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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 will be presented in a poster (Mansouri et al., Computational Toxicology I session) at the 2023 annual meeting of the Society of Toxicology (SOT). Another SOT poster (Lee et al., Bioinformatics session) will describe application of computational models to predict carcinogenicity and hepatotoxicity potential. View complete list of NICEATM activities at SOT.

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.
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).
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.
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.
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.
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.
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.  
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.
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.
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. 
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