Dataset Information

PBPK model reporting template for chemical risk assessment applications.

ABSTRACT: Physiologically-based pharmacokinetic (PBPK) modeling analysis does not stand on its own for regulatory purposes but is a robust tool to support drug/chemical safety assessment. While the development of PBPK models have grown steadily since their emergence, only a handful of models have been accepted to support regulatory purposes due to obstacles such as the lack of a standardized template for reporting PBPK analysis. Here, we expand the existing guidances designed for pharmaceutical applications by recommending additional elements that are relevant to environmental chemicals. This harmonized reporting template can be adopted and customized by public health agencies receiving PBPK model submission, and it can also serve as general guidance for submitting PBPK-related studies for publication in journals or other modeling sharing purposes. The current effort represents one of several ongoing collaborations among the PBPK modeling and risk assessment communities to promote, when appropriate, incorporating PBPK modeling to characterize the influence of pharmacokinetics on safety decisions made by regulatory agencies.

SUBMITTER: Tan YM

PROVIDER: S-EPMC8188465 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

PBPK model reporting template for chemical risk assessment applications.

Tan Yu-Mei YM Chan Melissa M Chukwudebe Amechi A Domoradzki Jeanne J Fisher Jeffrey J Hack C Eric CE Hinderliter Paul P Hirasawa Kota K Leonard Jeremy J Lumen Annie A Paini Alicia A Qian Hua H Ruiz Patricia P Wambaugh John J Zhang Fagen F Embry Michelle M

Regulatory toxicology and pharmacology : RTP 20200602

Physiologically-based pharmacokinetic (PBPK) modeling analysis does not stand on its own for regulatory purposes but is a robust tool to support drug/chemical safety assessment. While the development of PBPK models have grown steadily since their emergence, only a handful of models have been accepted to support regulatory purposes due to obstacles such as the lack of a standardized template for reporting PBPK analysis. Here, we expand the existing guidances designed for pharmaceutical applicatio ...[more]

PMID: 32502513

Similar Datasets

Project description:IntroductionWe previously developed a PBPK model template that consists of a single model "superstructure" with equations and logic found in many physiologically based pharmacokinetic (PBPK) models. Using the template, one can implement PBPK models with different combinations of structures and features.MethodsTo identify factors that influence computational time required for PBPK model simulations, we conducted timing experiments using various implementations of PBPK models for dichloromethane and chloroform, including template and stand-alone implementations, and simulating four different exposure scenarios. For each experiment, we measured the required computational time and evaluated the impacts of including various model features (e.g., number of output variables calculated) and incorporating various design choices (e.g., different methods for estimating blood concentrations).ResultsWe observed that model implementations that treat body weight and dependent quantities as constant (fixed) parameters can result in a 30% time savings compared with options that treat body weight and dependent quantities as time-varying. We also observed that decreasing the number of state variables by 36% in our PBPK model template led to a decrease of 20-35% in computational time. Other factors, such as the number of output variables, the method for implementing conditional statements, and the method for estimating blood concentrations, did not have large impacts on simulation time. In general, simulations with PBPK model template implementations of models required more time than simulations with stand-alone implementations, but the flexibility and (human) time savings in preparing and reviewing a model implemented using the PBPK model template may justify the increases in computational time requirements.ConclusionOur findings concerning how PBPK model design and implementation decisions impact computational speed can benefit anyone seeking to develop, improve, or apply a PBPK model, with or without the PBPK model template.

Project description:The number of legacy chemicals without toxicity reference values combined with the rate of new chemical development are overwhelming the capacity of the traditional risk assessment paradigm. More efficient approaches are needed to quantitatively estimate chemical risks. In this study, rats were dosed orally with multiple doses of six chemicals for 5 days, 2, 4, and 13 weeks. Target organs were analyzed for traditional histological and organ weight changes and transcriptional changes using microarrays. Histological and organ weight changes in this study and the tumor incidences in the original cancer bioassays were analyzed using benchmark dose (BMD) methods to identify noncancer and cancer points-of-departure. The dose-response changes in gene expression were also analyzed using BMD methods and the responses grouped based on signaling pathways. A comparison of transcriptional BMD values for the most sensitive pathway with BMD values for the noncancer and cancer apical endpoints showed a high degree of correlation at all time points. When the analysis included data from an earlier study with 8 additional chemicals, transcriptional BMD values for the most sensitive pathway were significantly correlated with noncancer (r = 0.827, p = 0.0031) and cancer-related (r = 0.940, p = 0.0002) BMD values at 13 weeks. The average ratio of apical-to-transcriptional BMD values was less than two suggesting that for the current chemicals, transcriptional perturbation did not occur at significantly lower doses than apical responses. Based on our results, we propose a practical framework for application of transcriptomic data to chemical risk assessment. Four to five week-old rats were exposed in dose-response and for multiple durations to 6 chemicals. The chemicals were 1,2,4-tribromobenzene (CAS No. 615-54-3), 2,3,4,6-tetrachlorophenol (CAS No. 58-90-2), bromobenzene (CAS No. 108-86-1), 4,4'-methylenebis(N,N-dimethyl)benzenamine (CAS No. 101-61-1), hydrazobenzene (CAS No. 122-66-7), N-nitrosodiphenylamine (CAS No. 86-30-6). For 1,2,4-tribromobenzene and 2,3,4,6-tetrachlorophenol, male Sprague Dawley (Rat/Crl:CD(SD)) rats were used. For bromobenzene and hydroazobenzene, male F344/DuCrl rats were used. For 4,4'-methylenebis(N,N-dimethyl)benzenamine and N-nitrosodiphenylamine, female F344/DuCrl rats were used. The exposure durations were 5 days, 2 weeks, 4 weeks, and 13 weeks. The dose and route of exposure are provided with the individual sample annotations. With each chemical treatment, a matched vehicle control group was run concurrently with the exposure. Gavage exposures were administered 7 days per week and feed exposures were provided 7 days per week. At the appropriate time point, animals were euthanized with a lethal intraperitoneal injection of sodium pentobarbital. For the liver, slices from each of the lobes were mixed together and used for microarray analysis. For the thyroid, the gland was transversely sectioned and the bottom portion was used for microarray analysis. For the bladder, the organ was longitudinally bisected, a slice was removed for histology and the remainder of the organ was minced and used for microarray analysis. Microarrays were run on five rats per dose per time point.

Dataset Information

PBPK model reporting template for chemical risk assessment applications.

Publications

PBPK model reporting template for chemical risk assessment applications.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets