Evaluation of TOPKAT, Toxtree, and Derek Nexus in Silico Models for Ocular Irritation and Development of a Knowledge-Based Framework To Improve the Prediction of Severe Irritation.

Assessment of ocular irritation is an essential component of any risk assessment. A number of (Q)SARs and expert systems have been developed and are described in the literature. Here, we focus on three in silico models (TOPKAT, BfR rulebase implemented in Toxtree, and Derek Nexus) and evaluate their performance using 1644 in-house and 123 European Centre for Toxicology and Ecotoxicology of Chemicals (ECETOC) compounds with existing in vivo ocular irritation classification data. Overall, the in silico models performed poorly. The best consensus predictions of severe ocular irritants were 52 and 65% for the in-house and ECETOC compounds, respectively. The prediction performance was improved by designing a knowledge-based chemical profiling framework that incorporated physicochemical properties and electrophilic reactivity mechanisms. The utility of the framework was assessed by applying it to the same test sets and three additional publicly available in vitro irritation data sets. The prediction of severe ocular irritants was improved to 73-77% if compounds were filtered on the basis of AlogP_MR (hydrophobicity with molar refractivity). The predictivity increased to 74-80% for compounds capable of preferentially undergoing hard electrophilic reactions, such as Schiff base formation and acylation. This research highlights the need for reliable ocular irritation models to be developed that take into account mechanisms of action and individual structural classes. It also demonstrates the value of profiling compounds with respect to their chemical reactivity and physicochemical properties that, in combination with existing models, results in better predictions for severe irritants.

Evaluation of OASIS QSAR Models Using ToxCast™ in Vitro Estrogen and Androgen Receptor Binding Data and Application in an Integrated Endocrine Screening Approach.

BACKGROUND: Integrative testing strategies (ITSs) for potential endocrine activity can use tiered in silico and in vitro models. Each component of an ITS should be thoroughly assessed. OBJECTIVES: We used the data from three in vitro ToxCast™ binding assays to assess OASIS, a quantitative structure-activity relationship (QSAR) platform covering both estrogen receptor (ER) and androgen receptor (AR) binding. For stronger binders (described here as AC50 < 1 µM), we also examined the relationship of QSAR predictions of ER or AR binding to the results from 18 ER and 10 AR transactivation assays, 72 ER-binding reference compounds, and the in vivo uterotrophic assay. METHODS: NovaScreen binding assay data for ER (human, bovine, and mouse) and AR (human, chimpanzee, and rat) were used to assess the sensitivity, specificity, concordance, and applicability domain of two OASIS QSAR models. The binding strength relative to the QSAR-predicted binding strength was examined for the ER data. The relationship of QSAR predictions of binding to transactivation- and pathway-based assays, as well as to in vivo uterotrophic responses, was examined. RESULTS: The QSAR models had both high sensitivity (> 75%) and specificity (> 86%) for ER as well as both high sensitivity (92-100%) and specificity (70-81%) for AR. For compounds within the domains of the ER and AR QSAR models that bound with AC50 < 1 µM, the QSAR models accurately predicted the binding for the parent compounds. The parent compounds were active in all transactivation assays where metabolism was incorporated and, except for those compounds known to require metabolism to manifest activity, all assay platforms where metabolism was not incorporated. Compounds in-domain and predicted to bind by the ER QSAR model that were positive in ToxCast™ ER binding at AC50 < 1 µM were active in the uterotrophic assay. CONCLUSIONS: We used the extensive ToxCast™ HTS binding data set to show that OASIS ER and AR QSAR models had high sensitivity and specificity when compounds were in-domain of the models. Based on this research, we recommend a tiered screening approach wherein a) QSAR is used to identify compounds in-domain of the ER or AR binding models and predicted to bind; b) those compounds are screened in vitro to assess binding potency; and c) the stronger binders (AC50 < 1 µM) are screened in vivo. This scheme prioritizes compounds for integrative testing and risk assessment. Importantly, compounds that are not in-domain, that are predicted either not to bind or to bind weakly, that are not active in in vitro, that require metabolism to manifest activity, or for which in vivo AR testing is in order, need to be assessed differently. CITATION: Bhhatarai B, Wilson DM, Price PS, Marty S, Parks AK, Carney E. 2016. Evaluation of OASIS QSAR models using ToxCast™ in vitro estrogen and androgen receptor binding data and application in an integrated endocrine screening approach. Environ Health Perspect 124:1453-1461; http://dx.doi.org/10.1289/EHP184.