Integration of Life-Stage Physiologically Based Pharmacokinetic Models with Adverse Outcome Pathways and Environmental Exposure Models to Screen for Environmental Hazards

Abstract

A computational framework was developed to assist in screening and prioritizing chemicals based on their dosimetry, toxicity, and potential exposures. The overall strategy started with contextualizing chemical activity observed in high-throughput toxicity screening (HTS) by mapping these assays to biological events described in Adverse Outcome Pathways (AOPs). Next, in vitro to in vivo (IVIVE) extrapolation was used to convert an in vitro dose to an external exposure level, which was compared with potential exposure levels to derive an AOP-based margins of exposure (MOE). In this study, the framework was applied to estimate MOEs for chemicals that can potentially cause developmental toxicity following a putative AOP for fetal vasculogenesis/angiogenesis. A physiologically based pharmacokinetic (PBPK) model was developed to describe chemical disposition during pregnancy, fetal, neonatal, and infant to adulthood stages. Using this life-stage PBPK model, maternal exposures were estimated that would yield fetal blood levels equivalent to the chemical concentration that altered in vitro activity of selected HTS assays related to the most sensitive vasculogenesis/angiogenesis putative AOP. The resulting maternal exposure estimates were then compared with potential exposure levels using literature data or exposure models to derive AOP-based MOEs.

Keywords: AOPs; PBPK; developmental toxicology; environmental toxicology; life-stage.

FIG. 1

A schematic depicting flow of information to estimate an AOP-based Margin of Exposure. The AOP-derived MOE is the ratio of the PBPK model-derived in vivo exposure needed for the chemical to illicit biological activity (as described by the AOP) to the chemical’s environmental exposure estimate. The steps needed for the derivation of the in vivo exposure levels are depicted using the solid lines. The dashed lines represent steps taken to estimate environmental exposure levels. An AOP is used to select chemicals and determine their toxicity potential using in vitro HTS data. The chemicals are queried for any documented tissue levels (eg, blood or urine in literature or using NHANES data). If this data are available, the PBPK model for the mother is used to reconstruct Life-Time exposure (ENV_EXPOSURE) (as was shown for PFOS). If this data is not available, elaborate exposure models such as SHEDS-HT is used to estimate environmental exposures (ENV_EXPOSURE). Similarly, using the in vitro levels in target tissue (eg, fetal blood), the AOP_EXPOSURE is constructed by the PBPK model. The AOP-based Margin of Exposure is estimated as the ratio of the AOP_EXPOSURE to ENV_EXPOSURE.

FIG. 2

Schematic of the Life-Stage PBPK Model compartments and time line. Growing child is modeled using a PBPK model until conception (assumed at age 25). The PBPK model is then converted to a pregnancy one and included a fetal sub-model. The fetal sub-model illustrates the directions of blood flow as the chemical(s) is distributed to fetal tissues with descriptions for shunts (ductus venosus DV, and ductus arteriosus DA) and the foramen ovale (FO) in the heart. Influence of the shunts and FO is reduced until they disappear as the fetus is born and the submodel continues to describe a neonate.

FIG. 3

Mathematical equations fit to data describing growth of tissues during pregnancy. Data were obtained from the ICRP (1989).

FIG. 4

Mathematical equations fit to data describing a, fetal volume; b, fetal blood volume.

FIG. 5

Mathematical equations (a*Vfet^b) fit to data describing fetal tissue volumes, where Vfet is the volume of fetus, for a, liver (a = 0.05, b = 0.99); b, fat (a = 0.01, b = 3.22); c, kidney (a = 0.01. b = 0.9); d, brain (a = 0.15, b = 0.77); and e, lung (a = 0.024, b = 0.752). Data were refit from Luecke et al. (1995).

FIG. 6

Mathematical equations fit to data describing fetal blood flow for a, heart left ventricular; b, ductus arteriosus; c, heart right ventricular; d, portal vein; and e, kidney.

FIG. 7

Determination of in vitro clearance rates by fitting data to equations that included descriptions for intra cellular diffusion for a, Fluazinam; b, Triclosan; and c, Pyridaben. The dashed and solid lines are fitted lines to data (given in circles) at the 1 and 10 µM for each chemical initial media concentrations, respectively. The estimated in vitro clearance rates were 0.012, 0.59, 0.01 µl/min per million cells for Fluazinam, Triclosan and Pyridaben, respectively.

FIG. 8

Temporal descriptions of the most sensitive parameters to blood levels of the maternal PBPK sub-model.

FIG. 9

Temporal descriptions of the most sensitive parameters to blood levels of the fetal submodel.

FIG. 10

Ontogeny profile for CYP1A2. Data were obtained from Hines (2012) and interpolated to provide a function of age as described by the solid line for use in the life-stage PBPK model.

FIG. 11

Impact of including CYP1A2 ontogeny function (dashed line) in comparison to direct scaling using liver hepatocyte content (solid line) on predicted blood levels during life-stages from neonate to adulthood.

FIG. 12

Life-time PFOS exposure (mg/kg/d) was estimated by calibrating the Life-Stage model using blood levels data for near-birth mothers.

FIG. 13

Using the estimated life-time exposure, the Life-Stage model was used to predict cord blood levels in comparison to PFOS data.

FIG. 14

The Life-Stage model prediction of maternal blood levels for PFOS during child growth, pregnancy and through lactation.