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Featuring:

Amber Daniel: "Defined Approaches for Predicting GHS and EPA Eye Irritation Classification of Agrochemicals"
ASCCT 13th Annual Meeting Poster Award Recipient

Hung-Lin Kan, PhD: "Inferring potential effects of PFASs via a novel chemical-phenotype inference system ZFinfer"
ASCCT 13th Annual Meeting Travel Award Recipient

Ricardo Scheufen Tieghi: “DeTox: an In-Silico Alternative to Animal Testing for Predicting Developmental Toxicity Potential”
The Suzanne Fitzpatrick Student Travel Award Recipient at the ASCCT 13th Annual Meeting

A brief Q&A session will follow each presentation.

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ABSTRACTS

Defined Approaches for Predicting GHS and EPA Eye Irritation Classification of Agrochemicals: Certain regulatory frameworks require in vivo testing to determine hazard labeling for agrochemical products. We conducted prospective in vitro testing to develop defined approaches (DAs) to predict GHS and EPA eye irritation classifications without animal testing. We developed four DAs, comprising bovine corneal opacity and permeability with histopathology alone (“DA-BCOP+”), or combined with EpiOcular™, SkinEthic time-to-toxicity for liquids, or EyeIRR-IS (“DA-EO+”, “DA-TTL+”, and “DA-EyeIRR-IS+”, respectively). We used prospective data to apply these four DAs. For both GHS and EPA, we orthogonally analyzed concordance of classification and labeling predictions across the four DAs and historical rabbit data. For both classification systems, majority orthogonal concordance was achieved for 97% (28/29) of formulations, and all four DAs were equally or more protective of human health than the rabbit test. These DAs therefore have high utility for predicting GHS and EPA classifications of agrochemical formulations. This project was funded with federal funds from the NIEHS, NIH under Contract No. HHSN273201500010C.

Inferring potential effects of PFASs via a novel chemical-phenotype inference system ZFinfer: Zebrafish is a useful model organism for toxicological research due to their small size, fast reproduction, and genetic similarity to humans. However, as environmental pollutants increase, it becomes difficult to identify all hazards using zebrafish models alone. In-silico models can help prioritize chemicals for further experimental evaluation and provide insight into underlying mechanisms. Chemical-phenotype inference system for zebrafish (ZFinfer) is a tool that uses chemical-protein interaction data from the STITCH database and gene-phenotype annotation data from the Zebrafish Information Network (ZFIN) to derive potentially affected phenotypes for quick data gap filling. ZFinfer currently includes 419,328 chemicals, 23,180 zebrafish proteins, and 3,104 zebrafish phenotypes. The system was validated using 777 ToxCast chemicals and 51 priority pollutants from the USEPA. The inference results demonstrated a sensitivity of 0.72 in critical morphological endpoints and a 93% rediscovery rate in the effect groups of known toxicity records in the ECOTOX knowledgebase. ZFinfer can help fill data gaps in environmental contaminants research, such as per- and poly-fluoroalkyl substances (PFAS), which are of concern due to their persistence and harmful effects on humans and the environment. For 164 PFAS chemicals, ZFinfer identified 5,188 potentially affected phenotypes. Additionally, ZFinfer can predict the affected phenotypes of unknown compounds based on chemical similarity. Using a Tanimoto similarity threshold of 0.65, ZFinfer predicted 1,917,718 chemical-protein interactions and identified 302,575 potentially affected phenotypes for 5,032 PFAS chemicals. ZFinfer could be used to prioritize chemicals for further evaluation, and it can be employed in drug discovery and environmental chemical hazard regulations. 

DeTox: an In-Silico Alternative to Animal Testing for Predicting Developmental Toxicity Potential: 

Background: Understanding potential developmental toxicity hazards associated with pharmaceutical and personal care products is vital for healthy pregnancies. These hazards can be predicted from chemical structures using Quantitative Structure-Activity Relationship (QSAR) models; however, developing reliable models is challenging due to the complexity of this endpoint.

Objectives: This study aims to collect and curate a database of compounds, classify them according to developmental toxicity potential, develop and validate QSAR models for predicting prenatal developmental toxicity, and implement these models via an online platform to support improved regulatory assessments.

Methods: By aggregating and curating data from the Food and Drug Administration (FDA), Teratogen Information System (TERIS), and select independent studies, we created the largest publicly available dataset of compounds annotated as developmental toxicants or non-toxicants.