Predicting individual responses to pravastatin using a physiologically based kinetic model for plasma cholesterol concentrations

A systems toxicology approach for identification of disruptions in cholesterol homeostasis after aggregated exposure to mixtures of perfluorinated compounds in humans

Per- and polyfluoroalkyl substances (PFAS) are used in various household and industrial products. In humans, positive associations were reported between PFAS, including perfluorsulfonic acid and perfluorooctanoic acid, and cholesterol, a cardiometabolic risk factor. Animal studies show the opposite. Human-centered approaches are needed to better understand the effects of PFAS mixtures on cholesterol. Here, a systems toxicology approach is described, using a gene-centered cholesterol biokinetic model. PFAS exposure-gene expression relations from published data were introduced into the model. An existing PFAS physiologically based kinetic model was augmented with lung and dermal compartments and integrated with the cholesterol model to enable exposure-effect modeling. The final model was populated with data reflecting lifetime mixture exposure from: tolerable weekly intake values; the environment; high occupational exposures (ski waxing, PFAS industry). Results indicate that low level exposures (tolerable weekly intake, environmental) did not change cholesterol. In contrast, occupational exposures clearly resulted in internal PFAS exposure and disruption of cholesterol homeostasis, largely in line with epidemiological observations. Despite model limitations (eg, dynamic range, directionality), changes in cholesterol homeostasis were predicted for ski waxers, hitherto unknown from epidemiological studies. Here, future studies involving lipid metabolism could improve risk assessment.

Microfluidic technology and simulation models in studying pharmacokinetics during pregnancy

Introduction: Preterm birth rates and maternal and neonatal mortality remain concerning global health issues, necessitating improved strategies for testing therapeutic compounds during pregnancy. Current 2D or 3D cell models and animal models often fail to provide data that can effectively translate into clinical trials, leading to pregnant women being excluded from drug development considerations and clinical studies. To address this limitation, we explored the utility of in silico simulation modeling and microfluidic-based organ-on-a-chip platforms to assess potential interventional agents. Methods: We developed a multi-organ feto-maternal interface on-chip (FMi-PLA-OOC) utilizing microfluidic channels to maintain intercellular interactions among seven different cell types (fetal membrane-decidua-placenta). This platform enabled the investigation of drug pharmacokinetics in vitro. Pravastatin, a model drug known for its efficacy in reducing oxidative stress and inflammation during pregnancy and currently in clinical trials, was used to test its transfer rate across both feto-maternal interfaces. The data obtained from FMi-PLA-OOC were compared with existing data from in vivo animal models and ex vivo placenta perfusion models. Additionally, we employed mechanistically based simulation software (Gastroplus®) to predict pravastatin pharmacokinetics in pregnant subjects based on validated nonpregnant drug data. Results: Pravastatin transfer across the FMi-PLA-OOC and predicted pharmacokinetics in the in silico models were found to be similar, approximately 18%. In contrast, animal models showed supraphysiologic drug accumulation in the amniotic fluid, reaching approximately 33%. Discussion: The results from this study suggest that the FMi-PLA-OOC and in silico models can serve as alternative methods for studying drug pharmacokinetics during pregnancy, providing valuable insights into drug transport and metabolism across the placenta and fetal membranes. These advanced platforms offer promising opportunities for safe, reliable, and faster testing of therapeutic compounds, potentially reducing the number of pregnant women referred to as "therapeutic orphans" due to the lack of consideration in drug development and clinical trials. By bridging the gap between preclinical studies and clinical trials, these approaches hold great promise in improving maternal and neonatal health outcomes.

Modeling cholesterol metabolism and atherosclerosis

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality among Western populations. Many risk factors have been identified for ASCVD; however, elevated low-density lipoprotein cholesterol (LDL-C) remains the gold standard. Cholesterol metabolism at the cellular and whole-body level is maintained by an array of interacting components. These regulatory mechanisms have complex behavior. Likewise, the mechanisms which underpin atherogenesis are nontrivial and multifaceted. To help overcome the challenge of investigating these processes mathematical modeling, which is a core constituent of the systems biology paradigm has played a pivotal role in deciphering their dynamics. In so doing models have revealed new insights about the key drivers of ASCVD. The aim of this review is fourfold; to provide an overview of cholesterol metabolism and atherosclerosis, to briefly introduce mathematical approaches used in this field, to critically discuss models of cholesterol metabolism and atherosclerosis, and to highlight areas where mathematical modeling could help to investigate in the future. This article is categorized under: Cardiovascular Diseases > Computational Models.

Improving the generation and selection of virtual populations in quantitative systems pharmacology models

Quantitative systems pharmacology (QSP) models aim to describe mechanistically the pathophysiology of disease and predict the effects of therapies on that disease. For most drug development applications, it is important to predict not only the mean response to an intervention but also the distribution of responses, due to inter-patient variability. Given the necessary complexity of QSP models, and the sparsity of relevant human data, the parameters of QSP models are often not well determined. One approach to overcome these limitations is to develop alternative virtual patients (VPs) and virtual populations (Vpops), which allow for the exploration of parametric uncertainty and reproduce inter-patient variability in response to perturbation. Here we evaluated approaches to improve the efficiency of generating Vpops. We aimed to generate Vpops without sacrificing diversity of the VPs' pathophysiologies and phenotypes. To do this, we built upon a previously published approach (Allen et al., 2016) by (a) incorporating alternative optimization algorithms (genetic algorithm and Metropolis-Hastings) or alternatively (b) augmenting the optimized objective function. Each method improved the baseline algorithm by requiring significantly fewer plausible patients (precursors to VPs) to create a reasonable Vpop.

Unlocking data sets by calibrating populations of models to data density: A study in atrial electrophysiology

The understanding of complex physical or biological systems nearly always requires a characterization of the variability that underpins these processes. In addition, the data used to calibrate these models may also often exhibit considerable variability. A recent approach to deal with these issues has been to calibrate populations of models (POMs), multiple copies of a single mathematical model but with different parameter values, in response to experimental data. To date, this calibration has been largely limited to selecting models that produce outputs that fall within the ranges of the data set, ignoring any trends that might be present in the data. We present here a novel and general methodology for calibrating POMs to the distributions of a set of measured values in a data set. We demonstrate our technique using a data set from a cardiac electrophysiology study based on the differences in atrial action potential readings between patients exhibiting sinus rhythm (SR) or chronic atrial fibrillation (cAF) and the Courtemanche-Ramirez-Nattel model for human atrial action potentials. Not only does our approach accurately capture the variability inherent in the experimental population, but we also demonstrate how the POMs that it produces may be used to extract additional information from the data used for calibration, including improved identification of the differences underlying stratified data. We also show how our approach allows different hypotheses regarding the variability in complex systems to be quantitatively compared.

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed "virtual patients." In order to reproduce the statistics of a clinical population, virtual patients are often weighted to form a virtual population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate virtual patients and, from this ensemble, demonstrate how to select a virtual population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious virtual patients become overrepresented in virtual populations.

Mixed effects of OATP1B1, BCRP and NTCP polymorphisms on the population pharmacokinetics of pravastatin in healthy volunteers

1. Pravastatin is a 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitor used for the treatment of hyperlipidaemia. This study aims to investigate the effects of genetic polymorphisms in OATP1B1, BCRP and NTCP on pravastatin population pharmacokinetics in healthy Chinese volunteers using a non-linear mixed-effect modelling (NONMEM) approach. A two-compartment model with a first-order absorption and elimination described plasma pravastatin concentrations well. 2. Genetic polymorphisms of rs4149056 (OATP1B1) and rs2306283 (OATP1B1) were found to be associated with a significant (p < 0.01) decrease in the apparent clearance from the central compartment (CL/F), while rs2296651 (NTCP) increased CL/F to a significant degree (p < 0.01). The combination of these three polymorphisms reduced the inter-individual variability of CL/F by 78.8%. 3. There was minimal effect of rs2231137 (BCRP) and rs2231142 (BCRP) on pravastatin pharmacokinetics (0.01 < p < 0.05), whereas rs11045819 (OATP1B1), rs1061018 (BCRP) and rs61745930 (NTCP) genotypes do not appear to be associated with pravastatin pharmacokinetics based on the population model (p > 0.05). 4. The current data suggest that the combination of rs4149056, rs2306283 and rs2296651 polymorphisms is an important determinant of pravastatin pharmacokinetics.