Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?

Abstract

Computational models have earned broad acceptance for assessing chemical toxicity during early stages of drug discovery or environmental safety assessment. The majority of publicly available QSAR toxicity models have been developed for datasets including mostly drugs or drug-like compounds. We have evaluated and compared chemical spaces occupied by cosmetics, drugs, and pesticides, and explored whether current computational models of toxicity endpoints can be universally applied to all these chemicals. Our analysis of the chemical space overlap and applicability domain (AD) of models built previously for twenty different toxicity endpoints showed that most of these models afforded high coverage (>90%) for all three classes of compounds analyzed herein. Only T. pyriformis models demonstrated lower coverage for drugs and pesticides (38% and 54%, respectively). These results show that, for the most part, historical QSAR models built with data available for different toxicity endpoints can be used for toxicity assessment of novel chemicals irrespective of the intended commercial use; however, the AD restriction is necessary to assure the expected prediction accuracy. Local models may need to be developed to capture chemicals that appear as outliers with respect to global models.

Keywords: Chemical space; Cosmetics; Drugs; Pesticides; Prediction; QSAR models.

Figure 1

Distribution of investigated compounds on cosmetics, drugs, and pesticides.

Figure 2

A) Chemical space of investigated compounds defined by ClogP and MW. B) Chemical space of investigated compounds in barycentric coordinates obtained from 2D DRAGON descriptors. Shadowed area represent the chemical space occupied by compounds from datasets used to generate current toxicity QSAR models. Two outliers (coordinates 1631, 160 and 960, −794) from the training sets of QSAR models are not shown.

Figure 3

Results of cluster analysis of 1,000 compounds including cosmetics, drugs, and pesticides. Heatmap and dendrogram of the distance matrix are both colored according to structural similarity (blue/violet = similar; yellow/red = dissimilar).

Figure 4

A) Distribution of cosmetics, drugs, pesticides, and T. pyriformis dataset (644 compounds) in chemical space. Four clusters of highly similar compounds are highlighted by black circles and numbered. B) Distribution of Tanimoto coefficients between industrial compounds and their nearest neighbor in the T. pyriformis dataset. C) Representative compounds for clusters 1–4.