1
|
Wu YE, Zheng YY, Li QY, Yao BF, Cao J, Liu HX, Hao GX, van den Anker J, Zheng Y, Zhao W. Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future. Adv Drug Deliv Rev 2024; 211:115364. [PMID: 38936664 DOI: 10.1016/j.addr.2024.115364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 06/09/2024] [Accepted: 06/19/2024] [Indexed: 06/29/2024]
Abstract
The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.
Collapse
Affiliation(s)
- Yue-E Wu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuan-Yuan Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qiu-Yue Li
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bu-Fan Yao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jing Cao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hui-Xin Liu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Medical Center, Washington, DC, USA; Departments of Pediatrics, Pharmacology & Physiology, George Washington University, School of Medicine and Health Sciences, Washington, DC, USA; Department of Paediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel, Basel, Switzerland
| | - Yi Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wei Zhao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
| |
Collapse
|
2
|
Lai Y, Koelmel JP, Walker DI, Price EJ, Papazian S, Manz KE, Castilla-Fernández D, Bowden JA, Nikiforov V, David A, Bessonneau V, Amer B, Seethapathy S, Hu X, Lin EZ, Jbebli A, McNeil BR, Barupal D, Cerasa M, Xie H, Kalia V, Nandakumar R, Singh R, Tian Z, Gao P, Zhao Y, Froment J, Rostkowski P, Dubey S, Coufalíková K, Seličová H, Hecht H, Liu S, Udhani HH, Restituito S, Tchou-Wong KM, Lu K, Martin JW, Warth B, Godri Pollitt KJ, Klánová J, Fiehn O, Metz TO, Pennell KD, Jones DP, Miller GW. High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 38984754 DOI: 10.1021/acs.est.4c01156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.
Collapse
Affiliation(s)
- Yunjia Lai
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Jeremy P Koelmel
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut 06520, United States
| | - Douglas I Walker
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Elliott J Price
- RECETOX, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Stefano Papazian
- Department of Environmental Science, Science for Life Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden
- National Facility for Exposomics, Metabolomics Platform, Science for Life Laboratory, Stockholm University, Solna 171 65, Sweden
| | - Katherine E Manz
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Delia Castilla-Fernández
- Department of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, 1010 Vienna, Austria
| | - John A Bowden
- Center for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine, University of Florida, Gainesville, Florida 32611, United States
| | | | - Arthur David
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S, 1085 Rennes, France
| | - Vincent Bessonneau
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S, 1085 Rennes, France
| | - Bashar Amer
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | | | - Xin Hu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Elizabeth Z Lin
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut 06520, United States
| | - Akrem Jbebli
- RECETOX, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Brooklynn R McNeil
- Biomarkers Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Dinesh Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Marina Cerasa
- Institute of Atmospheric Pollution Research, Italian National Research Council, 00015 Monterotondo, Rome, Italy
| | - Hongyu Xie
- Department of Environmental Science, Science for Life Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden
| | - Vrinda Kalia
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Renu Nandakumar
- Biomarkers Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Randolph Singh
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Zhenyu Tian
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Peng Gao
- Department of Environmental and Occupational Health, and Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania 15232, United States
| | - Yujia Zhao
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584CM, The Netherlands
| | | | | | - Saurabh Dubey
- Biomarkers Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Kateřina Coufalíková
- RECETOX, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Hana Seličová
- RECETOX, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Helge Hecht
- RECETOX, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Sheng Liu
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut 06520, United States
| | - Hanisha H Udhani
- Biomarkers Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Sophie Restituito
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Kam-Meng Tchou-Wong
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Kun Lu
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Jonathan W Martin
- Department of Environmental Science, Science for Life Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden
- National Facility for Exposomics, Metabolomics Platform, Science for Life Laboratory, Stockholm University, Solna 171 65, Sweden
| | - Benedikt Warth
- Department of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, 1010 Vienna, Austria
| | - Krystal J Godri Pollitt
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut 06520, United States
| | - Jana Klánová
- RECETOX, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California-Davis, Davis, California 95616, United States
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Kurt D Pennell
- School of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - Dean P Jones
- Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia 30322, United States
| | - Gary W Miller
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| |
Collapse
|
3
|
Amorim AM, Piochi LF, Gaspar AT, Preto A, Rosário-Ferreira N, Moreira IS. Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction. Chem Res Toxicol 2024; 37:827-849. [PMID: 38758610 PMCID: PMC11187637 DOI: 10.1021/acs.chemrestox.3c00352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/19/2024]
Abstract
The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.
Collapse
Affiliation(s)
- Ana M.
B. Amorim
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD
Programme in Biosciences, Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PURR.AI,
Rua Pedro Nunes, IPN Incubadora, Ed C, 3030-199 Coimbra, Portugal
| | - Luiz F. Piochi
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Ana T. Gaspar
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - António
J. Preto
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD Programme
in Experimental Biology and Biomedicine, Institute for Interdisciplinary
Research (IIIUC), University of Coimbra, Casa Costa Alemão, 3030-789 Coimbra, Portugal
| | - Nícia Rosário-Ferreira
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Irina S. Moreira
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| |
Collapse
|
4
|
Huang H, Zhao W, Qin N, Duan X. Recent Progress on Physiologically Based Pharmacokinetic (PBPK) Model: A Review Based on Bibliometrics. TOXICS 2024; 12:433. [PMID: 38922113 PMCID: PMC11209072 DOI: 10.3390/toxics12060433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/04/2024] [Accepted: 06/13/2024] [Indexed: 06/27/2024]
Abstract
Physiologically based pharmacokinetic/toxicokinetic (PBPK/PBTK) models are designed to elucidate the mechanism of chemical compound action in organisms based on the physiological, biochemical, anatomical, and thermodynamic properties of organisms. After nearly a century of research and practice, good results have been achieved in the fields of medicine, environmental science, and ecology. However, there is currently a lack of a more systematic review of progress in the main research directions of PBPK models, especially a more comprehensive understanding of the application in aquatic environmental research. In this review, a total of 3974 articles related to PBPK models from 1996 to 24 March 2024 were collected. Then, the main research areas of the PBPK model were categorized based on the keyword co-occurrence maps and cluster maps obtained by CiteSpace. The results showed that research related to medicine is the main application area of PBPK. Four major research directions included in the medical field were "drug assessment", "cross-species prediction", "drug-drug interactions", and "pediatrics and pregnancy drug development", in which "drug assessment" accounted for 55% of the total publication volume. In addition, bibliometric analyses indicated a rapid growth trend in the application in the field of environmental research, especially in predicting the residual levels in organisms and revealing the relationship between internal and external exposure. Despite facing the limitation of insufficient species-specific parameters, the PBPK model is still an effective tool for improving the understanding of chemical-biological effectiveness and will provide a theoretical basis for accurately assessing potential risks to ecosystems and human health. The combination with the quantitative structure-activity relationship model, Bayesian method, and machine learning technology are potential solutions to the previous research gaps.
Collapse
Affiliation(s)
| | | | - Ning Qin
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China; (H.H.); (W.Z.)
| | - Xiaoli Duan
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China; (H.H.); (W.Z.)
| |
Collapse
|
5
|
Qayyum A, Zamir A, Rasool MF, Imran I, Ahmad T, Alqahtani F. Investigating clinical pharmacokinetics of brivaracetam by using a pharmacokinetic modeling approach. Sci Rep 2024; 14:13357. [PMID: 38858493 PMCID: PMC11164859 DOI: 10.1038/s41598-024-63903-1] [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: 11/18/2023] [Accepted: 06/03/2024] [Indexed: 06/12/2024] Open
Abstract
The development of technology and the processing speed of computing machines have facilitated the evaluation of advanced pharmacokinetic (PK) models, making modeling processes simple and faster. The present model aims to analyze the PK of brivaracetam (BRV) in healthy and diseased populations. A comprehensive literature review was conducted to incorporate the BRV plasma concentration data and its input parameters into PK-Sim software, leading to the creation of intravenous (IV) and oral models for both populations. The developed physiologically based pharmacokinetic (PBPK) model of BRV was then assessed using the visual predictive checks, mean observed/predicted ratios (Robs/pre), and average fold error for PK parameters including the maximum systemic concentration (Cmax), the area under the curve at time 0 to t (AUC0-∞), and drug clearance (CL). The PBPK model of BRV demonstrated that mean Robs/pre ratios of the PK parameters remained within the acceptable limits when assessed against a twofold error margin. Furthermore, model predictions were carried out to assess how AUC0-∞ is affected following the administration of BRV in individuals with varying degrees of liver cirrhosis, ranging from different child-pugh (CP) scores like A, B, and C. Moreover, dose adjustments were recommended by considering the variations in Cmax and CL in various kidney disease stages (mild to severe).
Collapse
Affiliation(s)
- Attia Qayyum
- Department of Pharmacology, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, 60800, Pakistan
| | - Ammara Zamir
- Department of Pharmacy Practice, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, 60800, Pakistan
| | - Muhammad Fawad Rasool
- Department of Pharmacy Practice, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, 60800, Pakistan.
| | - Imran Imran
- Department of Pharmacology, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, 60800, Pakistan
| | - Tanveer Ahmad
- Instiitute for Advanced Biosciences (IAB), CNRS UMR5309, INSERM U1209, Grenoble Alpes University, 38700, La Tronche, France
| | - Faleh Alqahtani
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, 11451, Riyadh, Saudi Arabia.
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Bassani D, Parrott NJ, Manevski N, Zhang JD. Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules. Expert Opin Drug Discov 2024; 19:683-698. [PMID: 38727016 DOI: 10.1080/17460441.2024.2348157] [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: 10/23/2023] [Accepted: 04/23/2024] [Indexed: 05/22/2024]
Abstract
INTRODUCTION Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary. AREAS COVERED This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review. EXPERT OPINION ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.
Collapse
Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Neil John Parrott
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Nenad Manevski
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Jitao David Zhang
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| |
Collapse
|
8
|
Goto A, Moriya Y, Nakayama M, Iwasaki S, Yamamoto S. DMPK perspective on quantitative model analysis for chimeric antigen receptor cell therapy: Advances and challenges. Drug Metab Pharmacokinet 2024; 56:101003. [PMID: 38843652 DOI: 10.1016/j.dmpk.2024.101003] [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: 11/01/2023] [Revised: 01/26/2024] [Accepted: 02/10/2024] [Indexed: 06/24/2024]
Abstract
Chimeric antigen receptor (CAR) cells are genetically engineered immune cells that specifically target tumor-associated antigens and have revolutionized cancer treatment, particularly in hematological malignancies, with ongoing investigations into their potential applications in solid tumors. This review provides a comprehensive overview of the current status and challenges in drug metabolism and pharmacokinetics (DMPK) for CAR cell therapy, specifically emphasizing on quantitative modeling and simulation (M&S). Furthermore, the recent advances in quantitative model analysis have been reviewed, ranging from clinical data characterization to mechanism-based modeling that connects in vitro and in vivo nonclinical and clinical study data. Additionally, the future perspectives and areas for improvement in CAR cell therapy translation have been reviewed. This includes using formulation quality considerations, characterization of appropriate animal models, refinement of in vitro models for bottom-up approaches, and enhancement of quantitative bioanalytical methodology. Addressing these challenges within a DMPK framework is pivotal in facilitating the translation of CAR cell therapy, ultimately enhancing the patients' lives through efficient CAR cell therapies.
Collapse
Affiliation(s)
- Akihiko Goto
- Center of Excellence for Drug Metabolism, Pharmacokinetics and Modeling, Preclinical and Translational Sciences, Research, Takeda Pharmaceutical Company Limited, Kanagawa, Japan
| | - Yuu Moriya
- Center of Excellence for Drug Metabolism, Pharmacokinetics and Modeling, Preclinical and Translational Sciences, Research, Takeda Pharmaceutical Company Limited, Kanagawa, Japan
| | - Miyu Nakayama
- Center of Excellence for Drug Metabolism, Pharmacokinetics and Modeling, Preclinical and Translational Sciences, Research, Takeda Pharmaceutical Company Limited, Kanagawa, Japan
| | - Shinji Iwasaki
- Center of Excellence for Drug Metabolism, Pharmacokinetics and Modeling, Preclinical and Translational Sciences, Research, Takeda Pharmaceutical Company Limited, Kanagawa, Japan
| | - Syunsuke Yamamoto
- Center of Excellence for Drug Metabolism, Pharmacokinetics and Modeling, Preclinical and Translational Sciences, Research, Takeda Pharmaceutical Company Limited, Kanagawa, Japan.
| |
Collapse
|
9
|
Xue L, Singla RK, He S, Arrasate S, González-Díaz H, Miao L, Shen B. Warfarin-A natural anticoagulant: A review of research trends for precision medication. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 128:155479. [PMID: 38493714 DOI: 10.1016/j.phymed.2024.155479] [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: 07/27/2023] [Revised: 01/29/2024] [Accepted: 02/22/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability. PURPOSE The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage. METHODS A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature. RESULTS Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database. CONCLUSION Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.
Collapse
Affiliation(s)
- Ling Xue
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China; Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China; Department of Pharmacology, Faculty of Medicine, University of The Basque Country (UPV/EHU), Bilbao, Basque Country, Spain
| | - Rajeev K Singla
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China; School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab-144411, India
| | - Shan He
- IKERDATA S.l., ZITEK, University of The Basque Country (UPVEHU), Rectorate Building, 48940, Bilbao, Basque Country, Spain; Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Basque Country, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Basque Country, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Basque Country, Spain; BIOFISIKA: Basque Center for Biophysics CSIC, University of The Basque Country (UPV/EHU), Barrio Sarriena s/n, Leioa, Bizkaia 48940, Basque Country, Spain; IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Basque Country, Spain
| | - Liyan Miao
- Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China; Institute for Interdisciplinary Drug Research and Translational Sciences, Soochow University, Suzhou, China; College of Pharmaceutical Sciences, Soochow University, Suzhou, China.
| | - Bairong Shen
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
| |
Collapse
|
10
|
Toshimoto K. Beyond the basics: A deep dive into parameter estimation for advanced PBPK and QSP models. Drug Metab Pharmacokinet 2024; 56:101011. [PMID: 38833901 DOI: 10.1016/j.dmpk.2024.101011] [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: 11/06/2023] [Revised: 02/26/2024] [Accepted: 03/14/2024] [Indexed: 06/06/2024]
Abstract
Physiologically-based pharmacokinetic (PBPK) models and quantitative systems pharmacology (QSP) models have contributed to drug development strategies. The parameters of these models are commonly estimated by capturing observed values using the nonlinear least-squares method. Software packages for PBPK and QSP modeling provide a range of parameter estimation algorithms. To choose the most appropriate method, modelers need to understand the basic concept of each approach. This review provides a general introduction to the key points of parameter estimation with a focus on the PBPK and QSP models, and the respective parameter estimation algorithms. The latter part assesses the performance of five parameter estimation algorithms - the quasi-Newton method, Nelder-Mead method, genetic algorithm, particle swarm optimization, and Cluster Gauss-Newton method - using three examples of PBPK and QSP modeling. The assessment revealed that some parameter estimation results were significantly influenced by the initial values. Moreover, the choice of algorithms demonstrating good estimation results heavily depends on factors such as model structure and the parameters to be estimated. To obtain credible parameter estimation results, it is advisable to conduct multiple rounds of parameter estimation under different conditions, employing various estimation algorithms.
Collapse
Affiliation(s)
- Kota Toshimoto
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Ibaraki, Japan.
| |
Collapse
|
11
|
Li X, Lian T, Su B, Liu H, Wang Y, Wu X, He J, Wang Y, Xu Y, Yang S, Li Y. Construction of a physiologically based pharmacokinetic model of paclobutrazol and exposure estimation in the human body. Toxicology 2024; 505:153841. [PMID: 38796053 DOI: 10.1016/j.tox.2024.153841] [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: 04/01/2024] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 05/28/2024]
Abstract
Paclobutrazol (PBZ) is a plant growth regulator that can delay plant growth and improve plant resistance and yield. Although it has been widely used in the growth of medicinal plants, human beings may take it by taking traditional Chinese medicine. There are no published studies on PBZ exposure in humans or standardized limits for PBZ in medicinal plants. We measured the solubility, oil-water partition coefficient (logP), and pharmacokinetics of PBZ in rats and established a physiologically based pharmacokinetic (PBPK) model of PBZ in rats. This was followed by extrapolation to healthy Chinese adult males as a theoretical foundation for future risk assessment of PBZ. The results showed that PBZ had low solubility and high fat solubility. Pharmacokinetic experiments showed that PBZ was absorbed rapidly but eliminated slowly in rats. On this basis, the rat PBPK model was successfully constructed and extrapolated to healthy Chinese adult males to predict the plasma concentration-time curve and exposure of PBZ in humans. The construction of the PBPK model of PBZ in this study facilitates the determination of the standard formulation limits and risk assessment of PBZ residues in medicinal plants.
Collapse
Affiliation(s)
- Xiaomeng Li
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Jinghai District, Tuanbo New City, Tianjin 301617, PR China
| | - Tingting Lian
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Jinghai District, Tuanbo New City, Tianjin 301617, PR China
| | - Buda Su
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Jinghai District, Tuanbo New City, Tianjin 301617, PR China
| | - Hui Liu
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Jinghai District, Tuanbo New City, Tianjin 301617, PR China
| | - Yuming Wang
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Jinghai District, Tuanbo New City, Tianjin 301617, PR China
| | - Xiaoyan Wu
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Jinghai District, Tuanbo New City, Tianjin 301617, PR China
| | - Junjie He
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Jinghai District, Tuanbo New City, Tianjin 301617, PR China
| | - Yue Wang
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Jinghai District, Tuanbo New City, Tianjin 301617, PR China
| | - Yanyan Xu
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Jinghai District, Tuanbo New City, Tianjin 301617, PR China.
| | - Shenshen Yang
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Jinghai District, Tuanbo New City, Tianjin 301617, PR China.
| | - Yubo Li
- Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, West Zone, Jinghai District, Tuanbo New City, Tianjin 301617, PR China.
| |
Collapse
|
12
|
Thakur K, Telaprolu KC, Paterson D, Salem F, Arora S, Polak S. Development and verification of mechanistic vaginal absorption and metabolism model to predict systemic exposure after vaginal ring and gel application. Br J Clin Pharmacol 2024; 90:1428-1449. [PMID: 38450818 DOI: 10.1111/bcp.16029] [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: 11/01/2023] [Revised: 01/18/2024] [Accepted: 01/22/2024] [Indexed: 03/08/2024] Open
Abstract
AIMS The current work describes the development of mechanistic vaginal absorption and metabolism model within Simcyp Simulator to predict systemic concentrations following vaginal application of ring and gel formulations. METHODS Vaginal and cervix physiology parameters were incorporated in the model development. The study highlights the model assumptions including simulation results comparing systemic concentrations of 5 different compounds, namely, dapivirine, tenofovir, lidocaine, ethinylestradiol and etonogestrel, administered as vaginal ring or gel. Due to lack of data, the vaginal absorption parameters were calculated based on assumptions or optimized. The model uses release rate/in vitro release profiles with formulation characteristics to predict drug mass transfer across vaginal tissue into the systemic circulation. RESULTS For lidocaine and tenofovir vaginal gel, the predicted to observed AUC0-t and Cmax ratios were well within 2-fold error limits. The average fold error (AFE) and absolute AFE indicating bias and precision of predictions range from 0.62 to 1.61. For dapivirine, the pharmacokinetic parameters are under and overpredicted in some studies due to lack of formulation composition details and relevance of release rate used in ring model. The predicted to observed AUC0-t and Cmax ratios were well within 2-fold error limits for etonogestrel and ethinylestradiol vaginal ring (AFEs and absolute AFEs from 0.84 to 1.83). CONCLUSION The current study provides first of its kind physiologically based pharmacokinetic framework integrating physiology, population and formulation data to carry out in silico mechanistic vaginal absorption studies, with the potential for virtual bioequivalence assessment in the future.
Collapse
Affiliation(s)
| | | | | | - Farzaneh Salem
- Simcyp Division, Certara UK Limited, Sheffield, UK
- Drug Metabolism and Pharmacokinetics, GlaxoSmithKline, R&D, Stevenage, UK
| | - Sumit Arora
- Simcyp Division, Certara UK Limited, Sheffield, UK
- Janssen Pharmaceutical, Companies of Johnson & Johnson, Beerse, Belgium
| | - Sebastian Polak
- Simcyp Division, Certara UK Limited, Sheffield, UK
- Chair of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland
| |
Collapse
|
13
|
Rahim N, Naqvi SBS. In Vitro In Vivo Extrapolation and Bioequivalence Prediction for Immediate-Release Capsules of Cefadroxil Based on a Physiologically-Based Pharmacokinetic ACAT Model. AAPS PharmSciTech 2024; 25:100. [PMID: 38714602 DOI: 10.1208/s12249-024-02811-z] [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: 02/03/2024] [Accepted: 04/16/2024] [Indexed: 05/10/2024] Open
Abstract
Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic concept, which helps to judge the effects of biopharmceutical properties of drug product such as in vitro dissolution on its pharmacokinetic and in vivo performance. With the application of virtual bioequivalence (VBE) study, the drug product development using model-based approach can help in evaluating the possibility of extending BCS-based biowaiver. Therefore, the current study was intended to develop PBPK model as well as in vitro in vivo extrapolation (IVIVE) for BCS class III drug i.e. cefadroxil. A PBPK model was created in GastroPlus™ 9.8.3 utilizing clinical data of immediate-release cefadroxil formulations. By the examination of simulated and observed plasma drug concentration profiles, the predictability of the proposed model was assessed for the prediction errors. Furthermore, mechanistic deconvolution was used to create IVIVE, and the plasma drug concentration profiles and pharmacokinetic parameters were predicted for different virtual formulations with variable cefadroxil in vitro release. Virtual bioequivalence study was also executed to assess the bioequivalence of the generic verses the reference drug product (Duricef®). The developed PBPK model satisfactorily predicted Cmax and AUC0-t after cefadroxil single and multiple oral dose administrations, with all individual prediction errors within the limits except in a few cases. Second order polynomial correlation function obtained accurately predict in vivo drug release and plasma concentration profile of cefadroxil test and reference (Duricef®) formulation. The VBE study also proved test formulation bioequivalent to reference formulation and the statistical analysis on pharmacokinetic parameters reported 90% confidence interval for Cmax and AUC0-t in the FDA acceptable limits. The analysis found that a validated and verified PBPK model with a mechanistic background is as a suitable approach to accelerate generic drug development.
Collapse
Affiliation(s)
- Najia Rahim
- Department of Pharmacy Practice, Dow College of Pharmacy, Dow University of Health Sciences, Karachi, Pakistan.
| | | |
Collapse
|
14
|
Yang Y, Zhang X, Wang Y, Xi H, Xu M, Zheng L. Physiologically based pharmacokinetic modeling to predict the pharmacokinetics of codeine in different CYP2D6 phenotypes. Front Pharmacol 2024; 15:1342515. [PMID: 38756374 PMCID: PMC11096448 DOI: 10.3389/fphar.2024.1342515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 04/10/2024] [Indexed: 05/18/2024] Open
Abstract
Objectives Codeine, a prodrug used as an opioid agonist, is metabolized to the active product morphine by CYP2D6. This study aimed to establish physiologically based pharmacokinetic (PBPK) models of codeine and morphine and explore the influence of CYP2D6 genetic polymorphisms on the pharmacokinetics of codeine and morphine. Methods An initial PBPK modeling of codeine in healthy adults was established using PK-Sim® software and subsequently extrapolated to CYP2D6 phenotype-related PBPK modeling based on the turnover frequency (Kcat) of CYP2D6 for different phenotype populations (UM, EM, IM, and PM). The mean fold error (MFE) and geometric mean fold error (GMFE) methods were used to compare the differences between the predicted and observed values of the pharmacokinetic parameters to evaluate the accuracy of PBPK modeling. The validated models were then used to support dose safety for different CYP2D6 phenotypes. Results The developed and validated CYP2D6 phenotype-related PBPK model successfully predicted codeine and morphine dispositions in different CYP2D6 phenotypes. Compared with EMs, the predicted AUC0-∞ value of morphine was 98.6% lower in PMs, 60.84% lower in IMs, and 73.43% higher in UMs. Morphine plasma exposure in IMs administered 80 mg of codeine was roughly comparable to that in EMs administered 30 mg of codeine. CYP2D6 UMs may start dose titration to achieve an optimal individual regimen and avoid a single dose of over 20 mg. Codeine should not be used in PMs for pain relief, considering its insufficient efficacy. Conclusion PBPK modeling can be applied to explore the dosing safety of codeine and can be helpful in predicting the effect of CYP2D6 genetic polymorphisms on drug-drug interactions (DDIs) with codeine in the future.
Collapse
Affiliation(s)
- Yujie Yang
- Department of Pharmacy, The Third People’s Hospital of Chengdu, College of Medicine, Southwest Jiaotong University, Chengdu, China
| | - Xiqian Zhang
- Department of Pharmacy, The Third People’s Hospital of Chengdu, College of Medicine, Southwest Jiaotong University, Chengdu, China
| | - Yirong Wang
- Department of Pharmacy, The Third People’s Hospital of Chengdu, College of Medicine, Southwest Jiaotong University, Chengdu, China
| | - Heng Xi
- Department of Pharmacy, The Third People’s Hospital of Chengdu, College of Medicine, Southwest Jiaotong University, Chengdu, China
| | - Min Xu
- Department of Pharmacy, The Third People’s Hospital of Chengdu, College of Medicine, Southwest Jiaotong University, Chengdu, China
| | - Liang Zheng
- Department of Clinical Pharmacology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| |
Collapse
|
15
|
Chen G, Sun K, Michon I, Barter Z, Neuhoff S, Ghosh L, Ilic K, Song IH. Physiologically Based Pharmacokinetic Modeling for Maribavir to Inform Dosing in Drug-Drug Interaction Scenarios with CYP3A4 Inducers and Inhibitors. J Clin Pharmacol 2024; 64:590-600. [PMID: 38009271 DOI: 10.1002/jcph.2385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/14/2023] [Indexed: 11/28/2023]
Abstract
Maribavir, an orally available antiviral agent, has been approved in multiple countries for the treatment of patients with refractory post-transplant cytomegalovirus (CMV) infection and/or disease. Maribavir is primarily metabolized by CYP3A4; coadministration with CYP3A4 inducers and inhibitors may significantly alter maribavir exposure, thereby affecting its efficacy and safety. The effect of CYP3A4 inducers and inhibitors on maribavir exposure was evaluated based on a drug-drug interaction (DDI) study and physiologically-based pharmacokinetic (PBPK) modeling. The effect of rifampin (a strong inducer of CYP3A4 and moderate inducer of CYP1A2), administered at a 600 mg dose once daily, on maribavir pharmacokinetics was assessed in a clinical phase 1 DDI study in healthy participants. A full PBPK model for maribavir was developed and verified using in vitro and clinical pharmacokinetic data from phase 1 studies. The verified PBPK model was then used to simulate maribavir DDI interactions with various CYP3A4 inducers and inhibitors. The DDI study results showed that coadministration with rifampin decreased the maribavir maximum plasma concentration (Cmax), area under the plasma concentration-time curve (AUC), and trough concentration (Ctrough) by 39%, 60%, and 82%, respectively. Based on the results from the clinical DDI study, the coadministration of maribavir with rifampin is not recommended. The PBPK model did not predict a clinically significant effect of CYP3A4 inhibitors on maribavir exposure; however, it predicted that strong or moderate CYP3A4 inducers, including carbamazepine, efavirenz, phenobarbital, and phenytoin, may reduce maribavir exposure to a clinically significant extent, and may prompt the consideration of a maribavir dosing increase, in accordance with local approved labels and/or regulations.
Collapse
Affiliation(s)
- Grace Chen
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - Kefeng Sun
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | | | - Zoe Barter
- Certara UK Ltd., Simcyp Division, Sheffield, UK
| | | | - Lipika Ghosh
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - Katarina Ilic
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| | - Ivy H Song
- Takeda Development Center Americas, Inc., Cambridge, MA, USA
| |
Collapse
|
16
|
Kesharwani SS, Louit G, Ibrahim F. The Use of Global Sensitivity Analysis to Assess the Oral Absorption of Weakly Basic Compounds: A Case Example of Dipyridamole. Pharm Res 2024; 41:877-890. [PMID: 38538971 DOI: 10.1007/s11095-024-03688-0] [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: 01/23/2024] [Accepted: 03/04/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE To utilize the global system analysis (GSA) in oral absorption modeling to gain a deeper understanding of system behavior, improve model accuracy, and make informed decisions during drug development. METHODS GSA was utilized to give insight into which drug substance (DS), drug product (DP), and/or physiological parameter would have an impact on peak plasma concentration (Cmax) and area under the curve (AUC) of dipyridamole as a model weakly basic compound. GSA guided the design of in vitro experiments and oral absorption risk assessment using FormulatedProducts v2202.1.0. The solubility and precipitation profiles of dipyridamole in different bile salt concentrations were measured. The results were then used to build a mechanistic oral absorption model. RESULTS GSA warranted further investigation into the precipitation kinetics and its link to the levels of bile salt concentrations. Mechanistic modeling studies demonstrated that a precipitation-integrated modeling approach appropriately predicted the mean plasma profiles, Cmax, and AUC from the clinical studies. CONCLUSIONS This work shows the value of GSA utilization in early development to guide in vitro experimentation and build more confidence in identifying the critical parameters for the mathematical models.
Collapse
Affiliation(s)
- Siddharth S Kesharwani
- US Early Development Biopharmacy, Synthetics Platform, Sanofi, 350 Water St, Cambridge, MA, 02141, USA
| | - Guillaume Louit
- Siemens K.K, DI SW Division, 1-6-1 Miyahara, Osaka, 532-0003, Japan
| | - Fady Ibrahim
- US Early Development Biopharmacy, Synthetics Platform, Sanofi, 350 Water St, Cambridge, MA, 02141, USA.
| |
Collapse
|
17
|
Ng TM, Wang Z, Chan ECY. Physiologically-based pharmacokinetic modelling guided dose evaluations of nirmatrelvir/ritonavir in renal impairment for the management of COVID-19. Br J Clin Pharmacol 2024. [PMID: 38616514 DOI: 10.1111/bcp.16074] [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: 12/20/2023] [Revised: 03/06/2024] [Accepted: 03/21/2024] [Indexed: 04/16/2024] Open
Abstract
We aimed to address factors contributing to the pharmacokinetic changes of nirmatrelvir/ritonavir in renal impaired (RI) patients and recommend dosing adjustment via a physiologically-based pharmacokinetic (PBPK) modelling approach. A PBPK model of nirmatrelvir/ritonavir was developed via Simcyp® Simulator. Sensitivity analysis of the influence of hepatic CYP3A4 intrinsic clearance and abundance, as well as hepatic non-CYP3A4 metabolism (other human liver microsomes [HLM] CLint) was performed to evaluate the effects of RI on oral clearance of nirmatrelvir. Other HLM CLint, the most sensitive parameter, was adjusted, and the simulated plasma concentration profiles of nirmatrelvir in severe RI subjects were within the therapeutic index of 292-10 000 ng/mL for dosing regimens of loading doses of 300/100 mg followed by 150/100 mg or 75/100 mg twice daily of nirmatrelvir/ritonavir. Considering that nirmatrelvir is available as a 150 mg tablet, we recommend 300/100 mg followed by 150/100 mg twice daily as the dosing regimen to be investigated in severe RI.
Collapse
Affiliation(s)
- Tat Ming Ng
- Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore, Singapore, Singapore
- Division of Pharmacy, Tan Tock Seng Hospital, Novena, Singapore
| | - Ziteng Wang
- Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Eric Chun Yong Chan
- Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore, Singapore, Singapore
| |
Collapse
|
18
|
Alsmadi MM, Abudaqqa AA, Idkaidek N, Qinna NA, Al-Ghazawi A. The Effect of Inflammatory Bowel Disease and Irritable Bowel Syndrome on Pravastatin Oral Bioavailability: In vivo and in silico evaluation using bottom-up wbPBPK modeling. AAPS PharmSciTech 2024; 25:86. [PMID: 38605192 DOI: 10.1208/s12249-024-02803-z] [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: 01/08/2024] [Accepted: 04/01/2024] [Indexed: 04/13/2024] Open
Abstract
The common disorders irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) can modify the drugs' pharmacokinetics via their induced pathophysiological changes. This work aimed to investigate the impact of these two diseases on pravastatin oral bioavailability. Rat models for IBS and IBD were used to experimentally test the effects of IBS and IBD on pravastatin pharmacokinetics. Then, the observations made in rats were extrapolated to humans using a mechanistic whole-body physiologically-based pharmacokinetic (wbPBPK) model. The rat in vivo studies done herein showed that IBS and IBD decreased serum albumin (> 11% for both), decreased PRV binding in plasma, and increased pravastatin absolute oral bioavailability (0.17 and 0.53 compared to 0.01) which increased plasma, muscle, and liver exposure. However, the wbPBPK model predicted muscle concentration was much lower than the pravastatin toxicity thresholds for myotoxicity and rhabdomyolysis. Overall, IBS and IBD can significantly increase pravastatin oral bioavailability which can be due to a combination of increased pravastatin intestinal permeability and decreased pravastatin gastric degradation resulting in higher exposure. This is the first study in the literature investigating the effects of IBS and IBD on pravastatin pharmacokinetics. The high interpatient variability in pravastatin concentrations as induced by IBD and IBS can be reduced by oral administration of pravastatin using enteric-coated tablets. Such disease (IBS and IBD)-drug interaction can have more drastic consequences for narrow therapeutic index drugs prone to gastric degradation, especially for drugs with low intestinal permeability.
Collapse
Affiliation(s)
- Motasem M Alsmadi
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan.
- Nanotechnology Institute, Jordan University of Science and Technology, Irbid, Jordan.
| | - Alla A Abudaqqa
- Faculty of Pharmacy and Biomedical Sciences, University of Petra, Amman, Jordan
| | - Nasir Idkaidek
- Faculty of Pharmacy and Biomedical Sciences, University of Petra, Amman, Jordan
| | - Nidal A Qinna
- Faculty of Pharmacy and Biomedical Sciences, University of Petra, Amman, Jordan
- University of Petra Pharmaceutical Center (UPPC), University of Petra, Amman, Jordan
| | | |
Collapse
|
19
|
Wardani I, Hazimah Mohamed Nor N, Wright SL, Kooter IM, Koelmans AA. Nano- and microplastic PBK modeling in the context of human exposure and risk assessment. ENVIRONMENT INTERNATIONAL 2024; 186:108504. [PMID: 38537584 DOI: 10.1016/j.envint.2024.108504] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/30/2024] [Accepted: 02/14/2024] [Indexed: 04/26/2024]
Abstract
Insufficient data on nano- and microplastics (NMP) hinder robust evaluation of their potential health risks. Methodological disparities and the absence of established toxicity thresholds impede the comparability and practical application of research findings. The diverse attributes of NMP, such as variations in sizes, shapes, and compositions, complicate human health risk assessment. Although probability density functions (PDFs) show promise in capturing this diversity, their integration into risk assessment frameworks is limited. Physiologically based kinetic (PBK) models offer a potential solution to bridge the gap between external exposure and internal dosimetry for risk evaluation. However, the heterogeneity of NMP poses challenges for accurate biodistribution modeling. A literature review, encompassing both experimental and modeling studies, was conducted to examine biodistribution studies of monodisperse micro- and nanoparticles. The literature search in PubMed and Scopus databases yielded 39 studies that met the inclusion criteria. Evaluation criteria were adapted from previous Quality Assurance and Quality Control (QA-QC) studies, best practice guidelines from WHO (2010), OECD guidance (2021), and additional criteria specific to NMP risk assessment. Subsequently, a conceptual framework for a comprehensive NMP-PBK model was developed, addressing the multidimensionality of NMP particles. Parameters for an NMP-PBK model are presented. QA-QC evaluations revealed that most experimental studies scored relatively well (>0) in particle characterizations and environmental settings but fell short in criteria application for biodistribution modeling. The evaluation of modeling studies revealed that information regarding the model type and allometric scaling requires improvement. Three potential applications of PDFs in PBK modeling of NMP are identified: capturing the multidimensionality of the NMP continuum, quantifying the probabilistic definition of external exposure, and calculating the bio-accessibility fraction of NMP in the human body. A framework for an NMP-PBK model is proposed, integrating PDFs to enhance the assessment of NMP's impact on human health.
Collapse
Affiliation(s)
- Ira Wardani
- Department of aquatic ecology and water quality management, Wageningen University and Research, the Netherlands.
| | | | - Stephanie L Wright
- Environmental Research Group, School of Public Health, Imperial College London, London W12 0BZ, UK
| | - Ingeborg M Kooter
- TNO, Princetonlaan 6-8, 3584 CB Utrecht, the Netherlands; Department of Pharmacology and Toxicology, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University Medical Center, 6200 MD Maastricht, the Netherlands
| | - Albert A Koelmans
- Department of aquatic ecology and water quality management, Wageningen University and Research, the Netherlands
| |
Collapse
|
20
|
Wang X, Wu J, Ye H, Zhao X, Zhu S. Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999-2023). Pharm Res 2024; 41:609-622. [PMID: 38383936 DOI: 10.1007/s11095-024-03676-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/05/2024] [Indexed: 02/23/2024]
Abstract
PURPOSE The physiologically based pharmacokinetic (PBPK) modeling has received increasing attention owing to its excellent predictive abilities. However, there has been no bibliometric analysis about PBPK modeling. This research aimed to summarize the research development and hot points in PBPK model utilization overall through bibliometric analysis. METHODS We searched for publications related to the PBPK modeling from 1999 to 2023 in the Web of Science Core Collection (WoSCC) database. The Microsoft Office Excel, CiteSpace and VOSviewers were used to perform the analyses. RESULTS A total of 4,649 records from 1999 to 2023 were identified, and the largest number of publications focused in the period 2018-2023. The United States was the leading country, and the Environmental Protection Agency (EPA) was the leading institution. The journal Drug Metabolism and Disposition published and co-cited the most articles. Drug-drug interactions, special populations, and new drug development are the main topics in this research field. CONCLUSION We first visualize the research landscape and hotspots of the PBPK modeling through bibliometric methods. Our study provides a better understanding for researchers, especially beginners about the dynamization of PBPK modeling and presents the relevant trend in the future.
Collapse
Affiliation(s)
- Xin Wang
- Department of Pharmacy, The First Affiliated Hospital of Chongqing Medical University, No. 1, Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Jiangfan Wu
- School of Pharmacy, Chongqing Medical University, Chongqing, China
| | - Hongjiang Ye
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Xiaofang Zhao
- School of Pharmacy, Chongqing Medical University, Chongqing, China
- Qiandongnan Miao and Dong Autonomous Prefecture People's Hospital, Guizhou, 556000, China
| | - Shenyin Zhu
- Department of Pharmacy, The First Affiliated Hospital of Chongqing Medical University, No. 1, Youyi Road, Yuzhong District, Chongqing, 400016, China.
| |
Collapse
|
21
|
Shen C, Yang H, Shao W, Zheng L, Zhang W, Xie H, Jiang X, Wang L. Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms. Pharm Res 2024; 41:731-749. [PMID: 38443631 DOI: 10.1007/s11095-024-03680-8] [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: 12/09/2023] [Accepted: 02/19/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug. PURPOSE A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK). METHODS The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model's performance was evaluated by comparing predicted and observed values of plasma concentration-time (PCT) curves and PK parameters values. RESULTS In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96 h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups. CONCLUSIONS In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.
Collapse
Affiliation(s)
- Chaozhuang Shen
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Sichuan University, Chengdu, 610064, West China, China
| | - Hongyi Yang
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Sichuan University, Chengdu, 610064, West China, China
| | - Wenxin Shao
- Anhui Provincial Center for Drug Clinical Evaluation, Yijishan Hospital of Wannan Medical College, Wuhu, 241001, Anhui, China
| | - Liang Zheng
- Department of Clinical Pharmacology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China
| | - Wei Zhang
- Department of Clinical Pharmacology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China
| | - Haitang Xie
- Anhui Provincial Center for Drug Clinical Evaluation, Yijishan Hospital of Wannan Medical College, Wuhu, 241001, Anhui, China
| | - Xuehua Jiang
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Sichuan University, Chengdu, 610064, West China, China
| | - Ling Wang
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Sichuan University, Chengdu, 610064, West China, China.
| |
Collapse
|
22
|
Berridge B, Pierson J, Pettit S, Stockbridge N. Challenging the status quo: a framework for mechanistic and human-relevant cardiovascular safety screening. FRONTIERS IN TOXICOLOGY 2024; 6:1352783. [PMID: 38590785 PMCID: PMC10999590 DOI: 10.3389/ftox.2024.1352783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/11/2024] [Indexed: 04/10/2024] Open
Abstract
Traditional approaches to preclinical drug safety assessment have generally protected human patients from unintended adverse effects. However, these assessments typically occur too late to make changes in the formulation or in phase 1 and beyond, are highly dependent on animal studies and have the potential to lead to the termination of useful drugs due to liabilities in animals that are not applicable in patients. Collectively, these elements come at great detriment to both patients and the drug development sector. This phenomenon is particularly problematic in the area of cardiovascular safety assessment where preclinical attrition is high. We believe that a more efficient and translational approach can be defined. A multi-tiered assessment that leverages our understanding of human cardiovascular biology, applies human cell-based in vitro characterizations of cardiovascular responses to insult, and incorporates computational models of pharmacokinetic relationships would enable earlier and more translational identification of human-relevant liabilities. While this will take time to develop, the ultimate goal would be to implement such assays both in the lead selection phase as well as through regulatory phases.
Collapse
Affiliation(s)
| | - Jennifer Pierson
- Health and Environmental Sciences Institute, Washington, DC, United States
| | - Syril Pettit
- Health and Environmental Sciences Institute, Washington, DC, United States
| | - Norman Stockbridge
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, United States
| |
Collapse
|
23
|
van Borselen MD, Sluijterman LAÆ, Greupink R, de Wildt SN. Towards More Robust Evaluation of the Predictive Performance of Physiologically Based Pharmacokinetic Models: Using Confidence Intervals to Support Use of Model-Informed Dosing in Clinical Care. Clin Pharmacokinet 2024; 63:343-355. [PMID: 38361163 PMCID: PMC10954928 DOI: 10.1007/s40262-023-01326-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/30/2023] [Indexed: 02/17/2024]
Abstract
BACKGROUND AND OBJECTIVE With the rise in the use of physiologically based pharmacokinetic (PBPK) modeling over the past decade, the use of PBPK modeling to underpin drug dosing for off-label use in clinical care has become an attractive option. In order to use PBPK models for high-impact decisions, thorough qualification and validation of the model is essential to gain enough confidence in model performance. Currently, there is no agreed method for model acceptance, while clinicians demand a clear measure of model performance before considering implementing PBPK model-informed dosing. We aim to bridge this gap and propose the use of a confidence interval for the predicted-to-observed geometric mean ratio with predefined boundaries. This approach is similar to currently accepted bioequivalence testing procedures and can aid in improved model credibility and acceptance. METHODS Two different methods to construct a confidence interval are outlined, depending on whether individual observations or aggregate data are available from the clinical comparator data sets. The two testing procedures are demonstrated for an example evaluation of a midazolam PBPK model. In addition, a simulation study is performed to demonstrate the difference between the twofold criterion and our proposed method. RESULTS Using midazolam adult pharmacokinetic data, we demonstrated that creating a confidence interval yields more robust evaluation of the model than a point estimate, such as the commonly used twofold acceptance criterion. Additionally, we showed that the use of individual predictions can reduce the number of required test subjects. Furthermore, an easy-to-implement software tool was developed and is provided to make our proposed method more accessible. CONCLUSIONS With this method, we aim to provide a tool to further increase confidence in PBPK model performance and facilitate its use for directly informing drug dosing in clinical care.
Collapse
Affiliation(s)
- Marjolein D van Borselen
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.
| | | | - Rick Greupink
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Saskia N de Wildt
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
- Department of Pediatric and Neonatal Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands
| |
Collapse
|
24
|
Zhu X, Guo L, Zhang L, Xu Y. Physiologically Based Pharmacokinetic Modeling of Lacosamide in Patients With Hepatic and Renal Impairment and Pediatric Populations to Support Pediatric Dosing Optimization. Clin Ther 2024; 46:258-266. [PMID: 38369451 DOI: 10.1016/j.clinthera.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/20/2024]
Abstract
PURPOSE Lacosamide (LCM) is a new-generation anti-seizure medication that is efficacious in patients with focal seizures with or without secondary generalization. Until now, the efficacy, safety, and tolerability of LCM are still lacking in Chinese epilepsy patients, particularly for pediatric populations and patients with renal or hepatic impairment. METHODS This study was conducted to develop a physiologically based pharmacokinetic (PBPK) model to characterize the pharmacokinetics of LCM in Chinese populations and predict the pharmacokinetics of LCM in Chinese pediatric populations and patients with renal or hepatic impairment. Using data from clinical investigations, the developed PBPK model was validated by comparing predicted and observed blood concentration data. FINDINGS Doses should be reduced to approximately 82%, 75%, 63%, and 76% of the Chinese healthy adult dose in patients with mild, moderate, and severe renal impairment and end-stage renal disease; and approximately 89%, 72%, and 36% of the Chinese healthy adult dose in patients with Child Pugh-A, B, and C hepatic impairment. For pediatric populations, intravenous doses should be adjusted to 1.75 mg/kg for newborns, 2.5 mg/kg for toddlers, 2.2 mg/kg mg for preschool and school age, and 2 mg/kg mg for adolescents to achieve an equivalent plasma exposure of 2 mg/kg LCM in adults. The oral doses should be adjusted to 20 mg for toddlers, 32 mg for preschool, 45 mg for school age, and 95 mg for adolescents to achieve an approximately equivalent plasma exposure of 100 mg LCM in adults. IMPLICATIONS The PBPK model of LCM can be utilized to optimize dosage regimens for special populations.
Collapse
Affiliation(s)
- Xinyu Zhu
- Shengzhou Branch, the First Affiliated Hospital of Zhejiang University, School of Medicine, Shengzhou, Zhejiang, China
| | - Lingfeng Guo
- Shengzhou Branch, the First Affiliated Hospital of Zhejiang University, School of Medicine, Shengzhou, Zhejiang, China
| | - Lei Zhang
- Department of Pharmacy, the Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, Zhejiang, China
| | - Yichao Xu
- Center of Clinical Pharmacology, the Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, Zhejiang, China.
| |
Collapse
|
25
|
Li Y, Jin X, Wang F, Zhou H, Gu Y, Yang Y, Qian Z, Li W. Multi-channel Small Animal Drug Metabolism Real-Time Monitoring Fluorescence System. Mol Imaging Biol 2024; 26:138-147. [PMID: 38114709 DOI: 10.1007/s11307-023-01883-w] [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: 06/26/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE The data acquisition of drug metabolism analysis requires a lot of time and animal resources. However, there are often many deviations in the results of pharmacokinetic analysis. Conventional methods cannot measure the blood drug concentration data in multiple tissues at the same time, and the data is obtained by in vitro measurement, which produces time errors, in vitro data errors, and individual differences between animals. In the analysis of pharmacokinetic parameters, it will seriously affect the pass rate of clinical trials of R&D drugs and the accuracy of the dosing schedule. To the best of our knowledge, we have not found the study of in vivo blood drug concentration using multi-channel equipment. Therefore, the purpose of this paper is to build a set of multi-organ monitoring and analysis instruments for synchronously monitoring the metabolism of drugs in various tissues of small animals, so as to obtain real in vivo data of blood drug concentration in real time. PROCEDURES Using the fluorescence properties and laser-induced fluorescence principle of drugs, we designed six channels to monitor the changes of fluorescence-labeled drugs in their main metabolic organs, a multi-channel calibration method was proposed to improve the accuracy of the time-division multiplexing, the real-time collection of drug concentration in vivo is realized, and the drug metabolism curve in vivo can be observed. RESULTS The instrument satisfies the collection of small doses of drugs such as microgram; the detection sensitivity can reach 10 ng/ml, and can monitor and collect the drug metabolism of multiple small animal tissues at the same time, which greatly reduces the use of animals, reduces the differences between individuals, and reduces consumption cost and improve the detection efficiency of parameters, and obtain data information that is closer to the real biology. CONCLUSION The real-time continuous monitoring and data collection of the drug metabolism in the plasma of living small animals and the important organs such as kidney, liver, and spleen were realized. The research and development of new drugs and clinical research have higher practical value.
Collapse
Affiliation(s)
- Yiran Li
- Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Xiaofei Jin
- Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Feilong Wang
- Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Huijing Zhou
- Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Yueqing Gu
- Engineering College, China Pharmaceutical University, Nanjing, 211198, China
| | - Yamin Yang
- Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Zhiyu Qian
- Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
| | - Weitao Li
- Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
| |
Collapse
|
26
|
Xu Y, Zhang L, Dou X, Dong Y, Guo X. Physiologically based pharmacokinetic modeling of apixaban to predict exposure in populations with hepatic and renal impairment and elderly populations. Eur J Clin Pharmacol 2024; 80:261-271. [PMID: 38099940 PMCID: PMC10847219 DOI: 10.1007/s00228-023-03602-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 12/02/2023] [Indexed: 02/07/2024]
Abstract
BACKGROUND Apixaban is a factor Xa inhibitor with a limited therapeutic index that belongs to the family of oral direct anticoagulants. The pharmacokinetic (PK) behavior of apixaban may be altered in elderly populations and populations with renal or hepatic impairment, necessitating dosage adjustments. METHODS This study was conducted to examine how the physiologically based pharmacokinetic (PBPK) model describes the PKs of apixaban in adult and elderly populations and to determine the PKs of apixaban in elderly populations with renal and hepatic impairment. After PBPK models were constructed using the reported physicochemical properties of apixaban and clinical data, they were validated using data from clinical studies involving various dose ranges. Comparing predicted and observed blood concentration data and PK parameters was utilized to evaluate the model's fit performance. RESULTS Doses should be reduced to approximately 70% of the healthy adult population for the healthy elderly population to achieve the same PK exposure; approximately 88%, 71%, and 89% of that for the elderly populations with mild, moderate, and severe renal impairment, respectively; and approximately 96%, 81%, and 58% of that for the Child Pugh-A, Child Pugh-B, and Child Pugh-C hepatic impairment elderly populations, respectively to achieve the same PK exposure. CONCLUSION The findings indicate that the renal and hepatic function might be considered for apixaban therapy in Chinese elderly patients and the PBPK model can be used to optimize dosage regimens for specific populations.
Collapse
Affiliation(s)
- Yichao Xu
- Center of Clinical Pharmacology, the Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, Zhejiang, China
| | - Lei Zhang
- Department of Pharmacy, the Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, Zhejiang, China
| | - Xiaofan Dou
- Center for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yongze Dong
- Center for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xiangchai Guo
- Center for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
| |
Collapse
|
27
|
Djuris J, Cvijic S, Djekic L. Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration. Pharmaceuticals (Basel) 2024; 17:177. [PMID: 38399392 PMCID: PMC10892858 DOI: 10.3390/ph17020177] [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: 11/03/2023] [Revised: 12/23/2023] [Accepted: 12/29/2023] [Indexed: 02/25/2024] Open
Abstract
The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.
Collapse
Affiliation(s)
- Jelena Djuris
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia; (S.C.); (L.D.)
| | | | | |
Collapse
|
28
|
Fele-Paranj A, Saboury B, Uribe C, Rahmim A. Physiologically based radiopharmacokinetic (PBRPK) modeling to simulate and analyze radiopharmaceutical therapies: studies of non-linearities, multi-bolus injections, and albumin binding. EJNMMI Radiopharm Chem 2024; 9:6. [PMID: 38252191 PMCID: PMC10803696 DOI: 10.1186/s41181-023-00236-w] [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: 11/01/2023] [Accepted: 12/22/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND We aimed to develop a publicly shared computational physiologically based pharmacokinetic (PBPK) model to reliably simulate and analyze radiopharmaceutical therapies (RPTs), including probing of hot-cold ligand competitions as well as alternative injection scenarios and drug designs, towards optimal therapies. RESULTS To handle the complexity of PBPK models (over 150 differential equations), a scalable modeling notation called the "reaction graph" is introduced, enabling easy inclusion of various interactions. We refer to this as physiologically based radiopharmacokinetic (PBRPK) modeling, fine-tuned specifically for radiopharmaceuticals. As three important applications, we used our PBRPK model to (1) study the effect of competition between hot and cold species on delivered doses to tumors and organs at risk. In addition, (2) we evaluated an alternative paradigm of utilizing multi-bolus injections in RPTs instead of prevalent single injections. Finally, (3) we used PBRPK modeling to study the impact of varying albumin-binding affinities by ligands, and the implications for RPTs. We found that competition between labeled and unlabeled ligands can lead to non-linear relations between injected activity and the delivered dose to a particular organ, in the sense that doubling the injected activity does not necessarily result in a doubled dose delivered to a particular organ (a false intuition from external beam radiotherapy). In addition, we observed that fractionating injections can lead to a higher payload of dose delivery to organs, though not a differential dose delivery to the tumor. By contrast, we found out that increased albumin-binding affinities of the injected ligands can lead to such a differential effect in delivering more doses to tumors, and this can be attributed to several factors that PBRPK modeling allows us to probe. CONCLUSIONS Advanced computational PBRPK modeling enables simulation and analysis of a variety of intervention and drug design scenarios, towards more optimal delivery of RPTs.
Collapse
Affiliation(s)
- Ali Fele-Paranj
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, US
| | - Carlos Uribe
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Department of Functional Imaging, BC Cancer, Vancouver, BC, Canada
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Arman Rahmim
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada.
| |
Collapse
|
29
|
Tang W, Zhang X, Hong H, Chen J, Zhao Q, Wu F. Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:155. [PMID: 38251120 PMCID: PMC10819018 DOI: 10.3390/nano14020155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/08/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024]
Abstract
Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.
Collapse
Affiliation(s)
- Weihao Tang
- National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management, Institute of Eco-Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
| | - Xuejiao Zhang
- National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management, Institute of Eco-Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR 72079, USA
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Qing Zhao
- National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management, Institute of Eco-Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| |
Collapse
|
30
|
Gevertz JL, Kareva I. Minimally sufficient experimental design using identifiability analysis. NPJ Syst Biol Appl 2024; 10:2. [PMID: 38184643 PMCID: PMC10771435 DOI: 10.1038/s41540-023-00325-1] [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: 05/30/2023] [Accepted: 12/12/2023] [Indexed: 01/08/2024] Open
Abstract
Mathematical models are increasingly being developed and calibrated in tandem with data collection, empowering scientists to intervene in real time based on quantitative model predictions. Well-designed experiments can help augment the predictive power of a mathematical model but the question of when to collect data to maximize its utility for a model is non-trivial. Here we define data as model-informative if it results in a unique parametrization, assessed through the lens of practical identifiability. The framework we propose identifies an optimal experimental design (how much data to collect and when to collect it) that ensures parameter identifiability (permitting confidence in model predictions), while minimizing experimental time and costs. We demonstrate the power of the method by applying it to a modified version of a classic site-of-action pharmacokinetic/pharmacodynamic model that describes distribution of a drug into the tumor microenvironment (TME), where its efficacy is dependent on the level of target occupancy in the TME. In this context, we identify a minimal set of time points when data needs to be collected that robustly ensures practical identifiability of model parameters. The proposed methodology can be applied broadly to any mathematical model, allowing for the identification of a minimally sufficient experimental design that collects the most informative data.
Collapse
Affiliation(s)
- Jana L Gevertz
- Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA.
| | - Irina Kareva
- Quantitative Pharmacology Department, EMD Serono, Merck KGaA, Billerica, MA, USA
| |
Collapse
|
31
|
Šoša I. Quetiapine-Related Deaths: In Search of a Surrogate Endpoint. TOXICS 2024; 12:37. [PMID: 38250993 PMCID: PMC10819769 DOI: 10.3390/toxics12010037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/30/2023] [Accepted: 01/01/2024] [Indexed: 01/23/2024]
Abstract
Quetiapine is a second-generation antipsychotic drug available for two and half decades. Due to increased misuse, prescription outside the approved indications, and availability on the black market, it is being encountered in medicolegal autopsies more frequently. For instance, it has been linked to increased mortality rates, most likely due to its adverse effects on the cardiovascular system. Its pharmacokinetic features and significant postmortem redistribution challenge traditional sampling in forensic toxicology. Therefore, a systematic literature review was performed, inclusive of PubMed, the Web of Science-core collection, and the Scopus databases; articles were screened for the terms "quetiapine", "death", and "autopsy" to reevaluate each matrix used as a surrogate endpoint in the forensic toxicology of quetiapine-related deaths. Ultimately, this review considers the results of five studies that were well presented (more than two matrices, data available for all analyses, for instance). The highest quetiapine concentrations were usually measured in the liver tissue. As interpreted by their authors, the results of the considered studies showed a strong correlation between some matrices, but, unfortunately, the studies presented models with poor goodness of fit. The distribution of quetiapine in distinct body compartments/tissues showed no statistically significant relationship with the length of the postmortem interval. Furthermore, this study did not confirm the anecdotal correlation of peripheral blood concentrations with skeletal muscle concentrations. Otherwise, there was no consistency regarding selecting an endpoint for analysis.
Collapse
Affiliation(s)
- Ivan Šoša
- Department of Anatomy, Faculty of Medicine, University of Rijeka, 51000 Rijeka, Croatia
| |
Collapse
|
32
|
Fine-Shamir N, Dahan A. Solubility-enabling formulations for oral delivery of lipophilic drugs: considering the solubility-permeability interplay for accelerated formulation development. Expert Opin Drug Deliv 2024; 21:13-29. [PMID: 38124383 DOI: 10.1080/17425247.2023.2298247] [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: 10/25/2023] [Accepted: 12/19/2023] [Indexed: 12/23/2023]
Abstract
INTRODUCTION Tackling low water solubility of drug candidates is a major challenge in today's pharmaceutics/biopharmaceutics, especially by means of modern solubility-enabling formulations. However, drug absorption from these formulations oftentimes remains unchanged or even decreases, despite substantial solubility enhancement. AREAS COVERED In this article, we overview the simultaneous effects of the formulation on the solubility and the apparent permeability of the drug, and analyze the contribution of this solubility-permeability interplay to the success/failure of the formulation to increase the overall absorption and bioavailability. Three different patterns of interplay were identified: (1) solubility-permeability tradeoff in which every solubility gain comes with a price of concomitant permeability loss; (2) an advantageous interplay pattern in which the permeability remains unchanged alongside the solubility gain; and (3) an optimal interplay pattern in which the formulation increases both the solubility and the permeability. Passive vs. active intestinal permeability considerations in the context of the solubility-permeability interplay are also thoroughly discussed. EXPERT OPINION The solubility-permeability interplay pattern of a given formulation has a critical effect on its overall success/failure, and hence, taking into account both parameters in solubility-enabling formulation development is prudent and highly recommended.
Collapse
Affiliation(s)
- Noa Fine-Shamir
- Department of Clinical Pharmacology, School of Pharmacy, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Arik Dahan
- Department of Clinical Pharmacology, School of Pharmacy, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| |
Collapse
|
33
|
Zamir A, Alqahtani F, Rasool MF. Chronic kidney disease and physiologically based pharmacokinetic modeling: a critical review of existing models. Expert Opin Drug Metab Toxicol 2024; 20:95-105. [PMID: 38270999 DOI: 10.1080/17425255.2024.2311154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/24/2024] [Indexed: 01/27/2024]
Abstract
INTRODUCTION Physiologically based pharmacokinetic (PBPK) modeling is a paradigm shift in this era for determining the exposure of drugs in pediatrics, geriatrics, and patients with chronic diseases where clinical trials are difficult to conduct. AREAS COVERED This review has collated data regarding published PBPK models on chronic kidney disease (CKD), including the drug and system-specific input model parameters and model evaluation criteria. Four databases were used from 13th June 2023 to 10th July 2023 for identifying the relevant studies that met the inclusion/exclusion criteria. Alterations in plasma protein (albumin/alpha-1 acid glycoprotein), gastric emptying time, hematocrit, small intestinal transit time, the abundance of cytochrome (CYP) 450 enzymes, glomerular filtration rate, and physicochemical parameters for different drugs were explicitly elaborated from earlier reported studies. Moreover, model evaluation depicted that models in CKD for most of the included drugs were within the allowed two-fold error range. EXPERT OPINION This review will provide insights for researchers on applying PBPK models in managing patients with different levels of CKD to prevent undesirable side effects and increase the effectiveness of drug therapy.
Collapse
Affiliation(s)
- Ammara Zamir
- Department of Pharmacy Practice, Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Faleh Alqahtani
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud Universi-ty, Riyadh, Saudi Arabia
| | - Muhammad Fawad Rasool
- Department of Pharmacy Practice, Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| |
Collapse
|
34
|
Su M, Liu X, Zhao Y, Zhu Y, Wu M, Liu K, Yang G, Liu W, Wang L. In Silico and In Vivo Pharmacokinetic Evaluation of 84-B10, a Novel Drug Candidate against Acute Kidney Injury and Chronic Kidney Disease. Molecules 2023; 29:159. [PMID: 38202741 PMCID: PMC10780175 DOI: 10.3390/molecules29010159] [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: 11/09/2023] [Revised: 12/15/2023] [Accepted: 12/16/2023] [Indexed: 01/12/2024] Open
Abstract
Acute kidney injury (AKI) and chronic kidney disease (CKD) have become public health problems due to high morbidity and mortality. Currently, drugs recommended for patients with AKI or CKD are extremely limited, and candidates based on a new mechanism need to be explored. 84-B10 is a novel 3-phenylglutaric acid derivative that can activate the mitochondrial protease, Lon protease 1 (LONP1), and may protect against cisplatin-induced AKI and unilateral ureteral obstruction- or 5/6 nephrectomy [5/6Nx]-induced CKD model. Preclinical studies have shown that 84-B10 has a good therapeutic effect, low toxicity, and is a good prospect for further development. In the present study, the UHPLC-MS/MS method was first validated then applied to the pharmacokinetic study and tissue distribution of 84-B10 in rats. Physicochemical properties of 84-B10 were then acquired in silico. Based on these physicochemical and integral physiological parameters, a physiological based pharmacokinetic (PBPK) model was developed using the PK-Sim platform. The fitting accuracy was estimated with the obtained experimental data. Subsequently, the validated model was employed to predict the pharmacokinetic profiles in healthy and chronic kidney injury patients to evaluate potential clinical outcomes. Cmax in CKD patients was about 3250 ng/mL after a single dose of 84-B10 (0.41 mg/kg), and Cmax,ss was 1360 ng/mL after multiple doses. This study may serve in clinical dosage setting in the future.
Collapse
Affiliation(s)
- Man Su
- Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, Yantai 264005, China; (M.S.); (X.L.); (Y.Z.); (Y.Z.); (K.L.); (G.Y.)
| | - Xianru Liu
- Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, Yantai 264005, China; (M.S.); (X.L.); (Y.Z.); (Y.Z.); (K.L.); (G.Y.)
| | - Yuru Zhao
- Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, Yantai 264005, China; (M.S.); (X.L.); (Y.Z.); (Y.Z.); (K.L.); (G.Y.)
| | - Yatong Zhu
- Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, Yantai 264005, China; (M.S.); (X.L.); (Y.Z.); (Y.Z.); (K.L.); (G.Y.)
| | - Mengqiu Wu
- Nanjing Key Laboratory of Pediatrics, Children’s Hospital of Nanjing Medical University, Nanjing 210008, China;
| | - Kun Liu
- Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, Yantai 264005, China; (M.S.); (X.L.); (Y.Z.); (Y.Z.); (K.L.); (G.Y.)
| | - Gangqiang Yang
- Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, Yantai 264005, China; (M.S.); (X.L.); (Y.Z.); (Y.Z.); (K.L.); (G.Y.)
| | - Wanhui Liu
- Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, Yantai 264005, China; (M.S.); (X.L.); (Y.Z.); (Y.Z.); (K.L.); (G.Y.)
| | - Lin Wang
- Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, School of Pharmacy, Yantai University, Yantai 264005, China; (M.S.); (X.L.); (Y.Z.); (Y.Z.); (K.L.); (G.Y.)
| |
Collapse
|
35
|
Guo L, Zhu X, Zhang L, Xu Y. Physiologically based pharmacokinetic modeling of candesartan to predict the exposure in hepatic and renal impairment and elderly populations. Ther Adv Drug Saf 2023; 14:20420986231220222. [PMID: 38157240 PMCID: PMC10752084 DOI: 10.1177/20420986231220222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
Background Candesartan cilexetil is a widely used angiotensin II receptor blocker with minimal adverse effects and high tolerability for the treatment of hypertension. Candesartan is administered orally as the prodrug candesartan cilexetil, which is wholly and swiftly converted to the active metabolite candesartan by carboxylesterase during absorption in the intestinal tract. In populations with renal or hepatic impairment, candesartan's pharmacokinetic (PK) behavior may be altered, necessitating dosage adjustments. Objectives This study was conducted to examine how the physiologically based PK (PBPK) model characterizes the PKs of candesartan in adult and geriatric populations and to predict the PKs of candesartan in elderly populations with renal and hepatic impairment. Design After developing PBPK models using the reported physicochemical properties of candesartan and clinical data, these models were validated using data from clinical investigations involving various dose ranges. Methods Comparing predicted and observed blood concentration data and PK parameters was used to assess the fit performance of the models. Results Doses should be reduced to approximately 94% of Chinese healthy adults for the Chinese healthy elderly population; approximately 92%, 68%, and 64% of that of the Chinese healthy adult dose in elderly populations with mild, moderate, and severe renal impairment, respectively; and approximately 72%, 71%, and 52% of that of the Chinese healthy adult dose in elderly populations with Child-Pugh-A, Child-Pugh-B, and Child-Pugh-C hepatic impairment, respectively. Conclusion The results suggest that the PBPK model of candesartan can be utilized to optimize dosage regimens for special populations.
Collapse
Affiliation(s)
- Lingfeng Guo
- The First Affiliated Hospital of Zhejiang University Shengzhou Branch, School of Medicine, Shengzhou, Zhejiang, China
| | - Xinyu Zhu
- The First Affiliated Hospital of Zhejiang University Shengzhou Branch, School of Medicine, Shengzhou, Zhejiang, China
| | - Lei Zhang
- Department of Pharmacy, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, Zhejiang, China
| | - Yichao Xu
- Center of Clinical Pharmacology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, 88 Jiefang Road, Hangzhou, Zhejiang 310009, China
| |
Collapse
|
36
|
Di Stefano F, Rodrigues C, Galtier S, Guilleminot S, Robert V, Gasparini M, Saint-Hilary G. Incorporation of healthy volunteers data on receptor occupancy into a phase II proof-of-concept trial using a Bayesian dynamic borrowing design. Biom J 2023; 65:e2200305. [PMID: 37888795 DOI: 10.1002/bimj.202200305] [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: 11/04/2022] [Revised: 07/09/2023] [Accepted: 07/23/2023] [Indexed: 10/28/2023]
Abstract
Receptor occupancy in targeted tissues measures the proportion of receptors occupied by a drug at equilibrium and is sometimes used as a surrogate of drug efficacy to inform dose selection in clinical trials. We propose to incorporate data on receptor occupancy from a phase I study in healthy volunteers into a phase II proof-of-concept study in patients, with the objective of using all the available evidence to make informed decisions. A minimal physiologically based pharmacokinetic modeling is used to model receptor occupancy in healthy volunteers and to predict it in the patients of a phase II proof-of-concept study, taking into account the variability of the population parameters and the specific differences arising from the pathological condition compared to healthy volunteers. Then, given an estimated relationship between receptor occupancy and the clinical endpoint, an informative prior distribution is derived for the clinical endpoint in both the treatment and control arms of the phase II study. These distributions are incorporated into a Bayesian dynamic borrowing design to supplement concurrent phase II trial data. A simulation study in immuno-inflammation demonstrates that the proposed design increases the power of the study while maintaining a type I error at acceptable levels for realistic values of the clinical endpoint.
Collapse
Affiliation(s)
- Fulvio Di Stefano
- Dipartimento di Scienze Matematiche (DISMA) "Giuseppe Luigi Lagrange,", Politecnico di Torino, Torino, Italy
| | - Christelle Rodrigues
- Department of Quantitative Pharmacology, Institut de Recherches Internationales Servier, Suresnes, France
| | - Stephanie Galtier
- Department of Clinical Statistics, Institut de Recherches Internationales Servier, Suresnes, France
| | - Sandrine Guilleminot
- Department of Quantitative Pharmacology, Institut de Recherches Internationales Servier, Suresnes, France
| | - Veronique Robert
- Department of Clinical Statistics, Institut de Recherches Internationales Servier, Suresnes, France
| | - Mauro Gasparini
- Dipartimento di Scienze Matematiche (DISMA) "Giuseppe Luigi Lagrange,", Politecnico di Torino, Torino, Italy
| | - Gaelle Saint-Hilary
- Dipartimento di Scienze Matematiche (DISMA) "Giuseppe Luigi Lagrange,", Politecnico di Torino, Torino, Italy
- Department of Statistical Methodology, Saryga, Tournus, France
| |
Collapse
|
37
|
Cho CK, Kang P, Jang CG, Lee SY, Lee YJ, Choi CI. Physiologically based pharmacokinetic (PBPK) modeling to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes. Arch Pharm Res 2023; 46:939-953. [PMID: 38064121 DOI: 10.1007/s12272-023-01472-z] [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: 11/07/2023] [Accepted: 11/22/2023] [Indexed: 12/17/2023]
Abstract
Irbesartan, a potent and selective angiotensin II type-1 (AT1) receptor blocker (ARB), is one of the representative medications for the treatment of hypertension. Cytochrome P450 (CYP) 2C9 is primarily involved in the oxidation of irbesartan. CYP2C9 is highly polymorphic, and genetic polymorphism of this enzyme is the leading cause of significant alterations in the pharmacokinetics of irbesartan. This study aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes. The irbesartan PBPK model was established using the PK-Sim® software. Our previously reported pharmacogenomic data for irbesartan was leveraged in the development of the PBPK model and collected clinical pharmacokinetic data for irbesartan was used for the validation of the model. Physicochemical and ADME properties of irbesartan were obtained from previously reported data, predicted by the modeling software, or optimized to fit the observed plasma concentration-time profiles. Model evaluation was performed by comparing the predicted plasma concentration-time profiles and pharmacokinetic parameters to the observed results. Predicted plasma concentration-time profiles were visually similar to observed profiles. Predicted AUCinf in CYP2C9*1/*3 and CYP2C9*1/*13 genotypes were increased by 1.54- and 1.62-fold compared to CYP2C9*1/*1 genotype, respectively. All fold error values for AUC and Cmax in non-genotyped and CYP2C9 genotyped models were within the two-fold error criterion. We properly established the PBPK model of irbesartan in different CYP2C9 genotypes. It can be used to predict the pharmacokinetics of irbesartan for personalized pharmacotherapy in individuals of various races, ages, and CYP2C9 genotypes.
Collapse
Affiliation(s)
- Chang-Keun Cho
- School of Pharmacy, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Pureum Kang
- School of Pharmacy, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Choon-Gon Jang
- School of Pharmacy, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Seok-Yong Lee
- School of Pharmacy, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
| | - Yun Jeong Lee
- College of Pharmacy, Dankook University, Cheonan, 31116, Republic of Korea
| | - Chang-Ik Choi
- College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea.
| |
Collapse
|
38
|
Deb S, Hopefl R. Simulation of drug-drug interactions between breast cancer chemotherapeutic agents and antiemetic drugs. Daru 2023; 31:95-105. [PMID: 37223851 PMCID: PMC10624783 DOI: 10.1007/s40199-023-00463-1] [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: 08/01/2022] [Accepted: 05/06/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Chemotherapy-induced nausea and vomiting are commonly experienced side effects in breast cancer (BCa) patients. Antiemetic drugs used in BCa treatment are either inhibitors or inducers of cytochrome P450 (CYP) enzymes, while anticancer drugs are metabolized by CYPs. OBJECTIVES The purpose of the present work was to evaluate in silico drug-drug interaction (DDI) potential between BCa chemotherapeutic drugs and antiemetic agents. METHODS The Drug-Drug Interaction™ module of GastroPlus™ was employed to assess CYP-related interactions between antiemetic and anticancer therapy combinations. The CYP inhibitory or inducing parameters (IC50, Ki, EC50) used in simulations were obtained from the literature. RESULTS Analyses of twenty-three BCa drugs indicated that 22% of the chemotherapeutic drugs do not need an antiemetic agent due to their low emetogenicity, whereas 30% of the anticancer drugs are not metabolized by CYPs. The remaining eleven anticancer drugs metabolized by CYPs generated ninety-nine combinations with nine antiemetics. Simulation of DDIs suggest that about half of the pairs did not demonstrate any potential for DDI, whereas 30%, 10%, and 9% of the pairs showed weak, moderate, and strong interaction potential, respectively. In the present study, netupitant was the only antiemetic that showed strong inhibitory interactions (predicted AUC ratio > 5) with CYP3A4-metabolzied anticancer therapies (e.g., docetaxel, ribociclib, olaparib). Moderate to no interactions were observed with ondansetron, aprepitant, rolapitant, and dexamethasone in combination with anticancer agents. CONCLUSION It is critical to recognize that these interactions can get amplified in cancer patients because of the severity of the disease and chemotherapy toxicities. Clinicians need to be aware of the DDI likelihood of the drug combinations used in BCa treatment.
Collapse
Affiliation(s)
- Subrata Deb
- Department of Pharmaceutical Sciences, College of Pharmacy, Larkin University, Miami, FL, 33169, USA.
| | - Robert Hopefl
- Department of Pharmaceutical Sciences, College of Pharmacy, Larkin University, Miami, FL, 33169, USA
| |
Collapse
|
39
|
Chao FC, Manaia EB, Ponchel G, Hsieh CM. A physiologically-based pharmacokinetic model for predicting doxorubicin disposition in multiple tissue levels and quantitative toxicity assessment. Biomed Pharmacother 2023; 168:115636. [PMID: 37826938 DOI: 10.1016/j.biopha.2023.115636] [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: 07/20/2023] [Revised: 09/22/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023] Open
Abstract
Doxorubicin is a widely-used chemotherapeutic drug, however its high toxicity poses a significant challenge for its clinical use. To address this issue, a physiologically-based pharmacokinetic (PBPK) model was implemented to quantitatively assess doxorubicin toxicity at cellular scale. Due to its unique pharmacokinetic behavior (e.g. high volume of distribution and affinity to extra-plasma tissue compartments), we proposed a modified PBPK model structure and developed the model with multispecies extrapolation to compensate for the limitation of obtaining clinical tissue data. Our model predicted the disposition of doxorubicin in multiple tissues including clinical tissue data with an overall absolute average fold error (AAFE) of 2.12. The model's performance was further validated with 8 clinical datasets in combined with intracellular doxorubicin concentration with an average AAFE of 1.98. To assess the potential cellular toxicity, toxicity levels and area under curve (AUC) were defined for different dosing regimens in toxic and non-toxic scenarios. The cellular concentrations of doxorubicin in multiple organ sites associated with commonly observed adverse effects (AEs) were simulated and calculated the AUC for quantitative assessments. Our findings supported the clinical dosing regimen of 75 mg/m2 with a 21-day interval and suggest that slow infusion and separated single high doses may lower the risk of developing AEs from a cellular level, providing valuable insights for the risk assessment of doxorubicin chemotherapy. In conclusion, our work highlights the potential of PBPK modelling to provide quantitative assessments of cellular toxicity and supports the use of clinical dosing regimens to mitigate the risk of adverse effects.
Collapse
Affiliation(s)
- Fang-Ching Chao
- CNRS UMR 8612, Institut Galien Paris-Saclay, Université Paris-Saclay, Orsay 91400, France
| | - Eloísa Berbel Manaia
- CNRS UMR 8612, Institut Galien Paris-Saclay, Université Paris-Saclay, Orsay 91400, France
| | - Gilles Ponchel
- CNRS UMR 8612, Institut Galien Paris-Saclay, Université Paris-Saclay, Orsay 91400, France.
| | - Chien-Ming Hsieh
- School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei 11031, Taiwan; Ph.D. Program in Drug Discovery and Development Industry, College of Pharmacy, Taipei Medical University, Taipei 11031, Taiwan.
| |
Collapse
|
40
|
Uno Y, Uehara S, Ushirozako G, Murayama N, Suemizu H, Yamazaki H. Cytochrome P450 1A2 and 2C enzymes autoinduced by omeprazole in dog hepatocytes and human HepaRG and HepaSH cells are involved in omeprazole 5-hydroxylation and sulfoxidation. Xenobiotica 2023; 53:465-473. [PMID: 37800661 DOI: 10.1080/00498254.2023.2266840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 10/01/2023] [Indexed: 10/07/2023]
Abstract
The induction assay for the cytochromes P450 (P450s) is an important tool in drug discovery and development. The inductions of dog P450 1A2 and 3A12 by omeprazole and rifampicin were functionally characterised in dog hepatocytes and were compared with induction in human HepaRG and HepaSH cells.P450 1A2-dependent ethoxyresorufin O-deethylation was induced by R,S-omeprazole and P450 3 A-dependent midazolam 1'-hydroxylation was induced by rifampicin, and both reactions were significantly enhanced in cultured dog hepatocytes and human HepaRG and HepaSH cells.Recombinant dog P450 1A2 exhibited activities towards R- and S-omeprazole 5-hydroxylation with low Km values of 23-28 µM, whereas dog P450 2C21 and 3A12 efficiently mediated S-omeprazole 5-hydroxylation and sulfoxidation, respectively, with high Vmax values of 12-17 min-1.Although omeprazole 5-hydroxylation by human P450 2C19 (and sulfoxidation by P450 3A4) in human HepaSH cells were slightly (∼2-fold) induced by R,S-omeprazole, dog P450 1A2 was autoinduced by omeprazole in dog hepatocytes and showed enhanced R-omeprazole 5-hydroxylation activity (∼5-fold).These results indicate that omeprazole can be an autoinducer of P450 1A2 in hepatocytes, and this enzyme was found to be involved in omeprazole 5-hydroxylation and sulfoxidation in dog hepatocytes and human HepaRG and HepaSH cells.
Collapse
Affiliation(s)
- Yasuhiro Uno
- Joint Faculty of Veterinary Medicine, Kagoshima University, Kagoshima, Japan
| | - Shotaro Uehara
- Department of Applied Research for Laboratory Animals, Central Institute for Experimental Animals, Kawasaki, Japan
| | - Genki Ushirozako
- Joint Faculty of Veterinary Medicine, Kagoshima University, Kagoshima, Japan
| | - Norie Murayama
- Laboratory of Drug Metabolism and Pharmacokinetics, Showa Pharmaceutical University, Machida, Japan
| | - Hiroshi Suemizu
- Department of Applied Research for Laboratory Animals, Central Institute for Experimental Animals, Kawasaki, Japan
| | - Hiroshi Yamazaki
- Laboratory of Drug Metabolism and Pharmacokinetics, Showa Pharmaceutical University, Machida, Japan
| |
Collapse
|
41
|
Zhu Y, Pan X, Jia Y, Yang X, Song X, Ding J, Zhong W, Feng J, Zhu L. Exploring Route-Specific Pharmacokinetics of PFAS in Mice by Coupling in Vivo Tests and Physiologically Based Toxicokinetic Models. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:127012. [PMID: 38088889 PMCID: PMC10718298 DOI: 10.1289/ehp11969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/08/2023] [Accepted: 11/08/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Oral ingestion, inhalation, and skin contact are important exposure routes for humans to uptake per- and polyfluoroalkyl substances (PFAS). However, nasal and dermal exposure to PFAS remains unclear, and accurately predicting internal body burden of PFAS in humans via multiple exposure pathways is urgently required. OBJECTIVES We aimed to develop multiple physiologically based toxicokinetic (PBTK) models to unveil the route-specific pharmacokinetics and bioavailability of PFAS via respective oral, nasal, and dermal exposure pathways using a mouse model and sought to predict the internal concentrations in various tissues through multiple exposure routes and extrapolate it to humans. METHODS Mice were administered the mixed solution of perfluorohexane sulfonate, perfluorooctane sulfonate, and perfluorooctanoic acid through oral, nasal, and dermal exposure separately or jointly. The time-dependent concentrations of PFAS in plasma and tissues were determined to calibrate and validate the individual and combined PBTK models, which were applied in single- and repeated-dose scenarios. RESULTS The developed route-specific PBTK models successfully simulated the tissue concentrations of PFAS in mice following single or joint exposure routes as well as long-term repeated dose scenarios. The time to peak concentration of PFAS in plasma via dermal exposure was much longer (34.1-83.0 h) than that via nasal exposure (0.960 h). The bioavailability of PFAS via oral exposure was the highest (73.2%-98.0%), followed by nasal (33.9%-66.8%) and dermal exposure (4.59%-7.80%). This model was extrapolated to predict internal levels in human under real environment. DISCUSSION Based on these data, we predict the following: PFAS were absorbed quickly via nasal exposure, whereas a distinct hysteresis effect was observed for dermal exposure. Almost all the PFAS to which mice were exposed via gastrointestinal route were absorbed into plasma, which exhibited the highest bioavailability. Exhalation clearance greatly depressed the bioavailability of PFAS via nasal exposure, whereas the lowest bioavailability in dermal exposure was because of the interception of PFAS within the skin layers. https://doi.org/10.1289/EHP11969.
Collapse
Affiliation(s)
- Yumin Zhu
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Xiaoyu Pan
- Beijing Sankuai Online Technology Co., Ltd., Beijing, P. R. China
| | - Yibo Jia
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Xin Yang
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Xiaohua Song
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Jiaqi Ding
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Wenjue Zhong
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Jianfeng Feng
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Lingyan Zhu
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| |
Collapse
|
42
|
Nguyen JT, Tian DD, Tanna RS, Arian CM, Calamia JC, Rettie AE, Thummel KE, Paine MF. An Integrative Approach to Elucidate Mechanisms Underlying the Pharmacokinetic Goldenseal-Midazolam Interaction: Application of In Vitro Assays and Physiologically Based Pharmacokinetic Models to Understand Clinical Observations. J Pharmacol Exp Ther 2023; 387:252-264. [PMID: 37541764 PMCID: PMC10658920 DOI: 10.1124/jpet.123.001681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 06/11/2023] [Accepted: 07/06/2023] [Indexed: 08/06/2023] Open
Abstract
The natural product goldenseal is a clinical inhibitor of CYP3A activity, as evidenced by a 40%-60% increase in midazolam area under the plasma concentration versus time curve (AUC) after coadministration with goldenseal. The predominant goldenseal alkaloids berberine and (-)-β-hydrastine were previously identified as time-dependent CYP3A inhibitors using human liver microsomes. Whether these alkaloids contribute to the clinical interaction, as well as the primary anatomic site (hepatic vs. intestinal) and mode of CYP3A inhibition (reversible vs. time-dependent), remain uncharacterized. The objective of this study was to mechanistically assess the pharmacokinetic goldenseal-midazolam interaction using an integrated in vitro-in vivo-in silico approach. Using human intestinal microsomes, (-)-β-hydrastine was a more potent time-dependent inhibitor of midazolam 1'-hydroxylation than berberine (KI and kinact: 8.48 μM and 0.041 minutes-1, respectively, vs. >250 μM and ∼0.06 minutes-1, respectively). Both the AUC and Cmax of midazolam increased by 40%-60% after acute (single 3-g dose) and chronic (1 g thrice daily × 6 days) goldenseal administration to healthy adults. These increases, coupled with a modest or no increase (≤23%) in half-life, suggested that goldenseal primarily inhibited intestinal CYP3A. A physiologically based pharmacokinetic interaction model incorporating berberine and (-)-β-hydrastine successfully predicted the goldenseal-midazolam interaction to within 20% of that observed after both chronic and acute goldenseal administration. Simulations implicated (-)-β-hydrastine as the major alkaloid precipitating the interaction, primarily via time-dependent inhibition of intestinal CYP3A, after chronic and acute goldenseal exposure. Results highlight the potential interplay between time-dependent and reversible inhibition of intestinal CYP3A as the mechanism underlying natural product-drug interactions, even after acute exposure to the precipitant. SIGNIFICANCE STATEMENT: Natural products can alter the pharmacokinetics of an object drug, potentially resulting in increased off-target effects or decreased efficacy of the drug. The objective of this work was to evaluate fundamental mechanisms underlying the clinically observed goldenseal-midazolam interaction. Results support the use of an integrated approach involving established in vitro assays, clinical evaluation, and physiologically based pharmacokinetic modeling to elucidate the complex interplay between multiple phytoconstituents and various pharmacokinetic processes driving a drug interaction.
Collapse
Affiliation(s)
- James T Nguyen
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (J.T.N., D.-D.T., R.S.T., M.F.P.); Department of Pharmaceutics (C.M.A., J.C.C., K.E.T.) and Department of Medicinal Chemistry (A.E.R.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (A.E.R, K.E.T., M.F.P.)
| | - Dan-Dan Tian
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (J.T.N., D.-D.T., R.S.T., M.F.P.); Department of Pharmaceutics (C.M.A., J.C.C., K.E.T.) and Department of Medicinal Chemistry (A.E.R.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (A.E.R, K.E.T., M.F.P.)
| | - Rakshit S Tanna
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (J.T.N., D.-D.T., R.S.T., M.F.P.); Department of Pharmaceutics (C.M.A., J.C.C., K.E.T.) and Department of Medicinal Chemistry (A.E.R.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (A.E.R, K.E.T., M.F.P.)
| | - Christopher M Arian
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (J.T.N., D.-D.T., R.S.T., M.F.P.); Department of Pharmaceutics (C.M.A., J.C.C., K.E.T.) and Department of Medicinal Chemistry (A.E.R.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (A.E.R, K.E.T., M.F.P.)
| | - Justina C Calamia
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (J.T.N., D.-D.T., R.S.T., M.F.P.); Department of Pharmaceutics (C.M.A., J.C.C., K.E.T.) and Department of Medicinal Chemistry (A.E.R.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (A.E.R, K.E.T., M.F.P.)
| | - Allan E Rettie
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (J.T.N., D.-D.T., R.S.T., M.F.P.); Department of Pharmaceutics (C.M.A., J.C.C., K.E.T.) and Department of Medicinal Chemistry (A.E.R.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (A.E.R, K.E.T., M.F.P.)
| | - Kenneth E Thummel
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (J.T.N., D.-D.T., R.S.T., M.F.P.); Department of Pharmaceutics (C.M.A., J.C.C., K.E.T.) and Department of Medicinal Chemistry (A.E.R.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (A.E.R, K.E.T., M.F.P.)
| | - Mary F Paine
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington (J.T.N., D.-D.T., R.S.T., M.F.P.); Department of Pharmaceutics (C.M.A., J.C.C., K.E.T.) and Department of Medicinal Chemistry (A.E.R.), School of Pharmacy, University of Washington, Seattle, Washington; and Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (A.E.R, K.E.T., M.F.P.)
| |
Collapse
|
43
|
Gaohua L, Zhang M, Sychterz C, Chang M, Schmidt BJ. The Interplay of Permeability, Metabolism, Transporters, and Dosing in Determining the Dynamics of the Tissue/Plasma Partition Coefficient and Volume of Distribution-A Theoretical Investigation Using Permeability-Limited, Physiologically Based Pharmacokinetic Modeling. Int J Mol Sci 2023; 24:16224. [PMID: 38003416 PMCID: PMC10671645 DOI: 10.3390/ijms242216224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 10/26/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023] Open
Abstract
A permeability-limited physiologically based pharmacokinetic (PBPK) model featuring four subcompartments (corresponding to the intracellular and extracellular water of the tissue, the residual plasma, and blood cells) for each tissue has been developed in MATLAB/SimBiology and applied to various what-if scenario simulations. This model allowed us to explore the complex interplay of passive permeability, metabolism in tissue or residual blood, active uptake or efflux transporters, and different dosing routes (intravenous (IV) or oral (PO)) in determining the dynamics of the tissue/plasma partition coefficient (Kp) and volume of distribution (Vd) within a realistic pseudo-steady state. Based on the modeling exercise, the permeability, metabolism, and transporters demonstrated significant effects on the dynamics of the Kp and Vd for IV bolus administration and PO fast absorption, but these effects were not as pronounced for IV infusion or PO slow absorption. Especially for low-permeability compounds, uptake transporters were found to increase both the Kp and Vd at the pseudo-steady state (Vdss), while efflux transporters had the opposite effect of decreasing the Kp and Vdss. For IV bolus administration and PO fast absorption, increasing tissue metabolism was predicted to elevate the Kp and Vdss, which contrasted with the traditional derivation from the steady-state perfusion-limited PBPK model. Moreover, metabolism in the residual blood had more impact on the Kp and Vdss compared to metabolism in tissue. Due to its ability to offer a more realistic description of tissue dynamics, the permeability-limited PBPK model is expected to gain broader acceptance in describing clinical PK and observed Kp and Vdss, even for certain small molecules like cyclosporine, which are currently treated as perfusion-limited in commercial PBPK platforms.
Collapse
Affiliation(s)
- Lu Gaohua
- Clinical Pharmacology & Pharmacometrics, Bristol Myers Squibb, Lawrenceville, NJ 08540, USA; (M.Z.); (C.S.); (M.C.); (B.J.S.)
| | | | | | | | | |
Collapse
|
44
|
Wang X, Yu Y, Liu H, Bu F, Shen C, He Q, Zhu X, Jiang P, Han B, Xiang X. Prediction of Drug-Drug Interactions with Ensartinib as a Time-Dependent CYP3A Inhibitor Using Physiologically Based Pharmacokinetic Model. Drug Metab Dispos 2023; 51:1515-1526. [PMID: 37643879 DOI: 10.1124/dmd.123.001373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 08/31/2023] Open
Abstract
Ensartinib (X-396) is a second-generation anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor (TKI) indicated for the treatment of ALK-positive patients with locally advanced or metastatic non-small cell lung cancer. Although in vitro experiments and molecular docking suggested its potential as a cytochrome P450 inhibitor, no further investigation or clinical trials have been conducted to assess its drug-drug interaction (DDI) risk. In this study, we conducted a series of in vitro experiments to elucidate the inhibition mechanism of ensartinib. Furthermore, a physiologically-based pharmacokinetic (PBPK) model was developed based on in vitro, in silico, and in vivo parameters, verified using clinical data, and applied to predict the clinical DDI mediated by ensartinib. The in vitro incubation experiments suggested that ensartinib exhibited strong time-dependent inhibition. Simulation results from the PBPK model indicated a significant increase in the exposure of CYP3A substrates in the presence of ensartinib, with the maximal plasma concentration and area under the plasma concentration-time curve increasing up to 12-fold and 29-fold for sensitive substrates. Based on these findings, it is evident that co-administration of ensartinib and CYP3A substrates requires careful regulatory consideration. SIGNIFICANCE STATEMENT: Ensartinib was found to be a strong time-dependent inhibitor of CYP3A for the first time based on in vitro experiments, but there was no research conducted to estimate the risk of drug-drug interaction (DDI) of ensartinib in clinic. Therefore, the first ensartinib physiologically based pharmacokinetic model was developed and applied to predict various untested scenarios. The simulation result indicated that the exposure of CYP3A substrate increased significantly and urged the further clinical DDI study.
Collapse
Affiliation(s)
- Xiaowen Wang
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China (X.W., Q.H., X.Z., P.J., X.X.); Department of Pharmacy, Minhang Hospital, Fudan University, Shanghai, China (X.W., Y.Y., H.L., C.S., B.H.); Department of Pharmacy, Eye and ENT Hospital, Fudan University, Shanghai, China (F.B.); and Shanghai Medicilon Inc., Shanghai, China (P.J.)
| | - Yiqun Yu
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China (X.W., Q.H., X.Z., P.J., X.X.); Department of Pharmacy, Minhang Hospital, Fudan University, Shanghai, China (X.W., Y.Y., H.L., C.S., B.H.); Department of Pharmacy, Eye and ENT Hospital, Fudan University, Shanghai, China (F.B.); and Shanghai Medicilon Inc., Shanghai, China (P.J.)
| | - Hongrui Liu
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China (X.W., Q.H., X.Z., P.J., X.X.); Department of Pharmacy, Minhang Hospital, Fudan University, Shanghai, China (X.W., Y.Y., H.L., C.S., B.H.); Department of Pharmacy, Eye and ENT Hospital, Fudan University, Shanghai, China (F.B.); and Shanghai Medicilon Inc., Shanghai, China (P.J.)
| | - Fengjiao Bu
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China (X.W., Q.H., X.Z., P.J., X.X.); Department of Pharmacy, Minhang Hospital, Fudan University, Shanghai, China (X.W., Y.Y., H.L., C.S., B.H.); Department of Pharmacy, Eye and ENT Hospital, Fudan University, Shanghai, China (F.B.); and Shanghai Medicilon Inc., Shanghai, China (P.J.)
| | - Chunying Shen
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China (X.W., Q.H., X.Z., P.J., X.X.); Department of Pharmacy, Minhang Hospital, Fudan University, Shanghai, China (X.W., Y.Y., H.L., C.S., B.H.); Department of Pharmacy, Eye and ENT Hospital, Fudan University, Shanghai, China (F.B.); and Shanghai Medicilon Inc., Shanghai, China (P.J.)
| | - Qingfeng He
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China (X.W., Q.H., X.Z., P.J., X.X.); Department of Pharmacy, Minhang Hospital, Fudan University, Shanghai, China (X.W., Y.Y., H.L., C.S., B.H.); Department of Pharmacy, Eye and ENT Hospital, Fudan University, Shanghai, China (F.B.); and Shanghai Medicilon Inc., Shanghai, China (P.J.)
| | - Xiao Zhu
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China (X.W., Q.H., X.Z., P.J., X.X.); Department of Pharmacy, Minhang Hospital, Fudan University, Shanghai, China (X.W., Y.Y., H.L., C.S., B.H.); Department of Pharmacy, Eye and ENT Hospital, Fudan University, Shanghai, China (F.B.); and Shanghai Medicilon Inc., Shanghai, China (P.J.)
| | - Pin Jiang
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China (X.W., Q.H., X.Z., P.J., X.X.); Department of Pharmacy, Minhang Hospital, Fudan University, Shanghai, China (X.W., Y.Y., H.L., C.S., B.H.); Department of Pharmacy, Eye and ENT Hospital, Fudan University, Shanghai, China (F.B.); and Shanghai Medicilon Inc., Shanghai, China (P.J.)
| | - Bing Han
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China (X.W., Q.H., X.Z., P.J., X.X.); Department of Pharmacy, Minhang Hospital, Fudan University, Shanghai, China (X.W., Y.Y., H.L., C.S., B.H.); Department of Pharmacy, Eye and ENT Hospital, Fudan University, Shanghai, China (F.B.); and Shanghai Medicilon Inc., Shanghai, China (P.J.)
| | - Xiaoqiang Xiang
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China (X.W., Q.H., X.Z., P.J., X.X.); Department of Pharmacy, Minhang Hospital, Fudan University, Shanghai, China (X.W., Y.Y., H.L., C.S., B.H.); Department of Pharmacy, Eye and ENT Hospital, Fudan University, Shanghai, China (F.B.); and Shanghai Medicilon Inc., Shanghai, China (P.J.)
| |
Collapse
|
45
|
Xie R, Wang X, Xu Y, Zhang L, Ma M, Wang Z. In vitro to in vivo extrapolation for predicting human equivalent dose of phenolic endocrine disrupting chemicals: PBTK model development, biological pathways, outcomes and performance. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 897:165271. [PMID: 37422235 DOI: 10.1016/j.scitotenv.2023.165271] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/12/2023] [Accepted: 06/30/2023] [Indexed: 07/10/2023]
Abstract
In vitro to in vivo (IVIVE) leverages in vitro high-throughput biological responses to predict the corresponding in vivo exposures and further estimate the human safe dose. However, for phenolic endocrine disrupting chemicals (EDCs) linked with complicated biological pathways and adverse outcomes (AO), such as bisphenol A (BPA) and 4-nonylphenol (4-NP), plausible estimation of human equivalent doses (HED) by IVIVE approaches considering various biological pathways and endpoints is still challenging. To explore the capabilities and limitations of IVIVE, this study conducted physiologically based toxicokinetic (PBTK)-IVIVE approaches to derive pathway-specific HEDs using BPA and 4-NP as examples. In vitro HEDs of BPA and 4-NP varied in different adverse outcomes, pathways, and testing endpoints and ranged from 0.0013 to 1.0986 mg/kg bw/day and 0.0551 to 1.7483 mg/kg bw/day, respectively. In vitro HEDs associated with reproductive AOs initiated by PPARα activation and ER agonism were the most sensitive. Model verification suggested the potential of using effective in vitro data to determine reasonable approximation of in vivo HEDs for the same AO (fold differences of most AOs ranged in 0.14-2.74 and better predictions for apical endpoints). Furthermore, system-specific parameters of cardiac output and its fraction, body weight, as well as chemical-specific parameters of partition coefficient and liver metabolic were most sensitive for the PBTK simulations. The results indicated that the application of fit for-purpose PBTK-IVIVE approach could provide credible pathway-specific HEDs and contribute to high throughput prioritization of chemicals in a more realistic scenario.
Collapse
Affiliation(s)
- Ruili Xie
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaodan Wang
- China National Center for Food Safety Risk Assessment, Beijing 100022, China
| | - Yiping Xu
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
| | - Lei Zhang
- China National Center for Food Safety Risk Assessment, Beijing 100022, China.
| | - Mei Ma
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zijian Wang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| |
Collapse
|
46
|
van der Heijden JEM, Freriksen JJM, de Hoop-Sommen MA, Greupink R, de Wildt SN. Physiologically-Based Pharmacokinetic Modeling for Drug Dosing in Pediatric Patients: A Tutorial for a Pragmatic Approach in Clinical Care. Clin Pharmacol Ther 2023; 114:960-971. [PMID: 37553784 DOI: 10.1002/cpt.3023] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/02/2023] [Indexed: 08/10/2023]
Abstract
It is well-accepted that off-label drug dosing recommendations for pediatric patients should be based on the best available evidence. However, the available traditional evidence is often low. To bridge this gap, physiologically-based pharmacokinetic (PBPK) modeling is a scientifically well-founded tool that can be used to enable model-informed dosing (MID) recommendations in children in clinical practice. In this tutorial, we provide a pragmatic, PBPK-based pediatric modeling workflow. For this approach to be successfully implemented in pediatric clinical practice, a thorough understanding of the model assumptions and limitations is required. More importantly, careful evaluation of an MID approach within the context of overall benefits and the potential risks is crucial. The tutorial is aimed to help modelers, researchers, and clinicians, to effectively use PBPK simulations to support pediatric drug dosing.
Collapse
Affiliation(s)
- Joyce E M van der Heijden
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jolien J M Freriksen
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marika A de Hoop-Sommen
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rick Greupink
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Saskia N de Wildt
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Pediatric and Neonatal Intensive Care, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
| |
Collapse
|
47
|
Chen W, Ruan Z, Lou H, Yang D, Chen J, Shao R, Jiang B. Physiologically based pharmacokinetic modeling to characterize enterohepatic recirculation and predict food effect on the pharmacokinetics of hyzetimibe. Eur J Pharm Sci 2023; 190:106576. [PMID: 37678518 DOI: 10.1016/j.ejps.2023.106576] [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: 06/20/2023] [Revised: 08/17/2023] [Accepted: 08/31/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Hyzetimibe is a cholesterol absorption inhibitor indicated for the treatment of hypercholesterolemia. This study aims to describe the multiple-peak pharmacokinetics (PK) of hyzetimibe and its active metabolite M1 through physiologically-based pharmacokinetic (PBPK) modeling, and to compare the model predictions of a virtual food effect study with the results of a clinical food effect study. METHODS The plasma concentration data used for PBPK modeling were obtained from a single-dose, two-period crossover bioequivalence study in the fasted state. Advanced Compartmental Absorption and Transit model was used for absorption. Enterohepatic recirculation process was modeled by changing the gut physiological state from fasted to fed at meal time. Based on the established PBPK models, a virtual food effect study was simulated. A clinical food effect study was used for model external validation. RESULTS PK profiles of hyzetimibe and M1 under fasting condition could be well described by the PBPK model, and the errors of Cmax, AUC0-∞, and AUC0-t were within the two-fold range. Simulated geometric mean ratios (GMRs, fed/fasted) showed that a high-fat breakfast slightly affected the PK of hyzetimibe, expressed as increased Cmax of hyzetimibe (130.6%). Simulated GMRs and 90% confidence intervals of AUC were within the preset bioequivalent range. The results of the simulated virtual food effect trial were consistent with those of the clinical food effect trial. CONCLUSIONS The established PBPK model could describe the concentration-time profiles of hyzetimibe and M1 well with good prediction performance. A fully mechanistic model of enterohepatic recirculation warrants further investigation.
Collapse
Affiliation(s)
- Wenjun Chen
- Center of Clinical Pharmacology, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zourong Ruan
- Center of Clinical Pharmacology, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Honggang Lou
- Center of Clinical Pharmacology, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Dandan Yang
- Center of Clinical Pharmacology, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jinliang Chen
- Center of Clinical Pharmacology, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Rong Shao
- Center of Clinical Pharmacology, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Bo Jiang
- Center of Clinical Pharmacology, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
| |
Collapse
|
48
|
El Hoffy NM, Yacoub AS, Ghoneim AM, Ibrahim M, Ammar HO, Eissa N. Computational Amendment of Parenteral In Situ Forming Particulates' Characteristics: Design of Experiment and PBPK Physiological Modeling. Pharmaceutics 2023; 15:2513. [PMID: 37896273 PMCID: PMC10609842 DOI: 10.3390/pharmaceutics15102513] [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: 09/14/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
Lipid and/or polymer-based drug conjugates can potentially minimize side effects by increasing drug accumulation at target sites and thus augment patient compliance. Formulation factors can present a potent influence on the characteristics of the obtained systems. The selection of an appropriate solvent with satisfactory rheological properties, miscibility, and biocompatibility is essential to optimize drug release. This work presents a computational study of the effect of the basic formulation factors on the characteristics of the obtained in situ-forming particulates (IFPs) encapsulating a model drug using a 21.31 full factorial experimental design. The emulsion method was employed for the preparation of lipid and/or polymer-based IFPs. The IFP release profiles and parameters were computed. Additionally, a desirability study was carried out to choose the optimum formulation for further morphological examination, rheological study, and PBPK physiological modeling. Results revealed that the type of particulate forming agent (lipid/polymer) and the incorporation of structure additives like Brij 52 and Eudragit RL can effectively augment the release profile as well as the burst of the drug. The optimized formulation exhibited a pseudoplastic rheological behavior and yielded uniformly spherical-shaped dense particulates with a PS of 573.92 ± 23.5 nm upon injection. Physiological modeling simulation revealed the pioneer pharmacokinetic properties of the optimized formulation compared to the observed data. These results assure the importance of controlling the formulation factors during drug development, the potentiality of the optimized IFPs for the intramuscular delivery of piroxicam, and the reliability of PBPK physiological modeling in predicting the biological performance of new formulations with effective cost management.
Collapse
Affiliation(s)
- Nada M. El Hoffy
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Future University in Egypt, New Cairo 11835, Egypt; (A.S.Y.); (A.M.G.); (H.O.A.)
| | - Ahmed S. Yacoub
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Future University in Egypt, New Cairo 11835, Egypt; (A.S.Y.); (A.M.G.); (H.O.A.)
- Bone Muscle Research Center, The University of Texas at Arlington, Arlington, TX 76013, USA
| | - Amira M. Ghoneim
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Future University in Egypt, New Cairo 11835, Egypt; (A.S.Y.); (A.M.G.); (H.O.A.)
| | - Magdy Ibrahim
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Cairo University, Giza 11562, Egypt;
| | - Hussein O. Ammar
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Future University in Egypt, New Cairo 11835, Egypt; (A.S.Y.); (A.M.G.); (H.O.A.)
| | - Nermin Eissa
- Department of Biomedical Sciences, College of Health Sciences, Abu Dhabi University, Abu Dhabi P.O. Box 59911, United Arab Emirates
| |
Collapse
|
49
|
Li X, Jusko WJ. Utility of Minimal Physiologically Based Pharmacokinetic Models for Assessing Fractional Distribution, Oral Absorption, and Series-Compartment Models of Hepatic Clearance. Drug Metab Dispos 2023; 51:1403-1418. [PMID: 37460222 PMCID: PMC10506700 DOI: 10.1124/dmd.123.001403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/13/2023] [Indexed: 09/16/2023] Open
Abstract
Minimal physiologically based pharmacokinetic (mPBPK) models are physiologically relevant, require less information than full PBPK models, and offer flexibility in pharmacokinetics (PK). The well-stirred hepatic model (WSM) is commonly used in PBPK, whereas the more plausible dispersion model (DM) poses computational complexities. The series-compartment model (SCM) mimics the DM but is easier to operate. This work implements the SCM and mPBPK models for assessing fractional tissue distribution, oral absorption, and hepatic clearance using literature-reported blood and liver concentration-time data in rats for compounds mainly cleared by the liver. Further handled were various complexities, including nonlinear hepatic binding and metabolism, differing absorption kinetics, and sites of administration. The SCM containing one to five (n) liver subcompartments yields similar fittings and provides comparable estimates for hepatic extraction ratio (ER), prehepatic availability (Fg ), and first-order absorption rate constants (ka ). However, they produce decreased intrinsic clearances (CLint ) and liver-to-plasma partition coefficients (Kph ) with increasing n as expected. Model simulations demonstrated changes in intravenous and oral PK profiles with alterations in Kph and ka and with hepatic metabolic zonation. The permeability (PAMPA P) of the various compounds well explained the fitted fractional distribution (fd ) parameters. The SCM and mPBPK models offer advantages in distinguishing systemic, extrahepatic, and hepatic clearances. The SCM allows for incorporation of liver zonation and is useful in assessing changes in internal concentration gradients potentially masked by similar blood PK profiles. Improved assessment of intraorgan drug concentrations may offer insights into active moieties driving metabolism, biliary excretion, pharmacodynamics, and hepatic toxicity. SIGNIFICANCE STATEMENT: The minimal physiologically based pharmacokinetic model and the series-compartment model are useful in assessing oral absorption and hepatic clearance. They add flexibility in accounting for various drug- or system-specific complexities, including fractional distribution, nonlinear binding and saturable hepatic metabolism, and hepatic zonation. These models can offer improved insights into the intraorgan concentrations that reflect physiologically active moieties often driving disposition, pharmacodynamics, and toxicity.
Collapse
Affiliation(s)
- Xiaonan Li
- Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York
| | - William J Jusko
- Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York
| |
Collapse
|
50
|
Machado TR, Honorio T, Souza Domingos TF, Candido de Paula DDS, Cabral LM, Rodrigues CR, Abrahim-Vieira BA, Teles de Souza AM. Physiologically based pharmacokinetic modelling of semaglutide in children and adolescents with healthy and obese body weights. Br J Clin Pharmacol 2023; 89:3175-3194. [PMID: 37293836 DOI: 10.1111/bcp.15816] [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: 12/14/2022] [Revised: 04/23/2023] [Accepted: 05/31/2023] [Indexed: 06/10/2023] Open
Abstract
AIMS To develop paediatric physiologically based pharmacokinetic modelling (PBPK) models of semaglutide to estimate the pharmacokinetic profile for subcutaneous injections in children and adolescents with healthy and obese body weights. METHODS Pharmacokinetic modelling and simulations of semaglutide subcutaneous injections were performed using the Transdermal Compartmental Absorption & Transit model implemented in GastroPlus v.9.5 modules. A PBPK model of semaglutide was developed and verified in the adult population, by comparing the simulated plasma exposure with the observed data, and further scaled to the paediatric populations with normal and obese body weight. RESULTS The semaglutide PBPK model was successfully developed in adults and scaled to the paediatric population. Our paediatric PBPK simulations indicated a significant increase in maximum plasma concentrations for the 10-14 years' paediatric population with healthy body weights, which was higher than the observed values in adults at the reference dose. Since gastrointestinal adverse events are related to increased semaglutide concentrations, peak concentrations outside the target range may represent a safety risk for this paediatric age group. Besides, paediatric PBPK models indicated that body weight was inversely related to semaglutide maximum plasma concentration, corroborating the consensus on the influence of body weight on semaglutide PK in adults. CONCLUSION Paediatric PBPK was successfully achieved using a top-down approach and drug-related parameters. The development of unprecedented PBPK models will support paediatric clinical therapy for applying aid-safe dosing regimens for the paediatric population in diabetes treatment.
Collapse
Affiliation(s)
- Thayná Rocco Machado
- Laboratory of Molecular Modeling & QSAR (ModMolQSAR), Faculty of Pharmacy, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Thiago Honorio
- Laboratory of Molecular Modeling & QSAR (ModMolQSAR), Faculty of Pharmacy, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Dailane da Silva Candido de Paula
- Laboratory of Molecular Modeling & QSAR (ModMolQSAR), Faculty of Pharmacy, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Lucio Mendes Cabral
- Laboratory of Molecular Modeling & QSAR (ModMolQSAR), Faculty of Pharmacy, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Carlos R Rodrigues
- Laboratory of Molecular Modeling & QSAR (ModMolQSAR), Faculty of Pharmacy, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Bárbara A Abrahim-Vieira
- Laboratory of Molecular Modeling & QSAR (ModMolQSAR), Faculty of Pharmacy, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Alessandra Mendonça Teles de Souza
- Laboratory of Molecular Modeling & QSAR (ModMolQSAR), Faculty of Pharmacy, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| |
Collapse
|