101
|
Scherholz ML, Androulakis IP. Exploration of sexual dimorphism and inter-individual variability in multivariate parameter spaces for a pharmacokinetic compartment model. Math Biosci 2018; 308:70-80. [PMID: 30557560 DOI: 10.1016/j.mbs.2018.12.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 12/13/2018] [Accepted: 12/13/2018] [Indexed: 11/24/2022]
Abstract
Pharmacokinetic models are particularly useful to study the underlying and complex physiological mechanisms contributing to clinical differences across patient subgroups or special populations. Unfortunately, the inherent variability of biological systems and knowledge gaps in physiological data limit confidence in model predictions for special populations. Sourcing data to reflect the desired physiologies can be resource intensive, particularly for a larger model. Thus, a critical step in model development for special populations involves an in-depth analysis of model inputs, which can be guided by Monte Carlo simulations. Such an approach enables the generation of parameter values by stochastic sampling that are subsequently restricted to the combinations that describe biologically plausible or target model output. Our approach utilized a published pharmacokinetic compartmental model to demonstrate how sampling in conjunction with global sensitivity analysis can be used to explore sexual dimorphism and inter-individual variability in multivariate parameter spaces for differentiation of model input and behavior across phenotypes. Despite limiting the model output to relatively narrow ranges, male and female phenotypes were associated with wide variability in both individual parameter values and combinations of parameters. Through an integrated approach using a support vector machine, principal component analysis and global sensitivity analysis, our approach revealed that specific combinations of parameters gave rise to a certain phenotype, while individual parameters influenced the shape of plasma concentration profile. Augmenting analysis of the model input with global sensitivity analysis enabled an understanding of both sexual dimorphism and inter-individual variability in pharmacokinetics. While the current study revealed how model input could be separated by sex for a simple compartment model, the approach could be extended to other patient factors, such as age or disease, and to a more complex physiologically-based model that describes absorption, distribution, metabolism, and elimination with more detail.
Collapse
Affiliation(s)
- Megerle L Scherholz
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854, United States
| | - Ioannis P Androulakis
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854, United States; Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ 08854, United States.
| |
Collapse
|
102
|
Lin W, Yan JH, Heimbach T, He H. Pediatric Physiologically Based Pharmacokinetic Model Development: Current Status and Challenges. ACTA ACUST UNITED AC 2018. [DOI: 10.1007/s40495-018-0162-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
|
103
|
Matsumoto Y, Cabalu T, Sandhu P, Hartmann G, Iwasa T, Yoshitsugu H, Gibson C, Uemura N. Application of Physiologically Based Pharmacokinetic Modeling to Predict Pharmacokinetics in Healthy Japanese Subjects. Clin Pharmacol Ther 2018; 105:1018-1030. [PMID: 30252941 PMCID: PMC6587435 DOI: 10.1002/cpt.1240] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 09/15/2018] [Indexed: 11/25/2022]
Abstract
Pharmacokinetics (PKs) in Japanese healthy subjects were simulated for nine compounds using physiologically based PK (PBPK) models parameterized with physicochemical properties, preclinical absorption, distribution, metabolism, and excretion (ADME) data, and clinical PK data from non‐Japanese subjects. For each dosing regimen, 100 virtual trials were simulated and predicted/observed ratios for peak plasma concentration (Cmax) and area under the curve (AUC) were calculated. As qualification criteria, it was prespecified that >80% of simulated trials should demonstrate ratios to observed data ranging from 0.5–2.0. Across all compounds and dose regimens studied, 93% of simulated Cmax values in Japanese subjects fulfilled the criteria. Similarly, for AUC, 77% of single‐dosing regimens and 100% of multiple‐dosing regimens fulfilled the criteria. In summary, mechanistically incorporating the appropriate ADME properties into PBPK models, followed by qualification using non‐Japanese clinical data, can predict PKs in the Japanese population and lead to efficient trial design and conduct of Japanese phase I studies.
Collapse
Affiliation(s)
- Yuki Matsumoto
- Clinical Pharmacokinetics and Pharmacometrics Group, Clinical Pharmacology, Clinical Research Division, Japan Development, MSD K.K., Tokyo, Japan.,Faculty of Medicine, Graduate School of Medicine, Oita University, Oita, Japan
| | - Tamara Cabalu
- Department of Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Kenilworth, New Jersey, USA
| | - Punam Sandhu
- Department of Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Kenilworth, New Jersey, USA
| | - Georgy Hartmann
- Department of Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Kenilworth, New Jersey, USA
| | - Takashi Iwasa
- Clinical Pharmacokinetics and Pharmacometrics Group, Clinical Pharmacology, Clinical Research Division, Japan Development, MSD K.K., Tokyo, Japan
| | - Hiroyuki Yoshitsugu
- Clinical Pharmacokinetics and Pharmacometrics Group, Clinical Pharmacology, Clinical Research Division, Japan Development, MSD K.K., Tokyo, Japan
| | - Christopher Gibson
- Department of Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Kenilworth, New Jersey, USA
| | - Naoto Uemura
- Faculty of Medicine, Graduate School of Medicine, Oita University, Oita, Japan
| |
Collapse
|
104
|
A Physiologically Based Pharmacokinetic Model for Optimally Profiling Lamotrigine Disposition and Drug–Drug Interactions. Eur J Drug Metab Pharmacokinet 2018; 44:389-408. [DOI: 10.1007/s13318-018-0532-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
|
105
|
Coulet A, Shah NH, Wack M, Chawki MB, Jay N, Dumontier M. Predicting the need for a reduced drug dose, at first prescription. Sci Rep 2018; 8:15558. [PMID: 30349060 PMCID: PMC6197198 DOI: 10.1038/s41598-018-33980-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 10/06/2018] [Indexed: 01/21/2023] Open
Abstract
Prescribing the right drug with the right dose is a central tenet of precision medicine. We examined the use of patients’ prior Electronic Health Records to predict a reduction in drug dosage. We focus on drugs that interact with the P450 enzyme family, because their dosage is known to be sensitive and variable. We extracted diagnostic codes, conditions reported in clinical notes, and laboratory orders from Stanford’s clinical data warehouse to construct cohorts of patients that either did or did not need a dose change. After feature selection, we trained models to predict the patients who will (or will not) require a dose change after being prescribed one of 34 drugs across 23 drug classes. Overall, we can predict (AUC ≥ 0.70–0.95) a dose reduction for 23 drugs and 22 drug classes. Several of these drugs are associated with clinical guidelines that recommend dose reduction exclusively in the case of adverse reaction. For these cases, a reduction in dosage may be considered as a surrogate for an adverse reaction, which our system could indirectly help predict and prevent. Our study illustrates the role machine learning may take in providing guidance in setting the starting dose for drugs associated with response variability.
Collapse
Affiliation(s)
- Adrien Coulet
- Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France. .,Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
| | - Nigam H Shah
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA
| | - Maxime Wack
- Service d'Evaluation et d'Information Médicales, University Hospital of Nancy (CHRU), Nancy, France
| | - Mohammad B Chawki
- Service d'Evaluation et d'Information Médicales, University Hospital of Nancy (CHRU), Nancy, France
| | - Nicolas Jay
- Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France.,Service d'Evaluation et d'Information Médicales, University Hospital of Nancy (CHRU), Nancy, France
| | - Michel Dumontier
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.,Institute of Data Science, Maastricht University, Maastricht, Netherlands
| |
Collapse
|
106
|
Bracken LM, Chan BSH, Buckley NA. Physiologically based pharmacokinetic modelling of acute digoxin toxicity and the effect of digoxin-specific antibody fragments. Clin Toxicol (Phila) 2018; 57:117-124. [PMID: 30306803 DOI: 10.1080/15563650.2018.1503288] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
CONTEXT Recommended doses of digoxin-specific antibody fragments (digoxin-Fab) for treatment of acute digoxin poisoning are pharmacokinetically unsubstantiated and theoretically excessive. Physiologically based pharmacokinetic (PBPK) modelling creates clinical simulations which are closely related to physiological and pharmacokinetic behaviour. This paper details the formulation of a PBPK model of digoxin and explores its use as a simulation tool for acute digoxin toxicity and its management. MATERIALS AND METHODS A PBPK model of digoxin was constructed and validated for acute digoxin poisoning management by comparing simulations with observed individual acute overdose patients. These simulations were compared with standard two-compartment PK model simulations. RESULTS PBPK model simulations showed good agreement with post-absorption plasma concentrations of digoxin measured in 6 acute overdose patients. PBPK predictions were accurate to 1.5-fold or less of observed clinical values, proving to be more accurate than two-compartment simulations of the same patients which produced up to a 4.9-fold change. CONCLUSIONS Compared to conventional two-compartment modelling, PBPK modelling is superior in generating realistic simulations of acute digoxin toxicity and the response to digoxin-Fab. Simulation capacity provides realistic, continuous data which has the potential to substantiate alternative, less expensive, and safer digoxin-Fab dosing strategies for the treatment of acute digoxin toxicity.
Collapse
Affiliation(s)
- Lucy M Bracken
- a Pharmacology, Sydney Medical School, University of Sydney , Sydney , Australia.,b Department of Emergency Medicine , Clinical Toxicology Unit, Prince of Wales Hospital , New South Wales , Australia
| | - Betty S H Chan
- b Department of Emergency Medicine , Clinical Toxicology Unit, Prince of Wales Hospital , New South Wales , Australia.,c New South Wales Poisons Information Centre , New South Wales , Australia
| | - Nicholas A Buckley
- a Pharmacology, Sydney Medical School, University of Sydney , Sydney , Australia.,c New South Wales Poisons Information Centre , New South Wales , Australia
| |
Collapse
|
107
|
Michelet R, Van Bocxlaer J, Allegaert K, Vermeulen A. The use of PBPK modeling across the pediatric age range using propofol as a case. J Pharmacokinet Pharmacodyn 2018; 45:765-785. [PMID: 30298439 DOI: 10.1007/s10928-018-9607-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 09/25/2018] [Indexed: 12/12/2022]
Abstract
The project SAFEPEDRUG aims to provide guidelines for drug research in children, based on bottom-up and top-down approaches. Propofol, one of the studied model compounds, was selected because it is extensively metabolized in liver and kidney, with an important role for the glucuronidation pathway. Besides, being a lipophilic molecule, it is distributed into fat tissues, from where it redistributes into the systemic circulation. In the past, both bottom-up (Physiologically based pharmacokinetic, PBPK) and top-down approaches (population pharmacokinetic, popPK) were applied to describe its pharmacokinetics (PK). In this work, a combination of the two was used to check their performance to describe PK in children and neonates (both term and preterm) using propofol as a case compound. First, in vitro data was generated in human liver microsomes and recombinant enzymes and used to develop an adult PBPK model in Simcyp®. Activity adjustment factors (AAFs) were calculated to account for differences between in vitro and in vivo enzyme activity. Clinical data were analyzed using a 3-compartment model in NONMEM. These data were used to construct a retrograde PBPK model and for qualification of the PBPK models. Once an accurate in vivo clearance was obtained accounting for the contribution of the different metabolic pathways, the resulting PBPK models were challenged with new data for qualification. After that, the constructed adult PPBK model for propofol was extrapolated to the pediatric population. Both the default built-in and in vivo derived ontogeny functions were used to do so. The models were qualified by comparing their predicted PK parameters to published values, and by comparison of predicted concentration-time profiles to available clinical data. Clearance values were predicted well, especially when compared with values obtained from trials where long-term sampling was applied, whereas volume of distribution was lower compared to the most common popPK model predictions. Concentration-time profiles were predicted well up until and including the preterm neonatal population. In this work, it was thus shown that PBPK can be used to predict the PK up to and including the preterm neonatal population without the use of pediatric in vivo data. This work adds weight to the need for further development of PBPK models, especially regarding distribution modeling and the use of in vivo derived ontogeny functions.
Collapse
Affiliation(s)
- Robin Michelet
- Laboratory of Medical Biochemistry and Clinical Analysis, Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.
| | - Jan Van Bocxlaer
- Laboratory of Medical Biochemistry and Clinical Analysis, Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
| | - Karel Allegaert
- Department of Development & Regeneration, KU Leuven, Leuven, Belgium.,Division of Neonatology, Department of Pediatrics, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - An Vermeulen
- Laboratory of Medical Biochemistry and Clinical Analysis, Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
| |
Collapse
|
108
|
Adiwidjaja J, Boddy AV, McLachlan AJ. A Strategy to Refine the Phenotyping Approach and Its Implementation to Predict Drug Clearance: A Physiologically Based Pharmacokinetic Simulation Study. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:798-808. [PMID: 30260092 PMCID: PMC6310868 DOI: 10.1002/psp4.12355] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 09/10/2018] [Indexed: 12/13/2022]
Abstract
The phenotyping approach to predict drug metabolism activity is often hampered by a lack of correlation between the probe and the drug of interest. In this article, we present a strategy to refine the phenotyping approach based on a physiologically based pharmacokinetic simulation (implemented in Simcyp Simulator version 17) using previously published models. The apparent clearance (CL/F) of erlotinib was better predicted by the sum of caffeine and i.v. midazolam CL/F (r2 = 0.60) compared to that of either probe drug alone. The clearance of atorvastatin and repaglinide had a strong correlation (r2 = 0.70 and 0.63, respectively) with that of pitavastatin (a SLCO1B1 probe). Use of multiple probes for drugs that are predominantly metabolized by more than one cytochrome P450 (CYP) enzyme should be considered. In a case in which hepatic uptake transporters play a significant role in the disposition of a drug, the pharmacokinetic of a transporter probe will provide better predictions of the drug clearance.
Collapse
Affiliation(s)
- Jeffry Adiwidjaja
- Sydney Pharmacy School, The University of Sydney, Sydney, New South Wales, Australia
| | - Alan V Boddy
- Sydney Pharmacy School, The University of Sydney, Sydney, New South Wales, Australia.,School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia, Australia
| | - Andrew J McLachlan
- Sydney Pharmacy School, The University of Sydney, Sydney, New South Wales, Australia
| |
Collapse
|
109
|
Review: Using physiologically based models to predict population responses to phytochemicals by wild vertebrate herbivores. Animal 2018; 12:s383-s398. [PMID: 30251623 DOI: 10.1017/s1751731118002264] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
To understand how foraging decisions impact individual fitness of herbivores, nutritional ecologists must consider the complex in vivo dynamics of nutrient-nutrient interactions and nutrient-toxin interactions associated with foraging. Mathematical modeling has long been used to make foraging predictions (e.g. optimal foraging theory) but has largely been restricted to a single currency (e.g. energy) or using simple indices of nutrition (e.g. fecal nitrogen) without full consideration of physiologically based interactions among numerous co-ingested phytochemicals. Here, we describe a physiologically based model (PBM) that provides a mechanistic link between foraging decisions and demographic consequences. Including physiological mechanisms of absorption, digestion and metabolism of phytochemicals in PBMs allows us to estimate concentrations of ingested and interacting phytochemicals in the body. Estimated phytochemical concentrations more accurately link intake of phytochemicals to changes in individual fitness than measures of intake alone. Further, we illustrate how estimated physiological parameters can be integrated with the geometric framework of nutrition and into integral projection models and agent-based models to predict fitness and population responses of vertebrate herbivores to ingested phytochemicals. The PBMs will improve our ability to understand the foraging decisions of vertebrate herbivores and consequences of those decisions and may help identify key physiological mechanisms that underlie diet-based ecological adaptations.
Collapse
|
110
|
Calvier EAM, Nguyen TT, Johnson TN, Rostami-Hodjegan A, Tibboel D, Krekels EHJ, Knibbe CAJ. Can Population Modelling Principles be Used to Identify Key PBPK Parameters for Paediatric Clearance Predictions? An Innovative Application of Optimal Design Theory. Pharm Res 2018; 35:209. [PMID: 30218393 PMCID: PMC6156772 DOI: 10.1007/s11095-018-2487-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 08/27/2018] [Indexed: 12/26/2022]
Abstract
PURPOSE Physiologically-based pharmacokinetic (PBPK) models are essential in drug development, but require parameters that are not always obtainable. We developed a methodology to investigate the feasibility and requirements for precise and accurate estimation of PBPK parameters using population modelling of clinical data and illustrate this for two key PBPK parameters for hepatic metabolic clearance, namely whole liver unbound intrinsic clearance (CLint,u,WL) and hepatic blood flow (Qh) in children. METHODS First, structural identifiability was enabled through re-parametrization and the definition of essential trial design components. Subsequently, requirements for the trial components to yield precise estimation of the PBPK parameters and their inter-individual variability were established using a novel application of population optimal design theory. Finally, the performance of the proposed trial design was assessed using stochastic simulation and estimation. RESULTS Precise estimation of CLint,u,WL and Qh and their inter-individual variability was found to require a trial with two drugs, of which one has an extraction ratio (ER) ≤ 0.27 and the other has an ER ≥ 0.93. The proposed clinical trial design was found to lead to precise and accurate parameter estimates and was robust to parameter uncertainty. CONCLUSION The proposed framework can be applied to other PBPK parameters and facilitate the development of PBPK models.
Collapse
Affiliation(s)
- Elisa A M Calvier
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Gorlaeus Laboratories, Leiden University, Einstein weg 55, 2333 CC, Leiden, The Netherlands
| | - Thu Thuy Nguyen
- IAME, UMR 1137, INSERM, University Paris Diderot, Sorbonne Paris Cité, Paris, France
| | | | - Amin Rostami-Hodjegan
- Simcyp Limited, Sheffield, UK.,Manchester Pharmacy School, University of Manchester, Manchester, UK
| | - Dick Tibboel
- Intensive Care and Department of Pediatric Surgery, Erasmus University Medical Center - Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Elke H J Krekels
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Gorlaeus Laboratories, Leiden University, Einstein weg 55, 2333 CC, Leiden, The Netherlands
| | - Catherijne A J Knibbe
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Gorlaeus Laboratories, Leiden University, Einstein weg 55, 2333 CC, Leiden, The Netherlands. .,Department of Clinical Pharmacy, St. Antonius Hospital, Nieuwegein, The Netherlands.
| |
Collapse
|
111
|
Fuhr U, Hsin CH, Li X, Jabrane W, Sörgel F. Assessment of Pharmacokinetic Drug-Drug Interactions in Humans: In Vivo Probe Substrates for Drug Metabolism and Drug Transport Revisited. Annu Rev Pharmacol Toxicol 2018; 59:507-536. [PMID: 30156973 DOI: 10.1146/annurev-pharmtox-010818-021909] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Pharmacokinetic parameters of selective probe substrates are used to quantify the activity of an individual pharmacokinetic process (PKP) and the effect of perpetrator drugs thereon in clinical drug-drug interaction (DDI) studies. For instance, oral caffeine is used to quantify hepatic CYP1A2 activity, and oral dagibatran etexilate for intestinal P-glycoprotein (P-gp) activity. However, no probe substrate depends exclusively on the PKP it is meant to quantify. Lack of selectivity for a given enzyme/transporter and expression of the respective enzyme/transporter at several sites in the human body are the main challenges. Thus, a detailed understanding of the role of individual PKPs for the pharmacokinetics of any probe substrate is essential to allocate the effect of a perpetrator drug to a specific PKP; this is a prerequisite for reliably informed pharmacokinetic models that will allow for the quantitative prediction of perpetrator effects on therapeutic drugs, also in respective patient populations not included in DDI studies.
Collapse
Affiliation(s)
- Uwe Fuhr
- Department I of Pharmacology, University Hospital Cologne, 50931 Cologne, Germany;
| | - Chih-Hsuan Hsin
- Department I of Pharmacology, University Hospital Cologne, 50931 Cologne, Germany;
| | - Xia Li
- Department I of Pharmacology, University Hospital Cologne, 50931 Cologne, Germany;
| | - Wafaâ Jabrane
- Department I of Pharmacology, University Hospital Cologne, 50931 Cologne, Germany;
| | - Fritz Sörgel
- Institute for Biomedical and Pharmaceutical Research, 90562 Nürnberg-Heroldsberg, Germany
| |
Collapse
|
112
|
Biliouris K, Gaitonde P, Yin W, Norris DA, Wang Y, Henry S, Fey R, Nestorov I, Schmidt S, Rogge M, Lesko LJ, Trame MN. A Semi-Mechanistic Population Pharmacokinetic Model of Nusinersen: An Antisense Oligonucleotide for the Treatment of Spinal Muscular Atrophy. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:581-592. [PMID: 30043511 PMCID: PMC6157691 DOI: 10.1002/psp4.12323] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 07/06/2018] [Indexed: 01/21/2023]
Abstract
A pharmacokinetic (PK) model was developed for nusinersen, an antisense oligonucleotide (ASO) that is the first approved treatment for spinal muscular atrophy (SMA). The model was built with data from 92 nonhuman primates (NHPs) following intrathecal doses (0.3–7 mg) and characterized the PK in cerebrospinal fluid (CSF), plasma, total spinal cord, brain, and pons. The estimated volumes were 13.6, 937, 4.5, 53.8, and 2.11 mL, respectively. Global sensitivity analysis demonstrated that the CSF‐to‐plasma drug distribution rate (0.09 hour−1) is a major determinant of the maximum nusinersen concentration in central nervous system (CNS) tissues. Physiological age‐based and body weight‐based allometric scaling was implemented with exponent values of −0.08 and 1 for the rate constants and the volume of distribution, respectively. Simulations of the scaled model were in agreement with clinical observations from 52 pediatric phase I PK profiles. The developed model can be used to guide the design of clinical trials with ASOs.
Collapse
Affiliation(s)
- Konstantinos Biliouris
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA
| | - Puneet Gaitonde
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA
| | - Wei Yin
- Biogen Idec, Cambridge, Massachusetts, USA
| | | | - Yanfeng Wang
- Ionis Pharmaceuticals Inc., Carlsbad, California, USA
| | - Scott Henry
- Ionis Pharmaceuticals Inc., Carlsbad, California, USA
| | - Robert Fey
- Ionis Pharmaceuticals Inc., Carlsbad, California, USA
| | | | - Stephan Schmidt
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA
| | - Mark Rogge
- Biogen Idec, Cambridge, Massachusetts, USA
| | - Lawrence J Lesko
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA
| | - Mirjam N Trame
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA
| |
Collapse
|
113
|
Hasegawa C, Duffull SB. Automated Scale Reduction of Nonlinear QSP Models With an Illustrative Application to a Bone Biology System. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:562-572. [PMID: 30043496 PMCID: PMC6157701 DOI: 10.1002/psp4.12324] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Integrating quantitative systems pharmacology (QSP) into pharmacokinetics/pharmacodynamics (PKPD) has resulted in models that are highly complex and often not amenable to further exploration via estimation or design. Because QSP models are usually depicted using nonlinear differential equations it is not straightforward to apply some model reduction techniques, such as proper lumping. In this study, we explore the combined use of linearization and proper lumping as a general method to simplification of a nonlinear QSP model. We illustrate this with a bone biology model and the reduced model was then applied to describe bone mineral density (BMD) changes due to denosumab dosing. The methodologies used in this study can be applied to other multiscale models for developing a mechanism-based structural model for future analyses.
Collapse
Affiliation(s)
- Chihiro Hasegawa
- School of Pharmacy, University of Otago, Dunedin, New Zealand.,Translational Medicine Center, Ono Pharmaceutical Co., Ltd., Osaka, Japan
| | | |
Collapse
|
114
|
Prieto Garcia L, Janzén D, Kanebratt KP, Ericsson H, Lennernäs H, Lundahl A. Physiologically Based Pharmacokinetic Model of Itraconazole and Two of Its Metabolites to Improve the Predictions and the Mechanistic Understanding of CYP3A4 Drug-Drug Interactions. Drug Metab Dispos 2018; 46:1420-1433. [DOI: 10.1124/dmd.118.081364] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 07/27/2018] [Indexed: 11/22/2022] Open
|
115
|
Kaur N, Narang A, Bansal AK. Use of biorelevant dissolution and PBPK modeling to predict oral drug absorption. Eur J Pharm Biopharm 2018; 129:222-246. [DOI: 10.1016/j.ejpb.2018.05.024] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 04/16/2018] [Accepted: 05/21/2018] [Indexed: 11/29/2022]
|
116
|
Guimarães M, Statelova M, Holm R, Reppas C, Symilllides M, Vertzoni M, Fotaki N. Biopharmaceutical considerations in paediatrics with a view to the evaluation of orally administered drug products - a PEARRL review. ACTA ACUST UNITED AC 2018; 71:603-642. [PMID: 29971768 DOI: 10.1111/jphp.12955] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Accepted: 05/28/2018] [Indexed: 12/25/2022]
Abstract
OBJECTIVES In this review, the current biopharmaceutical approaches for evaluation of oral formulation performance in paediatrics are discussed. KEY FINDINGS The paediatric gastrointestinal (GI) tract undergoes numerous morphological and physiological changes throughout its development and growth. Some physiological parameters are yet to be investigated, limiting the use of the existing in vitro biopharmaceutical tools to predict the in vivo performance of paediatric formulations. Meals and frequencies of their administration evolve during childhood and affect oral drug absorption. Furthermore, the establishment of a paediatric Biopharmaceutics Classification System (pBCS), based on the adult Biopharmaceutics Classification System (BCS), requires criteria adjustments. The usefulness of computational simulation and modeling for extrapolation of adult data to paediatrics has been confirmed as a tool for predicting drug formulation performance. Despite the great number of successful physiologically based pharmacokinetic models to simulate drug disposition, the simulation of drug absorption from the GI tract is a complicating issue in paediatric populations. SUMMARY The biopharmaceutics tools for investigation of oral drug absorption in paediatrics need further development, refinement and validation. A combination of in vitro and in silico methods could compensate for the uncertainties accompanying each method on its own.
Collapse
Affiliation(s)
- Mariana Guimarães
- Department of Pharmacy and Pharmacology, University of Bath, Bath, UK
| | - Marina Statelova
- Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece
| | - René Holm
- Drug Product Development, Janssen Research and Development, Johnson & Johnson, Beerse, Belgium
| | - Christos Reppas
- Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece
| | - Moira Symilllides
- Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece
| | - Maria Vertzoni
- Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece
| | - Nikoletta Fotaki
- Department of Pharmacy and Pharmacology, University of Bath, Bath, UK
| |
Collapse
|
117
|
Smith RL, Cohen SM, Fukushima S, Gooderham NJ, Hecht SS, Guengerich FP, Rietjens IMCM, Bastaki M, Harman CL, McGowen MM, Taylor SV. The safety evaluation of food flavouring substances: the role of metabolic studies. Toxicol Res (Camb) 2018; 7:618-646. [PMID: 30090611 PMCID: PMC6062396 DOI: 10.1039/c7tx00254h] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 03/21/2018] [Indexed: 12/13/2022] Open
Abstract
The safety assessment of a flavour substance examines several factors, including metabolic and physiological disposition data. The present article provides an overview of the metabolism and disposition of flavour substances by identifying general applicable principles of metabolism to illustrate how information on metabolic fate is taken into account in their safety evaluation. The metabolism of the majority of flavour substances involves a series both of enzymatic and non-enzymatic biotransformation that often results in products that are more hydrophilic and more readily excretable than their precursors. Flavours can undergo metabolic reactions, such as oxidation, reduction, or hydrolysis that alter a functional group relative to the parent compound. The altered functional group may serve as a reaction site for a subsequent metabolic transformation. Metabolic intermediates undergo conjugation with an endogenous agent such as glucuronic acid, sulphate, glutathione, amino acids, or acetate. Such conjugates are typically readily excreted through the kidneys and liver. This paper summarizes the types of metabolic reactions that have been documented for flavour substances that are added to the human food chain, the methodologies available for metabolic studies, and the factors that affect the metabolic fate of a flavour substance.
Collapse
Affiliation(s)
- Robert L Smith
- Molecular Toxicology , Imperial College School of Medicine , London SW7 2AZ , UK
| | - Samuel M Cohen
- Dept. of Pathology and Microbiology , University of Nebraska Medical Centre , 983135 Nebraska Medical Centre , Omaha , NE 68198-3135 , USA
| | - Shoji Fukushima
- Japan Bioassay Research Centre , 2445 Hirasawa , Hadano , Kanagawa 257-0015 , Japan
| | - Nigel J Gooderham
- Dept. of Surgery and Cancer , Imperial College of Science , Sir Alexander Fleming Building , London SW7 2AZ , UK
| | - Stephen S Hecht
- Masonic Cancer Centre and Dept. of Laboratory Medicine and Pathology , University of Minnesota , Cancer and Cardiovascular Research Building , 2231 6th St , SE , Minneapolis , MN 55455 , USA
| | - F Peter Guengerich
- Department of Biochemistry , Vanderbilt University School of Medicine , 638B Robinson Research Building , 2200 Pierce Avenue , Nashville , Tennessee 37232-0146 , USA
| | - Ivonne M C M Rietjens
- Division of Toxicology , Wageningen University , Tuinlaan 5 , 6703 HE Wageningen , The Netherlands
| | - Maria Bastaki
- Flavor and Extract Manufacturers Association , 1101 17th Street , NW Suite 700 , Washington , DC 20036 , USA . ; ; Tel: +1 (202)293-5800
| | - Christie L Harman
- Flavor and Extract Manufacturers Association , 1101 17th Street , NW Suite 700 , Washington , DC 20036 , USA . ; ; Tel: +1 (202)293-5800
| | - Margaret M McGowen
- Flavor and Extract Manufacturers Association , 1101 17th Street , NW Suite 700 , Washington , DC 20036 , USA . ; ; Tel: +1 (202)293-5800
| | - Sean V Taylor
- Flavor and Extract Manufacturers Association , 1101 17th Street , NW Suite 700 , Washington , DC 20036 , USA . ; ; Tel: +1 (202)293-5800
| |
Collapse
|
118
|
Bouchene S, Marchand S, Couet W, Friberg LE, Gobin P, Lamarche I, Grégoire N, Björkman S, Karlsson MO. A Whole-Body Physiologically Based Pharmacokinetic Model for Colistin and Colistin Methanesulfonate in Rat. Basic Clin Pharmacol Toxicol 2018; 123:407-422. [PMID: 29665289 DOI: 10.1111/bcpt.13026] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 04/03/2018] [Indexed: 11/29/2022]
Abstract
Colistin is a polymyxin antibiotic used to treat patients infected with multidrug-resistant Gram-negative bacteria (MDR-GNB). The objective of this work was to develop a whole-body physiologically based pharmacokinetic (WB-PBPK) model to predict tissue distribution of colistin in rat. The distribution of a drug in a tissue is commonly characterized by its tissue-to-plasma partition coefficient, Kp . Colistin and its prodrug, colistin methanesulfonate (CMS) Kp priors, were measured experimentally from rat tissue homogenates or predicted in silico. The PK parameters of both compounds were estimated fitting in vivo their plasma concentration-time profiles from six rats receiving an i.v. bolus of CMS. The variability in the data was quantified by applying a nonlinear mixed effect (NLME) modelling approach. A WB-PBPK model was developed assuming a well-stirred and perfusion-limited distribution in tissue compartments. Prior information on tissue distribution of colistin and CMS was investigated following three scenarios: Kp was estimated using in silico Kp priors (I) or Kp was estimated using experimental Kp priors (II) or Kp was fixed to the experimental values (III). The WB-PBPK model best described colistin and CMS plasma concentration-time profiles in scenario II. Colistin-predicted concentrations in kidneys in scenario II were higher than in other tissues, which was consistent with its large experimental Kp prior. This might be explained by a high affinity of colistin for renal parenchyma and active reabsorption into the proximal tubular cells. In contrast, renal accumulation of colistin was not predicted in scenario I. Colistin and CMS clearance estimates were in agreement with published values. The developed model suggests using experimental priors over in silico Kp priors for kidneys to provide a better prediction of colistin renal distribution. Such models might serve in drug development for interspecies scaling and investigate the impact of disease state on colistin disposition.
Collapse
Affiliation(s)
- Salim Bouchene
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Sandrine Marchand
- INSERM U-1070, Pôle Biologie Santé, Poitiers, France.,Laboratoire de Toxicologie et Pharmacocinétique, CHU de Poitiers, Poitiers, France
| | - William Couet
- INSERM U-1070, Pôle Biologie Santé, Poitiers, France.,Laboratoire de Toxicologie et Pharmacocinétique, CHU de Poitiers, Poitiers, France
| | - Lena E Friberg
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Patrice Gobin
- INSERM U-1070, Pôle Biologie Santé, Poitiers, France
| | | | - Nicolas Grégoire
- INSERM U-1070, Pôle Biologie Santé, Poitiers, France.,Laboratoire de Toxicologie et Pharmacocinétique, CHU de Poitiers, Poitiers, France
| | - Sven Björkman
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| |
Collapse
|
119
|
Hsieh NH, Reisfeld B, Bois FY, Chiu WA. Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling. Front Pharmacol 2018; 9:588. [PMID: 29937730 PMCID: PMC6002508 DOI: 10.3389/fphar.2018.00588] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 05/16/2018] [Indexed: 11/13/2022] Open
Abstract
Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish “influential” parameters to be estimated and “non-influential” parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by “expert judgment”-based approach) vs. the subset of original influential parameters (OIP, determined by GSA from the OMP). We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP) vs. the full set influential parameters (FIP, determined by GSA from FMP). We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods) and efficiency (lowest computational cost to achieve convergence) in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational efficiency without discernable changes in prediction accuracy or precision. We further found six previously fixed parameters that were actually influential to the model predictions. Adding these additional influential parameters improved the model performance beyond that of the original publication while maintaining similar computational efficiency. We conclude that GSA provides an objective, transparent, and reproducible approach to improve the performance and computational efficiency of PBPK models.
Collapse
Affiliation(s)
- Nan-Hung Hsieh
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Brad Reisfeld
- Chemical and Biological Engineering and School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States
| | | | - Weihsueh A Chiu
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| |
Collapse
|
120
|
Lindmark B, Lundahl A, Kanebratt KP, Andersson TB, Isin EM. Human hepatocytes and cytochrome P450-selective inhibitors predict variability in human drug exposure more accurately than human recombinant P450s. Br J Pharmacol 2018; 175:2116-2129. [PMID: 29574682 PMCID: PMC5980217 DOI: 10.1111/bph.14203] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 02/27/2018] [Accepted: 03/02/2018] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND AND PURPOSE Drugs metabolically eliminated by several enzymes are less vulnerable to variable compound exposure in patients due to drug-drug interactions (DDI) or if a polymorphic enzyme is involved in their elimination. Therefore, it is vital in drug discovery to accurately and efficiently estimate and optimize the metabolic elimination profile. EXPERIMENTAL APPROACH CYP3A and/or CYP2D6 substrates with well described variability in vivo in humans due to CYP3A DDI and CYP2D6 polymorphism were selected for assessment of fraction metabolized by each enzyme (fmCYP ) in two in vitro systems: (i) human recombinant P450s (hrP450s) and (ii) human hepatocytes combined with selective P450 inhibitors. Increases in compound exposure in poor versus extensive CYP2D6 metabolizers and by the strong CYP3A inhibitor ketoconazole were mathematically modelled and predicted changes in exposure were compared with in vivo data. KEY RESULTS Predicted changes in exposure were within twofold of reported in vivo values using fmCYP estimated in human hepatocytes and there was a strong linear correlation between predicted and observed changes in exposure (r2 = 0.83 for CYP3A, r2 = 0.82 for CYP2D6). Predictions using fmCYP in hrP450s were not as accurate (r2 = 0.55 for CYP3A, r2 = 0.20 for CYP2D6). CONCLUSIONS AND IMPLICATIONS The results suggest that variability in human drug exposure due to DDI and enzyme polymorphism can be accurately predicted using fmCYP from human hepatocytes and CYP-selective inhibitors. This approach can be efficiently applied in drug discovery to aid optimization of candidate drugs with a favourable metabolic elimination profile and limited variability in patients.
Collapse
Affiliation(s)
- Bo Lindmark
- Cardiovascular, Renal and Metabolism, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Anna Lundahl
- Cardiovascular, Renal and Metabolism, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Kajsa P Kanebratt
- Cardiovascular, Renal and Metabolism, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Tommy B Andersson
- Cardiovascular, Renal and Metabolism, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Emre M Isin
- Cardiovascular, Renal and Metabolism, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Gothenburg, Sweden
| |
Collapse
|
121
|
Martinez MN, Gehring R, Mochel JP, Pade D, Pelligand L. Population variability in animal health: Influence on dose-exposure-response relationships: Part II: Modelling and simulation. J Vet Pharmacol Ther 2018; 41:E68-E76. [PMID: 29806231 DOI: 10.1111/jvp.12666] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 04/15/2018] [Indexed: 11/29/2022]
Abstract
During the 2017 Biennial meeting, the American Academy of Veterinary Pharmacology and Therapeutics hosted a 1-day session on the influence of population variability on dose-exposure-response relationships. In Part I, we highlighted some of the sources of population variability. Part II provides a summary of discussions on modelling and simulation tools that utilize existing pharmacokinetic data, can integrate drug physicochemical characteristics with species physiological characteristics and dosing information or that combine observed with predicted and in vitro information to explore and describe sources of variability that may influence the safe and effective use of veterinary pharmaceuticals.
Collapse
Affiliation(s)
- Marilyn N Martinez
- Center for Veterinary Medicine, US Food and Drug Administration, Rockville, Maryland
| | - Ronette Gehring
- Utrecht Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Jonathan P Mochel
- Department of Biomedical Sciences, College of Veterinary Medicine, Iowa State University, Ames, Iowa
| | | | - Ludovic Pelligand
- Department of Clinical Services and Sciences and Department of Comparative Biomedical Sciences, The Royal Veterinary College, Hatfield, UK
| |
Collapse
|
122
|
Leedale J, Sharkey KJ, Colley HE, Norton ÁM, Peeney D, Mason CL, Sathish JG, Murdoch C, Sharma P, Webb SD. A Combined In Vitro/In Silico Approach to Identifying Off-Target Receptor Toxicity. iScience 2018; 4:84-96. [PMID: 30240756 PMCID: PMC6147237 DOI: 10.1016/j.isci.2018.05.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 04/19/2018] [Accepted: 05/15/2018] [Indexed: 12/20/2022] Open
Abstract
Many xenobiotics can bind to off-target receptors and cause toxicity via the dysregulation of downstream transcription factors. Identification of subsequent off-target toxicity in these chemicals has often required extensive chemical testing in animal models. An alternative, integrated in vitro/in silico approach for predicting toxic off-target functional responses is presented to refine in vitro receptor identification and reduce the burden on in vivo testing. As part of the methodology, mathematical modeling is used to mechanistically describe processes that regulate transcriptional activity following receptor-ligand binding informed by transcription factor signaling assays. Critical reactions in the signaling cascade are identified to highlight potential perturbation points in the biochemical network that can guide and optimize additional in vitro testing. A physiologically based pharmacokinetic model provides information on the timing and localization of different levels of receptor activation informing whole-body toxic potential resulting from off-target binding.
Collapse
Affiliation(s)
- Joseph Leedale
- EPSRC Liverpool Centre for Mathematics in Healthcare, Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK.
| | - Kieran J Sharkey
- EPSRC Liverpool Centre for Mathematics in Healthcare, Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK
| | - Helen E Colley
- School of Clinical Dentistry, University of Sheffield, Sheffield S10 2TA, UK
| | - Áine M Norton
- MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool L69 3GE, UK
| | - David Peeney
- MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool L69 3GE, UK
| | - Chantelle L Mason
- Department of Applied Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK
| | - Jean G Sathish
- MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool L69 3GE, UK; Immuno and Molecular Toxicology, Drug Safety Evaluation, Bristol-Myers Squibb, 1 Squibb Drive, New Brunswick, NJ 08903, USA
| | - Craig Murdoch
- School of Clinical Dentistry, University of Sheffield, Sheffield S10 2TA, UK
| | - Parveen Sharma
- MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool L69 3GE, UK.
| | - Steven D Webb
- Department of Applied Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK
| |
Collapse
|
123
|
Brussee JM, Yu H, Krekels EHJ, de Roos B, Brill MJE, van den Anker JN, Rostami-Hodjegan A, de Wildt SN, Knibbe CAJ. First-Pass CYP3A-Mediated Metabolism of Midazolam in the Gut Wall and Liver in Preterm Neonates. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:374-383. [PMID: 29745466 PMCID: PMC6027733 DOI: 10.1002/psp4.12295] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 03/08/2018] [Accepted: 03/09/2018] [Indexed: 12/11/2022]
Abstract
To predict first‐pass and systemic cytochrome P450 (CYP) 3A‐mediated metabolism of midazolam in preterm neonates, a physiological population pharmacokinetic model was developed describing intestinal and hepatic midazolam clearance in preterm infants. On the basis of midazolam and 1‐OH‐midazolam concentrations from 37 preterm neonates (gestational age 26–34 weeks) receiving midazolam orally and/or via a 30‐minute intravenous infusion, intrinsic clearance in the gut wall and liver were found to be very low, with lower values in the gut wall (0.0196 and 6.7 L/h, respectively). This results in a highly variable and high total oral bioavailability of 92.1% (range, 67–95%) in preterm neonates, whereas this is around 30% in adults. This approach in which intestinal and hepatic clearance were separately estimated shows that the high bioavailability in preterm neonates is explained by, likely age‐related, low CYP3A activity in the liver and even lower CYP3A activity in the gut wall.
Collapse
Affiliation(s)
- Janneke M Brussee
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Huixin Yu
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Elke H J Krekels
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Berend de Roos
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Margreke J E Brill
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Johannes N van den Anker
- Intensive Care and Department of Pediatric Surgery, Erasmus MC - Sophia Children's Hospital, Rotterdam, The Netherlands.,Division of Paediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland.,Division of Clinical Pharmacology, Children's National Health System, Washington, DC
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK.,Simcyp Limited (A Certara Company), Sheffield, UK
| | - Saskia N de Wildt
- Intensive Care and Department of Pediatric Surgery, Erasmus MC - Sophia Children's Hospital, Rotterdam, The Netherlands.,Department of Pharmacology and Toxicology, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Catherijne A J Knibbe
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.,Department of Clinical Pharmacy, St. Antonius Hospital, Nieuwegein, The Netherlands
| |
Collapse
|
124
|
Snowden TJ, van der Graaf PH, Tindall MJ. Model reduction in mathematical pharmacology : Integration, reduction and linking of PBPK and systems biology models. J Pharmacokinet Pharmacodyn 2018; 45:537-555. [PMID: 29582349 PMCID: PMC6061126 DOI: 10.1007/s10928-018-9584-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 03/14/2018] [Indexed: 11/27/2022]
Abstract
In this paper we present a framework for the reduction and linking of physiologically based pharmacokinetic (PBPK) models with models of systems biology to describe the effects of drug administration across multiple scales. To address the issue of model complexity, we propose the reduction of each type of model separately prior to being linked. We highlight the use of balanced truncation in reducing the linear components of PBPK models, whilst proper lumping is shown to be efficient in reducing typically nonlinear systems biology type models. The overall methodology is demonstrated via two example systems; a model of bacterial chemotactic signalling in Escherichia coli and a model of extracellular regulatory kinase activation mediated via the extracellular growth factor and nerve growth factor receptor pathways. Each system is tested under the simulated administration of three hypothetical compounds; a strong base, a weak base, and an acid, mirroring the parameterisation of pindolol, midazolam, and thiopental, respectively. Our method can produce up to an 80% decrease in simulation time, allowing substantial speed-up for computationally intensive applications including parameter fitting or agent based modelling. The approach provides a straightforward means to construct simplified Quantitative Systems Pharmacology models that still provide significant insight into the mechanisms of drug action. Such a framework can potentially bridge pre-clinical and clinical modelling - providing an intermediate level of model granularity between classical, empirical approaches and mechanistic systems describing the molecular scale.
Collapse
Affiliation(s)
- Thomas J. Snowden
- Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AX UK
- Certara QSP, University of Kent Innovation Centre, Canterbury, CT2 7FG UK
| | - Piet H. van der Graaf
- Certara QSP, University of Kent Innovation Centre, Canterbury, CT2 7FG UK
- Leiden Academic Centre for Drug Research, Universiteit Leiden, 2333 CC Leiden, The Netherlands
| | - Marcus J. Tindall
- Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AX UK
- The Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, RG6 6UR UK
| |
Collapse
|
125
|
Doki K, Darwich AS, Achour B, Tornio A, Backman JT, Rostami-Hodjegan A. Implications of intercorrelation between hepatic CYP3A4-CYP2C8 enzymes for the evaluation of drug-drug interactions: a case study with repaglinide. Br J Clin Pharmacol 2018; 84:972-986. [PMID: 29381228 DOI: 10.1111/bcp.13533] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Revised: 12/21/2017] [Accepted: 01/21/2018] [Indexed: 12/18/2022] Open
Abstract
AIMS Statistically significant positive correlations are reported for the abundance of hepatic drug-metabolizing enzymes. We investigate, as an example, the impact of CYP3A4-CYP2C8 intercorrelation on the predicted interindividual variabilities of clearance and drug-drug interactions (DDIs) for repaglinide using physiologically based pharmacokinetic (PBPK) modelling. METHODS PBPK modelling and simulation were employed using Simcyp Simulator (v15.1). Virtual populations were generated assuming intercorrelations between hepatic CYP3A4-CYP2C8 abundances derived from observed values in 24 human livers. A repaglinide PBPK model was used to predict PK parameters in the presence and absence of gemfibrozil in virtual populations, and the results were compared with a clinical DDI study. RESULTS Coefficient of variation (CV) of oral clearance was 52.5% in the absence of intercorrelation between CYP3A4-CYP2C8 abundances, which increased to 54.2% when incorporating intercorrelation. In contrast, CV for predicted DDI (as measured by AUC ratio before and after inhibition) was reduced from 46.0% in the absence of intercorrelation between enzymes to 43.8% when incorporating intercorrelation: these CVs were associated with 5th/95th percentiles (2.48-11.29 vs. 2.49-9.69). The range of predicted DDI was larger in the absence of intercorrelation (1.55-77.06) than when incorporating intercorrelation (1.79-25.15), which was closer to clinical observations (2.6-12). CONCLUSIONS The present study demonstrates via a systematic investigation that population-based PBPK modelling incorporating intercorrelation led to more consistent estimation of extreme values than those observed in interindividual variabilities of clearance and DDI. As the intercorrelations more realistically reflect enzyme abundances, virtual population studies involving PBPK and DDI should avoid using Monte Carlo assignment of enzyme abundance.
Collapse
Affiliation(s)
- Kosuke Doki
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy & Optometry, University of Manchester, Manchester, UK.,Department of Pharmaceutical Sciences, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Adam S Darwich
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy & Optometry, University of Manchester, Manchester, UK
| | - Brahim Achour
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy & Optometry, University of Manchester, Manchester, UK
| | - Aleksi Tornio
- Department of Clinical Pharmacology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Janne T Backman
- Department of Clinical Pharmacology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy & Optometry, University of Manchester, Manchester, UK.,Simcyp Limited (A Certara Company), Sheffield, UK
| |
Collapse
|
126
|
Jelliffe R, Bayard D. New Perspectives in Clinical Pharmacokinetics-1: the Importance of Updating the Teaching in Pharmacokinetics that both Clearance and Elimination Rate Constant Approaches Are Mathematically Proven Equally Valid. AAPS JOURNAL 2018; 20:36. [PMID: 29484513 DOI: 10.1208/s12248-018-0185-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Accepted: 01/04/2018] [Indexed: 11/30/2022]
Abstract
The healing professions have only about four main therapeutic tools at their disposal-surgery, drugs, physical therapy, and psychotherapy. For the general profession of internal medicine, drug therapy is its primary tool. Providing an understanding of the state-of-the-art in therapeutic methods, grounded in solid scientific and mathematical rigor, is therefore of the utmost clinical importance for both physicians and clinical pharmacists. This is particularly true where rapidly evolving scientific changes require an up-to-date education upon which students can rely. Unfortunately, relatively little attention has been paid to training clinical pharmacokineticists and physicians to manage drug therapy optimally for patients under their care in their everyday practice. In this paper, we discuss one of these basic deficiencies from the perspective of the longstanding controversy in pharmacokinetic modeling: whether the volume and clearance approach or the volume and rate constant approach is somehow "better". We examine this controversy using the mathematical principle of invariance, which to our knowledge has not been done before. The conclusion of this analysis is that both approaches are rigorously proven mathematically to be equally valid. We also discuss some implications of these equally valid approaches from the framework of mechanistic and non-compartmental models. Ultimately, the conclusion is that the choice of one parameterization over the other is based on preference or usefulness for research or clinical practice, but no longer, because of this analysis, on science.
Collapse
Affiliation(s)
- Roger Jelliffe
- USC Laboratory of Applied Pharmacokinetics and Bioinformatics, University of Southern California School of Medicine, Children's Hospital of Los Angeles, 4650 Sunset Boulevard, #51, Los Angeles, CA, 90027, USA.
| | - David Bayard
- USC Laboratory of Applied Pharmacokinetics and Bioinformatics, University of Southern California School of Medicine, Children's Hospital of Los Angeles, 4650 Sunset Boulevard, #51, Los Angeles, CA, 90027, USA
| |
Collapse
|
127
|
Scherholz ML, Forder J, Androulakis IP. A framework for 2-stage global sensitivity analysis of GastroPlus™ compartmental models. J Pharmacokinet Pharmacodyn 2018; 45:309-327. [DOI: 10.1007/s10928-018-9573-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 01/19/2018] [Indexed: 12/12/2022]
|
128
|
Shebley M, Sandhu P, Emami Riedmaier A, Jamei M, Narayanan R, Patel A, Peters SA, Reddy VP, Zheng M, de Zwart L, Beneton M, Bouzom F, Chen J, Chen Y, Cleary Y, Collins C, Dickinson GL, Djebli N, Einolf HJ, Gardner I, Huth F, Kazmi F, Khalil F, Lin J, Odinecs A, Patel C, Rong H, Schuck E, Sharma P, Wu SP, Xu Y, Yamazaki S, Yoshida K, Rowland M. Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective. Clin Pharmacol Ther 2018; 104:88-110. [PMID: 29315504 PMCID: PMC6032820 DOI: 10.1002/cpt.1013] [Citation(s) in RCA: 223] [Impact Index Per Article: 37.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 12/05/2017] [Accepted: 01/03/2018] [Indexed: 12/15/2022]
Abstract
This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | - Ming Zheng
- Bristol-Myers Squibb, Princeton, NJ, USA
| | | | | | | | - Jun Chen
- Sanofi, Région de Montpellier, France
| | | | | | | | | | | | | | | | | | | | | | - Jing Lin
- Sunovion Pharmaceuticals Inc., Marlborough, MA, USA
| | | | - Chirag Patel
- Takeda Pharmaceuticals International Co., Cambridge, MA, USA
| | | | | | | | | | | | | | | | | |
Collapse
|
129
|
Leeder JS, Meibohm B. Challenges and Opportunities for Increasing the Knowledge Base Related to Drug Biotransformation and Pharmacokinetics during Growth and Development. ACTA ACUST UNITED AC 2018; 44:916-23. [PMID: 27302933 DOI: 10.1124/dmd.116.071159] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 05/11/2016] [Indexed: 01/22/2023]
Abstract
It is generally acknowledged that there is a need and role for informative pharmacokinetic models to improve predictions and simulation as well as individualization of drug therapy in pediatric populations of different ages and developmental stages. This special issue contains more than 20 papers responding to the challenge of providing new information on scaling factors, ontogeny functions for drug metabolizing enzymes and transporters, the mechanisms underlying the observed developmental trajectories for these gene products, age-dependent changes in physiologic processes affecting drug disposition in children, as well as in vitro and in vivo studies describing the relative contribution of ontogeny and genetic factors as sources of variability in drug disposition in children. Considered together, these contributions serve to illustrate some of the current limitations regarding sample availability, number, and quality, but also provide a framework that allows for the potential value of the results of a given study to be interpreted within the context of these limitations. Among the challenges for the future are improving our understanding of the mechanisms regulating age-dependent changes in factors influencing drug disposition and response, thereby facilitating generalization to systems lacking detailed data, better integrating age-dependent changes in pharmacokinetics with age-dependent changes in pharmacodynamics, and allowing better predictability and individualization of drug disposition and response across the pediatric age spectrum.
Collapse
Affiliation(s)
- J Steven Leeder
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Department of Pediatrics, Children's Mercy Hospitals and Clinics, University of Missouri-Kansas City, Kansas City, Missouri (J.S.L.); and Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Sciences Center, Memphis, Tennessee (B.M.)
| | - Bernd Meibohm
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Department of Pediatrics, Children's Mercy Hospitals and Clinics, University of Missouri-Kansas City, Kansas City, Missouri (J.S.L.); and Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Sciences Center, Memphis, Tennessee (B.M.)
| |
Collapse
|
130
|
Barnett S, Ogungbenro K, Ménochet K, Shen H, Lai Y, Humphreys WG, Galetin A. Gaining Mechanistic Insight Into Coproporphyrin I as Endogenous Biomarker for OATP1B-Mediated Drug-Drug Interactions Using Population Pharmacokinetic Modeling and Simulation. Clin Pharmacol Ther 2018; 104:564-574. [PMID: 29243231 PMCID: PMC6175062 DOI: 10.1002/cpt.983] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 11/21/2017] [Accepted: 12/05/2017] [Indexed: 12/19/2022]
Abstract
This study evaluated coproporphyrin I (CPI) as a selective endogenous biomarker of OATP1B‐mediated drug–drug interactions (DDIs) relative to clinical probe rosuvastatin using nonlinear mixed‐effect modeling. Plasma and urine CPI data in the presence/absence of rifampicin were modeled to describe CPI synthesis, elimination clearances, and obtain rifampicin in vivo OATP Ki. The biomarker showed stable interoccasion baseline concentrations and low interindividual variability (<25%) in subjects with wildtype SLCO1B1. Biliary excretion was the dominant CPI elimination route (maximal >85%). Estimated rifampicin in vivo unbound OATP Ki (0.13 μM) using CPI data was 2‐fold lower relative to rosuvastatin. Model‐based simulations and power calculations confirmed sensitivity of CPI to identify moderate and weak OATP1B inhibitors in an adequately powered clinical study. Current analysis provides the most detailed evaluation of CPI as an endogenous OATP1B biomarker to support optimal DDI study design; further pharmacogenomic and DDI data with a panel of inhibitors are required.
Collapse
Affiliation(s)
- Shelby Barnett
- Centre for Applied Pharmacokinetic Research, University of Manchester, UK
| | - Kayode Ogungbenro
- Centre for Applied Pharmacokinetic Research, University of Manchester, UK
| | | | - Hong Shen
- Pharmaceutical Candidate Optimization, Bristol-Myers Squibb, Princeton, New Jersey, USA
| | - Yurong Lai
- Pharmaceutical Candidate Optimization, Bristol-Myers Squibb, Princeton, New Jersey, USA
| | - W Griffith Humphreys
- Pharmaceutical Candidate Optimization, Bristol-Myers Squibb, Princeton, New Jersey, USA
| | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research, University of Manchester, UK
| |
Collapse
|
131
|
Conner TM, Nikolian VC, Georgoff PE, Pai MP, Alam HB, Sun D, Reed RC, Zhang T. Physiologically based pharmacokinetic modeling of disposition and drug-drug interactions for valproic acid and divalproex. Eur J Pharm Sci 2018; 111:465-481. [DOI: 10.1016/j.ejps.2017.10.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 09/15/2017] [Accepted: 10/06/2017] [Indexed: 11/28/2022]
|
132
|
Pearce RG, Setzer RW, Davis JL, Wambaugh JF. Evaluation and calibration of high-throughput predictions of chemical distribution to tissues. J Pharmacokinet Pharmacodyn 2017; 44:549-565. [PMID: 29032447 DOI: 10.1007/s10928-017-9548-7.evaluation] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 09/30/2017] [Indexed: 05/27/2023]
Abstract
Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.
Collapse
Affiliation(s)
- Robert G Pearce
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37831, USA
| | - R Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
| | - Jimena L Davis
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
- Syngenta, Research Triangle Park, NC, 27709, USA
| | - John F Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA.
| |
Collapse
|
133
|
Pearce RG, Setzer RW, Davis JL, Wambaugh JF. Evaluation and calibration of high-throughput predictions of chemical distribution to tissues. J Pharmacokinet Pharmacodyn 2017; 44:549-565. [PMID: 29032447 PMCID: PMC6186149 DOI: 10.1007/s10928-017-9548-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 09/30/2017] [Indexed: 12/25/2022]
Abstract
Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.
Collapse
Affiliation(s)
- Robert G Pearce
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37831, USA
| | - R Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
| | - Jimena L Davis
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
- Syngenta, Research Triangle Park, NC, 27709, USA
| | - John F Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA.
| |
Collapse
|
134
|
The feasibility of physiologically based pharmacokinetic modeling in forensic medicine illustrated by the example of morphine. Int J Legal Med 2017; 132:415-424. [DOI: 10.1007/s00414-017-1754-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 11/28/2017] [Indexed: 12/18/2022]
|
135
|
Hasegawa C, Duffull SB. Selection and Qualification of Simplified QSP Models When Using Model Order Reduction Techniques. AAPS JOURNAL 2017; 20:2. [PMID: 29181592 DOI: 10.1208/s12248-017-0170-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 11/05/2017] [Indexed: 01/04/2023]
Abstract
Quantitative systems pharmacology (QSP) models are increasingly used in drug development to provide a deep understanding of the mechanism of action of drugs and to identify appropriate disease targets. Such models are, however, not suitable for estimation purposes due to their high dimensionality. Based on any desired and specific input-output relationship, the system may be reduced to a model with fewer states and parameters. However, any simplification process will be a trade-off between model performance and complexity. In this study, we develop a weighted composite criterion which brings together the opposing indices of performance and dimensionality. The weighting factor can be determined by qualification of the simplified model based on a visual predictive check (VPC) using the precision of each parameter. The weighted criterion and model qualification techniques were illustrated with three examples: a simple compartmental pharmacokinetic model, a physiologically based pharmacokinetic (PBPK) example, and a semimechanistic model for bone mineral density. When considering the PBPK example, this automated search identified the same reduced model which had been detected in a previous report, as well as a simpler model which had not been previously identified. The simpler bone mineral density model provided an adequate description of the response even after 1 year from the initiation of treatment. The proposed criterion together with a VPC provides a natural way for model order reduction that can be fully automated and applied to multiscale models.
Collapse
Affiliation(s)
- Chihiro Hasegawa
- School of Pharmacy, University of Otago, Dunedin, New Zealand. .,Translational Medicine Center, Ono Pharmaceutical Co., Ltd., Osaka, Japan.
| | | |
Collapse
|
136
|
Rostami-Hodjegan A. Reverse Translation in PBPK and QSP: Going Backwards in Order to Go Forward With Confidence. Clin Pharmacol Ther 2017; 103:224-232. [PMID: 29023678 PMCID: PMC5813098 DOI: 10.1002/cpt.904] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 10/02/2017] [Accepted: 10/06/2017] [Indexed: 02/06/2023]
Abstract
Significant events have taken place shaping the recent industrialization of physiologically based pharmacokinetic in vitro-in vivo extrapolation (PBPK-IVIVE) use in drug development. Due to our knowledge gaps about drug-independent systems parameters, there are limitations in the use of purely IVIVE-based (bottom-up) approaches. This has encouraged combining the classical data analysis (top-down) with PBPK-IVIVE-linked models in order to optimize model parameters by taking advantage of observed clinical data. This concept, when initiated after clinical observations, can be viewed as "reverse translation," since it refers back to available systems information preclinical data before trying to describe the observations. This review demonstrates the advantages of such strategies in filling knowledge gaps and discusses the perceived hurdles in widening applications. It is paramount that no clinical data are assessed on their own, but in conjunction with other studies for that drug in different populations and/or other similar drugs in the same population.
Collapse
Affiliation(s)
- Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetics Research (CAPKR), University of Manchester, Manchester, UK.,Certara, Blades Enterprise Centre, Sheffield, UK
| |
Collapse
|
137
|
Lu T, Fraczkiewicz G, Salphati L, Budha N, Dalziel G, Smelick GS, Morrissey KM, Davis JD, Jin JY, Ware JA. Combining "Bottom-up" and "Top-down" Approaches to Assess the Impact of Food and Gastric pH on Pictilisib (GDC-0941) Pharmacokinetics. CPT Pharmacometrics Syst Pharmacol 2017; 6:747-755. [PMID: 28748626 PMCID: PMC5702897 DOI: 10.1002/psp4.12228] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 08/19/2017] [Accepted: 07/19/2017] [Indexed: 12/27/2022] Open
Abstract
Pictilisib, a weakly basic compound, is an orally administered, potent, and selective pan-inhibitor of phosphatidylinositol 3-kinases for oncology indications. To investigate the significance of high-fat food and gastric pH on pictilisib pharmacokinetics (PK) and enable label recommendations, a dedicated clinical study was conducted in healthy volunteers, whereby both top-down (population PK, PopPK) and bottom-up (physiologically based PK, PBPK) approaches were applied to enhance confidence of recommendation and facilitate the clinical development through scenario simulations. The PopPK model identified food (for absorption rate constant (Ka )) and proton pump inhibitors (PPI, for relative bioavailability (Frel ) and Ka ) as significant covariates. Food and PPI also impacted the variability of Frel . The PBPK model accounted for the supersaturation tendency of pictilisib, and gastric emptying physiology successfully predicted the food and PPI effect on pictilisib absorption. Our research highlights the importance of applying both quantitative approaches to address critical drug development questions.
Collapse
Affiliation(s)
- Tong Lu
- Department of Clinical PharmacologyGenentech IncSouth San FranciscoCaliforniaUSA
| | | | - Laurent Salphati
- Department of Drug Metabolism and PharmacokineticsGenentech IncSouth San FranciscoCaliforniaUSA
| | - Nageshwar Budha
- Department of Clinical PharmacologyGenentech IncSouth San FranciscoCaliforniaUSA
| | - Gena Dalziel
- Department of Small Molecule Pharmaceutical SciencesGenentech IncSouth San FranciscoCaliforniaUSA
| | - Gillian S. Smelick
- Department of Clinical PharmacologyGenentech IncSouth San FranciscoCaliforniaUSA
| | - Kari M. Morrissey
- Department of Clinical PharmacologyGenentech IncSouth San FranciscoCaliforniaUSA
| | - John D. Davis
- Department of Clinical PharmacologyGenentech IncSouth San FranciscoCaliforniaUSA
| | - Jin Y. Jin
- Department of Clinical PharmacologyGenentech IncSouth San FranciscoCaliforniaUSA
| | - Joseph A. Ware
- Department of Clinical PharmacologyGenentech IncSouth San FranciscoCaliforniaUSA
| |
Collapse
|
138
|
Yee KL, Li M, Cabalu T, Sahasrabudhe V, Lin J, Zhao P, Jadhav P. Evaluation of Model‐Based Prediction of Pharmacokinetics in the Renal Impairment Population. J Clin Pharmacol 2017; 58:364-376. [DOI: 10.1002/jcph.1022] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 08/28/2017] [Indexed: 11/06/2022]
Affiliation(s)
- Ka Lai Yee
- Pharmacokinetics, Pharmacodynamics, and Drug MetabolismMerck & Co., Inc. Kenilworth NJ USA
| | - Mengyao Li
- Division of PharmacometricsOffice of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration Silver Spring MD USA
| | - Tamara Cabalu
- Pharmacokinetics, Pharmacodynamics, and Drug MetabolismMerck & Co., Inc. Kenilworth NJ USA
| | | | - Jian Lin
- PharmacokineticsDynamicsand MetabolismPfizer Inc. Groton CT USA
| | - Ping Zhao
- Division of PharmacometricsOffice of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration Silver Spring MD USA
| | - Pravin Jadhav
- Corporate ProjectsOtsuka Pharmaceutical Development and Commercialization (OPDC) Princeton NJ USA
| |
Collapse
|
139
|
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]
|
140
|
Tsamandouras N, Guo Y, Wendling T, Hall S, Galetin A, Aarons L. Modelling of atorvastatin pharmacokinetics and the identification of the effect of a BCRP polymorphism in the Japanese population. Pharmacogenet Genomics 2017; 27:27-38. [PMID: 27787353 DOI: 10.1097/fpc.0000000000000252] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
AIM Ethnicity plays a modulating role in atorvastatin pharmacokinetics (PK), with Asian patients reported to have higher exposure compared with Caucasians. Therefore, it is difficult to safely extrapolate atorvastatin PK data and models across ethnic groups. This work aims to develop a population PK model for atorvastatin and its pharmacologically active metabolites specifically for the Japanese population. Subsequently, it aimed to identify genetic polymorphisms affecting atorvastatin PK in this population. METHODS Atorvastatin acid (ATA) and ortho-hydroxy-atorvastatin acid (o-OH-ATA) plasma concentrations, clinical/demographic characteristics and genotypes for 18 (3, 3, 1, 1, 7, 2 and 1 in the ABCB1, ABCG2, CYP3A4, CYP3A5, SLCO1B1, SLCO2B1 and PPARA genes, respectively) genetic polymorphisms were collected from 27 Japanese individuals (taking 10 mg atorvastatin once daily) and analysed using a population PK modelling approach. RESULTS The population PK model developed (one-compartment for ATA linked through metabolite formation to an additional compartment describing the disposition of o-OH-ATA) accurately described the observed data and the associated population variability. Our analysis suggested that patients carrying one variant allele for the rs2622604 polymorphism (ABCG2) show a 55% (95% confidence interval: 16-131%) increase in atorvastatin oral bioavailability relative to the value in individuals without the variant allele. CONCLUSION The current work reports the identification in the Japanese population of a BCRP polymorphism, not previously associated with the PK of any statin, that markedly increases ATA and o-OH-ATA exposure. The model developed may be of clinical importance to guide dosing recommendations tailored specifically for the Japanese.
Collapse
Affiliation(s)
- Nikolaos Tsamandouras
- aManchester Pharmacy School, Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK bEli Lilly and Company, Indianapolis, Indiana, USA
| | | | | | | | | | | |
Collapse
|
141
|
Wiśniowska B, Tylutki Z, Polak S. Humans Vary, So Cardiac Models Should Account for That Too! Front Physiol 2017; 8:700. [PMID: 28983251 PMCID: PMC5613127 DOI: 10.3389/fphys.2017.00700] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 08/30/2017] [Indexed: 12/25/2022] Open
Abstract
The utilization of mathematical modeling and simulation in drug development encompasses multiple mathematical techniques and the location of a drug candidate in the development pipeline. Historically speaking they have been used to analyze experimental data (i.e., Hill equation) and clarify the involved physical and chemical processes (i.e., Fick laws and drug molecule diffusion). In recent years the advanced utilization of mathematical modeling has been an important part of the regulatory review process. Physiologically based pharmacokinetic (PBPK) models identify the need to conduct specific clinical studies, suggest specific study designs and propose appropriate labeling language. Their application allows the evaluation of the influence of intrinsic (e.g., age, gender, genetics, disease) and extrinsic [e.g., dosing schedule, drug-drug interactions (DDIs)] factors, alone or in combinations, on drug exposure and therefore provides accurate population assessment. A similar pathway has been taken for the assessment of drug safety with cardiac safety being one the most advanced examples. Mechanistic mathematical model-informed safety evaluation, with a focus on drug potential for causing arrhythmias, is now discussed as an element of the Comprehensive in vitro Proarrhythmia Assay. One of the pillars of this paradigm is the use of an in silico model of the adult human ventricular cardiomyocyte to integrate in vitro measured data. Existing examples (in vitro—in vivo extrapolation with the use of PBPK models) suggest that deterministic, epidemiological and clinical data based variability models can be merged with the mechanistic models describing human physiology. There are other methods available, based on the stochastic approach and on population of models generated by randomly assigning specific parameter values (ionic current conductance and kinetic) and further pruning. Both approaches are briefly characterized in this manuscript, in parallel with the drug-specific variability.
Collapse
Affiliation(s)
- Barbara Wiśniowska
- Pharmacoepidemiology and Pharmacoeconomics Unit, Faculty of Pharmacy, Jagiellonian University Medical CollegeKrakow, Poland
| | - Zofia Tylutki
- Pharmacoepidemiology and Pharmacoeconomics Unit, Faculty of Pharmacy, Jagiellonian University Medical CollegeKrakow, Poland
| | - Sebastian Polak
- Pharmacoepidemiology and Pharmacoeconomics Unit, Faculty of Pharmacy, Jagiellonian University Medical CollegeKrakow, Poland.,SimcypCertara, Sheffield, United Kingdom
| |
Collapse
|
142
|
Emami Riedmaier A, Lindley DJ, Hall JA, Castleberry S, Slade RT, Stuart P, Carr RA, Borchardt TB, Bow DAJ, Nijsen M. Mechanistic Physiologically Based Pharmacokinetic Modeling of the Dissolution and Food Effect of a Biopharmaceutics Classification System IV Compound-The Venetoclax Story. J Pharm Sci 2017; 107:495-502. [PMID: 28993217 DOI: 10.1016/j.xphs.2017.09.027] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 09/08/2017] [Accepted: 09/18/2017] [Indexed: 10/18/2022]
Abstract
Venetoclax, a selective B-cell lymphoma-2 inhibitor, is a biopharmaceutics classification system class IV compound. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to mechanistically describe absorption and disposition of an amorphous solid dispersion formulation of venetoclax in humans. A mechanistic PBPK model was developed incorporating measured amorphous solubility, dissolution, metabolism, and plasma protein binding. A middle-out approach was used to define permeability. Model predictions of oral venetoclax pharmacokinetics were verified against clinical studies of fed and fasted healthy volunteers, and clinical drug interaction studies with strong CYP3A inhibitor (ketoconazole) and inducer (rifampicin). Model verification demonstrated accurate prediction of the observed food effect following a low-fat diet. Ratios of predicted versus observed Cmax and area under the curve of venetoclax were within 0.8- to 1.25-fold of observed ratios for strong CYP3A inhibitor and inducer interactions, indicating that the venetoclax elimination pathway was correctly specified. The verified venetoclax PBPK model is one of the first examples mechanistically capturing absorption, food effect, and exposure of an amorphous solid dispersion formulated compound. This model allows evaluation of untested drug-drug interactions, especially those primarily occurring in the intestine, and paves the way for future modeling of biopharmaceutics classification system IV compounds.
Collapse
Affiliation(s)
| | - David J Lindley
- Drug Product Development, AbbVie Inc., North Chicago, Illinois 60064
| | - Jeffrey A Hall
- Drug Product Development, AbbVie Inc., North Chicago, Illinois 60064
| | | | - Russell T Slade
- Drug Product Development, AbbVie Inc., North Chicago, Illinois 60064
| | - Patricia Stuart
- DMPK and Translational Modeling, AbbVie Inc., North Chicago, Illinois 60064
| | - Robert A Carr
- DMPK and Translational Modeling, AbbVie Inc., North Chicago, Illinois 60064
| | | | - Daniel A J Bow
- DMPK and Translational Modeling, AbbVie Inc., North Chicago, Illinois 60064
| | - Marjoleen Nijsen
- DMPK and Translational Modeling, AbbVie Inc., North Chicago, Illinois 60064
| |
Collapse
|
143
|
Ensuring quality pharmacokinetic analyses in antimicrobial drug development programs. Curr Opin Pharmacol 2017; 36:139-145. [DOI: 10.1016/j.coph.2017.10.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Revised: 10/04/2017] [Accepted: 10/27/2017] [Indexed: 01/11/2023]
|
144
|
Age, Weight, and CYP2D6 Genotype Are Major Determinants of Primaquine Pharmacokinetics in African Children. Antimicrob Agents Chemother 2017; 61:AAC.02590-16. [PMID: 28289025 PMCID: PMC5404566 DOI: 10.1128/aac.02590-16] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 03/06/2017] [Indexed: 12/22/2022] Open
Abstract
Low-dose primaquine is recommended to prevent Plasmodium falciparum malaria transmission in areas threatened by artemisinin resistance and areas aiming for malaria elimination. Community treatment campaigns with artemisinin-based combination therapy in combination with the gametocytocidal primaquine dose target all age groups, but no studies thus far have assessed the pharmacokinetics of this gametocytocidal drug in African children. We recruited 40 children participating in a primaquine efficacy trial in Burkina Faso to study primaquine pharmacokinetics. These children received artemether-lumefantrine and either a 0.25- or a 0.40-mg/kg primaquine dose. Seven blood samples were collected from each participant for primaquine and carboxy-primaquine plasma levels determinations: one sample was collected before primaquine administration and six after primaquine administration according to partially overlapping sampling schedules. Physiological population pharmacokinetic modeling was used to assess the impact of weight, age, and CYP2D6 genotype on primaquine and carboxy-primaquine pharmacokinetics. Despite linear weight normalized dosing, the areas under the plasma concentration-time curves and the peak concentrations for both primaquine and carboxy-primaquine increased with age and body weight. Children who were CYP2D6 poor metabolizers had higher levels of the parent compound, indicating a lower primaquine CYP2D6-mediated metabolism. Our data indicate that primaquine and carboxy-primaquine pharmacokinetics are influenced by age, weight, and CYP2D6 genotype and suggest that dosing strategies may have to be reconsidered to maximize the transmission-blocking properties of primaquine. (This study has been registered at ClinicalTrials.gov under registration no. NCT01935882.)
Collapse
|
145
|
Galetin A, Zhao P, Huang SM. Physiologically Based Pharmacokinetic Modeling of Drug Transporters to Facilitate Individualized Dose Prediction. J Pharm Sci 2017; 106:2204-2208. [PMID: 28390843 DOI: 10.1016/j.xphs.2017.03.036] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Revised: 03/22/2017] [Accepted: 03/27/2017] [Indexed: 01/12/2023]
Abstract
Physiologically based pharmacokinetic modeling is a commonly used strategy in the drug development and regulatory submissions. This commentary provides a critical overview of the current status of physiologically based pharmacokinetic methodologies to predict transporter-mediated pharmacokinetics, in addition to the impact of disease and genetics with respect to local and systemic concentration.
Collapse
Affiliation(s)
- Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK; Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland 20993.
| | - Ping Zhao
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland 20993
| | - Shiew-Mei Huang
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland 20993
| |
Collapse
|
146
|
Darwich AS, Ogungbenro K, Vinks AA, Powell JR, Reny JL, Marsousi N, Daali Y, Fairman D, Cook J, Lesko LJ, McCune JS, Knibbe CAJ, de Wildt SN, Leeder JS, Neely M, Zuppa AF, Vicini P, Aarons L, Johnson TN, Boiani J, Rostami-Hodjegan A. Why Has Model-Informed Precision Dosing Not Yet Become Common Clinical Reality? Lessons From the Past and a Roadmap for the Future. Clin Pharmacol Ther 2017; 101:646-656. [DOI: 10.1002/cpt.659] [Citation(s) in RCA: 121] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 02/07/2017] [Accepted: 02/07/2017] [Indexed: 12/17/2022]
Affiliation(s)
- A S Darwich
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry; University of Manchester; Manchester UK
| | - K Ogungbenro
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry; University of Manchester; Manchester UK
| | - A A Vinks
- Cincinnati Children's Hospital Medical Center; Cincinnati Ohio USA
- Department of Pediatrics; University of Cincinnati School of medicine; Cincinnati Ohio USA
| | - J R Powell
- Eshelman School of Pharmacy; University of North Carolina; Chapel Hill North Carolina USA
| | - J-L Reny
- Geneva Platelet Group, School of Medicine; University of Geneva; Geneva Switzerland
- Department of Internal Medicine, Rehabilitation and Geriatrics; Geneva University Hospitals; Geneva Switzerland
| | - N Marsousi
- Clinical Pharmacology and Toxicology; Geneva University Hospitals; Geneva Switzerland
| | - Y Daali
- Geneva Platelet Group, School of Medicine; University of Geneva; Geneva Switzerland
- Clinical Pharmacology and Toxicology; Geneva University Hospitals; Geneva Switzerland
| | - D Fairman
- Clinical Pharmacology Modeling and Simulation, GSK Stevenage; UK
| | - J Cook
- Clinical Pharmacology, Pfizer Inc; Groton Connecticut USA
| | - L J Lesko
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology; University of Florida at Lake Nona (Orlando); Orlando Florida USA
| | - J S McCune
- University of Washington Department of Pharmaceutics and Fred Hitchinson Cancer Research Center Clinical Research Division; Seattle Washington USA
| | - C A J Knibbe
- Department of Clinical Pharmacy, St. Antonius Hospital, Nieuwegein, the Netherlands and Division of Pharmacology, Leiden Academic Centre for Drug Research; Leiden University; the Netherlands
| | - S N de Wildt
- Department of Pharmacology and Toxicology; Radboud University; Nijmegen the Netherlands
- Intensive Care and Department of Pediatric Surgery, Erasmus MC Sophia Children's Hospital; Rotterdam the Netherlands
| | - J S Leeder
- Division of Pediatric Pharmacology and Medical Toxicology, Department of Pediatrics, Children's Mercy Hospitals and Clinics; Kansas City Missouri USA
- Department of Pharmacology; University of Missouri-Kansas City; Kansas City Missouri USA
| | - M Neely
- University of Southern California and the Children's Hospital of Los Angeles; Los Angeles California USA
| | - A F Zuppa
- Children's Hospital of Philadelphia; Philadelphia Pennsylvania USA
| | - P Vicini
- Clinical Pharmacology, Pharmacometrics and DMPK, MedImmune; Cambridge UK
| | - L Aarons
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry; University of Manchester; Manchester UK
| | - T N Johnson
- Certara, Blades Enterprise Centre; Sheffield UK
| | - J Boiani
- Epstein Becker & Green; Washington DC USA
| | - A Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry; University of Manchester; Manchester UK
- Epstein Becker & Green; Washington DC USA
| |
Collapse
|
147
|
Abstract
This review summarizes various mathematical models of cell-autonomous mammalian circadian clock. We present the basics necessary for understanding of the cell-autonomous mammalian circadian oscillator, modern experimental data essential for its reconstruction and some special problems related to the validation of mathematical circadian oscillator models. This work compares existing mathematical models of circadian oscillator and the results of the computational studies of the oscillating systems. Finally, we discuss applications of the mathematical models of mammalian circadian oscillator for solving specific problems in circadian rhythm biology.
Collapse
|
148
|
Evaluation of Drug-Drug Interaction Potential Between Sacubitril/Valsartan (LCZ696) and Statins Using a Physiologically Based Pharmacokinetic Model. J Pharm Sci 2017; 106:1439-1451. [PMID: 28089685 DOI: 10.1016/j.xphs.2017.01.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 12/23/2016] [Accepted: 01/03/2017] [Indexed: 01/13/2023]
Abstract
Sacubitril/valsartan (LCZ696) has been approved for the treatment of heart failure. Sacubitril is an in vitro inhibitor of organic anion-transporting polypeptides (OATPs). In clinical studies, LCZ696 increased atorvastatin Cmax by 1.7-fold and area under the plasma concentration-time curve by 1.3-fold, but had little or no effect on simvastatin or simvastatin acid exposure. A physiologically based pharmacokinetics modeling approach was applied to explore the underlying mechanisms behind the statin-specific LCZ696 drug interaction observations. The model incorporated OATP-mediated clearance (CLint,T) for simvastatin and simvastatin acid to successfully describe the pharmacokinetic profiles of either analyte in the absence or presence of LCZ696. Moreover, the model successfully described the clinically observed drug effect with atorvastatin. The simulations clarified the critical parameters responsible for the observation of a low, yet clinically relevant, drug-drug interaction DDI between sacubitril and atorvastatin and the lack of effect with simvastatin acid. Atorvastatin is administered in its active form and rapidly achieves Cmax that coincide with the low Cmax of sacubitril. In contrast, simvastatin requires a hydrolysis step to the acid form and therefore is not present at the site of interactions at sacubitril concentrations that are inhibitory. Similar models were used to evaluate the drug-drug interaction risk for additional OATP-transported statins which predicted to maximally result in a 1.5-fold exposure increase.
Collapse
|
149
|
Freise KJ, Shebley M, Salem AH. Quantitative Prediction of the Effect of CYP3A Inhibitors and Inducers on Venetoclax Pharmacokinetics Using a Physiologically Based Pharmacokinetic Model. J Clin Pharmacol 2017; 57:796-804. [DOI: 10.1002/jcph.858] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Accepted: 11/21/2016] [Indexed: 11/07/2022]
|
150
|
de Kanter R, Sidharta PN, Delahaye S, Gnerre C, Segrestaa J, Buchmann S, Kohl C, Treiber A. Physiologically-Based Pharmacokinetic Modeling of Macitentan: Prediction of Drug-Drug Interactions. Clin Pharmacokinet 2016; 55:369-80. [PMID: 26385839 DOI: 10.1007/s40262-015-0322-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Macitentan is a novel dual endothelin receptor antagonist for the treatment of pulmonary arterial hypertension (PAH). It is metabolized by cytochrome P450 (CYP) enzymes, mainly CYP3A4, to its active metabolite ACT-132577. METHODS A physiological-based pharmacokinetic (PBPK) model was developed by combining observations from clinical studies and physicochemical parameters as well as absorption, distribution, metabolism and excretion parameters determined in vitro. RESULTS The model predicted the observed pharmacokinetics of macitentan and its active metabolite ACT-132577 after single and multiple dosing. It performed well in recovering the observed effect of the CYP3A4 inhibitors ketoconazole and cyclosporine, and the CYP3A4 inducer rifampicin, as well as in predicting interactions with S-warfarin and sildenafil. The model was robust enough to allow prospective predictions of macitentan-drug combinations not studied, including an alternative dosing regimen of ketoconazole and nine other CYP3A4-interacting drugs. Among these were the HIV drugs ritonavir and saquinavir, which were included because HIV infection is a known risk factor for the development of PAH. CONCLUSION This example of the application of PBPK modeling to predict drug-drug interactions was used to support the labeling of macitentan (Opsumit).
Collapse
Affiliation(s)
- Ruben de Kanter
- Preclinical Pharmacokinetics and Metabolism, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland.
| | - Patricia N Sidharta
- Clinical Pharmacology, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland
| | - Stéphane Delahaye
- Preclinical Pharmacokinetics and Metabolism, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland
| | - Carmela Gnerre
- Preclinical Pharmacokinetics and Metabolism, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland
| | - Jerome Segrestaa
- Preclinical Pharmacokinetics and Metabolism, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland
| | - Stephan Buchmann
- Preformulation and Preclinical Galenics, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland
| | - Christopher Kohl
- Preclinical Pharmacokinetics and Metabolism, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland
| | - Alexander Treiber
- Preclinical Pharmacokinetics and Metabolism, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123, Allschwil, Switzerland
| |
Collapse
|