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Alsmadi MM. Salivary Therapeutic Monitoring of Buprenorphine in Neonates After Maternal Sublingual Dosing Guided by Physiologically Based Pharmacokinetic Modeling. Ther Drug Monit 2024; 46:512-521. [PMID: 38366333 DOI: 10.1097/ftd.0000000000001172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 11/08/2023] [Indexed: 02/18/2024]
Abstract
BACKGROUND Opioid use disorder (OUD) during pregnancy is associated with high mortality rates and neonatal opioid withdrawal syndrome (NOWS). Buprenorphine, an opioid, is used to treat OUD and NOWS. Buprenorphine active metabolite (norbuprenorphine) can cross the placenta and cause neonatal respiratory depression (EC 50 = 35 ng/mL) at high brain extracellular fluid (bECF) levels. Neonatal therapeutic drug monitoring using saliva decreases the likelihood of distress and infections associated with frequent blood sampling. METHODS An adult physiologically based pharmacokinetic model for buprenorphine and norbuprenorphine after intravenous and sublingual administration was constructed, vetted, and scaled to newborn and pregnant populations. The pregnancy model predicted that buprenorphine and norbuprenorphine doses would be transplacentally transferred to the newborns. The newborn physiologically based pharmacokinetic model was used to estimate the buprenorphine and norbuprenorphine levels in newborn plasma, bECF, and saliva after these doses. RESULTS After maternal sublingual administration of buprenorphine (4 mg/d), the estimated plasma concentrations of buprenorphine and norbuprenorphine in newborns exceeded the toxicity thresholds for 8 and 24 hours, respectively. However, the norbuprenorphine bECF levels were lower than the respiratory depression threshold. Furthermore, the salivary buprenorphine threshold levels in newborns for buprenorphine analgesia, norbuprenorphine analgesia, and norbuprenorphine hypoventilation were observed to be 22, 2, and 162 ng/mL. CONCLUSIONS Using neonatal saliva for buprenorphine therapeutic drug monitoring can facilitate newborn safety during the maternal treatment of OUD using sublingual buprenorphine. Nevertheless, the suitability of using adult values of respiratory depression EC 50 for newborns must be confirmed.
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Affiliation(s)
- Mo'tasem M Alsmadi
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan; and
- Nanotechnology Institute, Jordan University of Science and Technology, Irbid, Jordan
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Huang H, Zhao W, Qin N, Duan X. Recent Progress on Physiologically Based Pharmacokinetic (PBPK) Model: A Review Based on Bibliometrics. TOXICS 2024; 12:433. [PMID: 38922113 PMCID: PMC11209072 DOI: 10.3390/toxics12060433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/04/2024] [Accepted: 06/13/2024] [Indexed: 06/27/2024]
Abstract
Physiologically based pharmacokinetic/toxicokinetic (PBPK/PBTK) models are designed to elucidate the mechanism of chemical compound action in organisms based on the physiological, biochemical, anatomical, and thermodynamic properties of organisms. After nearly a century of research and practice, good results have been achieved in the fields of medicine, environmental science, and ecology. However, there is currently a lack of a more systematic review of progress in the main research directions of PBPK models, especially a more comprehensive understanding of the application in aquatic environmental research. In this review, a total of 3974 articles related to PBPK models from 1996 to 24 March 2024 were collected. Then, the main research areas of the PBPK model were categorized based on the keyword co-occurrence maps and cluster maps obtained by CiteSpace. The results showed that research related to medicine is the main application area of PBPK. Four major research directions included in the medical field were "drug assessment", "cross-species prediction", "drug-drug interactions", and "pediatrics and pregnancy drug development", in which "drug assessment" accounted for 55% of the total publication volume. In addition, bibliometric analyses indicated a rapid growth trend in the application in the field of environmental research, especially in predicting the residual levels in organisms and revealing the relationship between internal and external exposure. Despite facing the limitation of insufficient species-specific parameters, the PBPK model is still an effective tool for improving the understanding of chemical-biological effectiveness and will provide a theoretical basis for accurately assessing potential risks to ecosystems and human health. The combination with the quantitative structure-activity relationship model, Bayesian method, and machine learning technology are potential solutions to the previous research gaps.
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Affiliation(s)
| | | | - Ning Qin
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China; (H.H.); (W.Z.)
| | - Xiaoli Duan
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China; (H.H.); (W.Z.)
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Zhang W, Zhang Q, Cao Z, Zheng L, Hu W. Physiologically Based Pharmacokinetic Modeling in Neonates: Current Status and Future Perspectives. Pharmaceutics 2023; 15:2765. [PMID: 38140105 PMCID: PMC10747965 DOI: 10.3390/pharmaceutics15122765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/07/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023] Open
Abstract
Rational drug use in special populations is a clinical problem that doctors and pharma-cists must consider seriously. Neonates are the most physiologically immature and vulnerable to drug dosing. There is a pronounced difference in the anatomical and physiological profiles be-tween neonates and older people, affecting the absorption, distribution, metabolism, and excretion of drugs in vivo, ultimately leading to changes in drug concentration. Thus, dose adjustments in neonates are necessary to achieve adequate therapeutic concentrations and avoid drug toxicity. Over the past few decades, modeling and simulation techniques, especially physiologically based pharmacokinetic (PBPK) modeling, have been increasingly used in pediatric drug development and clinical therapy. This rigorously designed and verified model can effectively compensate for the deficiencies of clinical trials in neonates, provide a valuable reference for clinical research design, and even replace some clinical trials to predict drug plasma concentrations in newborns. This review introduces previous findings regarding age-dependent physiological changes and pathological factors affecting neonatal pharmacokinetics, along with their research means. The application of PBPK modeling in neonatal pharmacokinetic studies of various medications is also reviewed. Based on this, we propose future perspectives on neonatal PBPK modeling and hope for its broader application.
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Affiliation(s)
| | | | | | - Liang Zheng
- Department of Clinical Pharmacology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China; (W.Z.); (Q.Z.); (Z.C.)
| | - Wei Hu
- Department of Clinical Pharmacology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China; (W.Z.); (Q.Z.); (Z.C.)
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Alqahtani F, Alruwaili AH, Alasmari MS, Almazroa SA, Alsuhaibani KS, Rasool MF, Alruwaili AF, Alsanea S. A Physiologically Based Pharmacokinetic Model to Predict Systemic Ondansetron Concentration in Liver Cirrhosis Patients. Pharmaceuticals (Basel) 2023; 16:1693. [PMID: 38139819 PMCID: PMC10747545 DOI: 10.3390/ph16121693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023] Open
Abstract
INTRODUCTION Ondansetron is a drug that is routinely prescribed for the management of nausea and vomiting associated with cancer, radiation therapy, and surgical operations. It is mainly metabolized in the liver, and it might accumulate in patients with hepatic impairment and lead to unwanted adverse events. METHODS A physiologically based pharmacokinetic (PBPK) model was developed to predict the exposure of ondansetron in healthy and liver cirrhosis populations. The population-based PBPK simulator PK-Sim was utilized for simulating ondansetron exposure in healthy and liver cirrhosis populations. RESULTS The developed model successfully described the pharmacokinetics of ondansetron in healthy and liver cirrhosis populations. The predicted area under the curve, maximum systemic concentration, and clearance were within the allowed twofold range. The exposure of ondansetron in the population of Child-Pugh class C has doubled in comparison to Child-Pugh class A. The dose has to be adjusted for liver cirrhosis patients to ensure comparable exposure to a healthy population. CONCLUSION In this study, the developed PBPK model has described the pharmacokinetics of ondansetron successfully. The PBPK model has been successfully evaluated to be used as a tool for dose adjustments in liver cirrhosis patients.
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Affiliation(s)
- Faleh Alqahtani
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (F.A.); (A.H.A.); (S.A.A.); (K.S.A.)
| | - Abdullah H. Alruwaili
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (F.A.); (A.H.A.); (S.A.A.); (K.S.A.)
| | - Mohammed S. Alasmari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (F.A.); (A.H.A.); (S.A.A.); (K.S.A.)
| | - Sultan A. Almazroa
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (F.A.); (A.H.A.); (S.A.A.); (K.S.A.)
| | - Khaled S. Alsuhaibani
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (F.A.); (A.H.A.); (S.A.A.); (K.S.A.)
| | - Muhammad F. Rasool
- Department of Pharmacy Practice, Faculty of Pharmacy, Bahauddin Zakariya University, Multan 60800, Pakistan;
| | - Abdulkarim F. Alruwaili
- Clinical Pharmacy Unit, Department of Pharmaceutical Services, Dallah Hospital, Riyadh 12381, Saudi Arabia;
| | - Sary Alsanea
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (F.A.); (A.H.A.); (S.A.A.); (K.S.A.)
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van der Heijden JEM, Freriksen JJM, de Hoop-Sommen MA, Greupink R, de Wildt SN. Physiologically-Based Pharmacokinetic Modeling for Drug Dosing in Pediatric Patients: A Tutorial for a Pragmatic Approach in Clinical Care. Clin Pharmacol Ther 2023; 114:960-971. [PMID: 37553784 DOI: 10.1002/cpt.3023] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/02/2023] [Indexed: 08/10/2023]
Abstract
It is well-accepted that off-label drug dosing recommendations for pediatric patients should be based on the best available evidence. However, the available traditional evidence is often low. To bridge this gap, physiologically-based pharmacokinetic (PBPK) modeling is a scientifically well-founded tool that can be used to enable model-informed dosing (MID) recommendations in children in clinical practice. In this tutorial, we provide a pragmatic, PBPK-based pediatric modeling workflow. For this approach to be successfully implemented in pediatric clinical practice, a thorough understanding of the model assumptions and limitations is required. More importantly, careful evaluation of an MID approach within the context of overall benefits and the potential risks is crucial. The tutorial is aimed to help modelers, researchers, and clinicians, to effectively use PBPK simulations to support pediatric drug dosing.
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Affiliation(s)
- Joyce E M van der Heijden
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jolien J M Freriksen
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marika A de Hoop-Sommen
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rick Greupink
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Saskia N de Wildt
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Pediatric and Neonatal Intensive Care, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
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Deepika D, Kumar V. The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3473. [PMID: 36834167 PMCID: PMC9966583 DOI: 10.3390/ijerph20043473] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.
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Affiliation(s)
- Deepika Deepika
- Environmental Engineering Laboratory, Departament d’Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Pere Virgili Health Research Institute (IISPV), Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, 43204 Reus, Catalonia, Spain
| | - Vikas Kumar
- Environmental Engineering Laboratory, Departament d’Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Pere Virgili Health Research Institute (IISPV), Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, 43204 Reus, Catalonia, Spain
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Fairman K, Choi MK, Gonnabathula P, Lumen A, Worth A, Paini A, Li M. An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science. TOXICS 2023; 11:126. [PMID: 36851001 PMCID: PMC9964742 DOI: 10.3390/toxics11020126] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/16/2022] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations.
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Affiliation(s)
- Kiara Fairman
- Division of Biochemical Toxicology, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR 72079, USA
| | - Me-Kyoung Choi
- Division of Biochemical Toxicology, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR 72079, USA
| | - Pavani Gonnabathula
- Division of Biochemical Toxicology, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR 72079, USA
| | - Annie Lumen
- Division of Biochemical Toxicology, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR 72079, USA
| | - Andrew Worth
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy
| | | | - Miao Li
- Division of Biochemical Toxicology, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR 72079, USA
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Saleem MT, Shoaib MH, Yousuf RI, Ahmed FR, Ahmed K, Siddiqui F, Mahmood ZA, Sikandar M, Imtiaz MS. SeDeM tool-driven full factorial design for osmotic drug delivery of tramadol HCl: Formulation development, physicochemical evaluation, and in-silico PBPK modeling for predictive pharmacokinetic evaluation using GastroPlus™. Front Pharmacol 2022; 13:974715. [PMID: 36278217 PMCID: PMC9585207 DOI: 10.3389/fphar.2022.974715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
The study is based on using SeDeM expert system in developing controlled-release tramadol HCl osmotic tablets and its in-silico physiologically based pharmacokinetic (PBPK) modeling for in-vivo pharmacokinetic evaluation. A Quality by Design (QbD) based approach in developing SeDEM-driven full factorial osmotic drug delivery was applied. A 24 Full-factorial design was used to make the trial formulations of tramadol HCl osmotic tablets using NaCl as osmogen, Methocel K4M as rate controlling polymer, and avicel pH 101 as diluent. The preformulation characteristics of formulations (F1-F16) were determined by applying SeDeM Expert Tool. The formulation was optimized followed by in-vivo predictive pharmacokinetic assessment using PBPK “ACAT” model of GastroPlus™. The FTIR results showed no interaction among the ingredients. The index of good compressibility (ICG) values of all trial formulation blends were ≥5, suggesting direct compression is the best-suited method. Formulation F3 and F4 were optimized based on drug release at 2, 10, and 16 h with a zero-order kinetic release (r2 = 0.992 and 0.994). The SEM images confirmed micropores formation on the surface of the osmotic tablet after complete drug release. F3 and F4 were also stable (shelf life 29.41 and 23.46 months). The in vivo simulation of the pharmacokinetics of the PBPK in-silico model revealed excellent relative bioavailability of F3 and F4 with reference to tramadol HCl 50 mg IR formulations. The SeDeM expert tool was best utilized to evaluate the compression characteristics of selected formulation excipients and their blends for direct compression method in designing once-daily osmotically controlled-release tramadol HCl tablets. The in-silico GastroPlus™ PBPK modeling provided a thorough pharmacokinetic assessment of the optimized formulation as an alternative to tramadol HCl in vivo studies.
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Yuan Y, He Q, Zhang S, Li M, Tang Z, Zhu X, Jiao Z, Cai W, Xiang X. Application of Physiologically Based Pharmacokinetic Modeling in Preclinical Studies: A Feasible Strategy to Practice the Principles of 3Rs. Front Pharmacol 2022; 13:895556. [PMID: 35645843 PMCID: PMC9133488 DOI: 10.3389/fphar.2022.895556] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/14/2022] [Indexed: 11/18/2022] Open
Abstract
Pharmacokinetic characterization plays a vital role in drug discovery and development. Although involving numerous laboratory animals with error-prone, labor-intensive, and time-consuming procedures, pharmacokinetic profiling is still irreplaceable in preclinical studies. With physiologically based pharmacokinetic (PBPK) modeling, the in vivo profiles of drug absorption, distribution, metabolism, and excretion can be predicted. To evaluate the application of such an approach in preclinical investigations, the plasma pharmacokinetic profiles of seven commonly used probe substrates of microsomal enzymes, including phenacetin, tolbutamide, omeprazole, metoprolol, chlorzoxazone, nifedipine, and baicalein, were predicted in rats using bottom-up PBPK models built with in vitro data alone. The prediction's reliability was assessed by comparison with in vivo pharmacokinetic data reported in the literature. The overall predicted accuracy of PBPK models was good with most fold errors within 2, and the coefficient of determination (R2) between the predicted concentration data and the observed ones was more than 0.8. Moreover, most of the observation dots were within the prediction span of the sensitivity analysis. We conclude that PBPK modeling with acceptable accuracy may be incorporated into preclinical studies to refine in vivo investigations, and PBPK modeling is a feasible strategy to practice the principles of 3Rs.
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Affiliation(s)
- Yawen Yuan
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China
- Department of Pharmacy, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qingfeng He
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China
| | - Shunguo Zhang
- Department of Pharmacy, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Min Li
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China
| | - Zhijia Tang
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China
| | - Xiao Zhu
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China
| | - Zheng Jiao
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Weimin Cai
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China
| | - Xiaoqiang Xiang
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China
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Long T, Cristofoletti R, Cicali B, Michaud V, Dow P, Turgeon J, Schmidt S. Physiologically-based Pharmacokinetic Modeling to Assess the Impact of CYP2D6-Mediated Drug-Drug Interactions on Tramadol and O-Desmethyltramadol Exposures via Allosteric and Competitive Inhibition. J Clin Pharmacol 2021; 62:76-86. [PMID: 34383318 PMCID: PMC9293201 DOI: 10.1002/jcph.1951] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/06/2021] [Indexed: 11/11/2022]
Abstract
Tramadol is an opioid medication used to treat moderately severe pain. Cytochrome P450 (CYP) 2D6 inhibition could be important for tramadol, as it decreases the formation of its pharmacologically active metabolite, O‐desmethyltramadol, potentially resulting in increased opioid use and misuse. The objective of this study was to evaluate the impact of allosteric and competitive CYP2D6 inhibition on tramadol and O‐desmethyltramadol pharmacokinetics using quinidine and metoprolol as prototypical perpetrator drugs. A physiologically based pharmacokinetic model for tramadol and O‐desmethyltramadol was developed and verified in PK‐Sim version 8 and linked to respective models of quinidine and metoprolol to evaluate the impact of allosteric and competitive CYP2D6 inhibition on tramadol and O‐desmethyltramadol exposure. Our results show that there is a differentiated impact of CYP2D6 inhibitors on tramadol and O‐desmethyltramadol based on their mechanisms of inhibition. Following allosteric inhibition by a single dose of quinidine, the exposure of both tramadol (51% increase) and O‐desmethyltramadol (52% decrease) was predicted to be significantly altered after concomitant administration of a single dose of tramadol. Following multiple‐dose administration of tramadol and a single‐dose or multiple‐dose administration of quinidine, the inhibitory effect of quinidine was predicted to be long (≈42 hours) and to alter exposure of tramadol and O‐desmethyltramadol by up to 60%, suggesting that coadministration of quinidine and tramadol should be avoided clinically. In comparison, there is no predicted significant impact of metoprolol on tramadol and O‐desmethyltramadol exposure. In fact, tramadol is predicted to act as a CYP2D6 perpetrator and increase metoprolol exposure, which may necessitate the need for dose separation.
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Affiliation(s)
- Tao Long
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL, USA
| | - Rodrigo Cristofoletti
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL, USA
| | - Brian Cicali
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL, USA
| | - Veronique Michaud
- Tabula Rasa HealthCare, Precision Pharmacotherapy Research and Development Institute, Orlando, FL, USA.,Faculty of Pharmacy, Université de Montréal, Montréal, Quebec, Canada
| | - Pamela Dow
- Tabula Rasa HealthCare, Precision Pharmacotherapy Research and Development Institute, Orlando, FL, USA
| | - Jacques Turgeon
- Tabula Rasa HealthCare, Precision Pharmacotherapy Research and Development Institute, Orlando, FL, USA.,Faculty of Pharmacy, Université de Montréal, Montréal, Quebec, Canada
| | - Stephan Schmidt
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL, USA
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Wang K, Jiang K, Wei X, Li Y, Wang T, Song Y. Physiologically Based Pharmacokinetic Models Are Effective Support for Pediatric Drug Development. AAPS PharmSciTech 2021; 22:208. [PMID: 34312742 PMCID: PMC8312709 DOI: 10.1208/s12249-021-02076-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 06/16/2021] [Indexed: 12/30/2022] Open
Abstract
Pediatric drug development faces many difficulties. Traditionally, pediatric drug doses are simply calculated linearly based on the body weight, age, and body surface area of adults. Due to the ontogeny of children, this simple linear scaling may lead to drug overdose in pediatric patients. The physiologically based pharmacokinetic (PBPK) model, as a mathematical model, contributes to the research and development of pediatric drugs. An example of a PBPK model guiding drug dose selection in pediatrics has emerged and has been approved by the relevant regulatory agencies. In this review, we discuss the principle of the PBPK model, emphasize the necessity of establishing a pediatric PBPK model, introduce the absorption, distribution, metabolism, and excretion of the pediatric PBPK model, and understand the various applications and related prospects of the pediatric PBPK model.
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Liu XI, van den Anker JN, Burckart GJ, Dallmann A. Evaluation of Physiologically Based Pharmacokinetic Models to Predict the Absorption of BCS Class I Drugs in Different Pediatric Age Groups. J Clin Pharmacol 2021; 61 Suppl 1:S94-S107. [PMID: 34185902 DOI: 10.1002/jcph.1845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 02/17/2021] [Indexed: 11/06/2022]
Abstract
Age-related changes in many parameters affecting drug absorption remain poorly characterized. The objective of this study was to apply physiologically based pharmacokinetic (PBPK) models in pediatric patients to investigate the absorption and pharmacokinetics of 4 drugs belonging to the Biopharmaceutics Classification System (BCS) class I administered as oral liquid formulations. Pediatric PBPK models built with PK-Sim/MoBi were used to predict the pharmacokinetics of acetaminophen, emtricitabine, theophylline, and zolpidem in different pediatric populations. The model performance for predicting drug absorption and pharmacokinetics was assessed by comparing the predicted absorption profile with the deconvoluted dose fraction absorbed over time and predicted with observed plasma concentration-time profiles. Sensitivity analyses were performed to analyze the effects of changes in relevant input parameters on the model output. Overall, most pharmacokinetic parameters were predicted within a 2-fold error range. The absorption profiles were generally reasonably predicted, but relatively large differences were observed for acetaminophen. Sensitivity analyses showed that the predicted absorption profile was most sensitive to changes in the gastric emptying time (GET) and the specific intestinal permeability. The drug's solubility played only a minor role. These findings confirm that gastric emptying time, more than intestinal permeability or solubility, is a key factor affecting BCS class I drug absorption in children. As gastric emptying time is prolonged in the fed state, a better understanding of the interplay between food intake and gastric emptying time in children is needed, especially in the very young in whom the (semi)fed condition is the prevailing prandial state, and hence prolonged gastric emptying time seems more plausible than the fasting state.
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Affiliation(s)
- Xiaomei I Liu
- Division of Clinical Pharmacology, Children's National Hospital, Washington, District of Columbia, USA
| | - John N van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, District of Columbia, USA.,Division of Pediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland
| | - Gilbert J Burckart
- Office of Clinical Pharmacology, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - André Dallmann
- Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
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Zhou Z, Zhu J, Jiang M, Sang L, Hao K, He H. The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development. Pharmaceutics 2021; 13:pharmaceutics13050704. [PMID: 34065907 PMCID: PMC8151315 DOI: 10.3390/pharmaceutics13050704] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/29/2021] [Accepted: 05/05/2021] [Indexed: 12/12/2022] Open
Abstract
Human-derived in vitro models can provide high-throughput efficacy and toxicity data without a species gap in drug development. Challenges are still encountered regarding the full utilisation of massive data in clinical settings. The lack of translated methods hinders the reliable prediction of clinical outcomes. Therefore, in this study, in silico models were proposed to tackle these obstacles from in vitro to in vivo translation, and the current major cell culture methods were introduced, such as human-induced pluripotent stem cells (hiPSCs), 3D cells, organoids, and microphysiological systems (MPS). Furthermore, the role and applications of several in silico models were summarised, including the physiologically based pharmacokinetic model (PBPK), pharmacokinetic/pharmacodynamic model (PK/PD), quantitative systems pharmacology model (QSP), and virtual clinical trials. These credible translation cases will provide templates for subsequent in vitro to in vivo translation. We believe that synergising high-quality in vitro data with existing models can better guide drug development and clinical use.
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Affiliation(s)
- Zhengying Zhou
- Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China; (Z.Z.); (M.J.)
| | - Jinwei Zhu
- State Key Laboratory of Natural Medicines, Jiangsu Province Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China; (J.Z.); (L.S.)
| | - Muhan Jiang
- Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China; (Z.Z.); (M.J.)
| | - Lan Sang
- State Key Laboratory of Natural Medicines, Jiangsu Province Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China; (J.Z.); (L.S.)
| | - Kun Hao
- State Key Laboratory of Natural Medicines, Jiangsu Province Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China; (J.Z.); (L.S.)
- Correspondence: (K.H.); (H.H.)
| | - Hua He
- Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China; (Z.Z.); (M.J.)
- Correspondence: (K.H.); (H.H.)
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Kalam MN, Rasool MF, Alqahtani F, Imran I, Rehman AU, Ahmed N. Development and Evaluation of a Physiologically Based Pharmacokinetic Drug-Disease Model of Propranolol for Suggesting Model Informed Dosing in Liver Cirrhosis Patients. Drug Des Devel Ther 2021; 15:1195-1211. [PMID: 33762817 PMCID: PMC7982780 DOI: 10.2147/dddt.s297981] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/25/2021] [Indexed: 12/25/2022] Open
Abstract
AIM The study was aimed to understand the underlying causes for the differences in propranolol pharmacokinetics (PK) between healthy and cirrhosis populations by using a systematic whole-body physiologically based pharmacokinetic (PBPK) model-building approach for suggesting model informed propranolol dosing in liver cirrhosis patients with different stages of disease severity. METHODS A whole-body PBPK model was developed by using population simulator PK-Sim® by using reported physicochemical and clinical data for propranolol in healthy and liver cirrhosis populations. The model evaluation was done by visual verification and comparison of PK parameters using their observed/predicted ratios (Robs/pred). RESULTS The developed model has effectively described the disposition of propranolol after intravenous and oral application in healthy and liver cirrhosis populations. All the model predictions were comparable to the observed clinical data and the Robs/pred for all the PK parameters were within a 2-fold range. A significant increase in plasma concentration of propranolol and decrease in drug clearance was observed in progressive stages of liver cirrhosis. The developed model after evaluation with the reported clinical PK data was used for suggesting model informed propranolol dosing in different stages of liver cirrhosis based on systemic unbound drug concentration. CONCLUSION The developed PBPK model has successfully described propranolol PK in healthy and cirrhosis populations after IV and oral administration. The evaluated PBPK propranolol-cirrhosis model can have many implications in predicting propranolol dosing in liver cirrhosis patients with different stages of disease severity.
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Affiliation(s)
| | - Muhammad Fawad Rasool
- Department of Pharmacy Practice, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, 60800, Pakistan
| | - Faleh Alqahtani
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Imran Imran
- Department of Pharmacology, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, 60800, Pakistan
| | - Asim Ur Rehman
- Department of Pharmacy, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Naveed Ahmed
- Department of Pharmacy, Quaid-i-Azam University, Islamabad, 45320, Pakistan
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Xu M, Zheng L, Zeng J, Xu W, Jiang X, Wang L. Physiologically based pharmacokinetic modeling of tramadol to inform dose adjustment and drug-drug interactions according to CYP2D6 phenotypes. Pharmacotherapy 2021; 41:277-290. [PMID: 33316842 DOI: 10.1002/phar.2494] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/06/2020] [Accepted: 11/06/2020] [Indexed: 12/23/2022]
Abstract
OBJECTIVES The objective of this study was to establish physiologically based pharmacokinetic (PBPK) models of tramadol and its active metabolite O-desmethyltramadol (M1) and to explore the influence of CYP2D6 gene polymorphism on the pharmacokinetics of tramadol and M1. Furthermore, we used PBPK modeling to prospectively predict the extent of drug-drug interactions (DDIs) in the presence of genetic polymorphisms when tramadol was co-administered with the CYP2D6 inhibitors duloxetine and paroxetine. METHODS Plasma concentrations of tramadol and M1 were used to adjust the turnover frequency (Kcat ) of CYP2D6 for phenotype populations with different CYP2D6 genotypes. PBPK models were developed to capture the pharmacokinetics between CYP2D6 extensive metabolizers (EMs), intermediate metabolizers (IMs), poor metabolizers (PMs), and ultra-rapid metabolizers (UMs). The validated models were then used to support dose adjustment in different CYP2D6 phenotypes and to predict the extent of CYP2D6-mediated DDIs when tramadol was co-administered with paroxetine or duloxetine. RESULTS The PBPK models we built accurately describe tramadol and M1 exposure in the population with different CYP2D6 phenotypes. In our prediction, the area under the concentration-time curve (AUCinf-tDlast ) of M1 is 70% lower in PMs than in EMs, 27% lower in IMs, and 15% higher in UMs. Based on the models we built, we suggest that the oral dose of tramadol should be 50% higher for IMs and 25% lower for UMs to achieve an approximately equivalent plasma exposure of M1 as in EMs. When tramadol was co-administered with paroxetine or duloxetine, the magnitude of the inhibitor-substrate interaction was lowest in EMs (0.45), secondary in IMs (0.39), and highest in PMs (0.18) in terms of M1. CONCLUSION The current example uses the PBPK model to guide dose adjustment of tramadol and to predict the effect of CYP2D6 genetic polymorphisms on DDIs for rational clinical use of tramadol in the future.
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Affiliation(s)
- Miao Xu
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Liang Zheng
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Jin Zeng
- Department of Pharmacy, Chongqing University Cancer Hospital, Chongqing, China
| | - Wenwen Xu
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Xuehua Jiang
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Ling Wang
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
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Statelova M, Holm R, Fotaki N, Reppas C, Vertzoni M. Factors Affecting Successful Extrapolation of Ibuprofen Exposure from Adults to Pediatric Populations After Oral Administration of a Pediatric Aqueous Suspension. AAPS JOURNAL 2020; 22:146. [DOI: 10.1208/s12248-020-00522-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Accepted: 10/06/2020] [Indexed: 12/17/2022]
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17
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Statelova M, Holm R, Fotaki N, Reppas C, Vertzoni M. Successful Extrapolation of Paracetamol Exposure from Adults to Infants After Oral Administration of a Pediatric Aqueous Suspension Is Highly Dependent on the Study Dosing Conditions. AAPS JOURNAL 2020; 22:126. [DOI: 10.1208/s12248-020-00504-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 08/18/2020] [Indexed: 01/10/2023]
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18
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Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, Liang X, Tan M, Huang Z. Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front Chem 2020; 8:726. [PMID: 33062633 PMCID: PMC7517894 DOI: 10.3389/fchem.2020.00726] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 07/14/2020] [Indexed: 12/11/2022] Open
Abstract
Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.
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Affiliation(s)
- Fengxu Wu
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China
| | - Yuquan Zhou
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Langhui Li
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Xianhuan Shen
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Ganying Chen
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Xiaoqing Wang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Xianyang Liang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Mengyuan Tan
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Zunnan Huang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
- Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, China
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Physiologically-based pharmacokinetic models for children: Starting to reach maturation? Pharmacol Ther 2020; 211:107541. [DOI: 10.1016/j.pharmthera.2020.107541] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 03/19/2020] [Indexed: 12/13/2022]
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20
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Evaluation of the Effect of CYP2D6 Genotypes on Tramadol and O-Desmethyltramadol Pharmacokinetic Profiles in a Korean Population Using Physiologically-Based Pharmacokinetic Modeling. Pharmaceutics 2019; 11:pharmaceutics11110618. [PMID: 31744222 PMCID: PMC6920759 DOI: 10.3390/pharmaceutics11110618] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 11/13/2019] [Accepted: 11/15/2019] [Indexed: 01/04/2023] Open
Abstract
Tramadol is a μ-opioid receptor agonist and a monoamine reuptake inhibitor. O-desmethyltramadol (M1), the major active metabolite of tramadol, is produced by CYP2D6. A physiologically-based pharmacokinetic model was developed to predict changes in time-concentration profiles for tramadol and M1 according to dosage and CYP2D6 genotypes in the Korean population. Parallel artificial membrane permeation assay was performed to determine tramadol permeability, and the metabolic clearance of M1 was determined using human liver microsomes. Clinical study data were used to develop the model. Other physicochemical and pharmacokinetic parameters were obtained from the literature. Simulations for plasma concentrations of tramadol and M1 (after 100 mg tramadol was administered five times at 12-h intervals) were based on a total of 1000 virtual healthy Koreans using SimCYP® simulator. Geometric mean ratios (90% confidence intervals) (predicted/observed) for maximum plasma concentration at steady-state (Cmax,ss) and area under the curve at steady-state (AUClast,ss) were 0.79 (0.69-0.91) and 1.04 (0.85-1.28) for tramadol, and 0.63 (0.51-0.79) and 0.67 (0.54-0.84) for M1, respectively. The predicted time-concentration profiles of tramadol fitted well to observed profiles and those of M1 showed under-prediction. The developed model could be applied to predict concentration-dependent toxicities according to CYP2D6 genotypes and also, CYP2D6-related drug interactions.
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Physiologically Based Pharmacokinetic Modeling of Oxycodone in Children to Support Pediatric Dosing Optimization. Pharm Res 2019; 36:171. [DOI: 10.1007/s11095-019-2708-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 09/24/2019] [Indexed: 12/14/2022]
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22
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Yun YE, Edginton AN. Model qualification of the PK-Sim® pediatric module for pediatric exposure assessment of CYP450 metabolized compounds. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART A 2019; 82:789-814. [PMID: 31405354 DOI: 10.1080/15287394.2019.1652215] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Pediatric physiologically based pharmacokinetic (PBPK) models facilitate the estimation of pharmacokinetic (PK) parameters in children under specific exposure conditions. In human health risk assessment, PBPK modeling has been used to determine a chemical-specific human kinetic adjustment factor (HKAF). Due to increased demands in regulatory assessment, model evaluation and qualification have gained growing attention. The aim of this study was to undertake model qualification of pediatric PBPK models for compounds that are primarily metabolized by cytochrome P450 (CYP) enzymes. The objectives were to determine the appropriateness of the virtual individual creating algorithm in PK-Sim® in predicting PK parameters and their variability in children and identify critical system-specific inputs. PBPK models in adults were constructed for several pharmaceuticals (grouped by major clearance process such as CYP3A4). Several age groups of virtual individuals were created to represent children in pediatric clinical studies. The mean and variance of clearance (CL) from virtual populations were compared to observed values. Sensitivity analysis on area under the curve (AUC) was performed. System-specific parameters of virtual children that contribute to inter-individual PK properties were assessed. Eighty-one percent of the comparisons between simulated and observed clearance values were within twofold error. The mean fold errors were 1.1, 1, 0.7 and 1.8 in adolescents, children, infants and neonates, respectively. CL variability was reasonably predicted for 70% of the comparisons with comparable coefficients of variation between observed and predicted. The sensitivity analysis revealed that fraction unbound in plasma, parameters related to CYP enzyme-mediated metabolism and liver volumewere most important in the estimation of pediatric exposure. A comparison of variabilities in weight, height and liver volume in virtual children showed reliable agreement with observed data. The presented results of predictive performance and properties of virtual populations provide confidence in the use of PK-Sim for pediatric PBPK modeling in toxicological applications including PBPK-based-HKAF derivation.
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Affiliation(s)
- Yejin Esther Yun
- School of Pharmacy, University of Waterloo , Waterloo , Ontario , Canada
| | - Andrea N Edginton
- School of Pharmacy, University of Waterloo , Waterloo , Ontario , Canada
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O'Brien F, Clapham D, Krysiak K, Batchelor H, Field P, Caivano G, Pertile M, Nunn A, Tuleu C. Making Medicines Baby Size: The Challenges in Bridging the Formulation Gap in Neonatal Medicine. Int J Mol Sci 2019; 20:E2688. [PMID: 31159216 PMCID: PMC6600135 DOI: 10.3390/ijms20112688] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 05/17/2019] [Accepted: 05/24/2019] [Indexed: 12/12/2022] Open
Abstract
The development of age-appropriate formulations should focus on dosage forms that can deliver variable yet accurate doses that are safe and acceptable to the child, are matched to his/her development and ability, and avoid medication errors. However, in the past decade, the medication needs of neonates have largely been neglected. The aim of this review is to expand on what differentiates the needs of preterm and term neonates from those of the older paediatric subsets, in terms of environment of care, ability to measure and administer the dose (from the perspective of the patient and carer, the routes of administration, the device and the product), neonatal biopharmaceutics and regulatory challenges. This review offers insight into those challenges posed by the formulation of medicinal products for neonatal patients in order to support the development of clinically relevant products.
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Affiliation(s)
- Fiona O'Brien
- School of Pharmacy, Royal College of Surgeons in Ireland, 111 St Stephens Green Dublin 2, Ireland.
| | | | - Kamelia Krysiak
- School of Pharmacy, Royal College of Surgeons in Ireland, 111 St Stephens Green Dublin 2, Ireland.
| | - Hannah Batchelor
- College of Medical and Dental Sciences, Institute of Clinical Sciences, University of Birmingham, Birmingham B15 2TT, UK.
| | - Peter Field
- University College London School of Pharmacy, 29-39 Brunswick Square, London WC1N 1AX, UK.
| | - Grazia Caivano
- Chiesi Farmaceutici S.p.A. Largo Francesco Belloli 11/A-43122 Parma, Italy.
| | - Marisa Pertile
- Chiesi Farmaceutici S.p.A. Largo Francesco Belloli 11/A-43122 Parma, Italy.
| | - Anthony Nunn
- Department of Women's and Children's Health, University of Liverpool, Liverpool Women's Hospital, Liverpool L8 7SS, UK.
| | - Catherine Tuleu
- University College London School of Pharmacy, 29-39 Brunswick Square, London WC1N 1AX, UK.
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