Derivation of a Human In Vivo Benchmark Dose for Bisphenol A from ToxCast In Vitro Concentration Response Data Using a Computational Workflow for Probabilistic Quantitative In Vitro to In Vivo Extrapolation

Abstract

A computational workflow which integrates physiologically based kinetic (PBK) modelling; global sensitivity analysis (GSA), Approximate Bayesian Computation (ABC), Markov Chain Monte Carlo (MCMC) simulation and the Virtual Cell Based Assay (VCBA) for the estimation of the active, free in vitro concentration of chemical in the reaction medium was developed to facilitate quantitative in vitro to in vivo extrapolation (QIVIVE). The workflow was designed to estimate parameter and model uncertainty within a computationally efficient framework. The workflow was tested using a human PBK model for bisphenol A (BPA) and high throughput screening (HTS) in vitro concentration-response data, for estrogen and pregnane X receptor activation determined in human liver and kidney cell lines, from the ToxCast/Tox21 database. In vivo benchmark dose 10% lower confidence limits (BMDL₁₀) for oral uptake of BPA (ng/kg BW/day) were calculated from the in vivo dose-responses and compared to the human equivalent dose (HED) BMDL₁₀ for relative kidney weight change in the mouse derived by European Food Safety Authority (EFSA). Three from four in vivo BMDL₁₀ values calculated in this study were similar to the EFSA values whereas the fourth was much smaller. The derivation of an uncertainty factor (UF) to accommodate the uncertainties associated with measurements using human cell lines in vitro, extrapolated to in vivo, could be useful for the derivation of Health Based Guidance Values (HBGV).

Keywords: BPA (bisphenol A); PBK; QIVIVE; in silico; in vitro.

FIGURE 1

A schematic of the model for BPA and sub-model for BPAG and BPAS. The main model contained a lymphatic compartment () which received a portion of the oral dose of BPA from the stomach and GI tract which entered the systemic circulation after bypassing the liver. The model described metabolism of BPA to BPAG and BPAS in the gut with subsequent uptake into the hepatic portal vein as well as hepatic metabolism of BPA to BPAG and BPAS. Enterohepatic recirculation of BPA, BPAG and BPAS was also included.

FIGURE 2

The workflow. PBPK model evaluation was conducted using R (blue fill). This comprised sensitivity analysis of blood BPA following oral uptake, identification of marginal distributions using rejection sampling, calibration of model output using measured blood BPA concentrations followed by sensitivity analysis of in vivo target tissue dosimetry of liver (CVli) and kidney (CVki). Free concentrations of BPA in vitro were estimated from nominal concentrations using the VCBA (beige fill). Free BPA concentrations and the calibrated PBK model were input in the QIVIVE workflow to estimate in vivo dose responses (pale green fill). The latter were used to calculate a BMDL₁₀ using PROAST (pink fill).

FIGURE 3

PBK model for BPA was evaluated by simulating the data of Thayer et al. (2015). The panels show serum BPA, BPAG and BPAS from left to right for individual 1, body weight, 94 kg (upper panel), individual 3, body weight, 118 kg (middle panel) and individual 5, body weight, 86 kg (lower panel). The solid lines represent the posterior mode-fit and the shaded bands bounding the posterior mode-fit correspond to a numerically derived 95% credible interval.

FIGURE 4

Lowry plots of the most influential parameters governing tissue BPA in liver (CVli) (A) and kidney (CVki) (B). The Lowry plot shows the total effect of a parameter S_T, which is comprised the main effect S_M (green bar) and any interactions with other parameters Si (brown bar) given as a proportion of variance. The ribbon (light blue), representing variance due to parameter interactions, is bounded by the cumulative sum of the S_M (lower bound) and the minimum of the cumulative sum of the S_T (upper bound). The S_T for top three parameters for CVli (upper panel), and CVki (lower panel) accounted for 61 and 55% of variance, respectively.

FIGURE 5

Comparisons of concentration-time response profiles simulated in the rejection phase were run for each concentration. A typical example shows the target concentration of 0.011 mg/L bounded by two red lines representing the 7.5% range above and below the target concentration. (A) 5000 concentration-response profiles (upper panel), and (B) retained samples within a relative error of 7.5% (lower panel).

FIGURE 6

Typical predicted in vivo dose-response curves for PORALDOSE extrapolated from hepatic tissue concentration (CVli) for the in vitro datasets ATG_ERE_CIS_up (estrogen receptor activation) (A) and pregnane X receptor binding ATG_PXR_TRANS_up (B). The curves for the means only are shown. Benchmark dose values were calculated from such curves for lower and upper bounds (2.5 and 97.5%) of the credible intervals (Table 10).

FIGURE 7

Typical predicted in vivo dose-response curves for PORALDOSE extrapolated from kidney tissue concentration (CVki) for the in vitro datasets OT_ER_ERaERb_0480 (estrogen receptor activation) (A) and OT_ER_ERaERa_1440 (estrogen receptor activation) (B). The curves for the means only are shown. Benchmark dose values were calculated from such curves for lower and upper bounds (2.5 and 97.5%) of the credible intervals (Table 10).