Computational Models of Chemical Activity

Using structural data to generate activity predictions for new or poorly characterized chemicals can help researchers and regulators make decisions about further testing needs.

NICEATM provides support for continued development of the Open (Quantitative) Structure-activity/property Relationship App (OPERA). OPERA is a free and open-source/open-data suite of QSAR models providing predictions on physicochemical properties, environmental fate and toxicity endpoints.

Recent updates to OPERA were discussed in a poster (Mansouri et al., Computational Toxicology I session) presented at the 2023 annual meeting of the Society of Toxicology (SOT). Another SOT poster (Lee et al., Bioinformatics session) described application of computational models to predict carcinogenicity and hepatotoxicity potential.

List of projects for chemical activity computer models
Project	Description	Publication
Open-source quantitative structure-property relationship tools	NICEATM and collaborators at EPA developed OPERA, which uses molecular structures to predict the physicochemical features for a wide range of substances.	Mansouri K, et al. OPERA models for predicting physicochemical properties and environmental fate endpoints. J Cheminformatics. 2018 10:10.
QSAR models to predict physicochemical properties, environmental fate and toxicological endpoints	OPERA was updated to include QSAR models to predict physicochemical properties such as lipid-aqueous dissociation coefficient (logD) and acid dissociation constant (pKa).	Mansouri K, et al. Open-source QSPR models for pKa prediction using multiple machine learning approaches. J Cheminformatics. 2019 11:60.
QSAR models to predict critical parameters for in vitro to in vivo extrapolation (IVIVE)	To support an open-source workflow for IVIVE, NICEATM developed QSAR models to predict properties, such as human plasma fraction unbound and hepatic intrinsic clearance, that affect how substances behave in biological systems. A 2022 update of OPERA added a model for intestinal cell permeability.	Property predictions available via OPERA and used in ICE
QSAR models to screen for potential skin sensitizers	NICEATM and collaborators at the University of North Carolina-Chapel Hill (UNC-CH) developed QSAR models of human data that can either be combined with or used instead of animal data to screen for potential skin sensitizers.	Alves VM, et al. QSAR models of human data can enrich or replace LLNA testing for human skin sensitization. Green Chem. 2016 Oct;18:6501-6515. Borba JVB, et al. Pred-Skin: a web portal for accurate prediction of human skin sensitizers. Chem Res Toxicol. 2021; 34(2):258-267.
Open-source tools for predicting estrogen and androgen receptor pathway activity	NICEATM created open-source versions of published computational models to predict activity relevant to endocrine disruption. Predictions from these models are available through NICEATM's Integrated Chemical Environment (ICE) resource.	Judson RS, et al. Integrated model of chemical perturbations of a biological pathway using 18 in vitro high-throughput screening assays for the estrogen receptor. Toxicol Sci. 2015 Nov;148(1):137-154. Kleinstreuer NC, et al. Development and validation of a computational model for androgen receptor activity. Chem Res Toxicol. 2017 Apr 17;30(4):946–964.
International collaborations to predict endocrine activity	NICEATM and EPA ran global collaborations to leverage the expertise of the world-wide modeling community to predict estrogenic activity (CERAPP) and anti-androgenic activity (CoMPARA). Predictions from these models are available through ICE and will be available through the EPA Chemistry Dashboard. The consensus models from CERAPP and CoMPARA were also added to the standalone OPERA application.	Mansouri K, et al. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect. 2016 Jul 124:1023–1033. Mansouri K, et al. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. Environ Health Perspect. 2020.
International collaborations to predict acute toxicity	NICEATM and EPA ran a global collaboration to leverage the expertise of the world-wide modeling community to predict acute oral systemic toxicity (CATMoS). Predictions from CATMoS are available through ICE and will be available through the EPA Chemistry Dashboard. The consensus models from CATMoS were also added to the standalone OPERA application. NICEATM is currently organizing a similar project to develop models to predict acute inhalation toxicity.	Kleinstreuer NC, et al. Predictive models for acute oral systemic toxicity: A workshop to bridge the gap from research to regulation. Computational Toxicol. 2018 8:21-24. Mansouri K, et al. CATMoS: Collaborative Acute Toxicity Modeling Suite. Environ Health Perspect. 2021 Apr 129:47013.
Statistical models for classification of eye irritants	NICEATM developed statistical models that could potentially be used to classify chemicals as eye corrosives, irritants, or non-irritants according to EPA and GHS hazard classification endpoints. Results suggest that these models are useful for screening substances for eye irritation potential.	Sedykh et al. Mixtures-inclusive in silico models of ocular toxicity based on United States and international hazard categories. Chem Res Toxicol. 2022 35(6):992-1000.
QSAR prediction of assay interference for specific technology platforms	NICEATM and NIEHS scientists developed InterPred, a web tool to predict chemical autofluorescence and luminescence interference.	Borrel et al. InterPred: a webtool to predict chemical autofluorescence and luminescence interference. Nucleic Acids Res. 2021 Jul 48(W1):W586-W590.
QSAR prediction of acute systemic and topical toxicity	NICEATM and collaborators at UNC-CH, Duke University, and the Federal University of Goias in Brazil developed STopTox, a comprehensive collection of computational models that can predict the toxicity hazard of small organic molecules.	Borba et al. STopTox: an in silico alternative to animal testing for acute systemic and topical toxicity endpoints. Environ Health Perspect. 2022 Feb.
Structure-based models to predict cardiotoxicity	NICEATM and collaborators at NCATS developed QSAR prediction models for effects on the hERG potassium channel. The hERG channel plays an important role in cardiac rhythm regulation.	Krishna et al. High-throughput chemical screening and structure-based models to predict hERG inhibition. Biology. 2022 11(2):209.
Development of additional QSAR models	NICEATM and collaborators are developing or applying QSAR models to predict the following: Substrate selectivity for glucuronidation. Dermal irritation. Fish acute toxicity (LC50). Cancer hallmarks and key characteristics of carcinogens. Mechanisms of developmental toxicity. Mechanisms of carcinogenicity and drug-induced liver injury.	In progress