Dataset Information

PBPK Model of Coproporphyrin I: Evaluation of the Impact of SLCO1B1 Genotype, Ethnicity, and Sex on its Inter-Individual Variability.

ABSTRACT: Coproporphyrin I (CPI) is an endogenous biomarker of OATP1B activity and associated drug-drug interactions. In this study, a minimal physiologically-based pharmacokinetic model was developed to investigate the impact of OATP1B1 genotype (c.521T>C), ethnicity, and sex on CPI pharmacokinetics and interindividual variability in its baseline. The model implemented mechanistic descriptions of CPI hepatic transport between liver blood and liver tissue and renal excretion. Key model parameters (e.g., endogenous CPI synthesis rate, and CPI hepatic uptake clearance) were estimated by fitting the model simultaneously to three independent CPI clinical datasets (plasma and urine data) obtained from white (n = 16, men and women) and Asian-Indian (n = 26, all men) subjects, with c.521 variants (TT, TC, and CC). The optimized CPI model successfully described the observed data using c.521T>C genotype, ethnicity, and sex as covariates. CPI hepatic active was 79% lower in 521CC relative to the wild type and 42% lower in Asian-Indians relative to white subjects, whereas CPI synthesis was 23% higher in male relative to female subjects. Parameter sensitivity analysis showed marginal impact of the assumption of CPI synthesis site (blood or liver), resulting in comparable recovery of plasma and urine CPI data. Lower magnitude of CPI-drug interaction was simulated in 521CC subjects, suggesting the risk of underestimation of CPI-drug interaction without prior OATP1B1 genotyping. The CPI model incorporates key covariates contributing to interindividual variability in its baseline and highlights the utility of the CPI modeling to facilitate the design of prospective clinical studies to maximize the sensitivity of this biomarker.

SUBMITTER: Takita H

PROVIDER: S-EPMC7894406 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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PBPK Model of Coproporphyrin I: Evaluation of the Impact of SLCO1B1 Genotype, Ethnicity, and Sex on its Inter-Individual Variability.

Takita Hiroyuki H Barnett Shelby S Zhang Yueping Y Ménochet Karelle K Shen Hong H Ogungbenro Kayode K Galetin Aleksandra A

CPT: pharmacometrics & systems pharmacology 20210119 2

Coproporphyrin I (CPI) is an endogenous biomarker of OATP1B activity and associated drug-drug interactions. In this study, a minimal physiologically-based pharmacokinetic model was developed to investigate the impact of OATP1B1 genotype (c.521T>C), ethnicity, and sex on CPI pharmacokinetics and interindividual variability in its baseline. The model implemented mechanistic descriptions of CPI hepatic transport between liver blood and liver tissue and renal excretion. Key model parameters (e.g., e ...[more]

PMID: 33289952

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Project description:PurposeInter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research. To ensure patient safety it is important to identify adverse events or critical subgroups within the population as early as possible. Hence, a comprehensive understanding of the processes governing pharmacokinetics and pharmacodynamics is of utmost importance. In this paper we combine Bayesian statistics with detailed mechanistic physiologically-based pharmacokinetic (PBPK) models. On the example of pravastatin we demonstrate that this combination provides a powerful tool to investigate inter-individual variability in groups of patients and to identify clinically relevant homogenous subgroups in an unsupervised approach. Since PBPK models allow the identification of physiological, drug-specific and genotype-specific knowledge separately, our approach supports knowledge-based extrapolation to other drugs or populations.MethodsPBPK models are based on generic distribution models and extensive collections of physiological parameters and allow a mechanistic investigation of drug distribution and drug action. To systematically account for parameter variability within patient populations, a Bayesian-PBPK approach is developed rigorously quantifying the probability of a parameter given the amount of information contained in the measured data. Since these parameter distributions are high-dimensional, a Markov chain Monte Carlo algorithm is used, where the physiological and drug-specific parameters are considered in separate blocks.ResultsConsidering pravastatin pharmacokinetics as an application example, Bayesian-PBPK is used to investigate inter-individual variability in a cohort of 10 patients. Correlation analyses infer structural information about the PBPK model. Moreover, homogeneous subpopulations are identified a posteriori by examining the parameter distributions, which can even be assigned to a polymorphism in the hepatic organ anion transporter OATP1B1.ConclusionsThe presented Bayesian-PBPK approach systematically characterizes inter-individual variability within a population by updating prior knowledge about physiological parameters with new experimental data. Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified. The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs.

Project description:Endogenous glucocorticoids have diverse physiological effects and are important regulators of metabolism, immunity, cardiovascular function, musculoskeletal health and central nervous system activity. Synthetic glucocorticoids have received widespread attention for their potent anti-inflammatory activity and have become an important class of drugs used to augment endogenous glucocorticoid activity for the treatment of a host of chronic inflammatory conditions. Chronic use of synthetic glucocorticoids is associated with a number of adverse effects as a result of the persistent dysregulation of glucocorticoid sensitive pathways. A failure to consider the pronounced circadian rhythmicity of endogenous glucocorticoids can result in either supraphysiological glucocorticoid exposure or severe suppression of endogenous glucocorticoid secretion, and is thought be a causal factor in the incidence of adverse effects during chronic glucocorticoid therapy. Furthermore, given that synthetic glucocorticoids have potent feedback effects on the hypothalamic-pituitary-adrenal (HPA) axis, physiological factors which can give rise to individual variability in HPA axis activity such as sex, age, and disease state might also have substantial implications for therapy. We use a semi-mechanistic mathematical model of the rodent HPA axis to study how putative sex differences and individual variability in HPA axis regulation can influence the effects of long-term synthetic exposure on endogenous glucocorticoid circadian rhythms. Model simulations suggest that for the same drug exposure, simulated females exhibit less endogenous suppression than males considering differences in adrenal sensitivity and negative feedback to the hypothalamus and pituitary. Simulations reveal that homeostatic regulatory variability and chronic stress-induced regulatory adaptations in the HPA axis network can result in substantial differences in the effects of synthetic exposure on the circadian rhythm of endogenous glucocorticoids. In general, our results provide insight into how the dosage and exposure profile of synthetic glucocorticoids could be manipulated in a personalized manner to preserve the circadian dynamics of endogenous glucocorticoids during chronic therapy, thus potentially minimizing the incidence of adverse effects associated with long-term use of glucocorticoids.

Dataset Information

PBPK Model of Coproporphyrin I: Evaluation of the Impact of SLCO1B1 Genotype, Ethnicity, and Sex on its Inter-Individual Variability.

Publications

PBPK Model of Coproporphyrin I: Evaluation of the Impact of SLCO1B1 Genotype, Ethnicity, and Sex on its Inter-Individual Variability.

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