High-throughput PBTK models for in vitro to in vivo extrapolation

Abstract

Introduction: Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals.

Areas covered: This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data.

Expert opinion: HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.

Keywords: Generic physiologically based toxicokinetic models; high-throughput; in vitro to in vivo extrapolation; modeling software tools; open source tools; physiologically-based toxicokinetics; toxicokinetics.

Figure 1.. In vitro to in vivo extrapolation (IVIVE).

We perform that IVIVE using toxicokinetic (TK) modeling. TK models relate external dose to internal body concentration by describing “what the body does to the chemical”: absorption, distribution, metabolism, and excretion (ADME). For IVIVE of in vitro bioactive concentrations, we assume that bioactivity would occur in the body at a concentration equal to an in vitro bioactive concentration, and use TK modeling in reverse (that is, reverse dosimetry) to find the “equivalent dose” – an external dose that would produce the specified body concentration. While IVIVE broadly includes any use of in vitro data to predict phenomena in vivo, it is useful to distinguish between TK IVIVE (that is, the use of in vitro data to predict ADME) and toxicodynamic (TD) IVIVE, which includes the use of in vitro data to predict toxic effects in vivo.

Figure 2.. Reverse Dosimetry Toxicodynamic IVIVE.

Equivalent external dose is determined by solving the TK model in reverse by deriving the external dose (that is, TK model input) that produces a specified internal concentration (that is, TK model output). Reverse dosimetry and IVIVE using HTTK relies on the linearity of the models. We calculate a scaling factor to relate in vitro concentrations (μM) to administered equivalent doses (AED). The scaling factor is the inverse of the steady state plasma concentration (C_ss) predicted for a 1 mg/kg/day exposure dose rate. We use Monte Carlo to simulate variability and propagate uncertainty to calculate an upper 95th percentile C_ss,95 for individuals who get higher plasma concentrations from the same exposure.

Figure 3.. Schematic of httk R package.

Dashed boxes/arrows represent parameters that can be probabilistic in a Monte Carlo simulation.

Figure 4.. Example of a generic gas inhalation PBTK model. A single (generic) physiological structure is used for all appropriate chemicals.

Chemical-specific parameters can be predicted from a combination of in vitro measurements and QSARs. Q_rest is defined as the difference between Q_cardiac and the flow to the liver, kidney, and gut to preserve mass-balance.

Figure 5.. High-throughput chemical risk prioritization to rapidly prioritize large numbers of chemicals.

Evaluation of potential risk by comparing distributions of dose with potentially adverse effect and potential exposure. For TK we can account for both uncertainty and variability. If the hazard and exposure distributions are far apart, as shown on the left, then potential risk is lower – it means exposure probably doesn’t reach a level where there would be an adverse effect. If the lower tail of the hazard distribution starts to overlap the upper tail of the exposure distribution, as shown in the middle, then potential risk is medium. And if the hazard and exposure distributions totally overlap, as shown on the right, then potential risk is higher – it means that exposure is more likely to reach a level where there might be an adverse effect.