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Predicting Endocrine Disruption Using Conformal Prediction: A Prioritization Strategy to Identify Hazardous Chemicals with Confidence
Chemistry Department, Umeå University, 901 87 Umeå, Sweden.
Örebro University, School of Science and Technology. Department of Computer and Systems Sciences, Stockholm University, Box 7003, 164 07 Kista, Sweden; Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 75 124 Uppsala, Sweden. (MTM Research Centre)ORCID iD: 0000-0003-3107-331X
Chemistry Department, Umeå University, 901 87 Umeå, Sweden.
2023 (English)In: Chemical Research in Toxicology, ISSN 0893-228X, E-ISSN 1520-5010, Vol. 36, no 1, p. 53-65Article in journal (Refereed) Published
Abstract [en]

Receptor-mediated molecular initiating events (MIEs) and their relevance in endocrine activity (EA) have been highlighted in literature. More than 15 receptors have been associated with neurodevelopmental adversity and metabolic disruption. MIEs describe chemical interactions with defined biological outcomes, a relationship that could be described with quantitative structure-activity relationship (QSAR) models. QSAR uncertainty can be assessed using the conformal prediction (CP) framework, which provides similarity (i.e., nonconformity) scores relative to the defined classes per prediction. CP calibration can indirectly mitigate data imbalance during model development, and the nonconformity scores serve as intrinsic measures of chemical applicability domain assessment during screening. The focus of this work was to propose an in silico predictive strategy for EA. First, 23 QSAR models for MIEs associated with EA were developed using high-throughput data for 14 receptors. To handle the data imbalance, five protocols were compared, and CP provided the most balanced class definition. Second, the developed QSAR models were applied to a large data set (∼55,000 chemicals), comprising chemicals representative of potential risk for human exposure. Using CP, it was possible to assess the uncertainty of the screening results and identify model strengths and out of domain chemicals. Last, two clustering methods, t-distributed stochastic neighbor embedding and Tanimoto similarity, were used to identify compounds with potential EA using known endocrine disruptors as reference. The cluster overlap between methods produced 23 chemicals with suspected or demonstrated EA potential. The presented models could be utilized for first-tier screening and identification of compounds with potential biological activity across the studied MIEs.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2023. Vol. 36, no 1, p. 53-65
National Category
Pharmacology and Toxicology
Identifiers
URN: urn:nbn:se:oru:diva-102811DOI: 10.1021/acs.chemrestox.2c00267ISI: 000903383200001PubMedID: 36534483Scopus ID: 2-s2.0-85144410434OAI: oai:DiVA.org:oru-102811DiVA, id: diva2:1720635
Funder
European Commission, 825759 825489Swedish Foundation for Strategic Research, DIA 2018/11Available from: 2022-12-20 Created: 2022-12-20 Last updated: 2024-01-16Bibliographically approved

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