Rapid Screening of Human Transthyretin Disruptors through a Tiered in Silico Approach

Abstract

In silico models have been considered as a promising tool that can be used to screen and identify potential endocrine disrupting chemicals (EDCs) effectively and assist finally realizing the sustainable assessments and management of EDCs. However, most of the current modeling efforts are focused on constructing predictive models for the end points related to nuclear receptors. Considerable actions are needed to model the interfering effects of compounds on the nonreceptor mediated targets, e.g., hormone transporters. Here, a tiered screening approach was proposed and derived based on classification models and quantitative structure–activity relationship (QSAR) models for identifying potential disruptors of human transthyretin (hTTR), a critical thyroid hormone transporter. Regarding classification models, the sensitivity, specificity, and predictive accuracy of all five optimum models for corresponding training sets and validation sets were >0.800, implying that they had good classification performance. Concerning quantitative prediction, k-nearest neighbor and multiple linear regression QSAR models were developed. The statistical parameters of all constructed QSAR models met the acceptable criteria (e.g., leave-one out cross validation Q 2 (Q 2 LOO) and bootstrapping coefficient (Q 2 BOOT) > 0.600, determination coefficient (R 2) > 0.700, externally explained variance (Q 2 EXT), concordance correlation coefficient (CCC) > 0.850), indicating that those QSAR models had good robustness, goodness-of-fit, and external prediction ability. Finally, an extra data set with 38 data was used to evaluate the predictive performance of the tiered approach. The assessment results showed that more than 65% of compounds can be classified correctly. Thus, the tiered approach proposed here can be employed to distinguish whether a given compound within the applicability domain of corresponding models is a potential hTTR disruptor or not.

Graph Attention Network Model with Defined Applicability Domains for Screening PBT Chemicals

“…The AD FP method that has been proved to be successful in AD characterizations for QSAR models on peroxisome proliferator-activated receptor γ activities, human transthyretin disruptors, and mitochondrial toxicity was adopted first. The AD FP divides chemical spaces into different communities based on pairwise similarities ( S ) between all molecules and sets a threshold S cutoff to exclude molecules with low S values from other molecules in the same community.…”

Potential Disrupting Effects of Wastewater-Derived Disinfection Byproducts on Chinese Rare Minnow (Gobiocypris rarus) Transthyretin: An In Vitro and In Silico Study

“…E HOMO-adj is the chemical form-adjusted energies of the highest occupied molecular orbital, which is usually employed to describe the hydrogen bonding interactions and electrostatic interactions with TTR. 62 A given substance with a higher value of E HOMO-adj may form stronger hydrogen bonding interactions and electrostatic interactions with TTR. The compounds with E HOMO-adj values >−0.24 were active CrmTTR binders (Table S9).…”

Research Progress of the Endocrine-Disrupting Effects of Disinfection Byproducts

“…In this regard, EDCs are recognized as a serious threat to human health and the environment. In order to minimize the adverse health and environment impacts of EDCs, it is urgent to identify and screen potential EDCs from artificial chemical substances and unintentional production chemicals (e.g., DBPs) [ 55 ]. Actually, despite more than 800 individual DBPs having been identified in previous studies, only a few have been assessed for their potential endocrine-disrupting potency.…”