Abstract
A computational workflow was developed to facilitate the process of quantitative in vitro to in vivo extrapolation (QIVIVE), specifically the translation of in vitro concentration-response to in vivo dose-response relationships and subsequent derivation of a benchmark dose value (BMD). The workflow integrates physiologically based pharmacokinetic (PBPK) modeling; global sensitivity analysis (GSA), Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC) simulation. For a given set of in vitro concentration and response data the algorithm returns the posterior distribution of the corresponding in vivo, population-based dose-response values, for a given route of exposure. The novel aspect of the workflow is a rigorous statistical framework for accommodating uncertainty in both the parameters of the PBPK model (both parameter uncertainty and population variability) and in the structure of the PBPK model itself recognizing that the model is an approximation to reality. Both these sources of uncertainty propagate through the workflow and are quantified within the posterior distribution of in vivo dose for a fixed representative in vitro concentration. To demonstrate this process and for comparative purposes a similar exercise to previously published work describing the kinetics of ethylene glycol monoethyl ether (EGME) and its embryotoxic metabolite methoxyacetic acid (MAA) in rats was undertaken. The computational algorithm can be used to extrapolate from in vitro data to any organism, including human. Ultimately, this process will be incorporated into a user-friendly, freely available modeling platform, currently under development, that will simplify the process of QIVIVE.
Highlights
The prospect of an animal-free, in vitro bioassay based, human safety testing of chemicals strategy was increased with the publication of the US National Research Council (NRC) report titled “Toxicity Testing in the 21st Century: A Vision and a Strategy” (NRC, 2007)
In this work we demonstrate a workflow for the calculation of an in vivo point of departure comprising of four steps: (1) GSA to identify the most sensitive model parameters that govern variance of the dose metric; (2) refinement of parameter ranges through model calibration to experimental data; (3) quantitative in vitro to in vivo extrapolation (QIVIVE) using ABC; (4) calculation of a benchmark dose
When a PBPK model only has to fit a single data point, such as the peak (Cmax) plasma concentration of parent chemical or metabolite, corresponding to a value from in-vitro experiments, for fixed values of all other PBPK model parameters it is possible to estimate a unique external dose concentration that results in the target plasma concentration
Summary
The prospect of an animal-free, in vitro bioassay based, human safety testing of chemicals strategy was increased with the publication of the US National Research Council (NRC) report titled “Toxicity Testing in the 21st Century: A Vision and a Strategy” (NRC, 2007). On the other hand the majority of studies reporting the translation of in vitro concentration-response to in vivo doseresponse curves used a different approach more accurately described as “iterative forward dosimetry.” This approach assumes the model is an accurate emulation of reality in which all parameters, other than input dose or exposure, are fixed. We used PBPK modeling, Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC) simulation to convert in vitro concentration-response data to in vivo dose-response data To demonstrate this process we undertook a similar exercise to Louisse et al (2010) with the added objective of accommodating model and parameter value uncertainty within an efficient modeling framework. The motivation for this work is twofold: (1) development of a rigorous statistical framework for accommodating uncertainty in both the parameters of the PBPK model and lack of fit of the model to measured data, and a consideration of how this affects an in-vivo dose response relationship, and (2) to develop a workflow and code that will be incorporated into a userfriendly, freely available modeling platform called RVis, currently under development, that will simplify the process of translation of in vitro concentration-response to in vivo dose-response relationships
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