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Chacko IA, Ramachandran G, Sudheesh MS. Unmet technological demands in orodispersible films for age-appropriate paediatric drug delivery. Drug Deliv Transl Res 2024; 14:841-857. [PMID: 37957474 DOI: 10.1007/s13346-023-01451-3] [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] [Accepted: 10/11/2023] [Indexed: 11/15/2023]
Abstract
Age-appropriateness of a formulation is the ability to deliver variable but accurate doses to the paediatric population in a safe and acceptable manner to improve medical adherence and reduce medication errors. Paediatric drug delivery is a challenging area of formulation research due to the existing gap in knowledge. This includes the unknown safety of excipients in the paediatric population, the need for an age-appropriate formulation, the lack of an effective taste-masking method and the lack of paediatric pharmacokinetic data and patient acceptability. It is equally important to establish methods for predicting the biopharmaceutical performance of a paediatric formulation as a function of age. Overcoming the challenges of existing technologies and providing custom-made solutions for the development of age-appropriate formulation is, therefore, a daunting task. Orodispersible films (ODF) are promising as age-appropriate formulations, an unmet need in paediatric drug delivery. New technological improvements in taste masking, improving solubility and rate of dissolution of insoluble drugs, the flexibility of dosing and extemporaneous preparation of these films in a hospital good manufacturing practises (GMP) setup using 3D printing can increase its acceptance among clinicians, patients and caregivers. The current review discusses the problems and possibilities in ODF technology to address the outstanding issues of age-appropriateness, which is the hallmark of patient acceptance and medical adherence in paediatrics.
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Affiliation(s)
- Indhu Annie Chacko
- Department of Pharmaceutics, Amrita School of Pharmacy, AIMS Health Sciences Campus, Amrita Vishwa Vidyapeetham, 682041, Ponekkara, Kochi, India
| | - Gayathri Ramachandran
- Department of Pharmaceutics, Amrita School of Pharmacy, AIMS Health Sciences Campus, Amrita Vishwa Vidyapeetham, 682041, Ponekkara, Kochi, India
| | - M S Sudheesh
- Department of Pharmaceutics, Amrita School of Pharmacy, AIMS Health Sciences Campus, Amrita Vishwa Vidyapeetham, 682041, Ponekkara, Kochi, India.
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2
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Kang DW, Kim JH, Choi GW, Cho SJ, Cho HY. Physiologically-based pharmacokinetic model for evaluating gender-specific exposures of N-nitrosodimethylamine (NDMA). Arch Toxicol 2024; 98:821-835. [PMID: 38127128 DOI: 10.1007/s00204-023-03652-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023]
Abstract
N-nitrosodimethylamine (NDMA) is classified as a human carcinogen and could be produced by both natural and industrial processes. Although its toxicity and histopathology have been well-studied in animal species, there is insufficient data on the blood and tissue exposures that can be correlated with the toxicity of NDMA. The purpose of this study was to evaluate gender-specific pharmacokinetics/toxicokinetics (PKs/TKs), tissue distribution, and excretion after the oral administration of three different doses of NDMA in rats using a physiologically-based pharmacokinetic (PBPK) model. The major target tissues for developing the PBPK model and evaluating dose metrics of NDMA included blood, gastrointestinal (GI) tract, liver, kidney, lung, heart, and brain. The predictive performance of the model was validated using sensitivity analysis, (average) fold error, and visual inspection of observations versus predictions. Then, a Monte Carlo simulation was performed to describe the magnitudes of inter-individual variability and uncertainty of the single model predictions. The developed PBPK model was applied for the exposure simulation of daily oral NDMA to estimate blood concentration ranges affecting health effects following acute-duration (≤ 14 days), intermediate-duration (15-364 days), and chronic-duration (≥ 365 days) intakes. The results of the study could be used as a scientific basis for interpreting the correlation between in vivo exposures and toxicological effects of NDMA.
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Affiliation(s)
- Dong Wook Kang
- College of Pharmacy, CHA University, 335 Pangyo-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13488, Republic of Korea
| | - Ju Hee Kim
- College of Pharmacy, CHA University, 335 Pangyo-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13488, Republic of Korea
| | - Go-Wun Choi
- College of Pharmacy, CHA University, 335 Pangyo-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13488, Republic of Korea
| | - Seok-Jin Cho
- College of Pharmacy, CHA University, 335 Pangyo-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13488, Republic of Korea
| | - Hea-Young Cho
- College of Pharmacy, CHA University, 335 Pangyo-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13488, Republic of Korea.
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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.
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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.
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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.
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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
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Zakariya F, Salem FK, Alamrain AA, Sanker V, Abdelazeem ZG, Hosameldin M, Tan JK, Howard R, Huang H, Awuah WA. Refining mutanome-based individualised immunotherapy of melanoma using artificial intelligence. Eur J Med Res 2024; 29:25. [PMID: 38183141 PMCID: PMC10768232 DOI: 10.1186/s40001-023-01625-2] [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: 11/17/2023] [Accepted: 12/25/2023] [Indexed: 01/07/2024] Open
Abstract
Using the particular nature of melanoma mutanomes to develop medicines that activate the immune system against specific mutations is a game changer in immunotherapy individualisation. It offers a viable solution to the recent rise in resistance to accessible immunotherapy alternatives, with some patients demonstrating innate resistance to these drugs despite past sensitisation to these agents. However, various obstacles stand in the way of this method, most notably the practicality of sequencing each patient's mutanome, selecting immunotherapy targets, and manufacturing specific medications on a large scale. With the robustness and advancement in research techniques, artificial intelligence (AI) is a potential tool that can help refine the mutanome-based immunotherapy for melanoma. Mutanome-based techniques are being employed in the development of immune-stimulating vaccines, improving current options such as adoptive cell treatment, and simplifying immunotherapy responses. Although the use of AI in these approaches is limited by data paucity, cost implications, flaws in AI inference capabilities, and the incapacity of AI to apply data to a broad population, its potential for improving immunotherapy is limitless. Thus, in-depth research on how AI might help the individualisation of immunotherapy utilising knowledge of mutanomes is critical, and this should be at the forefront of melanoma management.
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Affiliation(s)
- Farida Zakariya
- Faculty of Pharmaceutical Sciences, Ahmadu Bello University, Zaria, Nigeria
- Division of Experimental Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - Fatma K Salem
- Faculty of Veterinary Medicine, South Valley University, Qena, 83523, Egypt
| | | | - Vivek Sanker
- Research Assistant, Dept. Of Neurosurgery, Trivandrum Medical College, Trivandrum, India
| | - Zainab G Abdelazeem
- Division of Molecular Biology, Department of Zoology, Faculty of Science, Alexandria University, Alexandria, Egypt
| | | | | | - Rachel Howard
- School of Clinical Medicine, University of Cambridge, Cambridge, England
| | - Helen Huang
- Faculty of Medicine and Health Science, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Wireko Andrew Awuah
- Medical Institute, Sumy State University, Zamonstanksya 7, Sumy, 40007, Ukraine.
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Reali F, Fochesato A, Kaddi C, Visintainer R, Watson S, Levi M, Dartois V, Azer K, Marchetti L. A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis. Front Pharmacol 2024; 14:1272091. [PMID: 38239195 PMCID: PMC10794428 DOI: 10.3389/fphar.2023.1272091] [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: 08/03/2023] [Accepted: 12/04/2023] [Indexed: 01/22/2024] Open
Abstract
Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework. Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b. Results: The model has been successfully tested and validated against 11 anti-TB compounds-rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices. Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.
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Affiliation(s)
- Federico Reali
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Anna Fochesato
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Mathematics, University of Trento, Povo, Italy
| | - Chanchala Kaddi
- Gates Medical Research Institute, Cambridge, MD, United States
| | - Roberto Visintainer
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Shayne Watson
- Gates Medical Research Institute, Cambridge, MD, United States
| | - Micha Levi
- Gates Medical Research Institute, Cambridge, MD, United States
| | | | - Karim Azer
- Gates Medical Research Institute, Cambridge, MD, United States
| | - Luca Marchetti
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Povo, Italy
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Lancheros Porras KD, Alves IA, Novoa DMA. PBPK Modeling as an Alternative Method of Interspecies Extrapolation that Reduces the Use of Animals: A Systematic Review. Curr Med Chem 2024; 31:102-126. [PMID: 37031391 DOI: 10.2174/0929867330666230408201849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/03/2023] [Accepted: 02/03/2023] [Indexed: 04/10/2023]
Abstract
INTRODUCTION Physiologically based pharmacokinetic (PBPK) modeling is a computational approach that simulates the anatomical structure of the studied species and presents the organs and tissues as compartments interconnected by arterial and venous blood flows. AIM The aim of this systematic review was to analyze the published articles focused on the development of PBPK models for interspecies extrapolation in the disposition of drugs and health risk assessment, presenting to this modeling an alternative to reduce the use of animals. METHODS For this purpose, a systematic search was performed in PubMed using the following search terms: "PBPK" and "Interspecies extrapolation". The revision was performed according to PRISMA guidelines. RESULTS In the analysis of the articles, it was found that rats and mice are the most commonly used animal models in the PBPK models; however, most of the physiological and physicochemical information used in the reviewed studies were obtained from previous publications. Additionally, most of the PBPK models were developed to extrapolate pharmacokinetic parameters to humans and the main application of the models was for toxicity testing. CONCLUSION PBPK modeling is an alternative that allows the integration of in vitro and in silico data as well as parameters reported in the literature to predict the pharmacokinetics of chemical substances, reducing in large quantity the use of animals that are required in traditional studies.
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Cuquerella-Gilabert M, Reig-López J, Serna J, Rueda-Ferreiro A, Merino-Sanjuan M, Mangas-Sanjuan V, Sánchez-Herrero S. Phys-DAT: A physiologically-based pharmacokinetic model for unraveling the dissolution, transit and absorption processes using PhysPK®. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107929. [PMID: 38006685 DOI: 10.1016/j.cmpb.2023.107929] [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: 07/26/2023] [Revised: 11/11/2023] [Accepted: 11/13/2023] [Indexed: 11/27/2023]
Abstract
BACKGROUND AND OBJECTIVE In silico methods have become the key for efficiently testing and qualifying drug properties. Due to the complexity of the LADME processes and drug characteristics associated to oral drug absorption, there is a growing demand in the development of Physiologically-based Pharmacokinetic (PBPK) software with greater flexibility. Thus, the aims of this work are (i) to develop a mechanistic-based modeling framework of dissolution, transit and absorption (Phys-DAT) processes in the PhysPK platform and (ii) to assess the predictive power of the acausal MOOM methodology embedded in Phys-DAT versus reference ODE-based PBPK software. METHODS A PBPK model was developed including unreleased, undissolved and dissolved thermodynamic states of the drug. The gastrointestinal tract (GI) was represented by nine compartments and first-order transit kinetics was assumed for the drug fractions. Dissolution processes were described using solubility-independent or solubility-dependent mechanisms and pH effects. Linear transit and linear absorption mechanisms including gradual decrease absorption rate were considered to represent the passive diffusion process. Internal validation of the Phys-DAT model was performed through simulation-based analysis, considering different theoretical scenarios. External validation was carried out using in silico and in vivo data of GI segments and plasma concentrations. Both BCS I and II class drugs were included. RESULTS The model predicts plasma-concentration profiles of each compartment for undissolved, dissolved, and absorbed fractions using PhysPK® v.2.4.1. Internal and external validations demonstrate that the model aligned with the theoretical assumptions and accurately predicted Cmax, Tmax, and AUC 0-t for both BCS I and II drugs. Average Fold Error (AFE), Absolute Average Fold Error (AAFE), and Percent Prediction Error (PPE) calculations indicate good predictive performance, with predicted/observed ratios falling within the acceptable range. CONCLUSIONS Phys-DAT represents a mechanistic model for predicting oral absorption, including the dissolution, pH effect, transit, and absorption processes. PhysPK has shown to be a tool with strong prediction accuracy, similar to the obtained by ODE-based PBPK reference software, and the results obtained with the Phys-DAT model for oral administered drugs showed predictive reliability in healthy volunteers, setting the basis to determine the interchangeability of the acausal MOOM methodology with other modeling approaches.
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Affiliation(s)
- Marina Cuquerella-Gilabert
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain; Interuniversity Research Institute for Molecular Recognition and Technological Development, Polytechnic University of Valencia-University of Valencia, Valencia, Spain; Simulation Department, Empresarios Agrupados Internacional S.A., Madrid, Spain
| | - Javier Reig-López
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain; Interuniversity Research Institute for Molecular Recognition and Technological Development, Polytechnic University of Valencia-University of Valencia, Valencia, Spain
| | - Jenifer Serna
- Simulation Department, Empresarios Agrupados Internacional S.A., Madrid, Spain
| | | | - Matilde Merino-Sanjuan
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain; Interuniversity Research Institute for Molecular Recognition and Technological Development, Polytechnic University of Valencia-University of Valencia, Valencia, Spain
| | - Victor Mangas-Sanjuan
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain; Interuniversity Research Institute for Molecular Recognition and Technological Development, Polytechnic University of Valencia-University of Valencia, Valencia, Spain.
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Chen M, Du R, Zhang T, Li C, Bao W, Xin F, Hou S, Yang Q, Chen L, Wang Q, Zhu A. The Application of a Physiologically Based Toxicokinetic Model in Health Risk Assessment. TOXICS 2023; 11:874. [PMID: 37888724 PMCID: PMC10611306 DOI: 10.3390/toxics11100874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/17/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023]
Abstract
Toxicokinetics plays a crucial role in the health risk assessments of xenobiotics. Classical compartmental models are limited in their ability to determine chemical concentrations in specific organs or tissues, particularly target organs or tissues, and their limited interspecific and exposure route extrapolation hinders satisfactory health risk assessment. In contrast, physiologically based toxicokinetic (PBTK) models quantitatively describe the absorption, distribution, metabolism, and excretion of chemicals across various exposure routes and doses in organisms, establishing correlations with toxic effects. Consequently, PBTK models serve as potent tools for extrapolation and provide a theoretical foundation for health risk assessment and management. This review outlines the construction and application of PBTK models in health risk assessment while analyzing their limitations and future perspectives.
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Affiliation(s)
- Mengting Chen
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350108, China
| | - Ruihu Du
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, China
| | - Tao Zhang
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, China
| | - Chutao Li
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350108, China
| | - Wenqiang Bao
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350108, China
| | - Fan Xin
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350108, China
| | - Shaozhang Hou
- Department of Pathology, School of Basic Medical Sciences, Ningxia Medical University, Yinchuan 750004, China
| | - Qiaomei Yang
- Department of Gynecology, Fujian Maternity and Child Health Hospital (Fujian Obstetrics and Gynecology Hospital), Fuzhou 350001, China
| | - Li Chen
- Department of Gynecology, Fujian Maternity and Child Health Hospital (Fujian Obstetrics and Gynecology Hospital), Fuzhou 350001, China
| | - Qi Wang
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, China
- Key Laboratory of State Administration of Traditional Chinese Medicine for Compatibility Toxicology, Beijing 100191, China
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Beijing 100191, China
| | - An Zhu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350108, China
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Liang N, Zhou S, Li T, Zhang Z, Zhao T, Li R, Li M, Shao F, Wang G, Sun J. Physiologically based pharmacokinetic modeling to assess the drug-drug interactions of anaprazole with clarithromycin and amoxicillin in patients undergoing eradication therapy of H. pylori infection. Eur J Pharm Sci 2023; 189:106534. [PMID: 37480962 DOI: 10.1016/j.ejps.2023.106534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 06/18/2023] [Accepted: 07/18/2023] [Indexed: 07/24/2023]
Abstract
OBJECTIVE This study aimed to assess the pharmacokinetic (PK) interactions of anaprazole, clarithromycin, and amoxicillin using physiologically based pharmacokinetic (PBPK) models. METHODS The PBPK models for anaprazole, clarithromycin, and amoxicillin were constructed using the GastroPlus™ software (Version 9.7) based on the physicochemical data and PK parameters obtained from literature, then were optimized and validated in healthy subjects to predict the plasma concentration-time profiles of these three drugs and assess the predictive performance of each model. According to the analysis of the properties of each drug, the developed and validated models were applied to evaluate potential drug-drug interactions (DDIs) of anaprazole, clarithromycin, and amoxicillin. RESULTS The developed PBPK models properly described the pharmacokinetics of anaprazole, clarithromycin, and amoxicillin well, and all predicted PK parameters (Cmax,ss, AUC0-τ,ss) ratios were within 2.0-fold of the observed values. Furthermore, the application of these models to predict the anaprazole-clarithromycin and anaprazole-amoxicillin DDIs demonstrates their good performance, with the predicted DDI Cmax,ss ratios and DDI AUC0-τ,ss ratios within 1.25-fold of the observed values, and all predicted DDI Cmax,ss, and AUC0-τ,ss ratios within 2.0-fold. The simulated results show no need to adjust the dosage when co-administered with anaprazole in patients undergoing eradication therapy of H. pylori infection since the dose remained in the therapeutic range. CONCLUSION The whole-body PBPK models of anaprazole, clarithromycin, and amoxicillin were built and qualified, which can predict DDIs that are mediated by gastric pH change and inhibition of metabolic enzymes, providing a mechanistic understanding of the DDIs observed in the clinic of clarithromycin, amoxicillin with anaprazole.
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Affiliation(s)
- Ningxia Liang
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China; Department of Clinical Pharmacology, School of Pharmacy College, Nanjing Medical University, Nanjing 211166, China
| | - Sufeng Zhou
- Phase I Clinical Trial Unit, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China
| | - Tongtong Li
- Department of Clinical Pharmacology, School of Pharmacy College, Nanjing Medical University, Nanjing 211166, China; Phase I Clinical Trial Unit, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China
| | - Zeru Zhang
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Tangping Zhao
- Department of Clinical Pharmacology, School of Pharmacy College, Nanjing Medical University, Nanjing 211166, China; Phase I Clinical Trial Unit, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China
| | - Run Li
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Mingfeng Li
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Feng Shao
- Department of Clinical Pharmacology, School of Pharmacy College, Nanjing Medical University, Nanjing 211166, China; Phase I Clinical Trial Unit, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China.
| | - Guangji Wang
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China.
| | - Jianguo Sun
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China.
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11
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Kumar P, Mehta D, Bissler JJ. Physiologically Based Pharmacokinetic Modeling of Extracellular Vesicles. BIOLOGY 2023; 12:1178. [PMID: 37759578 PMCID: PMC10525702 DOI: 10.3390/biology12091178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/13/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023]
Abstract
Extracellular vesicles (EVs) are lipid membrane bound-cell-derived structures that are a key player in intercellular communication and facilitate numerous cellular functions such as tumor growth, metastasis, immunosuppression, and angiogenesis. They can be used as a drug delivery platform because they can protect drugs from degradation and target specific cells or tissues. With the advancement in the technologies and methods in EV research, EV-therapeutics are one of the fast-growing domains in the human health sector. Therapeutic translation of EVs in clinics requires assessing the quality, safety, and efficacy of the EVs, in which pharmacokinetics is very crucial. We report here the application of physiologically based pharmacokinetic (PBPK) modeling as a principal tool for the prediction of absorption, distribution, metabolism, and excretion of EVs. To create a PBPK model of EVs, researchers would need to gather data on the size, shape, and composition of the EVs, as well as the physiological processes that affect their behavior in the body. The PBPK model would then be used to predict the pharmacokinetics of drugs delivered via EVs, such as the rate at which the drug is absorbed and distributed throughout the body, the rate at which it is metabolized and eliminated, and the maximum concentration of the drug in the body. This information can be used to optimize the design of EV-based drug delivery systems, including the size and composition of the EVs, the route of administration, and the dose of the drug. There has not been any dedicated review article that describes the PBPK modeling of EV. This review provides an overview of the absorption, distribution, metabolism, and excretion (ADME) phenomena of EVs. In addition, we will briefly describe the different computer-based modeling approaches that may help in the future of EV-based therapeutic research.
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Affiliation(s)
- Prashant Kumar
- Division of Biochemical Toxicology, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR 72079, USA;
| | - Darshan Mehta
- Division of Biochemical Toxicology, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR 72079, USA;
| | - John J. Bissler
- Department of Pediatrics, Division of Pediatrics Nephrology, University of Tennessee Health Science Center, Memphis, TN 38103, USA;
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12
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Viel A, Nouichi A, Le Van Suu M, Rolland JG, Sanders P, Laurentie M, Manceau J, Henri J. PBPK Model To Predict Marbofloxacin Distribution in Edible Tissues and Intestinal Exposure in Pigs. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:4358-4370. [PMID: 36877630 DOI: 10.1021/acs.jafc.2c06561] [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] [Indexed: 06/18/2023]
Abstract
Marbofloxacin (MAR) is a fluoroquinolone antibiotic used in food-producing animals in European Union, especially in pigs. In this study, MAR concentrations in plasma, comestible tissues, and intestinal segments were determined in pigs injected with MAR. Based on these data and the literature, a flow-limited PBPK model was developed to predict the tissue distribution of MAR and estimate the withdrawal period after label-use in Europe. A submodel describing the different segments of the intestinal lumen was also developed to assess the intestinal exposure of MAR for the commensal bacteria. During model calibration, only four parameters were estimated. Then, Monte Carlo simulations were performed to generate a virtual population of pigs. The simulation results were compared with the observations from an independent data set during the validation step. A global sensitivity analysis was also carried out to identify the most influential parameters. Overall, the PBPK model was able to adequately predict the MAR kinetics in plasma and edible tissues, as well as in small intestines. However, the simulated concentrations in the large intestine were mostly underestimated, highlighting the need for improvements in the field of PBPK modeling to assess the intestinal exposure of antimicrobials in food animals.
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Affiliation(s)
- Alexis Viel
- Fougères Laboratory, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), 10B rue Claude Bourgelat, Fougères 35306, France
| | - Anis Nouichi
- Fougères Laboratory, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), 10B rue Claude Bourgelat, Fougères 35306, France
| | - Mélanie Le Van Suu
- Fougères Laboratory, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), 10B rue Claude Bourgelat, Fougères 35306, France
| | - Jean-Guy Rolland
- Fougères Laboratory, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), 10B rue Claude Bourgelat, Fougères 35306, France
| | - Pascal Sanders
- Fougères Laboratory, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), 10B rue Claude Bourgelat, Fougères 35306, France
| | - Michel Laurentie
- Fougères Laboratory, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), 10B rue Claude Bourgelat, Fougères 35306, France
| | - Jacqueline Manceau
- Fougères Laboratory, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), 10B rue Claude Bourgelat, Fougères 35306, France
| | - Jérôme Henri
- Fougères Laboratory, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), 10B rue Claude Bourgelat, Fougères 35306, France
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13
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Application of Minimal Physiologically-Based Pharmacokinetic Model to Simulate Lung and Trachea Exposure of Pyronaridine and Artesunate in Hamsters. Pharmaceutics 2023; 15:pharmaceutics15030838. [PMID: 36986698 PMCID: PMC10058671 DOI: 10.3390/pharmaceutics15030838] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 03/01/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
A fixed-dose combination of pyronaridine and artesunate, one of the artemisinin-based combination therapies, has been used as a potent antimalarial treatment regimen. Recently, several studies have reported the antiviral effects of both drugs against severe acute respiratory syndrome coronavirus two (SARS-CoV-2). However, there are limited data on the pharmacokinetics (PKs), lung, and trachea exposures that could be correlated with the antiviral effects of pyronaridine and artesunate. The purpose of this study was to evaluate the pharmacokinetics, lung, and trachea distribution of pyronaridine, artesunate, and dihydroartemisinin (an active metabolite of artesunate) using a minimal physiologically-based pharmacokinetic (PBPK) model. The major target tissues for evaluating dose metrics are blood, lung, and trachea, and the nontarget tissues were lumped together into the rest of the body. The predictive performance of the minimal PBPK model was evaluated using visual inspection between observations and model predictions, (average) fold error, and sensitivity analysis. The developed PBPK models were applied for the multiple-dosing simulation of daily oral pyronaridine and artesunate. A steady state was reached about three to four days after the first dosing of pyronaridine and an accumulation ratio was calculated to be 1.8. However, the accumulation ratio of artesunate and dihydroartemisinin could not be calculated since the steady state of both compounds was not achieved by daily multiple dosing. The elimination half-life of pyronaridine and artesunate was estimated to be 19.8 and 0.4 h, respectively. Pyronaridine was extensively distributed to the lung and trachea with the lung-to-blood and trachea-to-blood concentration ratios (=Cavg,tissue/Cavg,blood) of 25.83 and 12.41 at the steady state, respectively. Also, the lung-to-blood and trachea-to-blood AUC ratios for artesunate (dihydroartemisinin) were calculated to be 3.34 (1.51) and 0.34 (0.15). The results of this study could provide a scientific basis for interpreting the dose–exposure–response relationship of pyronaridine and artesunate for COVID-19 drug repurposing.
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14
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Challenges and Possible Solutions to Direct-Acting Oral Anticoagulants (DOACs) Dosing in Patients with Extreme Bodyweight and Renal Impairment. Am J Cardiovasc Drugs 2023; 23:9-17. [PMID: 36515822 DOI: 10.1007/s40256-022-00560-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/04/2022] [Indexed: 12/15/2022]
Abstract
This article aims to highlight the dosing issues of direct oral anticoagulants (DOACs) in patients with renal impairment and/or obesity in an attempt to develop solutions employing advanced data-driven techniques. DOACs have become widely accepted by clinicians worldwide because of their superior clinical profiles, more predictable pharmacokinetics, and hence more convenient dosing relative to other anticoagulants. However, the optimal dosing of DOACs in extreme bodyweight patients and patients with renal impairment is difficult to achieve using the conventional dosing approach. The standard dosing approach (fixed-dose) is based on limited data from clinical studies. The existing formulae (models) for determining the appropriate doses for these patient groups leads to suboptimal dosing. This problem of mis-dosing is worsened by the lack of standardized laboratory parameters for monitoring the exposure to DOACs in renal failure and extreme bodyweight patients. Model-informed precision dosing (MIPD) encompasses a range of techniques like machine learning and pharmacometrics modelling, which could uncover key variables and relationships as well as shed more light on the pharmacokinetics and pharmacodynamics of DOACs in patients with extreme bodyweight or renal impairment. Ultimately, this individualized approach-if implemented in clinical practice-could optimise dosing for the DOACs for better safety and efficacy.
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15
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Bouazza N, Dokoumetzidis A, Knibbe CAJ, de Wildt SN, Ambery C, De Cock PA, Gasthuys E, Foissac F, Urien S, Hamberg AK, Poggesi I, Zhao W, Vermeulen A, Standing JF, Tréluyer JM. General clinical and methodological considerations on the extrapolation of pharmacokinetics and optimization of study protocols for small molecules and monoclonal antibodies in children. Br J Clin Pharmacol 2022; 88:4985-4996. [PMID: 36256514 DOI: 10.1111/bcp.15571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 09/05/2022] [Accepted: 09/20/2022] [Indexed: 12/01/2022] Open
Abstract
Pharmacometric modelling plays a key role in both the design and analysis of regulatory trials in paediatric drug development. Studies in adults provide a rich source of data to inform the paediatric investigation plans, including knowledge on drug pharmacokinetics (PK), safety and efficacy. In children, drug disposition differs widely from birth to adolescence but extrapolating adult to paediatric PK, safety and efficacy either with pharmacometric or physiologically based approaches can help design or in some cases reduce the need for clinical studies. Aspects to consider when extrapolating PK include the maturation of drug metabolizing enzyme expression, glomerular filtration, drug excretory systems, and the expression and activity of specific transporters in conjunction with other drug properties such as fraction unbound. Knowledge of these can be used to develop extrapolation tools such as allometric scaling plus maturation functions or physiologically based PK. PK/pharmacodynamic approaches and well-designed clinical trials in children are of key importance in paediatric drug development. In this white paper, state-of-the-art of current methods used for paediatric extrapolation will be discussed. This paper is part of a conect4children implementation of innovative methodologies including pharmacometric and physiologically based PK modelling in clinical trial design/paediatric drug development through dissemination of expertise and expert advice. The suggestions arising from this white paper should define a minimum set of standards in paediatric modelling and contribute to the regulatory science.
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Affiliation(s)
- Naïm Bouazza
- Pediatric and Perinatal Drug Evaluation and Pharmacology, Université Paris Cité, Paris, France.,Unité de Recherche Clinique Université Paris Cité Necker-Cochin, AP-HP, Paris, France.,CIC-1419 Inserm, Cochin-Necker, Paris, France
| | | | - Catherijne A J Knibbe
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.,Department of Clinical Pharmacy, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - Saskia N de Wildt
- Department of Pharmacology and Toxicology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Intensive Care and Paediatric Surgery, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Claire Ambery
- Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline plc, London, UK
| | - Pieter A De Cock
- Department of Basic and Applied Medical Sciences, Ghent University, Ghent, Belgium.,Department of Pharmacy, Ghent University Hospital, Ghent, Belgium.,Department of Pediatric Intensive Care, Ghent University Hospital, Ghent, Belgium
| | - Elke Gasthuys
- Department of Bioanalysis, Faculty of Pharmaceutical Sciences, University of Ghent, Ghent, Belgium
| | - Frantz Foissac
- Pediatric and Perinatal Drug Evaluation and Pharmacology, Université Paris Cité, Paris, France.,Unité de Recherche Clinique Université Paris Cité Necker-Cochin, AP-HP, Paris, France.,CIC-1419 Inserm, Cochin-Necker, Paris, France
| | - Saïk Urien
- Pediatric and Perinatal Drug Evaluation and Pharmacology, Université Paris Cité, Paris, France.,Unité de Recherche Clinique Université Paris Cité Necker-Cochin, AP-HP, Paris, France.,CIC-1419 Inserm, Cochin-Necker, Paris, France
| | - Anna-Karin Hamberg
- Department of Clinical Pharmacology, Uppsala University Hospital, Uppsala, Sweden
| | - Italo Poggesi
- Clinical Pharmacology and Pharmacometrics, Janssen Research & Development, Beerse, Belgium
| | - Wei Zhao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China.,Clinical Research Centre, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - An Vermeulen
- Department of Bioanalysis, Faculty of Pharmaceutical Sciences, University of Ghent, Ghent, Belgium
| | - Joseph F Standing
- Infection, Inflammation and Immunology, UCL Great Ormond Street Institute of Child Health, London, UK.,Department of Pharmacy, Great Ormond Street Hospital for Children, London, UK
| | - Jean-Marc Tréluyer
- Pediatric and Perinatal Drug Evaluation and Pharmacology, Université Paris Cité, Paris, France.,Unité de Recherche Clinique Université Paris Cité Necker-Cochin, AP-HP, Paris, France.,CIC-1419 Inserm, Cochin-Necker, Paris, France
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16
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Ryu HJ, Kang WH, Kim T, Kim JK, Shin KH, Chae JW, Yun HY. A compatibility evaluation between the physiologically based pharmacokinetic (PBPK) model and the compartmental PK model using the lumping method with real cases. Front Pharmacol 2022; 13:964049. [PMID: 36034786 PMCID: PMC9413202 DOI: 10.3389/fphar.2022.964049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/14/2022] [Indexed: 11/13/2022] Open
Abstract
Pharmacokinetic (PK) modeling is a useful method for investigating drug absorption, distribution, metabolism, and excretion. The most commonly used mathematical models in PK modeling are the compartment model and physiologically based pharmacokinetic (PBPK) model. Although the theoretical characteristics of each model are well known, there have been few comparative studies of the compatibility of the models. Therefore, we evaluated the compatibility of PBPK and compartment models using the lumping method with 20 model compounds. The PBPK model was theoretically reduced to the lumped model using the principle of grouping tissues and organs that show similar kinetic behaviors. The area under the concentration–time curve (AUC) based on the simulated concentration and PK parameters (drug clearance [CL], central volume of distribution [Vc], peripheral volume of distribution [Vp]) in each model were compared, assuming administration to humans. The AUC and PK parameters in the PBPK model were similar to those in the lumped model within the 2-fold range for 17 of 20 model compounds (85%). In addition, the relationship of the calculated Vd/fu (volume of distribution [Vd], drug-unbound fraction [fu]) and the accuracy of AUC between the lumped model and compartment model confirmed their compatibility. Accordingly, the compatibility between PBPK and compartment models was confirmed by the lumping method. This method can be applied depending on the requirement of compatibility between the two models.
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Affiliation(s)
- Hyo-jeong Ryu
- Department of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon, South Korea
| | - Won-ho Kang
- Department of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon, South Korea
| | - Taeheon Kim
- Department of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon, South Korea
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, Korean Advanced Institute of Science and Technology, Daejeon, South Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, South Korea
| | - Kwang-Hee Shin
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Kyungpook National University, Daegu, South Korea
| | - Jung-woo Chae
- Department of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon, South Korea
- *Correspondence: Jung-woo Chae, ; Hwi-yeol Yun,
| | - Hwi-yeol Yun
- Department of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon, South Korea
- *Correspondence: Jung-woo Chae, ; Hwi-yeol Yun,
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17
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Farhan M, Rani P, Moledina F, George T, Tummala HP, Mallayasamy S. Application of Physiologically Based Pharmacokinetic Modeling of Lamotrigine Using PK-Sim in Predicting the Impact of Drug Interactions and Dosage Adjustment. J Pharmacol Pharmacother 2022. [DOI: 10.1177/0976500x221111455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background Physiologically based pharmacokinetic (PBPK) models are helpful as mechanistic representations of pharmacokinetic parameters. There were no reports of lamotrigine (LTG) PBPK models developed in open source platforms like PK-Sim. Objectives The present work was aimed to build a LTG PBPK model and compare it to the clinical data from South Asian Indian patients and use this model to understand the drug interactions of LTG and explore the optimal doses. Methods and Material The PBPK model was developed using the PK-Sim software platform and qualified with LTG plasma concentration data from an Indian study. The European population database was chosen as the patient setting in the software. Physicochemical data of LTG and enzyme kinetic data were incorporated from the literature. Dosing protocols were as per the previous study. Interaction models for drug interactions with carbamazepine and valproate were also simulated. Results Most of the model predicted concentration-time profiles of LTG at steady-state were well within the observed concentrations. The developed models were suitably qualified. The drug interaction model was used to assess the impact of induction and inhibition of the pharmacokinetic profile of LTG. Conclusions The predicted plasma concentrations of the developed PBPK models using the European population database were very similar to the data from Indian patients. The developed LTG PBPK models are applicable in predicting the impact of drug interactions and can yield appropriate LTG doses to be administered.
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Affiliation(s)
- Mohammed Farhan
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Prathvi Rani
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Fatimazahra Moledina
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Thomas George
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Hari Prabhath Tummala
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Surulivelrajan Mallayasamy
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
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18
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Laeer S, Cawello W, Burckhardt BB, Ablonczy L, Bajcetic M, Breur JMPJ, Dalinghaus M, Male C, de Wildt SN, Breitkreutz J, Faisal M, Keatley-Clarke A, Klingmann I, Lagler FB. Enalapril and Enalaprilat Pharmacokinetics in Children with Heart Failure Due to Dilated Cardiomyopathy and Congestive Heart Failure after Administration of an Orodispersible Enalapril Minitablet (LENA-Studies). Pharmaceutics 2022; 14:pharmaceutics14061163. [PMID: 35745735 PMCID: PMC9228797 DOI: 10.3390/pharmaceutics14061163] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/22/2022] [Accepted: 05/27/2022] [Indexed: 12/29/2022] Open
Abstract
Angiotensin-converting enzyme inhibitors (ACEI), such as enalapril, are a cornerstone of treatment for pediatric heart failure which is still used off-label. Using a novel age-appropriate formulation of enalapril orodispersible minitablets (ODMTs), phase II/III open-label, multicenter pharmacokinetic (PK) bridging studies were performed in pediatric patients with heart failure due to dilated cardiomyopathy (DCM) and congenital heart disease (CHD) in five participating European countries. Children were treated for 8 weeks with ODMTs according to an age-appropriate dosing schedule. The primary objective was to describe PK parameters (area under the curve (AUC), maximal concentration (Cmax), time to reach maximal concentration (t-max)) of enalapril and its active metabolite enalaprilat. Of 102 patients, 89 patients (n = 26, DCM; n = 63 CHD) were included in the primary PK endpoint analysis. Rate and extent of enalapril and its active metabolite enalaprilat were described and etiology and age could be identified as potential PK modifying factors. The dosing schedule appeared to be tolerated well and did not result in any significant drug-related serious adverse events. The PK analysis and the lack of severe safety events supports the applied age-appropriate dosing schedule for the enalapril ODMTs.
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Affiliation(s)
- Stephanie Laeer
- Institute of Clinical Pharmacy and Pharmacotherapy, Heinrich-Heine-Universitaet Düsseldorf, 40225 Duesseldorf, Germany; (W.C.); (B.B.B.); (M.F.)
- Correspondence: ; Tel.: +49-211-8110740
| | - Willi Cawello
- Institute of Clinical Pharmacy and Pharmacotherapy, Heinrich-Heine-Universitaet Düsseldorf, 40225 Duesseldorf, Germany; (W.C.); (B.B.B.); (M.F.)
| | - Bjoern B. Burckhardt
- Institute of Clinical Pharmacy and Pharmacotherapy, Heinrich-Heine-Universitaet Düsseldorf, 40225 Duesseldorf, Germany; (W.C.); (B.B.B.); (M.F.)
| | - László Ablonczy
- Goettsegen György Hungarian Institute of Cardiology (HPHC), 1450 Budapest, Hungary;
| | - Milica Bajcetic
- Univerzitetska Dečja Klinika (UDK), University Children Hospital, School of Medicine, University of Belgrade, 11129 Belgrade, Serbia;
| | - Johannes M. P. J. Breur
- University Medical Center Utrecht, Wilhelmina Children’s Hospital, 3584 CX Utrecht, The Netherlands;
| | - Michiel Dalinghaus
- Division of Pediatric Cardiology, Erasmus MC Sophia Children’s Hospital, 3000 CA Rotterdam, The Netherlands;
| | - Christoph Male
- Department of Paediatrics and Adolescent Medicine, Medical University of Vienna, 1090 Vienna, Austria;
| | - Saskia N. de Wildt
- Intensive Care and Department of Pediatric Surgery, Erasmus MC Sophia Children’s Hospital, 3015 GJ Rotterdam, The Netherlands;
- Department of Pharmacology and Toxicology, Radboud Institute of Health Sciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
| | | | - Muhammed Faisal
- Institute of Clinical Pharmacy and Pharmacotherapy, Heinrich-Heine-Universitaet Düsseldorf, 40225 Duesseldorf, Germany; (W.C.); (B.B.B.); (M.F.)
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19
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Yang Y, Zhang X. Integration of Engineered Delivery with the Pharmacokinetics of Medical Candidates via Physiology-Based Pharmacokinetics. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:57-69. [PMID: 35437718 DOI: 10.1007/978-1-0716-2265-0_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic computational model that can be used to predict a drug product's ADME (absorption, distribution, metabolism, and excretion) and pharmacokinetics (PK). In recent years, PBPK modeling and simulation has been used increasingly to address many biopharmaceutics and clinical pharmacology questions, such as the effect of formulations, intrinsic factors (age, organ dysfunction, etc.), and extrinsic factors (comedications, food) on the PK of an investigational drug product. In this chapter, we will briefly introduce various PBPK models for ADME prediction and general procedures for PBPK modeling and simulations. The readers are encouraged to read updated literature on new applications of PBPK modeling and simulation which is still an emerging area in pharmaceutical development.
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Affiliation(s)
- Yuching Yang
- Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Xinyuan Zhang
- Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
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20
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Choi S, Han S, Lee SJ, Lim B, Bae SH, Han S, Yim DS. DallphinAtoM: Physiologically based pharmacokinetics software predicting human PK parameters based on physicochemical properties, in vitro and animal in vivo data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 216:106662. [PMID: 35151112 DOI: 10.1016/j.cmpb.2022.106662] [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: 09/15/2021] [Revised: 11/12/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES In silico experiments and simulations using physiologically based pharmacokinetic (PBPK) and allometric approaches have played an important role in pharmaceutical research and drug development. These methods integrate diverse data from preclinical and clinical development, and have been widely applied to in vitro-in vivo extrapolation (IVIVE) of absorption, distribution, metabolism, and excretion (ADME). METHODS To develop a user-friendly open tool predicting human PK, we assessed various references on PBPK and allometric methods published so far. They were integrated into a software system named "DallphinAtoM" (Drugs with ALLometry and PHysiology Inside-Animal to huMan), which has a user-friendly platform that can handle complex PBPK models and allometric models with a relatively small amount of essential information of the drug. The models of DallphinAtoM support the integration of data gained during the nonclinical development phase, enable translation from animal to human, and allow the prediction of concentration-time profiles with predicted PK parameters. RESULTS We presented two illustrative applications using DallphinAtoM: (1) human PK simulation of an orally administered drug using PBPK method; and (2) simulation of intravenous infusion following a two-compartment model using the allometric scaling method. CONCLUSIONS We conclude that this is a straightforward and transparent tool allowing fast and reliable human PK simulation based on the latest knowledge on biochemical processes and physiology and provides valuable information for decision making during the early-phase drug development.
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Affiliation(s)
- Suein Choi
- Department of Clinical Pharmacology and Therapeutics, Seoul St. Mary's Hospital, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea; PIPET (Pharmacometrics Institute for Practical Education and Training), College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Sungpil Han
- Department of Clinical Pharmacology and Therapeutics, Seoul St. Mary's Hospital, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea; PIPET (Pharmacometrics Institute for Practical Education and Training), College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - So Jin Lee
- PIPET (Pharmacometrics Institute for Practical Education and Training), College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea; Q-fitter Inc., Seoul 06578, Republic of Korea
| | - Byunghee Lim
- PIPET (Pharmacometrics Institute for Practical Education and Training), College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | | | - Seunghoon Han
- Department of Clinical Pharmacology and Therapeutics, Seoul St. Mary's Hospital, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea; PIPET (Pharmacometrics Institute for Practical Education and Training), College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Dong-Seok Yim
- Department of Clinical Pharmacology and Therapeutics, Seoul St. Mary's Hospital, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea; PIPET (Pharmacometrics Institute for Practical Education and Training), College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea.
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Lex TR, Rodriguez JD, Zhang L, Jiang W, Gao Z. Development of In Vitro Dissolution Testing Methods to Simulate Fed Conditions for Immediate Release Solid Oral Dosage Forms. AAPS J 2022; 24:40. [PMID: 35277760 DOI: 10.1208/s12248-022-00690-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 02/10/2022] [Indexed: 11/30/2022] Open
Abstract
In vitro dissolution testing is widely used to mimic and predict in vivo performance of oral drug products in the gastrointestinal (GI) tract. This literature review assesses the current in vitro dissolution methodologies being employed to simulate and predict in vivo drug dissolution under fasted and fed conditions, with emphasis on immediate release (IR) solid oral dosage forms. Notable human GI physiological conditions under fasted and fed states have been reviewed and summarized. Literature results showed that dissolution media, mechanical forces, and transit times are key dissolution test parameters for simulating specific postprandial conditions. A number of biorelevant systems, including the fed stomach model (FSM), GastroDuo device, dynamic gastric model (DGM), simulated gastrointestinal tract models (TIM), and the human gastric simulator (HGS), have been developed to mimic the postprandial state of the stomach. While these models have assisted in expanding physiological relevance of in vitro dissolution tests, in general, these models lack the ability to fully replicate physiological conditions/processes. Furthermore, the translatability of in vitro data to an in vivo system remains challenging. Additionally, physiologically based pharmacokinetic (PBPK) modeling has been employed to evaluate the effect of food on drug bioavailability and bioequivalence. Here, we assess the current status of in vitro dissolution methodologies and absorption PBPK modeling approaches to identify knowledge gaps and facilitate further development of in vitro dissolution methods that factor in fasted and fed states. Prediction of in vivo drug performance under fasted and fed conditions via in vitro dissolution testing and modeling may potentially help efforts in harmonizing global regulatory recommendations regarding in vivo fasted and fed bioequivalence studies for solid oral IR products.
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Affiliation(s)
- Timothy R Lex
- Division of Complex Drug Analysis, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, St. Louis, Missouri, 63110, USA
| | - Jason D Rodriguez
- Division of Complex Drug Analysis, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, St. Louis, Missouri, 63110, USA
| | - Lei Zhang
- Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20993, USA
| | - Wenlei Jiang
- Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20993, USA.
| | - Zongming Gao
- Division of Complex Drug Analysis, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, St. Louis, Missouri, 63110, USA.
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22
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Villa Nova M, Lin TP, Shanehsazzadeh S, Jain K, Ng SCY, Wacker R, Chichakly K, Wacker MG. Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence. Front Digit Health 2022; 4:799341. [PMID: 35252958 PMCID: PMC8894322 DOI: 10.3389/fdgth.2022.799341] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/26/2022] [Indexed: 12/12/2022] Open
Abstract
Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.
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Affiliation(s)
- Mônica Villa Nova
- Department of Pharmacy, State University of Maringá, Maringá, Brazil
| | - Tzu Ping Lin
- Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Saeed Shanehsazzadeh
- Biological Resources Imaging Laboratory, Mark Wainwright Analytical Centre, UNSW Sydney, Sydney, NSW, Australia
| | - Kinjal Jain
- Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Samuel Cheng Yong Ng
- Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore
| | | | | | - Matthias G. Wacker
- Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore
- *Correspondence: Matthias G. Wacker
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23
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Chaphekar N, Caritis S, Venkataramanan R. Model-Informed Dose Optimization in Pregnancy. J Clin Pharmacol 2021; 60 Suppl 1:S63-S76. [PMID: 33205432 DOI: 10.1002/jcph.1777] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 10/07/2020] [Indexed: 12/12/2022]
Abstract
Pregnancy is associated with several physiological changes that can alter the pharmacokinetics (PK) and pharmacodynamics of drugs. These may require dosing changes in pregnant women to achieve drug exposures comparable to the nonpregnant population. There is, however, limited information available on the PK and pharmacodynamics of drugs used during pregnancy. Practical difficulties in performing PK studies and potential liability issues are often the reasons for the availability of limited information. Over the past several years, there has been a rapid development in the application of various modeling strategies such as population PK and physiologically based PK modeling to provide guidance on drug dosing in this special patient population. Population PK models rely on measured PK data, whereas physiologically based PK models integrate physiological, preclinical, and clinical data to quantify changes in PK of drugs in various patient populations. These modeling strategies offer a promising approach to identify the drugs with PK changes during pregnancy and guide dose adjustment in pregnant women. This review focuses on PBPK modeling to guide drug therpay in pregnancy.
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Affiliation(s)
- Nupur Chaphekar
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Steve Caritis
- Department of Obstetrics, Gynecology and Reproductive Sciences, School of Medicine, Magee Womens Hospital of UPMC, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Raman Venkataramanan
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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24
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Chaphekar N, Dodeja P, Shaik IH, Caritis S, Venkataramanan R. Maternal-Fetal Pharmacology of Drugs: A Review of Current Status of the Application of Physiologically Based Pharmacokinetic Models. Front Pediatr 2021; 9:733823. [PMID: 34805038 PMCID: PMC8596611 DOI: 10.3389/fped.2021.733823] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 09/16/2021] [Indexed: 12/31/2022] Open
Abstract
Pregnancy and the postpartum period are associated with several physiological changes that can alter the pharmacokinetics (PK) and pharmacodynamics (PD) of drugs. For certain drugs, dosing changes may be required during pregnancy and postpartum to achieve drug exposures comparable to what is observed in non-pregnant subjects. There is very limited data on fetal exposure of drugs during pregnancy, and neonatal exposure through transfer of drugs via human milk during breastfeeding. Very few systematic clinical pharmacology studies have been conducted in pregnant and postpartum women due to ethical issues, concern for the fetus safety as well as potential legal ramifications. Over the past several years, there has been an increase in the application of modeling and simulation approaches such as population PK (PopPK) and physiologically based PK (PBPK) modeling to provide guidance on drug dosing in those special patient populations. Population PK models rely on measured PK data, whereas physiologically based PK models incorporate physiological, preclinical, and clinical data into the model to predict drug exposure during pregnancy. These modeling strategies offer a promising approach to identify the drugs with PK changes during pregnancy to guide dose optimization in pregnancy, when there is lack of clinical data. PBPK modeling is also utilized to predict the fetal exposure of drugs and drug transfer via human milk following maternal exposure. This review focuses on the current status of the application of PBPK modeling to predict maternal and fetal exposure of drugs and thereby guide drug therapy during pregnancy.
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Affiliation(s)
- Nupur Chaphekar
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, United States
| | - Prerna Dodeja
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, United States
| | - Imam H Shaik
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, United States
| | - Steve Caritis
- Department of Obstetrics, Gynecology and Reproductive Sciences, Magee Women's Hospital of UPMC, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Raman Venkataramanan
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, United States.,Department of Obstetrics, Gynecology and Reproductive Sciences, Magee Women's Hospital of UPMC, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.,Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
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25
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Prediction of lisinopril pediatric dose from the reference adult dose by employing a physiologically based pharmacokinetic model. BMC Pharmacol Toxicol 2020; 21:56. [PMID: 32727574 PMCID: PMC7389632 DOI: 10.1186/s40360-020-00429-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 07/02/2020] [Indexed: 01/10/2023] Open
Abstract
Background This study aimed to assess the pediatric lisinopril doses using an adult physiological based pharmacokinetic (PBPK) model. As the empirical rules of dose calculation cannot calculate gender-specific pediatric doses and ignores the age-related physiological differences. Methods A PBPK model of lisinopril for the healthy adult population was developed for oral (fed and fasting) and IV administration using PK-Sim MoBI® and was scaled down to a virtual pediatric population for prediction of lisinopril doses in neonates to infants, infants to toddler, children at pre-school age, children at school age and the adolescents. The pharmacokinetic parameters were predicted for the above groups at decremental doses of 20 mg, 10 mg, 5 mg, 2.5 mg, and 1.5 mg in order to accomplish doses producing the pharmacokinetic parameters, similar (or comparable) to that of the adult population. The above simulated pediatric doses were compared to the doses computed using the conventional four methods, such as Young’s rule, Clark’s rule, and weight-based and body surface area-based equations and the dose reported in different studies. Results Though the doses predicted for all subpopulations of children were comparable to those calculated by Young’s rule, yet the conventional methods overestimated the pediatric doses when compared to the respective PBPK-predicted doses. The findings of previous real time pharmacokinetic studies in pediatric patients supported the present simulated dose. Conclusion Thus, PBPK seems to have predictability potential for pediatric dose since it takes into consideration the physiological changes related to age and gender.
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26
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Utembe W, Clewell H, Sanabria N, Doganis P, Gulumian M. Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials. NANOMATERIALS 2020; 10:nano10071267. [PMID: 32610468 PMCID: PMC7407857 DOI: 10.3390/nano10071267] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/03/2020] [Accepted: 06/13/2020] [Indexed: 02/08/2023]
Abstract
There have been efforts to develop physiologically based pharmacokinetic (PBPK) models for nanomaterials (NMs). Since NMs have quite different kinetic behaviors, the applicability of the approaches and techniques that are utilized in current PBPK models for NMs is warranted. Most PBPK models simulate a size-independent endocytosis from tissues or blood. In the lungs, dosimetry and the air-liquid interface (ALI) models have sometimes been used to estimate NM deposition and translocation into the circulatory system. In the gastrointestinal (GI) tract, kinetics data are needed for mechanistic understanding of NM behavior as well as their absorption through GI mucus and their subsequent hepatobiliary excretion into feces. Following absorption, permeability (Pt) and partition coefficients (PCs) are needed to simulate partitioning from the circulatory system into various organs. Furthermore, mechanistic modelling of organ- and species-specific NM corona formation is in its infancy. More recently, some PBPK models have included the mononuclear phagocyte system (MPS). Most notably, dissolution, a key elimination process for NMs, is only empirically added in some PBPK models. Nevertheless, despite the many challenges still present, there have been great advances in the development and application of PBPK models for hazard assessment and risk assessment of NMs.
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Affiliation(s)
- Wells Utembe
- National Institute for Occupational Health, P.O. Box 4788, Johannesburg 2000, South Africa; (W.U.); (N.S.)
| | - Harvey Clewell
- Ramboll US Corporation, Research Triangle Park, NC 27709, USA;
| | - Natasha Sanabria
- National Institute for Occupational Health, P.O. Box 4788, Johannesburg 2000, South Africa; (W.U.); (N.S.)
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, Zografou Campus, 15780 Athens, Greece;
| | - Mary Gulumian
- National Institute for Occupational Health, P.O. Box 4788, Johannesburg 2000, South Africa; (W.U.); (N.S.)
- Hematology and Molecular Medicine, University of the Witwatersrand, Johannesburg 2000, South Africa
- Correspondence: ; Tel.: +27-11-712-6428
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27
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Recent advances in long-acting nanoformulations for delivery of antiretroviral drugs. J Control Release 2020; 324:379-404. [PMID: 32461114 DOI: 10.1016/j.jconrel.2020.05.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 05/14/2020] [Accepted: 05/15/2020] [Indexed: 02/07/2023]
Abstract
In spite of introduction of combination antiretroviral therapy (cART) against human immunodeficiency virus (HIV) infection; inaccessibility and poor adherence to oral cART costs 10 in 100,000 death worldwide. Failure in adherence leads to viral rebound, emergence of drug resistance and anticipated HIV infection in high risk individuals. Various Long-acting antiretroviral (LA ARV) nanoformulations including nano-prodrug, solid drug nanoparticles (SDN), nanocrystals, aspherical nanoparticles, polymeric and lipidic nanoparticles have shown plasma/tissue drug concentration in the therapeutic range for several weeks during pre-clinical evaluation. LA ARV nanoformulations therefore have replaced cART as better alternative for the treatment of HIV infection. Cabenuva™ is recently approved by Health Canada containing LA cabotegravir+LA rilpivirine nanocrystals (ViiV healthcare) for once monthly administration by intramuscular route. The LA nanoformulation due to its nanosize insist on better stability, delivery to lymphatic, slow release into systemic circulation via lymphatic-circulatory system conjoint and secondary drug depot within infiltered immune cells at site of administration and systemic circulation in contrast to conventional drugs. However, the pharmacokinetic, biodistribution and efficacy of LA nanoformulations hinge onto physicochemical properties of the drugs and route of administration. Therefore, current review emphasizes on these contradistinctive factors that affects the reproducibility, safety, efficacy and toxicity of LA anti-HIV nanoformulations. Moreover, it expatiates on application of profuse nanoformulations for long-acting effect with promising preclinical discoveries and two clinical leads. To add on, utilization of physiology-based and mechanism-based pharmacokinetic modelling and in vivo animal models which could lead to enhanced safety and efficacy of LA ARV nanoformulations in humans have been included.
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28
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Smits A, Annaert P, Van Cruchten S, Allegaert K. A Physiology-Based Pharmacokinetic Framework to Support Drug Development and Dose Precision During Therapeutic Hypothermia in Neonates. Front Pharmacol 2020; 11:587. [PMID: 32477113 PMCID: PMC7237643 DOI: 10.3389/fphar.2020.00587] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 04/16/2020] [Indexed: 12/21/2022] Open
Abstract
Therapeutic hypothermia (TH) is standard treatment for neonates (≥36 weeks) with perinatal asphyxia (PA) and hypoxic-ischemic encephalopathy. TH reduces mortality and neurodevelopmental disability due to reduced metabolic rate and decreased neuronal apoptosis. Since both hypothermia and PA influence physiology, they are expected to alter pharmacokinetics (PK). Tools for personalized dosing in this setting are lacking. A neonatal hypothermia physiology-based PK (PBPK) framework would enable precision dosing in the clinic. In this literature review, the stepwise approach, benefits and challenges to develop such a PBPK framework are covered. It hereby contributes to explore the impact of non-maturational PK covariates. First, the current evidence as well as knowledge gaps on the impact of PA and TH on drug absorption, distribution, metabolism and excretion in neonates is summarized. While reduced renal drug elimination is well-documented in neonates with PA undergoing hypothermia, knowledge of the impact on drug metabolism is limited. Second, a multidisciplinary approach to develop a neonatal hypothermia PBPK framework is presented. Insights on the effect of hypothermia on hepatic drug elimination can partly be generated from in vitro (human/animal) profiling of hepatic drug metabolizing enzymes and transporters. Also, endogenous biomarkers may be evaluated as surrogate for metabolic activity. To distinguish the impact of PA versus hypothermia on drug metabolism, in vivo neonatal animal data are needed. The conventional pig is a well-established model for PA and the neonatal Göttingen minipig should be further explored for PA under hypothermia conditions, as it is the most commonly used pig strain in nonclinical drug development. Finally, a strategy is proposed for establishing and fine-tuning compound-specific PBPK models for this application. Besides improvement of clinical exposure predictions of drugs used during hypothermia, the developed PBPK models can be applied in drug development. Add-on pharmacotherapies to further improve outcome in neonates undergoing hypothermia are under investigation, all in need for dosing guidance. Furthermore, the hypothermia PBPK framework can be used to develop temperature-driven PBPK models for other populations or indications. The applicability of the proposed workflow and the challenges in the development of the PBPK framework are illustrated for midazolam as model drug.
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Affiliation(s)
- Anne Smits
- Neonatal Intensive Care Unit, University Hospitals Leuven, Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Pieter Annaert
- Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Steven Van Cruchten
- Applied Veterinary Morphology, Department of Veterinary Sciences, University of Antwerp, Wilrijk, Belgium
| | - Karel Allegaert
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
- Department of Clinical Pharmacy, Erasmus MC-Sophia Children's Hospital, Rotterdam, Netherlands
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29
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Sassen SDT, Zwaan CM, van der Sluis IM, Mathôt RAA. Pharmacokinetics and population pharmacokinetics in pediatric oncology. Pediatr Blood Cancer 2020; 67:e28132. [PMID: 31876123 DOI: 10.1002/pbc.28132] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 11/19/2019] [Accepted: 11/24/2019] [Indexed: 12/28/2022]
Abstract
Pharmacokinetic research has become increasingly important in pediatric oncology as it can have direct clinical implications and is a crucial component in individualized medicine. Population pharmacokinetics has become a popular method especially in children, due to the potential for sparse sampling, flexible sampling times, computing of heterogeneous data, and identification of variability sources. However, population pharmacokinetic reports can be complex and difficult to interpret. The aim of this article is to provide a basic explanation of population pharmacokinetics, using clinical examples from the field of pediatric oncology, to facilitate the translation of pharmacokinetic research into the daily clinic.
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Affiliation(s)
- Sebastiaan D T Sassen
- Department of Pediatric Oncology, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - C Michel Zwaan
- Department of Pediatric Oncology, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands.,Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | | | - Ron A A Mathôt
- Department of Hospital Pharmacy, Amsterdam University Medical Centers, Amsterdam, The Netherlands
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30
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Díaz de León-Ortega R, D'Arcy DM, Lamprou DA, Fotaki N. In vitro - in vivo relations for the parenteral liposomal formulation of Amphotericin B: A clinically relevant approach with PBPK modeling. Eur J Pharm Biopharm 2020; 159:177-187. [PMID: 32147578 DOI: 10.1016/j.ejpb.2020.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 02/22/2020] [Accepted: 03/03/2020] [Indexed: 12/26/2022]
Abstract
In vitro release testing is a useful tool for the quality control of controlled release parenteral formulations, but in vitro release test conditions that reflect or are able to predict the in vivo performance are advantageous. Therefore, it is important to investigate the factors that could affect drug release from formulations and relate them to in vivo performance. In this study the effect of media composition including albumin presence, type of buffer and hydrodynamics on drug release were evaluated on a liposomal Amphotericin B formulation (Ambisome®). A physiologically based pharmacokinetic (PBPK) model was developed using plasma concentration profiles from healthy subjects, in order to investigate the impact of each variable from the in vitro release tests on the prediction of the in vivo performance. It was found that albumin presence was the most important factor for the release of Amphotericin B from Ambisome®; both hydrodynamics setups, coupled with the PBPK model, had comparable predictive ability for simulating in vivo plasma concentration profiles. The PBPK model was extrapolated to a hypothetical hypoalbuminaemic population and the Amphotericin B plasma concentration and its activity against fungal cells were simulated. Selected in vitro release tests for these controlled release parenteral formulations were able to predict the in vivo AmB exposure, and this PBPK driven approach to release test development could benefit development of such formulations.
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Affiliation(s)
| | - D M D'Arcy
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin 2, Ireland
| | - D A Lamprou
- School of Pharmacy, Queen's University Belfast, Belfast, United Kingdom
| | - N Fotaki
- Department of Pharmacy and Pharmacology, University of Bath, Bath, United Kingdom.
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31
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State-of-the-Art Review on Physiologically Based Pharmacokinetic Modeling in Pediatric Drug Development. Clin Pharmacokinet 2020; 58:1-13. [PMID: 29777528 DOI: 10.1007/s40262-018-0677-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Physiologically based pharmacokinetic modeling and simulation is an important tool for predicting the pharmacokinetics, pharmacodynamics, and safety of drugs in pediatrics. Physiologically based pharmacokinetic modeling is applied in pediatric drug development for first-time-in-pediatric dose selection, simulation-based trial design, correlation with target organ toxicities, risk assessment by investigating possible drug-drug interactions, real-time assessment of pharmacokinetic-safety relationships, and assessment of non-systemic biodistribution targets. This review summarizes the details of a physiologically based pharmacokinetic modeling approach in pediatric drug research, emphasizing reports on pediatric physiologically based pharmacokinetic models of individual drugs. We also compare and contrast the strategies employed by various researchers in pediatric physiologically based pharmacokinetic modeling and provide a comprehensive overview of physiologically based pharmacokinetic modeling strategies and approaches in pediatrics. We discuss the impact of physiologically based pharmacokinetic models on regulatory reviews and product labels in the field of pediatric pharmacotherapy. Additionally, we examine in detail the current limitations and future directions of physiologically based pharmacokinetic modeling in pediatrics with regard to the ability to predict plasma concentrations and pharmacokinetic parameters. Despite the skepticism and concern in the pediatric community about the reliability of physiologically based pharmacokinetic models, there is substantial evidence that pediatric physiologically based pharmacokinetic models have been used successfully to predict differences in pharmacokinetics between adults and children for several drugs. It is obvious that the use of physiologically based pharmacokinetic modeling to support various stages of pediatric drug development is highly attractive and will rapidly increase, provided the robustness and reliability of these techniques are well established.
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32
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Rasool MF, Khalid S, Majeed A, Saeed H, Imran I, Mohany M, Al-Rejaie SS, Alqahtani F. Development and Evaluation of Physiologically Based Pharmacokinetic Drug-Disease Models for Predicting Rifampicin Exposure in Tuberculosis and Cirrhosis Populations. Pharmaceutics 2019; 11:pharmaceutics11110578. [PMID: 31694244 PMCID: PMC6921057 DOI: 10.3390/pharmaceutics11110578] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Revised: 10/22/2019] [Accepted: 10/30/2019] [Indexed: 11/25/2022] Open
Abstract
The physiologically based pharmacokinetic (PBPK) approach facilitates the construction of novel drug–disease models by allowing incorporation of relevant pathophysiological changes. The aim of the present work was to explore and identify the differences in rifampicin pharmacokinetics (PK) after the application of its single dose in healthy and diseased populations by using PBPK drug–disease models. The Simcyp® simulator was used as a platform for modeling and simulation. The model development process was initiated by predicting rifampicin PK in healthy population after intravenous (i.v) and oral administration. Subsequent to successful evaluation in healthy population, the pathophysiological changes in tuberculosis and cirrhosis population were incorporated into the developed model for predicting rifampicin PK in these populations. The model evaluation was performed by using visual predictive checks and the comparison of mean observed/predicted ratios (ratio(Obs/pred)) of the PK parameters. The predicted PK parameters in the healthy population were in adequate harmony with the reported clinical data. The incorporation of pathophysiological changes in albumin concentration in the tuberculosis population revealed improved prediction of clearance. The developed PBPK drug–disease models have efficiently described rifampicin PK in tuberculosis and cirrhosis populations after administering single drug dose, as the ratio(Obs/pred) for all the PK parameters were within a two-fold error range. The mechanistic nature of the developed PBPK models may facilitate their extension to other diseases and drugs.
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Affiliation(s)
- Muhammad F. Rasool
- Department of Pharmacy Practice, Faculty of Pharmacy, Bahauddin Zakariya University, Multan 60800, Pakistan;
- Correspondence: (M.F.R.); (F.A.); Tel.: +92-619-210-129 (M.F.R.); +96-611-469-7749 (F.A.)
| | - Sundus Khalid
- Department of Pharmaceutics, Faculty of Pharmacy, Bahauddin Zakariya University, Multan 60800, Pakistan;
| | - Abdul Majeed
- Department of Pharmacy Practice, Faculty of Pharmacy, Bahauddin Zakariya University, Multan 60800, Pakistan;
| | - Hamid Saeed
- Section of Pharmaceutics, University College of Pharmacy, Allama Iqbal Campus, University of the Punjab, Lahore 54000, Pakistan;
| | - Imran Imran
- Department of Pharmacology, Faculty of Pharmacy, Bahauddin Zakariya University, Multan 60800, Pakistan;
| | - Mohamed Mohany
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (M.M.); (S.S.A.-R.)
| | - Salim S. Al-Rejaie
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (M.M.); (S.S.A.-R.)
| | - Faleh Alqahtani
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (M.M.); (S.S.A.-R.)
- Correspondence: (M.F.R.); (F.A.); Tel.: +92-619-210-129 (M.F.R.); +96-611-469-7749 (F.A.)
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Tylutki Z, Szlęk J, Polak S. CardiacPBPK: A tool for the prediction and visualization of time-concentration profiles of drugs in heart tissue. Comput Biol Med 2019; 115:103484. [PMID: 31606584 DOI: 10.1016/j.compbiomed.2019.103484] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 10/03/2019] [Accepted: 10/04/2019] [Indexed: 11/18/2022]
Abstract
BACKGROUND AND OBJECTIVE Prediction of drug concentration in heart tissue is important in terms of drug safety and efficacy. This work presents the Open-Source CardiacPBPK platform for the prediction of the time-concentration profile of drugs, which could potentially reduce the risk of drug development failure due to cardiotoxicity. The objective of the CardiacPBPK development is to accelerate and simplify the in-silico toxicological assessment of new drugs, and to provide supportive material for the research community to use. METHODS The CardiacPBPK software provides a modular implementation of the PBPK model of heart tissue. It can be easily accessed via the Internet or installed locally. The graphical user interface and tabular design are easy to configure and use. RESULTS CardiacPBPK is a tool designed to predict and visualize the time-concentration profiles of a parent compound, and one metabolite, in venous plasma and heart tissue after oral or intravenous drug administration. CardiacPBPK is built on the R-environment framework and supports shiny application features such as interactive visualization of the results, and web applications interface by default. A shiny application refers to a computer program created with the use of shiny package in R. The application is freely available at https://github.com/jszlek/CardiacPBPK and https://sourceforge.net/projects/cardiacpbpk/. This open-source application runs on all platforms supporting R-environment (Linux, Windows, Mac OS X, Solaris). CONCLUSIONS We demonstrate the application of CardiacPBPK by simulating the study of amitriptyline intoxication in the case of CYP2D6 genetic polymorphism.
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Affiliation(s)
- Zofia Tylutki
- Unit of Pharmacoepidemiology and Pharmacoeconomics, Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland; Certara UK - Simcyp Division, Sheffield, UK
| | - Jakub Szlęk
- Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Krakow, Poland.
| | - Sebastian Polak
- Unit of Pharmacoepidemiology and Pharmacoeconomics, Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland; Certara UK - Simcyp Division, Sheffield, UK
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Huang W, Isoherranen N. Sampling Site Has a Critical Impact on Physiologically Based Pharmacokinetic Modeling. J Pharmacol Exp Ther 2019; 372:30-45. [PMID: 31604807 DOI: 10.1124/jpet.119.262154] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Accepted: 10/09/2019] [Indexed: 12/11/2022] Open
Abstract
It has been shown that arterial (central) and venous (peripheral) plasma drug concentrations can be very different. While pharmacokinetic studies typically measure drug concentrations from the peripheral vein such as the arm vein, physiologically based pharmacokinetic (PBPK) models generally output simulated concentrations from the central venous compartment that physiologically represents the right atrium, a merge of the superior and inferior vena cava. In this study, a physiologically based peripheral forearm sampling site model was developed and verified using nicotine, ketamine, lidocaine, and fentanyl as model drugs. This verified model allows output of simulated peripheral venous concentrations that can be meaningfully compared with observed pharmacokinetic data from the arm vein. The generalized effect of PBPK model sampling site on simulation output was investigated. Drugs and metabolites with large volumes of distribution showed considerable concentration discrepancy between the simulated central venous compartment and the peripheral arm vein after intravenous or oral administration, resulting in significant differences in values for C max and time taken to reach C max (t max ) In addition, the simulated central venous metabolite profile showed an unexpected profile that was not observed in the peripheral arm vein. Using fentanyl as a model compound, we show that using the wrong sampling site in PBPK models can lead to biased model evaluation and subsequent erroneous model parameter optimization. Such an error in model parameters along with the discrepant sampling site could dramatically mislead the pharmacokinetic prediction in unstudied clinical scenarios, affecting the assessment of drug safety and efficacy. Overall, this study shows that PBPK model publications should specify the model sampling sites and match them with those employed in clinical studies. SIGNIFICANCE STATEMENT: Our study shows that sampling from the central venous compartment (right atrium) during physiologically based pharmacokinetic model development gives rise to biased model evaluation and erroneous model parameterization when observed data are collected from the peripheral arm vein. This can lead to a clinically significant error in predictions of plasma concentration-time profiles in unstudied scenarios. To address this error, we developed and verified a novel peripheral sampling site model to simulate arm vein drug concentrations that can be applied to different drug dosing scenarios.
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Affiliation(s)
- Weize Huang
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington
| | - Nina Isoherranen
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington
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Stader F, Penny MA, Siccardi M, Marzolini C. A Comprehensive Framework for Physiologically-Based Pharmacokinetic Modeling in Matlab. CPT Pharmacometrics Syst Pharmacol 2019; 8:444-459. [PMID: 30779335 PMCID: PMC6657005 DOI: 10.1002/psp4.12399] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 02/05/2019] [Indexed: 01/24/2023] Open
Abstract
Physiologically-based pharmacokinetic (PBPK) models are useful tools to predict clinical scenarios for special populations for whom there are high hurdles to conduct clinical trials such as children or the elderly. However, the coding of a PBPK model in a mathematical programming language can be challenging. This tutorial illustrates how to build a whole-body PBPK model in Matlab to answer specific pharmacological questions involving drug disposition and magnitudes of drug-drug interactions in different patient populations.
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Affiliation(s)
- Felix Stader
- Division of Infectious Diseases and Hospital EpidemiologyDepartments of Medicine and Clinical ResearchUniversity Hospital BaselBaselSwitzerland,Infectious Disease Modelling UnitDepartment of Epidemiology and Public HealthSwiss Tropical and Public Health InstituteBaselSwitzerland,University of BaselBaselSwitzerland
| | - Melissa A. Penny
- Infectious Disease Modelling UnitDepartment of Epidemiology and Public HealthSwiss Tropical and Public Health InstituteBaselSwitzerland,University of BaselBaselSwitzerland
| | - Marco Siccardi
- Department of Molecular and Clinical PharmacologyInstitute of Translational MedicineUniversity of LiverpoolLiverpoolUK
| | - Catia Marzolini
- Division of Infectious Diseases and Hospital EpidemiologyDepartments of Medicine and Clinical ResearchUniversity Hospital BaselBaselSwitzerland,University of BaselBaselSwitzerland
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Abstract
Cancer continues to be among the leading healthcare problems worldwide, and efforts continue not just to find better drugs, but also better drug delivery methods. The need for delivering cytotoxic agents selectively to cancerous cells, for improved safety and efficacy, has triggered the application of nanotechnology in medicine. This effort has provided drug delivery systems that can potentially revolutionize cancer treatment. Nanocarriers, due to their capacity for targeted drug delivery, can shift the balance of cytotoxicity from healthy to cancerous cells. The field of cancer nanomedicine has made significant progress, but challenges remain that impede its clinical translation. Several biophysical barriers to the transport of nanocarriers to the tumor exist, and a much deeper understanding of nano-bio interactions is necessary to change the status quo. Mathematical modeling has been instrumental in improving our understanding of the physicochemical and physiological underpinnings of nanomaterial behavior in biological systems. Here, we present a comprehensive review of literature on mathematical modeling works that have been and are being employed towards a better understanding of nano-bio interactions for improved tumor delivery efficacy.
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Wu Q, Peters SA. A Retrospective Evaluation of Allometry, Population Pharmacokinetics, and Physiologically-Based Pharmacokinetics for Pediatric Dosing Using Clearance as a Surrogate. CPT Pharmacometrics Syst Pharmacol 2019; 8:220-229. [PMID: 30762304 PMCID: PMC6482279 DOI: 10.1002/psp4.12385] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 01/10/2019] [Indexed: 12/13/2022] Open
Abstract
Physiologically-based pharmacokinetic models are increasingly applied for pediatric dose selection along with traditional methods such as allometry and population pharmacokinetic models. We report a retrospective evaluation of the three methods. Pediatric population pharmacokinetic models sourced from literature for a subset of eight compounds were used to predict clearances for children < 2 years when they were within the modeled age range (interpolation, N = 11) or including those outside the modeled age range (interpolation and extrapolation, N = 18). Pediatric/adult clearance ratios were evaluated with a strict performance criterion of 0.8-1.25 and with twofold criteria. For children > 2 years, 58-75% of the clinical studies (N = 10) met the strict criteria, and > 80% of the clinical studies were predicted within twofold by all three methods. For children < 2 years, physiologically-based pharmacokinetic, allometry with age-dependent exponents, and pediatric population pharmacokinetic models predict 54%, 82%, and 64% within twofold of the observed, respectively.
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Affiliation(s)
- Qier Wu
- Quantitative PharmacologyMerck KGaADarmstadtGermany
- University of Paris DescartesParisFrance
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McDaniel M, Carter J, Keller JM, White SA, Baird A. Open Source Pharmacokinetic/Pharmacodynamic Framework: Tutorial on the BioGears Engine. CPT Pharmacometrics Syst Pharmacol 2019; 8:12-25. [PMID: 30411537 PMCID: PMC6363067 DOI: 10.1002/psp4.12371] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 10/22/2018] [Indexed: 01/24/2023] Open
Abstract
BioGears is an open-source, lumped parameter, full-body human physiology engine. Its purpose is to provide realistic and comprehensive simulations for medical training, research, and education. BioGears incorporates a physiologically based pharmacokinetic/pharmacodynamic (PK/PD) model that is designed to be applicable to a diversity of drug classes and patients and is extensible to future drugs. In addition, BioGears also supports drug interactions with various patient insults and interventions allowing for a realistic research framework and accurate dose-patient responses. This tutorial will demonstrate how the generic BioGears PK/PD model can be extended to a new substance for prediction of drug administration outcomes.
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Affiliation(s)
| | | | - Jonathan M Keller
- Pulmonary and Critical Care MedicineWISH Simulation CenterUniversity of WashingtonSeattleWashingtonUSA
| | | | - Austin Baird
- Applied Research Associates, Inc.RaleighNorth CarolinaUSA
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Krzyszczyk P, Acevedo A, Davidoff EJ, Timmins LM, Marrero-Berrios I, Patel M, White C, Lowe C, Sherba JJ, Hartmanshenn C, O'Neill KM, Balter ML, Fritz ZR, Androulakis IP, Schloss RS, Yarmush ML. The growing role of precision and personalized medicine for cancer treatment. TECHNOLOGY 2018; 6:79-100. [PMID: 30713991 PMCID: PMC6352312 DOI: 10.1142/s2339547818300020] [Citation(s) in RCA: 183] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Cancer is a devastating disease that takes the lives of hundreds of thousands of people every year. Due to disease heterogeneity, standard treatments, such as chemotherapy or radiation, are effective in only a subset of the patient population. Tumors can have different underlying genetic causes and may express different proteins in one patient versus another. This inherent variability of cancer lends itself to the growing field of precision and personalized medicine (PPM). There are many ongoing efforts to acquire PPM data in order to characterize molecular differences between tumors. Some PPM products are already available to link these differences to an effective drug. It is clear that PPM cancer treatments can result in immense patient benefits, and companies and regulatory agencies have begun to recognize this. However, broader changes to the healthcare and insurance systems must be addressed if PPM is to become part of standard cancer care.
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Affiliation(s)
- Paulina Krzyszczyk
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Alison Acevedo
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Erika J Davidoff
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Lauren M Timmins
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Ileana Marrero-Berrios
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Misaal Patel
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Corina White
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Christopher Lowe
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Joseph J Sherba
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Clara Hartmanshenn
- Department of Chemical & Biochemical Engineering, Rutgers University, 98 Brett Road, Piscataway, NJ 08854, USA
| | - Kate M O'Neill
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Max L Balter
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Zachary R Fritz
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Ioannis P Androulakis
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
- Department of Chemical & Biochemical Engineering, Rutgers University, 98 Brett Road, Piscataway, NJ 08854, USA
| | - Rene S Schloss
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
| | - Martin L Yarmush
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA
- Department of Chemical & Biochemical Engineering, Rutgers University, 98 Brett Road, Piscataway, NJ 08854, USA
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Pilla Reddy V, Bui K, Scarfe G, Zhou D, Learoyd M. Physiologically Based Pharmacokinetic Modeling for Olaparib Dosing Recommendations: Bridging Formulations, Drug Interactions, and Patient Populations. Clin Pharmacol Ther 2018; 105:229-241. [PMID: 29717476 PMCID: PMC6585620 DOI: 10.1002/cpt.1103] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 04/21/2018] [Indexed: 12/13/2022]
Abstract
We report physiologically based pharmacokinetic-modeling analyses to determine olaparib (tablet or capsule) drug-drug interactions (DDIs). Verified DDI simulations provided dose recommendations for olaparib coadministration with clinically relevant CYP3A4 modulators to eliminate potential risk to patient safety or olaparib efficacy. When olaparib is given with strong/moderate CYP3A inhibitors, the dose should be reduced to 100/150 mg b.i.d. (tablet), and 150/200 mg b.i.d. (capsule). Olaparib administration is not recommended with strong/moderate CYP3A inducers. No dose reductions are required with weak CYP3A inhibitors/inducers. Olaparib was shown to be a weak inhibitor of CYP3A (1.6-fold increase in exposure of a sensitive CYP3A probe) and to have no effect on P-glycoprotein or UGT1A1 substrates. Finally, this model was used to simulate exposure in scenarios where clinical data of olaparib are lacking, such as severe renal or hepatic impairment populations, and provided initial dosing recommendations in pediatric patients.
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Affiliation(s)
- Venkatesh Pilla Reddy
- Modelling and Simulation, Oncology DMPK, IMED Biotech Unit, AstraZeneca, Cambridge, UK
| | - Khanh Bui
- Quantitative Clinical Pharmacology, Oncology IMED Biotech Unit, AstraZeneca, Waltham, Massachusetts, USA
| | - Graeme Scarfe
- Modelling and Simulation, Oncology DMPK, IMED Biotech Unit, AstraZeneca, Cambridge, UK
| | - Diansong Zhou
- Quantitative Clinical Pharmacology, Oncology IMED Biotech Unit, AstraZeneca, Waltham, Massachusetts, USA
| | - Maria Learoyd
- Quantitative Clinical Pharmacology, Oncology IMED Biotech Unit, AstraZeneca, Cambridge, UK
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41
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Evaluation of the whole body physiologically based pharmacokinetic (WB-PBPK) modeling of drugs. J Theor Biol 2018; 451:1-9. [DOI: 10.1016/j.jtbi.2018.04.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Revised: 04/22/2018] [Accepted: 04/23/2018] [Indexed: 11/17/2022]
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Khazaee M, Ng CA. Evaluating parameter availability for physiologically based pharmacokinetic (PBPK) modeling of perfluorooctanoic acid (PFOA) in zebrafish. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2018; 20:105-119. [PMID: 29265128 DOI: 10.1039/c7em00474e] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Physiologically based pharmacokinetic (PBPK) models are considered useful tools to describe the absorption, distribution, metabolism and excretion of xenobiotics. For accurate predictions, PBPK models require species-specific and compound-specific parameters. Zebrafish are considered an appropriate vertebrate model for investigating the toxicity of a wide variety of compounds. However, no specific mechanistic model exists for the pharmacokinetics of perfluoroalkyl acids (PFAAs) in zebrafish, despite growing concern about this class of ubiquitous environmental contaminants. The purpose of this study was to evaluate the current state of knowledge for the parameters that would be needed to construct such a model for zebrafish. We chose perfluorooctanoic acid (PFOA) as a model PFAA with greater data availability. We have updated a previous PBPK model for rainbow trout to simulate PFOA fate in zebrafish following waterborne exposure. For the first time, the model considers hepatobiliary circulation. In order to evaluate the availability of parameters to implement this model, we performed an extensive literature review to find zebrafish-specific parameters. As in previous approaches, we broadened our search to include mammalian and other fish studies when zebrafish-specific data were lacking. Based on the method used to measure or estimate parameters, or based on their species-specific origin, we scored and ranked the quality of available parameters. These scores were then used in Monte Carlo and partial rank correlation analyses to identify the most critical data gaps. The liver, where fatty acid binding proteins (FABPs) and plasma proteins are considered, represented the best model-data agreement. Lack of agreement in other tissues suggest better parameters are needed. The results of our study highlight the lack of zebrafish-specific parameters. Based on sensitivity and uncertainty analysis, parameters associated with PFAA-protein interactions and passive diffusion need further refinement to enable development of predictive models for these emerging chemicals in zebrafish.
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Affiliation(s)
- Manoochehr Khazaee
- University of Pittsburgh, Department of Civil and Environmental Engineering, 3700 O'Hara St, Pittsburgh, PA 15261, USA.
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43
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Prediction of drug–drug interaction potential using physiologically based pharmacokinetic modeling. Arch Pharm Res 2017; 40:1356-1379. [DOI: 10.1007/s12272-017-0976-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2017] [Accepted: 10/19/2017] [Indexed: 12/22/2022]
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Clipp RB, Bray A, Metoyer R, Thames MC, Webb JB. Pharmacokinetic and pharmacodynamic modeling in BioGears. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:1467-1470. [PMID: 28268603 DOI: 10.1109/embc.2016.7590986] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Pharmacokinetics/pharmacodynamics models were designed and integrated into the BioGears® physiology engine to address the need for real time drug effects for varying patients and injury profiles. Ten drugs were validated using experimental and subject matter expert data. The plasma concentration curves had a good fit with experimental data and 48 of 50 physiologic parameters displayed a less than 10% error compared to the validation data.
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45
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Griffin BT, Guo J, Presas E, Donovan MD, Alonso MJ, O'Driscoll CM. Pharmacokinetic, pharmacodynamic and biodistribution following oral administration of nanocarriers containing peptide and protein drugs. Adv Drug Deliv Rev 2016; 106:367-380. [PMID: 27320644 DOI: 10.1016/j.addr.2016.06.006] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Revised: 06/07/2016] [Accepted: 06/10/2016] [Indexed: 12/17/2022]
Abstract
The influence of nanoparticle (NP) formulations on the pharmacokinetic, pharmacodynamic and biodistribution profiles of peptide- and protein-like drugs following oral administration is critically reviewed. The possible mechanisms of absorption enhancement and the effects of the physicochemical properties of the NP are examined. The potential advantages and challenges of physiologically-based pharmacokinetic (PBPK) modelling to help predict efficacy in man are discussed. The importance of developing and expanding the regulatory framework to help translate the technology into the clinic and accelerate the availability of oral nanoparticulate formulations is emphasized. In conclusion, opportunities for future work to improve the state of the art of oral nanomedicines are identified.
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46
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Hartmanshenn C, Scherholz M, Androulakis IP. Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine. J Pharmacokinet Pharmacodyn 2016; 43:481-504. [PMID: 27647273 DOI: 10.1007/s10928-016-9492-y] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 09/06/2016] [Indexed: 12/17/2022]
Abstract
Personalized medicine strives to deliver the 'right drug at the right dose' by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the quality by design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.
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Affiliation(s)
- Clara Hartmanshenn
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ, 08854, USA
| | - Megerle Scherholz
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ, 08854, USA
| | - Ioannis P Androulakis
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ, 08854, USA. .,Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ, 08854, USA.
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Lu XF, Zhan J, Zhou Y, Bi KS, Chen XH. Use of a semi-physiological pharmacokinetic model to investigate the influence of itraconazole on tacrolimus absorption, distribution and metabolism in mice. Xenobiotica 2016; 47:752-762. [PMID: 27533047 DOI: 10.1080/00498254.2016.1226003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
1. The aim of this study was to investigate the influence of itraconazole (ITCZ) on tacrolimus absorption, distribution and metabolism by developing a semi-physiological pharmacokinetic model of tacrolimus in mice. 2. Mice were randomly divided into four groups, namely control group (CG, taking 3 mg kg-1 tacrolimus only), low-dose group (LDG, taking tacrolimus with 12.5 mg kg-1 ITCZ), medium-dose group (MDG, taking tacrolimus with 25 mg kg-1 ITCZ) and high-dose group (HDG, taking tacrolimus with 50 mg kg-1 ITCZ). 3. Liver clearance (CLli) decreased significantly (**p < 0.01) in LDG (35.3%), MDG (45.2%) and HDG (58.7%) mice compared to CG mice. With respect to gut clearance (CLgu), significant (**p < 0.01) decrease was also revealed in LDG (35.9%), MDG (50.2%) and HDG (64.6%) mice. A significant (**p < 0.01) higher tacrolimus brain-to-blood partition coefficient (Kt,br) was found in MDG (25.3%) and HDG (55.9%) mice than in CG mice. Moreover, a significant (*p < 0.05) increase (16.3%) was found in the absorption rate constant (Ka) in HDG mice compared to CG mice. There was a significant (**p < 0.01) association between ITCZ dose and the change in CLgu (ΔCLgu, r= -0.790), the change in CLli (ΔCLli, r= -0.787) and the change in Kt,br (ΔKt,br, r = 0.727), while the association between ITCZ dose and the change in Ka (ΔKa) was not significant (p > 0.05). 4. These findings could be useful in predicting the efficacy and toxicity of tacrolimus, and drug-drug interaction of ITCZ and tarcolimus in human.
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Affiliation(s)
- Xue-Feng Lu
- a Department of Pharmaceutical Analysis , School of Pharmacy, Shenyang Pharmaceutical University , Shenyang , China
| | - Jian Zhan
- b Department of Pharmaceutics , School of Pharmacy, Shenyang Pharmaceutical University , Shenyang , China , and
| | - Yang Zhou
- c Department of Measurement and Control , School of Physics, Liaoning University , Shenyang , China
| | - Kai-Shun Bi
- a Department of Pharmaceutical Analysis , School of Pharmacy, Shenyang Pharmaceutical University , Shenyang , China
| | - Xiao-Hui Chen
- a Department of Pharmaceutical Analysis , School of Pharmacy, Shenyang Pharmaceutical University , Shenyang , China
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Rasool MF, Khalil F, Läer S. A physiologically based pharmacokinetic drug-disease model to predict carvedilol exposure in adult and paediatric heart failure patients by incorporating pathophysiological changes in hepatic and renal blood flows. Clin Pharmacokinet 2016; 54:943-62. [PMID: 25773479 PMCID: PMC4559583 DOI: 10.1007/s40262-015-0253-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Background and Objective Chronic diseases are associated with pathophysiological changes that could have profound impacts on drug pharmacokinetic behaviour, with a potential need to modify the administered drug therapy. It is important to acknowledge that most patients with chronic illnesses do not have a single, predominant condition but suffer from multiple comorbidities. The rapid advancement in physiologically based pharmacokinetic (PBPK) modelling, as well as the increasing quantitative knowledge of disease-related pathophysiological changes, facilitate building of drug–disease models. However, there are only a few published examples of PBPK models incorporating the pathophysiological changes that occur with chronic diseases. The objective of this study was to develop PBPK models that incorporate the haemodynamic changes in hepatic and renal blood flows occurring in chronic heart failure (CHF) and to evaluate these changes in adults and children, using carvedilol as a model drug. Methods After a comprehensive literature search to select the model input parameters, two PBPK models were developed. Model 1 was based on human liver and intestinal microsome clearances, and model 2 was based on clearance by specific cytochrome P450 enzymes. After evaluation of both models in healthy adults, the reduced hepatic and renal blood flows were incorporated into the developed models to predict carvedilol exposure in the adult CHF population. The adult carvedilol models were scaled down to children by using Simcyp® (Simcyp Ltd, Sheffield, UK). In order to show the impact of reduced organ blood flows on carvedilol disposition, the predictions in the CHF population were made with and without reductions in organ blood flows. Results The predictions made by both models in healthy adults were comparable and within the 2-fold error range. In adults with CHF, the mean observed/predicted ratio [ratio(Obs/Pred)] for oral clearance (CL/F) without reductions in organ blood flows was outside the 2-fold error range, i.e. 0.34 (95 % confidence interval [CI] 0.31–0.37), with use of both models. The mean CL/F ratio(Obs/Pred) values after incorporation of reduced organ blood flows were 1.0 (95 % CI 0.92–1.08) and 0.95 (95 % CI 0.88–1.03) with use of models 1 and 2, respectively. The mean ratio(Obs/Pred) values for the pharmacokinetic parameters were not improved after incorporation of reduced blood flows in paediatric patients, except in those above 17 years of age, who were categorized according to the New York Heart Association classification of CHF, where the CL/F ratio(Obs/Pred) values in two patients were closer to unity. Conclusion There was a strong connection between a decrease in hepatic clearance of carvedilol and an increase in the severity of CHF, especially in adults and in paediatric patients above 17 years of age. The incorporated reductions in hepatic and renal blood flows occurring in moderate and severe CHF patients resulted in improved predictions of carvedilol exposure. The developed models can be extended to predict exposures of drugs with high hepatic extraction in the CHF population. Electronic supplementary material The online version of this article (doi:10.1007/s40262-015-0253-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Muhammad Fawad Rasool
- Department of Clinical Pharmacy and Pharmacotherapy, Heinrich-Heine University, 40225, Düsseldorf, Germany,
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Lu XF, Bi K, Chen X. Physiologically based pharmacokinetic model of docetaxel and interspecies scaling: comparison of simple injection with folate receptor-targeting amphiphilic copolymer-modified liposomes. Xenobiotica 2016; 46:1093-1104. [DOI: 10.3109/00498254.2016.1155128] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Xue-Feng Lu
- Department of Pharmaceutical Analysis, School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, China
| | - Kaishun Bi
- Department of Pharmaceutical Analysis, School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, China
| | - Xiaohui Chen
- Department of Pharmaceutical Analysis, School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, China
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Wadsworth I, Hampson LV, Jaki T. Extrapolation of efficacy and other data to support the development of new medicines for children: A systematic review of methods. Stat Methods Med Res 2016; 27:398-413. [PMID: 26994211 DOI: 10.1177/0962280216631359] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE When developing new medicines for children, the potential to extrapolate from adult data to reduce the experimental burden in children is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. We reviewed the literature to identify statistical methods that could be used to optimise extrapolations in paediatric drug development programmes. METHODS Web of Science was used to identify papers proposing methods relevant for using data from a 'source population' to support inferences for a 'target population'. Four key areas of methods development were targeted: paediatric clinical trials, trials extrapolating efficacy across ethnic groups or geographic regions, the use of historical data in contemporary clinical trials and using short-term endpoints to support inferences about long-term outcomes. RESULTS Searches identified 626 papers of which 52 met our inclusion criteria. From these we identified 102 methods comprising 58 Bayesian and 44 frequentist approaches. Most Bayesian methods (n = 54) sought to use existing data in the source population to create an informative prior distribution for a future clinical trial. Of these, 46 allowed the source data to be down-weighted to account for potential differences between populations. Bayesian and frequentist versions of methods were found for assessing whether key parameters of source and target populations are commensurate (n = 34). Fourteen frequentist methods synthesised data from different populations using a joint model or a weighted test statistic. CONCLUSIONS Several methods were identified as potentially applicable to paediatric drug development. Methods which can accommodate a heterogeneous target population and which allow data from a source population to be down-weighted are preferred. Methods assessing the commensurability of parameters may be used to determine whether it is appropriate to pool data across age groups to estimate treatment effects.
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Affiliation(s)
- Ian Wadsworth
- Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster, UK
| | - Lisa V Hampson
- Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster, UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster, UK
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