Towards translating in vitro measures of thyroid hormone system disruption to in vivo responses in the pregnant rat via a biologically based dose response (BBDR) model.

Despite the number of in vitro assays that have been recently developed to identify chemicals that interfere with the hypothalamic-pituitary-thyroid axis (HPT), the translation of those in vitro results into in vivo responses (in vitro to in vivo extrapolation, IVIVE) has received limited attention from the modeling community. To help advance this field a steady state biologically based dose response (BBDR) model for the HPT axis was constructed for the pregnant rat on gestation day (GD) 20. The BBDR HPT axis model predicts plasma levels of thyroid stimulating hormone (TSH) and the thyroid hormones, thyroxine (T4) and triiodothyronine (T3). Thyroid hormones are important for normal growth and development of the fetus. Perchlorate, a potent inhibitor of thyroidal uptake of iodide by the sodium iodide symporter (NIS) protein, was used as a case study for the BBDR HPT axis model. The inhibitory blocking of the NIS by perchlorate was associated with dose-dependent steady state decreases in thyroid hormone production in the thyroid gland. The BBDR HPT axis model predictions for TSH, T3, and T4 plasma concentrations in pregnant Sprague Dawley (SD) rats were within 2-fold of observations for drinking water perchlorate exposures ranging from 10 to 30,000 µg/kg/d. In Long Evans (LE) pregnant rats, for both control and perchlorate drinking water exposures, ranging from 85 to 82,000 µg/kg/d, plasma thyroid hormone and TSH concentrations were predicted within 2 to 3.4- fold of observations. This BBDR HPT axis model provides a successful IVIVE template for thyroid hormone disruption in pregnant rats.

Impact of Obesity on Brexpiprazole Pharmacokinetics: Proposal for Improved Initiation of Treatment.

Brexpiprazole is an oral antipsychotic agent indicated for use in patients with schizophrenia or as adjunctive treatment for major depressive disorder. As obesity (body mass index ≥35 kg/m2 ) has the potential to affect drug pharmacokinetics and is a common comorbidity of both schizophrenia and major depressive disorder, it is important to understand changes in brexpiprazole disposition in this population. This study uses a whole-body physiologically based pharmacokinetic model to compare the pharmacokinetics of brexpiprazole in obese and normal-weight (body mass index 18-25 kg/m2 ) individuals known to be cytochrome P450 2D6 extensive metabolizers (EMs) and poor metabolizers (PMs). The physiologically based pharmacokinetic simulations demonstrated significant differences in the time to effective concentrations between obese and normal-weight individuals within metabolizer groups according to the label-recommended titration. Simulations using an alternative dosing strategy of 1 week of twice-daily dosing in obese EMs or 2 weeks of twice-daily dosing in obese poor metabolizers, followed by a return to once-daily dosing, yielded more consistent plasma concentrations between normal-weight and obese patients without exceeding the area under the plasma concentration-time curve observed in the normal-weight EMs. These alternative dosing strategies reduce the time to effective concentrations in obese patients and may improve clinical response to brexpiprazole.

Brexpiprazole Pharmacokinetics in CYP2D6 Poor Metabolizers: Using Physiologically Based Pharmacokinetic Modeling to Optimize Time to Effective Concentrations.

Brexpiprazole is an oral antipsychotic agent indicated for use in patients with schizophrenia, or as adjunctive treatment for major depressive disorder. As cytochrome P450 (CYP) 2D6 contributes significantly to brexpiprazole metabolism, there is a label-recommended 50% reduction in dose among patients with the CYP2D6 poor metabolizer phenotype. This study uses a whole-body physiologically based pharmacokinetic (PBPK) model to compare the pharmacokinetics of brexpiprazole in patients known to be extensive metabolizers (EMs) and poor metabolizers (PMs). A PBPK model was constructed, verified, and validated against brexpiprazole clinical data, and simulations of 500 subjects were performed to establish the median time to effective concentrations in EMs and PMs. The PBPK simulations captured brexpiprazole PK well and demonstrated significant differences in the time to effective concentrations between EMs and PMs according to the label-recommended titration. Additionally, these simulations suggest that CYP2D6 PMs consistently achieve lower minimum concentrations during the dosing interval than CYP2D6 EMs. Simulations using an alternative dosing strategy of twice-daily dosing (as opposed to once daily) in PMs during the first week of brexpiprazole dosing yielded more consistent plasma concentrations between EMs and PMs, without exceeding the area under the plasma concentration-time curve observed in the EMs. Taken together, the results of these PBPK simulations suggest that product labeling for brexpiprazole titration in CYP2D6 PMs likely overcompensates for the decreased clearance seen in this population. We propose an alternative dosing strategy that decreases the time to effective concentrations and recommend a reevaluation of steady-state PK in this population to potentially allow for higher daily doses in CYP2D6 PMs.

How Many Urine Samples Are Needed to Accurately Assess Exposure to Non-Persistent Chemicals? The Biomarker Reliability Assessment Tool (BRAT) for Scientists, Research Sponsors, and Risk Managers.

In epidemiologic and exposure research, biomonitoring is often used as the basis for assessing human exposure to environmental chemicals. Studies frequently rely on a single urinary measurement per participant to assess exposure to non-persistent chemicals. However, there is a growing consensus that single urine samples may be insufficient for adequately estimating exposure. The question then arises: how many samples would be needed for optimal characterization of exposure? To help researchers answer this question, we developed a tool called the Biomarker Reliability Assessment Tool (BRAT). The BRAT is based on pharmacokinetic modeling simulations, is freely available, and is designed to help researchers determine the approximate number of urine samples needed to optimize exposure assessment. The BRAT performs Monte Carlo simulations of exposure to estimate internal levels and resulting urinary concentrations in individuals from a population based on user-specified inputs (e.g., biological half-life, within- and between-person variability in exposure). The BRAT evaluates-through linear regression and quantile classification-the precision/accuracy of the estimation of internal levels depending on the number of urine samples. This tool should guide researchers towards more robust biomonitoring and improved exposure classification in epidemiologic and exposure research, which should in turn improve the translation of that research into decision-making.

Quantitative bias analysis of the association between subclinical thyroid disease and two perfluoroalkyl substances in a single study.

Exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) has been associated with the occurrence of thyroid disease in some epidemiologic studies. We hypothesized that in a specific epidemiologic study based on the National Health and Nutrition Examination Survey, the association of subclinical thyroid disease with serum concentration of PFOA and PFOS was due to reverse causality. Thyroid hormone affects glomerular filtration, which in turn affects excretion of PFOA and PFOS. We evaluated this by linking a model of thyroid disease status over the lifetime to physiologically based pharmacokinetic models of PFOA and PFOS. Using Monte Carlo methods, we simulated the target study population and analyzed the data using multivariable logistic regression. The target and simulated populations were similar with respect to age, estimated glomerular filtration rate, serum concentrations of PFOA and PFOS, and prevalence of subclinical thyroid disease. Our findings suggest that in the target study the associations with subclinical hypothyroidism were overstated and the results for subclinical hyperthyroidism were, in general, understated. For example, for subclinical hypothyroidism in men, the reported odds ratio per ln(PFOS) increase was 1.98 (95% CI 1.19-3.28), whereas in the simulated data the bias due to reverse causality gave an odds ratio of 1.19 (1.16-1.23). Our results provide evidence of bias due to reverse causality in a specific cross-sectional study of subclinical thyroid disease with exposure to PFOA and PFOS among adults.

Quantitative systems toxicology.

The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related pathways. Towards this vision, collaborative quantitative systems pharmacology and toxicology modeling endeavors (QSP/QST) have been initiated amongst numerous organizations worldwide. In this article, we discuss how quantitative structure-activity relationship (QSAR), network-based, and pharmacokinetic/pharmacodynamic modeling approaches can be integrated into the framework of QST models. Additionally, we review the application of QST models to predict cardiotoxicity and hepatotoxicity of drugs throughout their development. Cell and organ specific QST models are likely to become an essential component of modern toxicity testing, and provides a solid foundation towards determining individualized therapeutic windows to improve patient safety.

Lifetime PCB 153 bioaccumulation and pharmacokinetics in pilot whales: Bayesian population PBPK modeling and Markov chain Monte Carlo simulations.

Physiologically based pharmacokinetic (PBPK) models for wild animal populations such as marine mammals typically have a high degree of model uncertainty and variability due to the scarcity of information and the embryonic nature of this field. Parameters values used in marine mammals models are usually taken from other mammalian species (e.g. rats or mice) and might not be entirely suitable to properly explain the kinetics of pollutants in marine mammals. Therefore, several parameters for a PBPK model for the bioaccumulation and pharmacokinetics of PCB 153 in long-finned pilot whales were estimated in the present study using the Bayesian approach executed with Markov chain Monte Carlo (MCMC) simulations. This method uses 'prior' information of the parameters, either from the literature or from previous model runs. The advantage is that this method uses such 'prior' parameters to calculate probability distributions to determine 'posterior' values that best explain the field observations. Those field observations or datasets were PCB 153 concentrations in blubber of long-finned pilot whales from Sandy Cape and Stanley, Tasmania, Australia. The model predictions showed an overall decrease in PCB 153 levels in blubber over the lifetime of the pilot whales. All parameters from the Sandy Cape model were updated using the Stanley dataset, except for the concentration of PCB 153 in the milk. The model presented here is a promising and preliminary start to PBPK modeling in long-finned pilot whales that would provide a basis for non-invasive studies in these protected marine mammals.

A multi-route model of nicotine-cotinine pharmacokinetics, pharmacodynamics and brain nicotinic acetylcholine receptor binding in humans.

The pharmacokinetics of nicotine, the pharmacologically active alkaloid in tobacco responsible for addiction, are well characterized in humans. We developed a physiologically based pharmacokinetic/pharmacodynamic model of nicotine pharmacokinetics, brain dosimetry and brain nicotinic acetylcholine receptor (nAChRs) occupancy. A Bayesian framework was applied to optimize model parameters against multiple human data sets. The resulting model was consistent with both calibration and test data sets, but in general underestimated variability. A pharmacodynamic model relating nicotine levels to increases in heart rate as a proxy for the pharmacological effects of nicotine accurately described the nicotine related changes in heart rate and the development and decay of tolerance to nicotine. The PBPK model was utilized to quantitatively capture the combined impact of variation in physiological and metabolic parameters, nicotine availability and smoking compensation on the change in number of cigarettes smoked and toxicant exposure in a population of 10,000 people presented with a reduced toxicant (50%), reduced nicotine (50%) cigarette Across the population, toxicant exposure is reduced in some but not all smokers. Reductions are not in proportion to reductions in toxicant yields, largely due to partial compensation in response to reduced nicotine yields. This framework can be used as a key element of a dosimetry-driven risk assessment strategy for cigarette smoke constituents.