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Dadashova K, Smith RC, Haider MA. Local Identifiability Analysis, Parameter Subset Selection and Verification for a Minimal Brain PBPK Model. Bull Math Biol 2024; 86:12. [PMID: 38170402 DOI: 10.1007/s11538-023-01234-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/03/2023] [Indexed: 01/05/2024]
Abstract
Physiologically-based pharmacokinetic (PBPK) modeling is important for studying drug delivery in the central nervous system, including determining antibody exposure, predicting chemical concentrations at target locations, and ensuring accurate dosages. The complexity of PBPK models, involving many variables and parameters, requires a consideration of parameter identifiability; i.e., which parameters can be uniquely determined from data for a specified set of concentrations. We introduce the use of a local sensitivity-based parameter subset selection algorithm in the context of a minimal PBPK (mPBPK) model of the brain for antibody therapeutics. This algorithm is augmented by verification techniques, based on response distributions and energy statistics, to provide a systematic and robust technique to determine identifiable parameter subsets in a PBPK model across a specified time domain of interest. The accuracy of our approach is evaluated for three key concentrations in the mPBPK model for plasma, brain interstitial fluid and brain cerebrospinal fluid. The determination of accurate identifiable parameter subsets is important for model reduction and uncertainty quantification for PBPK models.
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Affiliation(s)
- Kamala Dadashova
- Department of Mathematics, North Carolina State University, Box 8205, Raleigh, NC, 27695, USA
| | - Ralph C Smith
- Department of Mathematics, North Carolina State University, Box 8205, Raleigh, NC, 27695, USA
| | - Mansoor A Haider
- Department of Mathematics, North Carolina State University, Box 8205, Raleigh, NC, 27695, USA.
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2
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Quinney SK, Bies RR, Grannis SJ, Bartlett CW, Mendonca E, Rogerson CM, Backes CH, Shah DK, Tillman EM, Costantine MM, Aruldhas BW, Allam R, Grant A, Abbasi MY, Kandasamy M, Zang Y, Wang L, Shendre A, Li L. The MPRINT Hub Data, Model, Knowledge and Research Coordination Center: Bridging the gap in maternal-pediatric therapeutics research through data integration and pharmacometrics. Pharmacotherapy 2023; 43:391-402. [PMID: 36625779 PMCID: PMC10192201 DOI: 10.1002/phar.2765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/13/2022] [Accepted: 12/08/2022] [Indexed: 01/11/2023]
Abstract
Maternal and pediatric populations have historically been considered "therapeutic orphans" due to their limited inclusion in clinical trials. Physiologic changes during pregnancy and lactation and growth and maturation of children alter pharmacokinetics (PK) and pharmacodynamics (PD) of drugs. Precision therapy in these populations requires knowledge of these effects. Efforts to enhance maternal and pediatric participation in clinical studies have increased over the past few decades. However, studies supporting precision therapeutics in these populations are often small and, in isolation, may have limited impact. Integration of data from various studies, for example through physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling or bioinformatics approaches, can augment the value of data from these studies, and help identify gaps in understanding. To catalyze research in maternal and pediatric precision therapeutics, the Obstetric and Pediatric Pharmacology and Therapeutics Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) established the Maternal and Pediatric Precision in Therapeutics (MPRINT) Hub. Herein, we provide an overview of the status of maternal-pediatric therapeutics research and introduce the Indiana University-Ohio State University MPRINT Hub Data, Model, Knowledge and Research Coordination Center (DMKRCC), which aims to facilitate research in maternal and pediatric precision therapeutics through the integration and assessment of existing knowledge, supporting pharmacometrics and clinical trials design, development of new real-world evidence resources, educational initiatives, and building collaborations among public and private partners, including other NICHD-funded networks. By fostering use of existing data and resources, the DMKRCC will identify critical gaps in knowledge and support efforts to overcome these gaps to enhance maternal-pediatric precision therapeutics.
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Affiliation(s)
- Sara K Quinney
- Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Robert R Bies
- Department of Pharmaceutical Sciences, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Computational and Data Sciences, University at Buffalo, State University of New York at Buffalo, Buffalo, New York, USA
| | - Shaun J Grannis
- Department of Family Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA
- Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana, USA
| | - Christopher W Bartlett
- The Steve & Cindy Rasmussen Institute for Genomic Medicine, Battelle Center for Computational Biology, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, Ohio, USA
- Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Eneida Mendonca
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - Colin M Rogerson
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Carl H Backes
- Division of Neonatology, Nationwide Children’s Hospital; Departments of Pediatrics and Obstetrics and Gynecology, The Ohio State University College of Medicine; Center for Perinatal Research and The Ohio Perinatal Research Network, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, USA; The Heart Center at Nationwide Children’s Hospital, Columbus, Ohio, USA
| | - Dhaval K Shah
- Department of Pharmaceutical Sciences, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York, USA
| | - Emma M Tillman
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Maged M Costantine
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, The Ohio State University, Columbus, Ohio, USA
| | - Blessed W Aruldhas
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
- Department of Pharmacology and Clinical Pharmacology, Christian Medical College, Vellore, India
| | - Reva Allam
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Amelia Grant
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Mohammed Yaseen Abbasi
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Murugesh Kandasamy
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Yong Zang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lei Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Aditi Shendre
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
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Wedagedera JR, Afuape A, Chirumamilla SK, Momiji H, Leary R, Dunlavey M, Matthews R, Abduljalil K, Jamei M, Bois FY. Population PBPK modeling using parametric and nonparametric methods of the Simcyp Simulator, and Bayesian samplers. CPT Pharmacometrics Syst Pharmacol 2022; 11:755-765. [PMID: 35385609 PMCID: PMC9197540 DOI: 10.1002/psp4.12787] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 03/01/2022] [Accepted: 03/07/2022] [Indexed: 11/23/2022] Open
Abstract
Physiologically‐based pharmacokinetic (PBPK) models usually include a large number of parameters whose values are obtained using in vitro to in vivo extrapolation. However, such extrapolations can be uncertain and may benefit from inclusion of evidence from clinical observations via parametric inference. When clinical interindividual variability is high, or the data sparse, it is essential to use a population pharmacokinetics inferential framework to estimate unknown or uncertain parameters. Several approaches are available for that purpose, but their relative advantages for PBPK modeling are unclear. We compare the results obtained using a minimal PBPK model of a canonical theophylline dataset with quasi‐random parametric expectation maximization (QRPEM), nonparametric adaptive grid estimation (NPAG), Bayesian Metropolis‐Hastings (MH), and Hamiltonian Markov Chain Monte Carlo sampling. QRPEM and NPAG gave consistent population and individual parameter estimates, mostly agreeing with Bayesian estimates. MH simulations ran faster than the others methods, which together had similar performance.
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Affiliation(s)
| | | | | | | | - Robert Leary
- CERTARA UK Limited, Simcyp Division, Sheffield, UK
| | | | | | | | - Masoud Jamei
- CERTARA UK Limited, Simcyp Division, Sheffield, UK
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Cristea S, Krekels EHJ, Allegaert K, De Paepe P, de Jaeger A, De Cock P, Knibbe CAJ. Estimation of Ontogeny Functions for Renal Transporters Using a Combined Population Pharmacokinetic and Physiology-Based Pharmacokinetic Approach: Application to OAT1,3. AAPS JOURNAL 2021; 23:65. [PMID: 33948771 PMCID: PMC8096729 DOI: 10.1208/s12248-021-00595-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 04/13/2021] [Indexed: 11/30/2022]
Abstract
To date, information on the ontogeny of renal transporters is limited. Here, we propose to estimate the in vivo functional ontogeny of transporters using a combined population pharmacokinetic (popPK) and physiology-based pharmacokinetic (PBPK) modeling approach called popPBPK. Clavulanic acid and amoxicillin were used as probes for glomerular filtration, combined glomerular filtration, and active secretion through OAT1,3, respectively. The predictive value of the estimated OAT1,3 ontogeny function was assessed by PBPK predictions of renal clearance (CLR) of other OAT1,3 substrates: cefazolin and piperacillin. Individual CLRpost-hoc values, obtained from a published popPK model on the concomitant use of clavulanic acid and amoxicillin in critically ill children between 1 month and 15 years, were used as dependent variables in the popPBPK analysis. CLR was re-parameterized according to PBPK principles, resulting in the estimation of OAT1,3-mediated intrinsic clearance (CLint,OAT1,3,invivo) and its ontogeny. CLint,OAT1,3,invivo ontogeny was described by a sigmoidal function, reaching half of adult level around 7 months of age, comparable to findings based on renal transporter-specific protein expression data. PBPK-based CLR predictions including this ontogeny function were reasonably accurate for piperacillin in a similar age range (2.5 months–15 years) as well as for cefazolin in neonates as compared to published data (%RMSPE of 21.2 and 22.8%, respectively and %PE within ±50%). Using this novel approach, we estimated an in vivo functional ontogeny profile for CLint,OAT1,3,invivo that yields accurate CLR predictions for different OAT1,3 substrates across different ages. This approach deserves further study on functional ontogeny of other transporters.
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Affiliation(s)
- Sînziana Cristea
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands
| | - Elke H J Krekels
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands
| | - Karel Allegaert
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.,Department of Pharmacy and Pharmaceutical Sciences, KU Leuven, Leuven, Belgium.,Department of Clinical Pharmacy, Erasmus MC, Rotterdam, The Netherlands
| | - Peter De Paepe
- Department of Pediatric Intensive Care, Ghent University Hospital, Ghent, Belgium
| | - Annick de Jaeger
- Heymans Institute of Pharmacology, Ghent University, Ghent, Belgium
| | - Pieter De Cock
- Department of Pediatric Intensive Care, Ghent University Hospital, Ghent, Belgium.,Heymans Institute of Pharmacology, Ghent University, Ghent, Belgium.,Department of Pharmacy, Ghent University Hospital, Ghent, Belgium
| | - Catherijne A J Knibbe
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands. .,Department of Clinical Pharmacy, St. Antonius Hospital, Nieuwegein, The Netherlands.
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Chapron BD, Chapron A, Leeder JS. Recent advances in the ontogeny of drug disposition. Br J Clin Pharmacol 2021; 88:4267-4284. [PMID: 33733546 DOI: 10.1111/bcp.14821] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/12/2021] [Accepted: 02/22/2021] [Indexed: 12/11/2022] Open
Abstract
Developmental changes that occur throughout childhood have long been known to impact drug disposition. However, pharmacokinetic studies in the paediatric population have historically been limited due to ethical concerns arising from incorporating children into clinical trials. As such, much of the early work in the field of developmental pharmacology was reliant on difficult-to-interpret in vitro and in vivo animal studies. Over the last 2 decades, our understanding of the mechanistic processes underlying age-related changes in drug disposition has advanced considerably. Progress has largely been driven by technological advances in mass spectrometry-based methods for quantifying proteins implicated in drug disposition, and in silico tools that leverage these data to predict age-related changes in pharmacokinetics. This review summarizes our current understanding of the impact of childhood development on drug disposition, particularly focusing on research of the past 20 years, but also highlighting select examples of earlier foundational research. Equally important to the studies reviewed herein are the areas that we cannot currently describe due to the lack of research evidence; these gaps provide a map of drug disposition pathways for which developmental trends still need to be characterized.
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Affiliation(s)
- Brian D Chapron
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Department of Pediatrics, Children's Mercy Hospital, Kansas City, MO, USA
| | - Alenka Chapron
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Department of Pediatrics, Children's Mercy Hospital, Kansas City, MO, USA
| | - J Steven Leeder
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Department of Pediatrics, Children's Mercy Hospital, Kansas City, MO, USA.,Schools of Medicine and Pharmacy, University of Missouri-Kansas City, MO, USA
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Krekels EHJ, Knibbe CAJ. Pharmacokinetics and Pharmacodynamics of Drugs in Obese Pediatric Patients: How to Map Uncharted Clinical Territories. Handb Exp Pharmacol 2020; 261:231-255. [PMID: 31598838 DOI: 10.1007/164_2019_250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Clinicians are increasingly faced with challenges regarding the pharmacological treatment of obese pediatric patients. To provide guidance for these treatments, a better understanding of the impact of obesity on pharmacological processes in children is needed. Results on pharmacological studies in adults show however ambiguous patterns regarding the impact of obesity on ADME processes or on drug pharmacodynamics. Additionally, based on the limited research performed in obese pediatric patients, it becomes clear that findings from obese adults cannot be expected to always translate directly to similar findings in obese children. To improve knowledge on drug pharmacology in obese pediatric patients, studies should focus on quantifying the impact of maturation, obesity, and other relevant variables on primary pharmacological parameters and on disentangling systemic (renal and/or hepatic) and presystemic (gut and/or first-pass hepatic) clearance. For this, data is required from well-designed clinical trials that include patients with not only a wide range in age but also a range in excess body weight, upon oral and intravenous dosing. Population modelling approaches are ideally suitable for this purpose and can also be used to link the pharmacokinetics to pharmacodynamics and to derive drug dosing regimens. Generalizability of research findings can be achieved by including mechanistic aspects in the data analysis, for instance, using either extrapolation approaches in population modelling or by applying physiologically based modelling principles. It is imperative that more and smarter studies are performed in obese pediatric patients to provide safe and effective treatment for this special patient population.
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Affiliation(s)
- Elke H J Krekels
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - 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.
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