Tiered approaches for screening and prioritizing chemicals through integration of pharmacokinetics and exposure information with in vitro dose-response data

Authors: Leonard JA, Tan YM
Publication: Computational Toxicology


With the advent of high throughput (HT) methodologies to evaluate hazard and exposure concerns more rapidly and for a greater number of chemicals at a time, in vivo toxicity data is lacking for a large number of chemicals currently on market or in production. One of the challenges associated with use of non-animal data involves extrapolating concentrations with the ability to perturb a molecular target in an in vitro environment to concentrations relevant to the organism as a whole, which requires consideration of exposure and pharmacokinetic (PK) behaviors (i.e., absorption, distribution, metabolism, and elimination [ADME]) in the interpretation of in vitro information. Simple PK and more complex physiologically based pharmacokinetic (PBPK) models are useful tools that allow for extrapolating an internal biologically-effective dose to an external concentration able to elicit that toxicological outcome in vivo. While such models have traditionally been parameterized with in vivo human and non-human PK information, generation of this data is unable to keep pace with the more rapid introduction of new chemicals to market. As a result, model parameterization is relying more and more upon data generated through in vitro and in silico means. The development and application of PK and PBPK models can depend on a number of factors, such as the needs of the investigator, the scientific challenge in question, and the availability of data. Taking these factors into consideration, a tiered approach can be taken to evaluate chemical risk. This review discusses such a tiered approach that spans from qualitative screening of data-poor chemicals to quantitative modeling and prioritization of data-rich chemicals, as well as how HT methods can be used to parameterize PK and PBPK models. As confidence increases in the parameterization of models with non-animal data, their utility in risk-based screening practices will be realized.