Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors

Abstract

The intrinsic metabolic clearance rate (Clint) and the fraction of the chemical unbound in plasma (f up) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure–activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

“…However, new efforts are examining systematic approaches to metals and PK models that generally allow for chemical transformation, including cycling [ 255 ]. In addition, for PK models, the rapid growth in informatics has allowed the development of many approaches relating chemical structure features to important properties, including in vitro PK measurements [ 41 , 78 , 84 ]. Quantitative structure–property relationships (QSPRs) are rapidly developing, and both new models and consensus predictors based on multiple models should be expected [ 79 , 256 ].…”

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

“…In the study of prediction of changes in cell activity, high-throughput cellular phenotype images or chemical response signatures were learned to predict cellular phenotypes or activity patterns that could be linked to in vivo outcomes. , Also, recently, toxicokinetics prediction studies to extrapolate in vivo toxicity based on in vitro HTS data are being conducted. In these studies, two key parameters related to toxicokinetics, intrinsic clearance rate (Cl int ) and the fraction of the chemical unbound in plasma (fub), were predicted. , However, studies using data other than chemical structure information have not been explored much, so more efforts are needed for research using these data to predict the toxicity of chemicals.…”

Scientific Opinion of the Scientific Panel on Plant Protection Products and their Residues (PPR Panel) on testing and interpretation of comparative in vitro metabolism studies

“…high‐throughput techniques, in vitro , OMICs and in silico methods such as PBK and QIVIVE models) as currently investigated worldwide (OECD, US EPA, EFSA). Relevant examples include large open‐source platforms such as the US‐EPA CompTox Chemicals Dashboard, ECHA’s REACH database and EFSA’s OpenFoodTox and new QSAR models predicting TK and TD properties of chemicals such as the VEGA hub and the OPERA suite from US‐EPA (Dawson et al, 2021; Williams et al, 2021). In practice, case studies could explore how to integrate NAMs for a range of regulatory contexts (pesticide active substances, food additives, contaminants) using the existing OECD Harmonised Template (OHT) 201 for structuring mechanistic data and the OHT 58 for kinetic data to implement these tools in a structured and transparent manner.…”