Department of Pharmacy, Nanjing First Hospital, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing 210006, China
| | - Run Li
- Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China
| | - Hong Zhou
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Wenxin Du
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing 210006, China
- Department of Pharmacy, Nanjing First Hospital, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing 210006, China
| | - Yajie Gu
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing 210006, China
- Department of Pharmacy, Nanjing First Hospital, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing 210006, China
| | - Yingshuo Jiang
- Department of Cardiothoracic Surgery, Nanjing First Hospital, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing 210006, China
| | - Yongqing Wang
- Research Division of Clinical Pharmacology, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Xin Chen
- Department of Cardiothoracic Surgery, Nanjing First Hospital, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing 210006, China
| | - Jianguo Sun
- Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China
| | - Junrong Zhu
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing 210006, China
- Department of Pharmacy, Nanjing First Hospital, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing 210006, China
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Kammala AK, Richardson LS, Radnaa E, Han A, Menon R. Microfluidic technology and simulation models in studying pharmacokinetics during pregnancy. Front Pharmacol 2023; 14:1241815. [PMID: 37663251 PMCID: PMC10469630 DOI: 10.3389/fphar.2023.1241815] [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: 06/17/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023] Open
Abstract
Introduction: Preterm birth rates and maternal and neonatal mortality remain concerning global health issues, necessitating improved strategies for testing therapeutic compounds during pregnancy. Current 2D or 3D cell models and animal models often fail to provide data that can effectively translate into clinical trials, leading to pregnant women being excluded from drug development considerations and clinical studies. To address this limitation, we explored the utility of in silico simulation modeling and microfluidic-based organ-on-a-chip platforms to assess potential interventional agents. Methods: We developed a multi-organ feto-maternal interface on-chip (FMi-PLA-OOC) utilizing microfluidic channels to maintain intercellular interactions among seven different cell types (fetal membrane-decidua-placenta). This platform enabled the investigation of drug pharmacokinetics in vitro. Pravastatin, a model drug known for its efficacy in reducing oxidative stress and inflammation during pregnancy and currently in clinical trials, was used to test its transfer rate across both feto-maternal interfaces. The data obtained from FMi-PLA-OOC were compared with existing data from in vivo animal models and ex vivo placenta perfusion models. Additionally, we employed mechanistically based simulation software (Gastroplus®) to predict pravastatin pharmacokinetics in pregnant subjects based on validated nonpregnant drug data. Results: Pravastatin transfer across the FMi-PLA-OOC and predicted pharmacokinetics in the in silico models were found to be similar, approximately 18%. In contrast, animal models showed supraphysiologic drug accumulation in the amniotic fluid, reaching approximately 33%. Discussion: The results from this study suggest that the FMi-PLA-OOC and in silico models can serve as alternative methods for studying drug pharmacokinetics during pregnancy, providing valuable insights into drug transport and metabolism across the placenta and fetal membranes. These advanced platforms offer promising opportunities for safe, reliable, and faster testing of therapeutic compounds, potentially reducing the number of pregnant women referred to as "therapeutic orphans" due to the lack of consideration in drug development and clinical trials. By bridging the gap between preclinical studies and clinical trials, these approaches hold great promise in improving maternal and neonatal health outcomes.
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Affiliation(s)
- Ananth K. Kammala
- Division of Basic Science and Translational Research, Department of Obstetrics and Gynecology, The University of Texas Medical Branch at Galveston, Galveston, TX, United States
| | - Lauren S. Richardson
- Division of Basic Science and Translational Research, Department of Obstetrics and Gynecology, The University of Texas Medical Branch at Galveston, Galveston, TX, United States
| | - Enkhtuya Radnaa
- Division of Basic Science and Translational Research, Department of Obstetrics and Gynecology, The University of Texas Medical Branch at Galveston, Galveston, TX, United States
| | - Arum Han
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, United States
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Ramkumar Menon
- Division of Basic Science and Translational Research, Department of Obstetrics and Gynecology, The University of Texas Medical Branch at Galveston, Galveston, TX, United States
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Talha Zahid M, Zamir A, Majeed A, Imran I, Alsanea S, Ahmad T, Alqahtani F, Fawad Rasool M. A physiologically based pharmacokinetic model of cefepime to predict its pharmacokinetics in healthy, pediatric and disease populations. Saudi Pharm J 2023; 31:101675. [PMID: 37576858 PMCID: PMC10415223 DOI: 10.1016/j.jsps.2023.06.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/12/2023] [Indexed: 08/15/2023] Open
Abstract
The physiologically based pharmacokinetic modeling (PBPK) approach can predict drug pharmacokinetics (PK) by combining changes in blood flow and pathophysiological alterations for developing drug-disease models. Cefepime hydrochloride is a parenteral cephalosporin that is used to treat pneumonia, sepsis, and febrile neutropenia, among other things. The current study sought to identify the factors that impact cefepime pharmacokinetics (PK) following dosing in healthy, diseased (CKD and obese), and pediatric populations. For model construction and simulation, the modeling tool PK-SIM was utilized. Estimating cefepime PK following intravenous (IV) application in healthy subjects served as the primary step in the model-building procedure. The prediction of cefepime PK in chronic kidney disease (CKD) and obese populations were performed after the integration of the relevant pathophysiological changes. Visual predictive checks and a comparison of the observed and predicted values of the PK parameters were used to verify the developed model. The results of the PK parameters were consistent with the reported clinical data in healthy subjects. The developed PBPK model successfully predicted cefepime PK as observed from the ratio of the observed and predicted PK parameters as they were within a two-fold error range.
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Affiliation(s)
- Muhammad Talha Zahid
- Department of Pharmacy Practice, Faculty of Pharmacy, Bahauddin Zakariya University, 60800, Multan, Pakistan
| | - Ammara Zamir
- Department of Pharmacy Practice, Faculty of Pharmacy, Bahauddin Zakariya University, 60800, Multan, Pakistan
| | - Abdul Majeed
- Department of Pharmaceutics, Faculty of Pharmacy, Bahauddin Zakariya University, 60800, Multan, Pakistan
| | - Imran Imran
- Department of Pharmacology, Faculty of Pharmacy, Bahauddin Zakariya University, 60800, Multan, Pakistan
| | - Sary Alsanea
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Tanveer Ahmad
- Institute for Advanced Biosciences (IAB), CNRS UMR5309, INSERM U1209, Grenoble Alpes University, La Tronche 38700, France
| | - Faleh Alqahtani
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Muhammad Fawad Rasool
- Department of Pharmacy Practice, Faculty of Pharmacy, Bahauddin Zakariya University, 60800, Multan, Pakistan
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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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Patient-specific in vitro drug release testing coupled with in silico PBPK modeling to forecast the in vivo performance of oral extended-release levodopa formulations in Parkinson's disease patients. Eur J Pharm Biopharm 2022; 180:101-118. [PMID: 36150616 DOI: 10.1016/j.ejpb.2022.09.015] [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: 02/22/2022] [Revised: 08/24/2022] [Accepted: 09/15/2022] [Indexed: 11/24/2022]
Abstract
Biorelevant in vitro release models are valuable analytical tools for oral drug development but often tailored to gastrointestinal conditions in 'average' healthy adults. However, predicting in vivo performance in individual patients whose gastrointestinal conditions do not match those of healthy adults would be of great value for optimizing oral drug therapy for such patients. This study focused on establishing patient-specific in vitro and in silico models to predict the in vivo performance of levodopa extended-release products in Parkinsońs disease patients. Current knowledge on gastrointestinal conditions in these patients was incorporated into model development. Relevant in vivo pharmacokinetic data and patient-specific in vitro release data from a novel in vitro test setup were integrated into patient-specific physiologically-based pharmacokinetic models. AUC, cmax and tmax of the computed plasma profiles were calculated using PK-Sim®. For the products studied, levodopa plasma concentration-time profiles modeled using this novel approach compared far better with published average plasma profiles in Parkinsońs disease patients than those derived from in vitro release data obtained from the 'average' healthy adult setup. Although further work is needed, results of this study highlight the importance of addressing patient-specific gastrointestinal conditions when aiming to predict drug release in such specific patient groups.
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Sahu M, Gupta R, Ambasta RK, Kumar P. Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2022; 190:57-100. [PMID: 36008002 DOI: 10.1016/bs.pmbts.2022.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India.
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12
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Ryu HJ, Kang WH, Kim T, Kim JK, Shin KH, Chae JW, Yun HY. A compatibility evaluation between the physiologically based pharmacokinetic (PBPK) model and the compartmental PK model using the lumping method with real cases. Front Pharmacol 2022; 13:964049. [PMID: 36034786 PMCID: PMC9413202 DOI: 10.3389/fphar.2022.964049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/14/2022] [Indexed: 11/13/2022] Open
Abstract
Pharmacokinetic (PK) modeling is a useful method for investigating drug absorption, distribution, metabolism, and excretion. The most commonly used mathematical models in PK modeling are the compartment model and physiologically based pharmacokinetic (PBPK) model. Although the theoretical characteristics of each model are well known, there have been few comparative studies of the compatibility of the models. Therefore, we evaluated the compatibility of PBPK and compartment models using the lumping method with 20 model compounds. The PBPK model was theoretically reduced to the lumped model using the principle of grouping tissues and organs that show similar kinetic behaviors. The area under the concentration–time curve (AUC) based on the simulated concentration and PK parameters (drug clearance [CL], central volume of distribution [Vc], peripheral volume of distribution [Vp]) in each model were compared, assuming administration to humans. The AUC and PK parameters in the PBPK model were similar to those in the lumped model within the 2-fold range for 17 of 20 model compounds (85%). In addition, the relationship of the calculated Vd/fu (volume of distribution [Vd], drug-unbound fraction [fu]) and the accuracy of AUC between the lumped model and compartment model confirmed their compatibility. Accordingly, the compatibility between PBPK and compartment models was confirmed by the lumping method. This method can be applied depending on the requirement of compatibility between the two models.
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Affiliation(s)
- Hyo-jeong Ryu
- Department of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon, South Korea
| | - Won-ho Kang
- Department of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon, South Korea
| | - Taeheon Kim
- Department of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon, South Korea
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, Korean Advanced Institute of Science and Technology, Daejeon, South Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, South Korea
| | - Kwang-Hee Shin
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Kyungpook National University, Daegu, South Korea
| | - Jung-woo Chae
- Department of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon, South Korea
- *Correspondence: Jung-woo Chae, ; Hwi-yeol Yun,
| | - Hwi-yeol Yun
- Department of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon, South Korea
- *Correspondence: Jung-woo Chae, ; Hwi-yeol Yun,
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13
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Wang J, Nolte TM, Owen SF, Beaudouin R, Hendriks AJ, Ragas AM. A Generalized Physiologically Based Kinetic Model for Fish for Environmental Risk Assessment of Pharmaceuticals. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:6500-6510. [PMID: 35472258 PMCID: PMC9118555 DOI: 10.1021/acs.est.1c08068] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
An increasing number of pharmaceuticals found in the environment potentially impose adverse effects on organisms such as fish. Physiologically based kinetic (PBK) models are essential risk assessment tools, allowing a mechanistic approach to understanding chemical effects within organisms. However, fish PBK models have been restricted to a few species, limiting the overall applicability given the countless species. Moreover, many pharmaceuticals are ionizable, and fish PBK models accounting for ionization are rare. Here, we developed a generalized PBK model, estimating required parameters as functions of fish and chemical properties. We assessed the model performance for five pharmaceuticals (covering neutral and ionic structures). With biotransformation half-lives (HLs) from EPI Suite, 73 and 41% of the time-course estimations were within a 10-fold and a 3-fold difference from measurements, respectively. The performance improved using experimental biotransformation HLs (87 and 59%, respectively). Estimations for ionizable substances were more accurate than any of the existing species-specific PBK models. The present study is the first to develop a generalized fish PBK model focusing on mechanism-based parameterization and explicitly accounting for ionization. Our generalized model facilitates its application across chemicals and species, improving efficiency for environmental risk assessment and supporting an animal-free toxicity testing paradigm.
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Affiliation(s)
- Jiaqi Wang
- Department
of Environmental Science, Radboud Institute for Biological and Environmental
Sciences, Radboud University, Nijmegen 6500 GL, The Netherlands
| | - Tom M. Nolte
- Department
of Environmental Science, Radboud Institute for Biological and Environmental
Sciences, Radboud University, Nijmegen 6500 GL, The Netherlands
| | - Stewart F. Owen
- AstraZeneca,
Global Sustainability, Macclesfield, Cheshire SK10 2NA, United Kingdom
| | - Rémy Beaudouin
- Institut
national de l’environnement industriel et des risques (INERIS), Verneuil-en-Halatte 60550, France
| | - A. Jan Hendriks
- Department
of Environmental Science, Radboud Institute for Biological and Environmental
Sciences, Radboud University, Nijmegen 6500 GL, The Netherlands
| | - Ad M.J. Ragas
- Department
of Environmental Science, Radboud Institute for Biological and Environmental
Sciences, Radboud University, Nijmegen 6500 GL, The Netherlands
- Department
of Environmental Sciences, Faculty of Science, Open University, Heerlen 6419 AT, The Netherlands
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14
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Zazo H, Colino CI, Gutiérrez-Millán C, Cordero AA, Bartneck M, Lanao JM. Physiologically Based Pharmacokinetic (PBPK) Model of Gold Nanoparticle-Based Drug Delivery System for Stavudine Biodistribution. Pharmaceutics 2022; 14:pharmaceutics14020406. [PMID: 35214138 PMCID: PMC8875329 DOI: 10.3390/pharmaceutics14020406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/10/2022] [Accepted: 02/11/2022] [Indexed: 11/16/2022] Open
Abstract
Computational modelling has gained attention for evaluating nanoparticle-based drug delivery systems. Physiologically based pharmacokinetic (PBPK) modelling provides a mechanistic approach for evaluating drug biodistribution. The aim of this work is to develop a specific PBPK model to simulate stavudine biodistribution after the administration of a 40 nm gold nanoparticle-based drug delivery system in rats. The model parameters used have been obtained from literature, in vitro and in vivo studies, and computer optimization. Based on these, the PBPK model was built, and the compartments included were considered as permeability rate-limited tissues. In comparison with stavudine solution, a higher biodistribution of stavudine into HIV reservoirs and the modification of pharmacokinetic parameters such as the mean residence time (MRT) have been observed. These changes are particularly noteworthy in the liver, which presents a higher partition coefficient (from 0.27 to 0.55) and higher MRT (from 1.28 to 5.67 h). Simulated stavudine concentrations successfully describe these changes in the in vivo study results. The average fold error of predicted concentrations after the administration of stavudine-gold nanoparticles was within the 0.5–2-fold error in all of the tissues. Thus, this PBPK model approach may help with the pre-clinical extrapolation to other administration routes or the species of stavudine gold nanoparticles.
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Affiliation(s)
- Hinojal Zazo
- Area of Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, Avda Lcdo Méndez Nieto, 37007 Salamanca, Spain; (H.Z.); (C.G.-M.); (A.A.C.)
- Institute of Biomedical Research of Salamanca (IBSAL), 37007 Salamanca, Spain
| | - Clara I. Colino
- Area of Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, Avda Lcdo Méndez Nieto, 37007 Salamanca, Spain; (H.Z.); (C.G.-M.); (A.A.C.)
- Institute of Biomedical Research of Salamanca (IBSAL), 37007 Salamanca, Spain
- Correspondence: (C.I.C.); (J.M.L.); Tel.: +34-923-294-536 (C.I.C.)
| | - Carmen Gutiérrez-Millán
- Area of Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, Avda Lcdo Méndez Nieto, 37007 Salamanca, Spain; (H.Z.); (C.G.-M.); (A.A.C.)
- Institute of Biomedical Research of Salamanca (IBSAL), 37007 Salamanca, Spain
| | - Andres A. Cordero
- Area of Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, Avda Lcdo Méndez Nieto, 37007 Salamanca, Spain; (H.Z.); (C.G.-M.); (A.A.C.)
| | - Matthias Bartneck
- Department of Medicine III, Medical Faculty, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany;
| | - José M. Lanao
- Area of Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, Avda Lcdo Méndez Nieto, 37007 Salamanca, Spain; (H.Z.); (C.G.-M.); (A.A.C.)
- Institute of Biomedical Research of Salamanca (IBSAL), 37007 Salamanca, Spain
- Correspondence: (C.I.C.); (J.M.L.); Tel.: +34-923-294-536 (C.I.C.)
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15
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Christ B, Collatz M, Dahmen U, Herrmann KH, Höpfl S, König M, Lambers L, Marz M, Meyer D, Radde N, Reichenbach JR, Ricken T, Tautenhahn HM. Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function. Front Physiol 2021; 12:733868. [PMID: 34867441 PMCID: PMC8637208 DOI: 10.3389/fphys.2021.733868] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/26/2021] [Indexed: 01/17/2023] Open
Abstract
Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.
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Affiliation(s)
- Bruno Christ
- Cell Transplantation/Molecular Hepatology Lab, Department of Visceral, Transplant, Thoracic and Vascular Surgery, University of Leipzig Medical Center, Leipzig, Germany
| | - Maximilian Collatz
- RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
- Optisch-Molekulare Diagnostik und Systemtechnologié, Leibniz Institute of Photonic Technology (IPHT), Jena, Germany
- InfectoGnostics Research Campus Jena, Jena, Germany
| | - Uta Dahmen
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, Jena University Hospital, Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Sebastian Höpfl
- Faculty of Engineering Design, Production Engineering and Automotive Engineering, Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany
| | - Matthias König
- Systems Medicine of the Liver Lab, Institute for Theoretical Biology, Humboldt-University Berlin, Berlin, Germany
| | - Lena Lambers
- Faculty of Aerospace Engineering and Geodesy, Institute of Mechanics, Structural Analysis and Dynamics, University of Stuttgart, Stuttgart, Germany
| | - Manja Marz
- RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
| | - Daria Meyer
- RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
| | - Nicole Radde
- Faculty of Engineering Design, Production Engineering and Automotive Engineering, Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany
| | - Jürgen R. Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Tim Ricken
- Faculty of Aerospace Engineering and Geodesy, Institute of Mechanics, Structural Analysis and Dynamics, University of Stuttgart, Stuttgart, Germany
| | - Hans-Michael Tautenhahn
- Department of General, Visceral and Vascular Surgery, Jena University Hospital, Jena, Germany
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16
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Chowdhury EA, Noorani B, Alqahtani F, Bhalerao A, Raut S, Sivandzade F, Cucullo L. Understanding the brain uptake and permeability of small molecules through the BBB: A technical overview. J Cereb Blood Flow Metab 2021; 41:1797-1820. [PMID: 33444097 PMCID: PMC8327119 DOI: 10.1177/0271678x20985946] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
The brain is the most important organ in our body requiring its unique microenvironment. By the virtue of its function, the blood-brain barrier poses a significant hurdle in drug delivery for the treatment of neurological diseases. There are also different theories regarding how molecules are typically effluxed from the brain. In this review, we comprehensively discuss how the different pharmacokinetic techniques used for measuring brain uptake/permeability of small molecules have evolved with time. We also discuss the advantages and disadvantages associated with these different techniques as well as the importance to utilize the right method to properly assess CNS exposure to drug molecules. Even though very strong advances have been made we still have a long way to go to ensure a reduction in failures in central nervous system drug development programs.
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Affiliation(s)
- Ekram Ahmed Chowdhury
- Department of Pharmaceutical Sciences, Texas Tech University Health Sciences Center, Amarillo, USA
| | - Behnam Noorani
- Department of Pharmaceutical Sciences, Texas Tech University Health Sciences Center, Amarillo, USA
| | - Faleh Alqahtani
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Aditya Bhalerao
- Department of Foundational Medical Studies, Oakland University William Beaumont School of Medicine, Rochester, USA
| | - Snehal Raut
- Department of Pharmaceutical Sciences, Texas Tech University Health Sciences Center, Amarillo, USA
| | - Farzane Sivandzade
- Department of Foundational Medical Studies, Oakland University William Beaumont School of Medicine, Rochester, USA
| | - Luca Cucullo
- Department of Foundational Medical Studies, Oakland University William Beaumont School of Medicine, Rochester, USA
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17
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Breen M, Ring CL, Kreutz A, Goldsmith MR, Wambaugh JF. High-throughput PBTK models for in vitro to in vivo extrapolation. Expert Opin Drug Metab Toxicol 2021; 17:903-921. [PMID: 34056988 DOI: 10.1080/17425255.2021.1935867] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals. AREAS COVERED This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data. EXPERT OPINION HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.
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Affiliation(s)
- Miyuki Breen
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Caroline L Ring
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Anna Kreutz
- Oak Ridge Institute for Science and Education (ORISE) fellow at the Center for Computational Toxicology and Exposure, Office of Research and Development, Research Triangle Park, NC, USA
| | - Michael-Rock Goldsmith
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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18
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Yoshida K, Doi Y, Iwazaki N, Yasuhara H, Ikenaga Y, Shimizu H, Nakada T, Watanabe T, Tateno C, Sanoh S, Kotake Y. Prediction of human pharmacokinetics for low-clearance compounds using pharmacokinetic data from chimeric mice with humanized livers. Clin Transl Sci 2021; 15:79-91. [PMID: 34080287 PMCID: PMC8742647 DOI: 10.1111/cts.13070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/02/2021] [Accepted: 04/28/2021] [Indexed: 11/25/2022] Open
Abstract
Development of low-clearance (CL) compounds that can be slowly metabolized is a major goal in the pharmaceutical industry. However, the pursuit of low intrinsic CL (CLint ) often leads to significant challenges in evaluating the pharmacokinetics of such compounds. Although in vitro-in vivo extrapolation is widely used to predict human CL, its application has been limited for low-CLint compounds because of the low turnover of parent compounds in metabolic stability assays. To address this issue, we focused on chimeric mice with humanized livers (PXB-mice), which have been increasingly reported to accurately predict human CL in recent years. The predictive accuracy for nine low-CLint compounds with no significant turnover in a human hepatocyte assay was investigated using PXB-mouse methods such as single-species allometric scaling (PXB-SSS) approach and a novel physiologically based scaling (PXB-PBS) approach that assumes that the CLint per hepatocyte is equal between humans and PXB-mice. The percentages of compounds with predicted CL within 2- and 3-fold ranges of the observed CL for low-CLint compounds were 89% and 100%, respectively, for both PXB-SSS and PXB-PBS approaches. Moreover, the predicted CL was mostly consistent among the methods. Conversely, percentages of compounds with predicted CL within 2- and 3-fold ranges of the observed CL for low-CLint compounds were 50% and 63%, respectively for multispecies allometric scaling (MA). Overall, these PXB-mouse methods were much more accurate than conventional MA approaches, suggesting that PXB-mice are useful tool for predicting the human CL of low-CLint compounds that are slowly metabolized.
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Affiliation(s)
- Kosuke Yoshida
- DMPK Research Laboratories, Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Kanagawa, Japan.,Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yuki Doi
- DMPK Research Laboratories, Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Kanagawa, Japan
| | - Norihiko Iwazaki
- DMPK Research Laboratories, Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Kanagawa, Japan
| | - Hidenori Yasuhara
- DMPK Research Laboratories, Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Kanagawa, Japan
| | - Yuka Ikenaga
- DMPK Research Laboratories, Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Kanagawa, Japan
| | - Hidetoshi Shimizu
- DMPK Research Laboratories, Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Kanagawa, Japan
| | - Tomohisa Nakada
- DMPK Research Laboratories, Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Kanagawa, Japan
| | - Tomoko Watanabe
- DMPK Research Laboratories, Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Kanagawa, Japan
| | - Chise Tateno
- Research and Development Department, PhoenixBio Co., Ltd, Hiroshima, Japan
| | - Seigo Sanoh
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yaichiro Kotake
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
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19
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Hammon K, de Hart G, Vuillemenot BR, Kennedy D, Musson D, O'Neill CA, Katz ML, Henshaw JW. Dose selection for intracerebroventricular cerliponase alfa in children with CLN2 disease, translation from animal to human in a rare genetic disease. Clin Transl Sci 2021; 14:1810-1821. [PMID: 34076336 PMCID: PMC8504808 DOI: 10.1111/cts.13028] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/08/2021] [Accepted: 02/12/2021] [Indexed: 11/28/2022] Open
Abstract
Neuronal ceroid lipofuscinosis type 2 (CLN2 disease) is an ultra‐rare pediatric neurodegenerative disorder characterized by deficiency of the lysosomal enzyme tripeptidyl peptidase‐1 (TPP1). In the absence of adequate TPP1, lysosomal storage material accumulation occurs in the central nervous system (CNS) accompanied by neurodegeneration and neurological decline that culminates in childhood death. Cerliponase alfa is a recombinant human TPP1 enzyme replacement therapy administered via intracerebroventricular infusion and approved for the treatment of CLN2 disease. Here, we describe two allometric methods, calculated by scaling brain mass across species, that informed the human dose selection and exposure prediction of cerliponase alfa from preclinical studies in monkeys and a dog model of CLN2 disease: (1) scaling of dose using a human‐equivalent dose factor; and (2) scaling of compartmental pharmacokinetic (PK) model parameters. Source PK data were obtained from cerebrospinal fluid (CSF) samples from dogs and monkeys, and the human exposure predictions were confirmed with CSF data from the first‐in‐human clinical study. Nonclinical and clinical data were analyzed using noncompartmental analysis and nonlinear mixed‐effect modeling approaches. Both allometric methods produced CSF exposure predictions within twofold of the observed exposure parameters maximum plasma concentration (Cmax) and area under the curve (AUC). Furthermore, cross‐species qualification produced consistent and reasonable PK profile predictions, which supported the allometric scaling of model parameters. The challenges faced in orphan drug development place an increased importance on, and opportunity for, data translation from research and nonclinical development. Our approach to dose translation and human exposure prediction for cerliponase alfa may be applicable to other CNS administered therapies being developed.
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Affiliation(s)
- Kevin Hammon
- BioMarin Pharmaceutical Inc., Novato, California, USA
| | - Greg de Hart
- BioMarin Pharmaceutical Inc., Novato, California, USA
| | | | - Derek Kennedy
- BioMarin Pharmaceutical Inc., Novato, California, USA
| | - Don Musson
- BioMarin Pharmaceutical Inc., Novato, California, USA
| | | | - Martin L Katz
- Mason Eye Institute, University of Missouri School of Medicine, Columbia, Missouri, USA.,Department of Bioengineering, University of Missouri, Columbia, Missouri, USA
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20
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Zhou Z, Zhu J, Jiang M, Sang L, Hao K, He H. The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development. Pharmaceutics 2021; 13:pharmaceutics13050704. [PMID: 34065907 PMCID: PMC8151315 DOI: 10.3390/pharmaceutics13050704] [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] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/29/2021] [Accepted: 05/05/2021] [Indexed: 12/12/2022] Open
Abstract
Human-derived in vitro models can provide high-throughput efficacy and toxicity data without a species gap in drug development. Challenges are still encountered regarding the full utilisation of massive data in clinical settings. The lack of translated methods hinders the reliable prediction of clinical outcomes. Therefore, in this study, in silico models were proposed to tackle these obstacles from in vitro to in vivo translation, and the current major cell culture methods were introduced, such as human-induced pluripotent stem cells (hiPSCs), 3D cells, organoids, and microphysiological systems (MPS). Furthermore, the role and applications of several in silico models were summarised, including the physiologically based pharmacokinetic model (PBPK), pharmacokinetic/pharmacodynamic model (PK/PD), quantitative systems pharmacology model (QSP), and virtual clinical trials. These credible translation cases will provide templates for subsequent in vitro to in vivo translation. We believe that synergising high-quality in vitro data with existing models can better guide drug development and clinical use.
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Affiliation(s)
- Zhengying Zhou
- Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China; (Z.Z.); (M.J.)
| | - Jinwei Zhu
- State Key Laboratory of Natural Medicines, Jiangsu Province Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China; (J.Z.); (L.S.)
| | - Muhan Jiang
- Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China; (Z.Z.); (M.J.)
| | - Lan Sang
- State Key Laboratory of Natural Medicines, Jiangsu Province Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China; (J.Z.); (L.S.)
| | - Kun Hao
- State Key Laboratory of Natural Medicines, Jiangsu Province Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China; (J.Z.); (L.S.)
- Correspondence: (K.H.); (H.H.)
| | - Hua He
- Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China; (Z.Z.); (M.J.)
- Correspondence: (K.H.); (H.H.)
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21
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Dawson D, Ingle BL, Phillips KA, Nichols JW, Wambaugh JF, Tornero-Velez R. Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:6505-6517. [PMID: 33856768 PMCID: PMC8548983 DOI: 10.1021/acs.est.0c06117] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
The intrinsic metabolic clearance rate (Clint) and the fraction of the chemical unbound in plasma (fup) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.
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Affiliation(s)
- Daniel Dawson
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709
| | - Brandall L. Ingle
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709
| | - Katherine A. Phillips
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709
| | - John W. Nichols
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709
| | - John F. Wambaugh
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709
| | - Rogelio Tornero-Velez
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709
- Corresponding Author Address correspondence to Rogelio Tornero-Velez at 109 T.W. Alexander Drive, Mail Code E205-01, Research Triangle Park, NC, 27709;
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22
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Abstract
Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.
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23
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Cheng Y, Wang CY, Li ZR, Pan Y, Liu MB, Jiao Z. Can Population Pharmacokinetics of Antibiotics be Extrapolated? Implications of External Evaluations. Clin Pharmacokinet 2020; 60:53-68. [PMID: 32960439 DOI: 10.1007/s40262-020-00937-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND OBJECTIVE External evaluation is an important issue in the population pharmacokinetic analysis of antibiotics. The purpose of this review was to summarize the current approaches and status of external evaluations and discuss the implications of external evaluation results for the future individualization of dosing regimens. METHODS We systematically searched the PubMed and EMBASE databases for external evaluation studies of population analysis and extracted the relevant information from these articles. A total of 32 studies were included in this review. RESULTS Vancomycin was investigated in 17 (53.1%) articles and was the most studied drug. Other studied drugs included gentamicin, tobramycin, amikacin, amoxicillin, ceftaroline, meropenem, fluconazole, voriconazole, and rifampicin. Nine (28.1%) studies were prospective, and the sample size varied widely between studies. Thirteen (40.6%) studies evaluated the population pharmacokinetic models by systematically searching for previous studies. Seven (21.9%) studies were multicenter studies, and 27 (84.4%) adopted the sparse sampling strategy. Almost all external evaluation studies of antibiotics (93.8%) used metrics for prediction-based diagnostics, while relatively fewer studies were based on simulations (46.9%) and Bayesian forecasting (25.0%). CONCLUSION The results of external evaluations in previous studies revealed the poor extrapolation performance of existing models of prediction- and simulation-based diagnostics, whereas the posterior Bayesian method could improve predictive performance. There is an urgent need for the development of standards and guidelines for external evaluation studies.
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Affiliation(s)
- Yu Cheng
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200040, China.,Department of Pharmacy, Fujian Medical University Union Hospital, 29 Xin Quan Road, Gulou, Fuzhou, 350001, China
| | - Chen-Yu Wang
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200040, China
| | - Zi-Ran Li
- College of Pharmacy, Fudan University, Shanghai, China
| | - Yan Pan
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200040, China
| | - Mao-Bai Liu
- Department of Pharmacy, Fujian Medical University Union Hospital, 29 Xin Quan Road, Gulou, Fuzhou, 350001, China.
| | - Zheng Jiao
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200040, China.
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24
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Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, Liang X, Tan M, Huang Z. Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front Chem 2020; 8:726. [PMID: 33062633 PMCID: PMC7517894 DOI: 10.3389/fchem.2020.00726] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 07/14/2020] [Indexed: 12/11/2022] Open
Abstract
Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.
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Affiliation(s)
- Fengxu Wu
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China
| | - Yuquan Zhou
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Langhui Li
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Xianhuan Shen
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Ganying Chen
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Xiaoqing Wang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Xianyang Liang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Mengyuan Tan
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Zunnan Huang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
- Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, China
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25
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Prediction of lisinopril pediatric dose from the reference adult dose by employing a physiologically based pharmacokinetic model. BMC Pharmacol Toxicol 2020; 21:56. [PMID: 32727574 PMCID: PMC7389632 DOI: 10.1186/s40360-020-00429-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 07/02/2020] [Indexed: 01/10/2023] Open
Abstract
Background This study aimed to assess the pediatric lisinopril doses using an adult physiological based pharmacokinetic (PBPK) model. As the empirical rules of dose calculation cannot calculate gender-specific pediatric doses and ignores the age-related physiological differences. Methods A PBPK model of lisinopril for the healthy adult population was developed for oral (fed and fasting) and IV administration using PK-Sim MoBI® and was scaled down to a virtual pediatric population for prediction of lisinopril doses in neonates to infants, infants to toddler, children at pre-school age, children at school age and the adolescents. The pharmacokinetic parameters were predicted for the above groups at decremental doses of 20 mg, 10 mg, 5 mg, 2.5 mg, and 1.5 mg in order to accomplish doses producing the pharmacokinetic parameters, similar (or comparable) to that of the adult population. The above simulated pediatric doses were compared to the doses computed using the conventional four methods, such as Young’s rule, Clark’s rule, and weight-based and body surface area-based equations and the dose reported in different studies. Results Though the doses predicted for all subpopulations of children were comparable to those calculated by Young’s rule, yet the conventional methods overestimated the pediatric doses when compared to the respective PBPK-predicted doses. The findings of previous real time pharmacokinetic studies in pediatric patients supported the present simulated dose. Conclusion Thus, PBPK seems to have predictability potential for pediatric dose since it takes into consideration the physiological changes related to age and gender.
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26
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Carrara L, Magni P, Teutonico D, Pasotti L, Della Pasqua O, Kloprogge F. Ethambutol disposition in humans: Challenges and limitations of whole-body physiologically-based pharmacokinetic modelling in early drug development. Eur J Pharm Sci 2020; 150:105359. [PMID: 32361179 DOI: 10.1016/j.ejps.2020.105359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 01/27/2020] [Accepted: 04/22/2020] [Indexed: 02/06/2023]
Abstract
Whole-body physiologically based pharmacokinetic (WB-PBPK) models have become an important tool in drug development, as they enable characterization of pharmacokinetic profiles across different organs based on physiological (systems-specific) and physicochemical (drug-specific) properties. However, it remains unclear which data are needed for accurate predictions when applying the approach to novel candidate molecules progressing into the clinic. In this work, as case study, we investigated the predictive performance of WB-PBPK models both for prospective and retrospective evaluation of the pharmacokinetics of ethambutol, considering scenarios that reflect different stages of development, including settings in which the data are limited to in vitro experiments, in vivo preclinical data, and when some clinical data are available. Overall, the accuracy of PBPK model-predicted systemic and tissue exposure was heavily dependant on prior knowledge about the eliminating organs. Whilst these findings may be specific to ethambutol, the challenges and potential limitations identified here may be relevant to a variety of drugs, raising questions about (1) the minimum requirements for prospective use of WB-PBPK models during the characterization of drug disposition and (2) implication of uncertainty for dose selection in humans.
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Affiliation(s)
- Letizia Carrara
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
| | - Paolo Magni
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
| | - Donato Teutonico
- Translational Medicine and Early Development, Sanofi R&D, France
| | - Lorenzo Pasotti
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
| | - Oscar Della Pasqua
- Clinical Pharmacology & Therapeutics Group, School of Pharmacy, University College London, United Kingdom; Clinical Pharmacology Modelling & Simulation. GlaxoSmithKline, United Kingdom.
| | - Frank Kloprogge
- Clinical Pharmacology & Therapeutics Group, School of Pharmacy, University College London, United Kingdom; Institute for Global Health, University College London, United Kingdom
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27
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Translational approach from preclinical to clinical: comparison of dose finding methods of a new Bcl2 inhibitor using PK-PD modeling and interspecies extrapolation. Invest New Drugs 2020; 38:1796-1806. [PMID: 32451663 DOI: 10.1007/s10637-020-00953-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 05/18/2020] [Indexed: 12/19/2022]
Abstract
The attrition rate of anticancer drugs during the clinical development remains very high. Interspecies extrapolation of anticancer drug pharmacodynamics (PD) could help to bridge the gap between preclinical and clinical settings and to improve drug development. Indeed, when combined with a physiologically-based-pharmacokinetics (PBPK) approach, PD interspecies extrapolation could be a powerful tool for predicting drug behavior in clinical trials. The present study aimed to explore this field for anticipating the clinical efficacy of a new Bcl-2 inhibitor, S 55746, for which dose ranging studies in xenografted mice and clinical data from a phase 1 trial involving cancer patients were available. Different strategies based on empirical or more mechanistic assumptions (based on PBPK-PD modelling) were developped and compared: the Rocchetti approach (ROC); the Orthogonal Rocchetti approach (oROC), a variant of ROC based on an orthogonal regression; the Consistent across species approach, bringing out an efficacy parameter assumed to be consistent across species; and the Scaling species-specific parameters approach, assuming the concentration-efficacy link is the same in mice as in humans, after allometric scaling. Empirical approaches (ROC and oROC) gave similar predictive performances and seemed to overestimate the active S 55746 dose compared to mechanistic approaches, while strategies elaborated from semi-mechanistic concepts and PBPK-PD modelling did not seem to be invalidated by clinical efficacy data. Also, empirical methods only predict a single dose level for the subsequent clinical studies, whereas mechanism-based strategies are more informative about the dose response relationship, highlighting the potential interest of such approaches in drug development.
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28
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Lee JB, Zhou S, Chiang M, Zang X, Kim TH, Kagan L. Interspecies prediction of pharmacokinetics and tissue distribution of doxorubicin by physiologically-based pharmacokinetic modeling. Biopharm Drug Dispos 2020; 41:192-205. [PMID: 32342986 DOI: 10.1002/bdd.2229] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 04/18/2020] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
The aim of the study was to develop a physiologically-based pharmacokinetic (PBPK) model to describe and predict whole-body disposition of doxorubicin following intravenous administration. The PBPK model was established using previously published data in mice and included 10 tissue compartments: lungs, heart, brain, muscle, kidneys, pancreas, intestine, liver, spleen, adipose tissue, and plasma. Individual tissues were described by either perfusion-limited or permeability-limited models. All parameters were simultaneously estimated and the final model was able to describe murine data with good precision. The model was used for predicting doxorubicin disposition in rats, rabbits, dogs, and humans using interspecies scaling approaches and was qualified using plasma and tissue observed data. Reasonable prediction of the plasma pharmacokinetics and tissue distribution was achieved across all species. In conclusion, the PBPK model developed based on a rich dataset obtained from mice, was able to reasonably predict the disposition of doxorubicin in other preclinical species and humans. Applicability of the model for special populations, such as patients with hepatic impairment, was also demonstrated. The proposed model will be a valuable tool for optimization of exposure profiles of doxorubicin in human patients.
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Affiliation(s)
- Jong Bong Lee
- Department of Pharmaceutics, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, NJ, 08854, USA
| | - Simon Zhou
- Translational Development and Clinical Pharmacology, Celgene Corporation, NJ, 07920, USA
| | - Manting Chiang
- Department of Pharmaceutics, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, NJ, 08854, USA.,Center of Excellence for Pharmaceutical Translational Research and Education, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, NJ, 08854, USA
| | - Xiaowei Zang
- Department of Pharmaceutics, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, NJ, 08854, USA
| | - Tae Hwan Kim
- College of Pharmacy, Daegu Catholic University, Gyeongsan, Republic of Korea, 38430
| | - Leonid Kagan
- Department of Pharmaceutics, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, NJ, 08854, USA.,Center of Excellence for Pharmaceutical Translational Research and Education, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, NJ, 08854, USA
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29
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Díaz de León-Ortega R, D'Arcy DM, Lamprou DA, Fotaki N. In vitro - in vivo relations for the parenteral liposomal formulation of Amphotericin B: A clinically relevant approach with PBPK modeling. Eur J Pharm Biopharm 2020; 159:177-187. [PMID: 32147578 DOI: 10.1016/j.ejpb.2020.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 02/22/2020] [Accepted: 03/03/2020] [Indexed: 12/26/2022]
Abstract
In vitro release testing is a useful tool for the quality control of controlled release parenteral formulations, but in vitro release test conditions that reflect or are able to predict the in vivo performance are advantageous. Therefore, it is important to investigate the factors that could affect drug release from formulations and relate them to in vivo performance. In this study the effect of media composition including albumin presence, type of buffer and hydrodynamics on drug release were evaluated on a liposomal Amphotericin B formulation (Ambisome®). A physiologically based pharmacokinetic (PBPK) model was developed using plasma concentration profiles from healthy subjects, in order to investigate the impact of each variable from the in vitro release tests on the prediction of the in vivo performance. It was found that albumin presence was the most important factor for the release of Amphotericin B from Ambisome®; both hydrodynamics setups, coupled with the PBPK model, had comparable predictive ability for simulating in vivo plasma concentration profiles. The PBPK model was extrapolated to a hypothetical hypoalbuminaemic population and the Amphotericin B plasma concentration and its activity against fungal cells were simulated. Selected in vitro release tests for these controlled release parenteral formulations were able to predict the in vivo AmB exposure, and this PBPK driven approach to release test development could benefit development of such formulations.
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Affiliation(s)
| | - D M D'Arcy
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin 2, Ireland
| | - D A Lamprou
- School of Pharmacy, Queen's University Belfast, Belfast, United Kingdom
| | - N Fotaki
- Department of Pharmacy and Pharmacology, University of Bath, Bath, United Kingdom.
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30
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Germovsek E, Barker CIS, Sharland M, Standing JF. Pharmacokinetic-Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance. Clin Pharmacokinet 2020; 58:39-52. [PMID: 29675639 PMCID: PMC6325987 DOI: 10.1007/s40262-018-0659-0] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Pharmacokinetic/pharmacodynamic (PKPD) modeling is important in the design and conduct of clinical pharmacology research in children. During drug development, PKPD modeling and simulation should underpin rational trial design and facilitate extrapolation to investigate efficacy and safety. The application of PKPD modeling to optimize dosing recommendations and therapeutic drug monitoring is also increasing, and PKPD model-based dose individualization will become a core feature of personalized medicine. Following extensive progress on pediatric PK modeling, a greater emphasis now needs to be placed on PD modeling to understand age-related changes in drug effects. This paper discusses the principles of PKPD modeling in the context of pediatric drug development, summarizing how important PK parameters, such as clearance (CL), are scaled with size and age, and highlights a standardized method for CL scaling in children. One standard scaling method would facilitate comparison of PK parameters across multiple studies, thus increasing the utility of existing PK models and facilitating optimal design of new studies.
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Affiliation(s)
- Eva Germovsek
- Infection, Inflammation and Rheumatology Section, UCL Great Ormond Street Institute of Child Heath, University College London, London, UK. .,Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, PO Box 591, 751 24, Uppsala, Sweden.
| | - Charlotte I S Barker
- Infection, Inflammation and Rheumatology Section, UCL Great Ormond Street Institute of Child Heath, University College London, London, UK.,Paediatric Infectious Diseases Research Group, Institute for Infection and Immunity, St George's, University of London, London, UK.,St George's University Hospitals NHS Foundation Trust, London, UK
| | - Mike Sharland
- Paediatric Infectious Diseases Research Group, Institute for Infection and Immunity, St George's, University of London, London, UK.,St George's University Hospitals NHS Foundation Trust, London, UK
| | - Joseph F Standing
- Infection, Inflammation and Rheumatology Section, UCL Great Ormond Street Institute of Child Heath, University College London, London, UK.,Paediatric Infectious Diseases Research Group, Institute for Infection and Immunity, St George's, University of London, London, UK
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31
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Le J, Bradley JS. Optimizing Antibiotic Drug Therapy in Pediatrics: Current State and Future Needs. J Clin Pharmacol 2019; 58 Suppl 10:S108-S122. [PMID: 30248202 DOI: 10.1002/jcph.1128] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 03/01/2018] [Indexed: 12/19/2022]
Abstract
The selection of the right antibiotic and right dose necessitates clinicians understand the contribution of pharmacokinetic variability stemming from age-related physiologic maturation and the pharmacodynamics to optimize drug exposure for clinical response. The complexity of selecting the right dose arises from the multiplicity of pediatric age groups, from premature neonates to adolescents. Body size and age (which relate to organ function) must be incorporated to optimize antibiotic dosing in this vulnerable population. In the effort to optimize and individualize drug dosing regimens, clinical pharmacometrics that incorporate population-based pharmacokinetic modeling, Bayesian estimation, and Monte Carlo simulations are utilized as a quantitative approach to understanding and predicting the pharmacology and clinical and microbiologic efficacy of antibiotics. In addition, opportunistic study designs and alternative blood sampling strategies can serve as practical approaches to ensure successful conduct of pediatric studies. This review article examines relevant literature on optimization of antibiotic pharmacotherapy in pediatric populations published within the last decade. Specific pediatric antibiotic data, including beta-lactam antibiotics, aminoglycosides, and vancomycin, are critically evaluated.
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Affiliation(s)
- Jennifer Le
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA, USA
| | - John S Bradley
- Department of Pediatrics, Division of Infectious Diseases, University of California at San Diego, La Jolla, CA, USA.,Rady Children's Hospital San Diego, San Diego, CA, USA
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32
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Perkins EJ, Ashauer R, Burgoon L, Conolly R, Landesmann B, Mackay C, Murphy CA, Pollesch N, Wheeler JR, Zupanic A, Scholz S. Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2019; 38:1850-1865. [PMID: 31127958 PMCID: PMC6771761 DOI: 10.1002/etc.4505] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 03/26/2019] [Accepted: 05/21/2019] [Indexed: 05/20/2023]
Abstract
An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway-based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850-1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
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Affiliation(s)
- Edward J. Perkins
- US Army Engineer Research and Development CenterVicksburgMississippiUSA
| | - Roman Ashauer
- Environment DepartmentUniversity of York, HeslingtonYorkUK
- ToxicodynamicsYorkUK
| | - Lyle Burgoon
- US Army Engineer Research and Development CenterVicksburgMississippiUSA
| | - Rory Conolly
- Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and DevelopmentUS Environmental Protection Agency, Research Triangle ParkNorth CarolinaUSA
| | | | - Cameron Mackay
- Unilever Safety and Environmental Assurance Centre, SharnbrookBedfordUK
| | - Cheryl A. Murphy
- Department of Fisheries and WildlifeMichigan State UniversityEast LansingMichiganUSA
| | - Nathan Pollesch
- Mid‐Continent Ecology Division, National Health and Environmental Effects Laboratory, Office of Research and DevelopmentUS Environmental Protection AgencyDuluthMinnesotaUSA
| | | | - Anze Zupanic
- Department of Environmental ToxicologySwiss Federal Institute for Aquatic Science and TechnologyDübendorfSwitzerland
| | - Stefan Scholz
- Department of Bioanalytical EcotoxicologyHelmholtz Centre for Environmental Research‐UFZLeipzigGermany
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33
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Valic MS, Zheng G. Research tools for extrapolating the disposition and pharmacokinetics of nanomaterials from preclinical animals to humans. Theranostics 2019; 9:3365-3387. [PMID: 31244958 PMCID: PMC6567967 DOI: 10.7150/thno.34509] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 04/26/2019] [Indexed: 11/30/2022] Open
Abstract
A critical step in the translational science of nanomaterials from preclinical animal studies to humans is the comprehensive investigation of their disposition (or ADME) and pharmacokinetic behaviours. Disposition and pharmacokinetic data are ideally collected in different animal species (rodent and nonrodent), at different dose levels, and following multiple administrations. These data are used to assess the systemic exposure and effect to nanomaterials, primary determinants of their potential toxicity and therapeutic efficacy. At toxic doses in animal models, pharmacokinetic (termed toxicokinetic) data are related to toxicologic findings that inform the design of nonclinical toxicity studies and contribute to the determination of the maximum recommended starting dose in clinical phase 1 trials. Nanomaterials present a unique challenge for disposition and pharmacokinetic investigations owing to their prolonged circulation times, nonlinear pharmacokinetic profiles, and their extensive distribution into tissues. Predictive relationships between nanomaterial physicochemical properties and behaviours in vivo are lacking and are confounded by anatomical, physiological, and immunological differences amongst preclinical animal models and humans. These challenges are poorly understood and frequently overlooked by investigators, leading to inaccurate assumptions of disposition, pharmacokinetic, and toxicokinetics profiles across species that can have profoundly detrimental impacts for nonclinical toxicity studies and clinical phase 1 trials. Herein are highlighted two research tools for analysing and interpreting disposition and pharmacokinetic data from multiple species and for extrapolating this data accurately in humans. Empirical methodologies and mechanistic mathematical modelling approaches are discussed with emphasis placed on important considerations and caveats for representing nanomaterials, such as the importance of integrating physiological variables associated with the mononuclear phagocyte system (MPS) into extrapolation methods for nanomaterials. The application of these tools will be examined in recent examples of investigational and clinically approved nanomaterials. Finally, strategies for applying these extrapolation tools in a complementary manner to perform dose predictions and in silico toxicity assessments in humans will be explained. A greater familiarity with the available tools and prior experiences of extrapolating nanomaterial disposition and pharmacokinetics from preclinical animal models to humans will hopefully result in a more straightforward roadmap for the clinical translation of promising nanomaterials.
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Affiliation(s)
- Michael S. Valic
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, CANADA, M5G 1L7
| | - Gang Zheng
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, CANADA, M5G 1L7
- Department of Medical Biophysics, Institute of Biomaterials and Biomedical Engineering, and Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, CANADA, M5G 1L7
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Kapraun DF, Wambaugh JF, Setzer RW, Judson RS. Empirical models for anatomical and physiological changes in a human mother and fetus during pregnancy and gestation. PLoS One 2019; 14:e0215906. [PMID: 31048866 PMCID: PMC6497258 DOI: 10.1371/journal.pone.0215906] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 04/10/2019] [Indexed: 12/28/2022] Open
Abstract
Many parameters treated as constants in traditional physiologically based pharmacokinetic models must be formulated as time-varying quantities when modeling pregnancy and gestation due to the dramatic physiological and anatomical changes that occur during this period. While several collections of empirical models for such parameters have been published, each has shortcomings. We sought to create a repository of empirical models for tissue volumes, blood flow rates, and other quantities that undergo substantial changes in a human mother and her fetus during the time between conception and birth, and to address deficiencies with similar, previously published repositories. We used maximum likelihood estimation to calibrate various models for the time-varying quantities of interest, and then used the Akaike information criterion to select an optimal model for each quantity. For quantities of interest for which time-course data were not available, we constructed composite models using percentages and/or models describing related quantities. In this way, we developed a comprehensive collection of formulae describing parameters essential for constructing a PBPK model of a human mother and her fetus throughout the approximately 40 weeks of pregnancy and gestation. We included models describing blood flow rates through various fetal blood routes that have no counterparts in adults. Our repository of mathematical models for anatomical and physiological quantities of interest provides a basis for PBPK models of human pregnancy and gestation, and as such, it can ultimately be used to support decision-making with respect to optimal pharmacological dosing and risk assessment for pregnant women and their developing fetuses. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
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Affiliation(s)
- Dustin F. Kapraun
- National Center for Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
- * E-mail:
| | - John F. Wambaugh
- National Center for Computational Toxicology, US Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
| | - R. Woodrow Setzer
- National Center for Computational Toxicology, US Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
| | - Richard S. Judson
- National Center for Computational Toxicology, US Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
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Bravo-Gómez ME, Camacho-García LN, Castillo-Alanís LA, Mendoza-Meléndez MÁ, Quijano-Mateos A. Revisiting a physiologically based pharmacokinetic model for cocaine with a forensic scope. Toxicol Res (Camb) 2019; 8:432-446. [PMID: 31160976 PMCID: PMC6505388 DOI: 10.1039/c8tx00309b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Accepted: 02/11/2019] [Indexed: 11/21/2022] Open
Abstract
A whole-body permeability-rate-limited physiologically based pharmacokinetic (PBPK) model for cocaine was developed and adjusted with the pharmacokinetic data from studies with animals and reparametrized scaling to humans with the aim to predict the concentration-time profiles of the drug in blood and different tissues in humans. Estimated time course concentrations could be used as an interpretation tool by forensic toxicologists. The model estimations were compared successfully with pharmacokinetic parameters and time to peak for some effects reported in the literature. Once developed, the PBPK model was employed to predict the time course tissue concentrations reported in previous distribution studies introducing individualizing data. The heart and brain concentrations estimated by the model match adequately with the time and duration of some effects such as chronotropic and psychoactive effects, respectively. This work is the first attempt for employing PBPK modeling as a tool for forensic interpretation. Future modeling of other cocaine metabolite profiles or interaction when co-administered with other substances, such as alcohol, might be developed in the future.
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Affiliation(s)
- María Elena Bravo-Gómez
- Forensic Science Department , School of Medicine , National Autonomous University of Mexico , Av. Universidad 3000 , ZC 04510 , Mexico City , Mexico . ; ; ; Tel: +52-(55)56232300 Ext.81916
| | - Laura Nayeli Camacho-García
- School of Chemistry , National Autonomous University of Mexico , Av. Universidad 3000 , ZC 04510 , Mexico City , Mexico .
| | - Luz Alejandra Castillo-Alanís
- Forensic Science Department , School of Medicine , National Autonomous University of Mexico , Av. Universidad 3000 , ZC 04510 , Mexico City , Mexico . ; ; ; Tel: +52-(55)56232300 Ext.81916
| | - Miguel Ángel Mendoza-Meléndez
- Research and Advanced Studies Centre , Politechnical National Institute (CINVESTAV-IPN). Av. Instituto Politécnico Nacional 2508 , Col. San Pedro Zacatenco , Gustavo A. Madero , ZC 07360 , Mexico City , Mexico .
| | - Alejandra Quijano-Mateos
- Forensic Science Department , School of Medicine , National Autonomous University of Mexico , Av. Universidad 3000 , ZC 04510 , Mexico City , Mexico . ; ; ; Tel: +52-(55)56232300 Ext.81916
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Pierrillas PB, Henin E, Ball K, Ogier J, Amiel M, Kraus-Berthier L, Chenel M, Bouzom F, Tod M. Prediction of Human Nonlinear Pharmacokinetics of a New Bcl-2 Inhibitor Using PBPK Modeling and Interspecies Extrapolation Strategy. Drug Metab Dispos 2019; 47:648-656. [PMID: 30940629 DOI: 10.1124/dmd.118.085605] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 03/26/2019] [Indexed: 01/05/2023] Open
Abstract
S 55746 ((S)-N-(4-hydroxyphenyl)-3-(6-(3-(morpholinomethyl)-1,2,3,4-tetrahydroisoquinoline-2-carbonyl)benzo[d][1,3]dioxol-5-yl)-N-phenyl-5,6,7,8-tetrahydroindolizine-1-carboxamide) is a new selective Bcl-2 (B-cell lymphoma 2) inhibitor developed by Servier Laboratories and used to restore apoptosis functions in cancer patients. The aim of this work was to develop a translational approach using physiologically based (PB) pharmacokinetic (PK) modeling for interspecies extrapolation to anticipate the nonlinear PK behavior of this new compound in patients. A PBPK mouse model was first built using a hybrid approach, defining scaling factors (determined from in vitro data) to correct in vitro clearance parameters and predicted Kp (partition coefficient) values. The qualification of the hybrid model using these empirically determined scaling factors was satisfactorily completed with rat and dog data, allowing extrapolation of the PBPK model to humans. Human PBPK simulations were then compared with clinical trial data from a phase 1 trial in which the drug was given orally and daily to cancer patients. Human PBPK predictions were within the 95% prediction interval for the eight dose levels, taking into account both the nonlinear dose and time dependencies occurring in S 55746 kinetics. Thus, the proposed PK interspecies extrapolation strategy, based on preclinical and in vitro information and physiologic assumptions, could be a useful tool for predicting human plasma concentrations at the early stage of drug development.
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Affiliation(s)
- Philippe B Pierrillas
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Emilie Henin
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Kathryn Ball
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Julien Ogier
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Magali Amiel
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Laurence Kraus-Berthier
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Marylore Chenel
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - François Bouzom
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Michel Tod
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
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Hussaarts KGAM, Veerman GDM, Jansman FGA, van Gelder T, Mathijssen RHJ, van Leeuwen RWF. Clinically relevant drug interactions with multikinase inhibitors: a review. Ther Adv Med Oncol 2019; 11:1758835918818347. [PMID: 30643582 PMCID: PMC6322107 DOI: 10.1177/1758835918818347] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 10/17/2018] [Indexed: 12/11/2022] Open
Abstract
Multikinase inhibitors (MKIs), including the tyrosine kinase inhibitors (TKIs), have rapidly become an established factor in daily (hemato)-oncology practice. Although the oral route of administration offers improved flexibility and convenience for the patient, challenges arise in the use of MKIs. As MKIs are prescribed extensively, patients are at increased risk for (severe) drug–drug interactions (DDIs). As a result of these DDIs, plasma pharmacokinetics of MKIs may vary significantly, thereby leading to high interpatient variability and subsequent risk for increased toxicity or a diminished therapeutic outcome. Most clinically relevant DDIs with MKIs concern altered absorption and metabolism. The absorption of MKIs may be decreased by concomitant use of gastric acid-suppressive agents (e.g. proton pump inhibitors) as many kinase inhibitors show pH-dependent solubility. In addition, DDIs concerning drug (uptake and efflux) transporters may be of significant clinical relevance during MKI therapy. Furthermore, since many MKIs are substrates for cytochrome P450 isoenzymes (CYPs), induction or inhibition with strong CYP inhibitors or inducers may lead to significant alterations in MKI exposure. In conclusion, DDIs are of major concern during MKI therapy and need to be monitored closely in clinical practice. Based on the current knowledge and available literature, practical recommendations for management of these DDIs in clinical practice are presented in this review.
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Affiliation(s)
- Koen G A M Hussaarts
- Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - G D Marijn Veerman
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Frank G A Jansman
- Department of Clinical Pharmacy, Deventer Hospital, Deventer, The Netherlands
| | - Teun van Gelder
- Department of Hospital Pharmacy, Erasmus MC, Rotterdam, The Netherlands
| | - Ron H J Mathijssen
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
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Pierrillas PB, Henin E, Ogier J, Kraus-Berthier L, Chenel M, Bouzom F, Tod M. Tumor Growth Inhibition Modelling Based on Receptor Occupancy and Biomarker Activity of a New Bcl-2 Inhibitor in Mice. J Pharmacol Exp Ther 2018; 367:414-424. [DOI: 10.1124/jpet.118.251694] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Accepted: 09/10/2018] [Indexed: 12/16/2022] Open
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Volpe DA, Qosa H. Challenges with the precise prediction of ABC-transporter interactions for improved drug discovery. Expert Opin Drug Discov 2018; 13:697-707. [PMID: 29943645 DOI: 10.1080/17460441.2018.1493454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Given that membrane efflux transporters can influence a drug's pharmacokinetics, efficacy and safety, identifying potential substrates and inhibitors of these transporters is a critical element in the drug discovery and development process. Additionally, it is important to predict the inhibition potential of new drugs to avoid clinically significant drug interactions. The goal of preclinical studies is to characterize a new drug as a substrate or inhibitor of efflux transporters. Areas covered: This article reviews preclinical systems that are routinely utilized to determine whether a new drug is substrate or inhibitor of efflux transporters including in silico models, in vitro membrane and cell assays, and animal models. Also included is an examination of studies comparing in vitro inhibition data to clinical drug interaction outcomes. Expert opinion: While a number of models are employed to classify a drug as an efflux substrate or inhibitor, there are challenges in predicting clinical drug interactions. Improvements could be made in these predictions through a tier approach to classify new drugs, validation of preclinical assays, and refinement of threshold criteria for clinical interaction studies.
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Affiliation(s)
- Donna A Volpe
- a Office of Clinical Pharmacology, Center for Drug Evaluation and Research , Food and Drug Administration , Silver Spring , MD , USA
| | - Hisham Qosa
- a Office of Clinical Pharmacology, Center for Drug Evaluation and Research , Food and Drug Administration , Silver Spring , MD , USA
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Cabrera-Pérez MÁ, Pham-The H. Computational modeling of human oral bioavailability: what will be next? Expert Opin Drug Discov 2018; 13:509-521. [PMID: 29663836 DOI: 10.1080/17460441.2018.1463988] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
INTRODUCTION The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.
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Affiliation(s)
- Miguel Ángel Cabrera-Pérez
- a Unit of Modeling and Experimental Biopharmaceutics , Chemical Bioactive Center, Central University of Las Villas , Santa Clara , Cuba.,b Department of Pharmacy and Pharmaceutical Technology , University of Valencia , Burjassot , Spain.,c Department of Engineering, Area of Pharmacy and Pharmaceutical Technology , Miguel Hernández University , Alicante , Spain
| | - Hai Pham-The
- d Department of Pharmaceutical Chemistry , Hanoi University of Pharmacy , Hanoi , Vietnam
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Donovan MD, Abduljalil K, Cryan JF, Boylan GB, Griffin BT. Application of a physiologically-based pharmacokinetic model for the prediction of bumetanide plasma and brain concentrations in the neonate. Biopharm Drug Dispos 2018; 39:125-134. [DOI: 10.1002/bdd.2119] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 12/06/2017] [Accepted: 12/19/2017] [Indexed: 12/30/2022]
Affiliation(s)
- Maria D. Donovan
- Pharmacodelivery Group, School of Pharmacy; University College Cork; Cork Ireland
- Department of Anatomy and Neuroscience; University College Cork; Cork Ireland
| | | | - John F. Cryan
- Department of Anatomy and Neuroscience; University College Cork; Cork Ireland
- Alimentary Pharmabiotic Centre; University College Cork; Cork Ireland
| | - Geraldine B. Boylan
- Department of Paediatrics and Child Health; University College Cork; Cork Ireland
- Irish Centre for Fetal and Neonatal Translational Research; University College Cork and Cork University Maternity Hospital; Cork Ireland
| | - Brendan T. Griffin
- Pharmacodelivery Group, School of Pharmacy; University College Cork; Cork Ireland
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Calvier EAM, Krekels EHJ, Yu H, Välitalo PAJ, Johnson TN, Rostami-Hodjegan A, Tibboel D, van der Graaf PH, Danhof M, Knibbe CAJ. Drugs Being Eliminated via the Same Pathway Will Not Always Require Similar Pediatric Dose Adjustments. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:175-185. [PMID: 29399979 PMCID: PMC5869561 DOI: 10.1002/psp4.12273] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 12/12/2017] [Accepted: 12/14/2017] [Indexed: 12/25/2022]
Abstract
For scaling drug plasma clearance (CLp) from adults to children, extrapolations of population pharmacokinetic (PopPK) covariate models between drugs sharing an elimination pathway have enabled accelerated development of pediatric models and dosing recommendations. This study aims at identifying conditions for which this approach consistently leads to accurate pathway specific CLp scaling from adults to children for drugs undergoing hepatic metabolism. A physiologically based pharmacokinetic (PBPK) simulation workflow utilizing mechanistic equations defining hepatic metabolism was developed. We found that drugs eliminated via the same pathway require similar pediatric dose adjustments only in specific cases, depending on drugs extraction ratio, unbound fraction, type of binding plasma protein, and the fraction metabolized by the isoenzyme pathway for which CLp is scaled. Overall, between‐drug extrapolation of pediatric covariate functions for CLp is mostly applicable to low and intermediate extraction ratio drugs eliminated by one isoenzyme and binding to human serum albumin in children older than 1 month.
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Affiliation(s)
- Elisa A M Calvier
- Division Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Elke H J Krekels
- Division Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Huixin Yu
- Division Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Pyry A J Välitalo
- Division Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | | | - Amin Rostami-Hodjegan
- Simcyp Limited, Sheffield, United Kingdom.,Manchester Pharmacy School, University of Manchester, Manchester, United Kingdom
| | - Dick Tibboel
- Intensive Care and Department of Pediatric Surgery, Erasmus University Medical Center - Sophia Children's Hospital, Rotterdam, The Netherlands
| | | | - Meindert Danhof
- Division Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Catherijne A J Knibbe
- Division Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.,Department of Clinical Pharmacy, St. Antonius Hospital, Nieuwegein, The Netherlands
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de Lange ECM, van den Brink W, Yamamoto Y, de Witte WEA, Wong YC. Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics. Expert Opin Drug Discov 2017; 12:1207-1218. [PMID: 28933618 DOI: 10.1080/17460441.2017.1380623] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.
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Affiliation(s)
- Elizabeth C M de Lange
- a Leiden Academic Center of Drug Research, Translational Pharmacology , Leiden University , Leiden , The Netherlands
| | - Willem van den Brink
- a Leiden Academic Center of Drug Research, Translational Pharmacology , Leiden University , Leiden , The Netherlands
| | - Yumi Yamamoto
- a Leiden Academic Center of Drug Research, Translational Pharmacology , Leiden University , Leiden , The Netherlands
| | - Wilhelmus E A de Witte
- a Leiden Academic Center of Drug Research, Translational Pharmacology , Leiden University , Leiden , The Netherlands
| | - Yin Cheong Wong
- a Leiden Academic Center of Drug Research, Translational Pharmacology , Leiden University , Leiden , The Netherlands
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Jaroch K, Jaroch A, Bojko B. Cell cultures in drug discovery and development: The need of reliable in vitro-in vivo extrapolation for pharmacodynamics and pharmacokinetics assessment. J Pharm Biomed Anal 2017; 147:297-312. [PMID: 28811111 DOI: 10.1016/j.jpba.2017.07.023] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Revised: 07/16/2017] [Accepted: 07/19/2017] [Indexed: 12/21/2022]
Abstract
For ethical and cost-related reasons, use of animals for the assessment of mode of action, metabolism and/or toxicity of new drug candidates has been increasingly scrutinized in research and industrial applications. Implementation of the 3 "Rs"1; rule (Reduction, Replacement, Refinement) through development of in silico or in vitro assays has become an essential element of risk assessment. Physiologically based pharmacokinetic (PBPK2) modeling is the most potent in silico tool used for extrapolation of pharmacokinetic parameters to animal or human models from results obtained in vitro. Although, many types of in vitro assays are conducted during drug development, use of cell cultures is the most reliable one. Two-dimensional (2D) cell cultures have been a part of drug development for many years. Nowadays, their role is decreasing in favor of three-dimensional (3D) cell cultures and co-cultures. 3D cultures exhibit protein expression patterns and intercellular junctions that are closer to in vivo states in comparison to classical monolayer cultures. Co-cultures allow for examinations of the mutual influence of different cell lines. However, the complexity and high costs of co-cultures and 3D equipment exclude such methods from high-throughput screening (HTS).3In vitro absorption, distribution, metabolism, and excretion assessment, as well as drug-drug interaction (DDI), are usually performed with the use of various cell culture based assays. Progress in in silico and in vitro methods can lead to better in vitro-in vivo extrapolation (IVIVE4) outcomes and have a potential to contribute towards a significant reduction in the number of laboratory animals needed for drug research. As such, concentrated efforts need to be spent towards the development of an HTS in vitro platform with satisfactory IVIVE features.
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Affiliation(s)
- Karol Jaroch
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Jurasza 2 Street, 85-089 Bydgoszcz, Poland
| | - Alina Jaroch
- Department and Institute of Nutrition and Dietetics, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Dębowa 3 Street, 85-626 Bydgoszcz, Poland; Department and Clinic of Geriatrics, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Curie Sklodowskiej 9 Street, 85-094 Bydgoszcz, Poland
| | - Barbara Bojko
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Jurasza 2 Street, 85-089 Bydgoszcz, Poland.
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Jeong YS, Yim CS, Ryu HM, Noh CK, Song YK, Chung SJ. Estimation of the minimum permeability coefficient in rats for perfusion-limited tissue distribution in whole-body physiologically-based pharmacokinetics. Eur J Pharm Biopharm 2017; 115:1-17. [DOI: 10.1016/j.ejpb.2017.01.026] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 01/25/2017] [Accepted: 01/28/2017] [Indexed: 01/12/2023]
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Clinical Exposure Boost Predictions by Integrating Cytochrome P450 3A4-Humanized Mouse Studies With PBPK Modeling. J Pharm Sci 2016; 105:1398-404. [PMID: 27019957 DOI: 10.1016/j.xphs.2016.01.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Revised: 01/19/2016] [Accepted: 01/20/2016] [Indexed: 01/01/2023]
Abstract
NVS123 is a poorly water-soluble protease 56 inhibitor in clinical development. Data from in vitro hepatocyte studies suggested that NVS123 is mainly metabolized by CYP3A4. As a consequence of limited solubility, NVS123 therapeutic plasma exposures could not be achieved even with high doses and optimized formulations. One approach to overcome NVS123 developability issues was to increase plasma exposure by coadministrating it with an inhibitor of CYP3A4 such as ritonavir. A clinical boost effect was predicted by using physiologically based pharmacokinetic (PBPK) modeling. However, initial boost predictions lacked sufficient confidence because a key parameter, fraction of drug metabolized by CYP3A4 (fmCYP3A4), could not be estimated with accuracy on account of disconnects between in vitro and in vivo preclinical data. To accurately estimate fmCYP3A4 in human, an in vivo boost effect study was conducted using CYP3A4-humanized mouse model which showed a 33- to 56-fold exposure boost effect. Using a top-down approach, human fmCYP3A4 for NVS123 was estimated to be very high and included in the human PBPK modeling to support subsequent clinical study design. The combined use of the in vivo boost study in CYP3A4-humanized mouse model mice along with PBPK modeling accurately predicted the clinical outcome and identified a significant NVS123 exposure boost (∼42-fold increase) with ritonavir.
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Lamberti G, Cascone S, Marra F, Titomanlio G, d’Amore M, Barba AA. Gastrointestinal behavior and ADME phenomena: II. In silico simulation. J Drug Deliv Sci Technol 2016. [DOI: 10.1016/j.jddst.2016.06.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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Knight-Schrijver V, Chelliah V, Cucurull-Sanchez L, Le Novère N. The promises of quantitative systems pharmacology modelling for drug development. Comput Struct Biotechnol J 2016; 14:363-370. [PMID: 27761201 PMCID: PMC5064996 DOI: 10.1016/j.csbj.2016.09.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 09/08/2016] [Accepted: 09/19/2016] [Indexed: 01/01/2023] Open
Abstract
Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.
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Affiliation(s)
| | - V. Chelliah
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - N. Le Novère
- Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
- Corresponding author.
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Willemin ME, Lumen A. Development of a PBPK model of thiocyanate in rats with an extrapolation to humans: A computational study to quantify the mechanism of action of thiocyanate kinetics in thyroid. Toxicol Appl Pharmacol 2016; 307:19-34. [DOI: 10.1016/j.taap.2016.07.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Revised: 07/07/2016] [Accepted: 07/15/2016] [Indexed: 12/13/2022]
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Drug Disposition Classification Systems in Discovery and Development: A Comparative Review of the BDDCS, ECCS and ECCCS Concepts. Pharm Res 2016; 33:2583-93. [DOI: 10.1007/s11095-016-2001-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 07/12/2016] [Indexed: 10/21/2022]
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