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In Silico to In Vitro and In Vivo

Description/Rationale and Research Approach:

The focus of this research is to develop predictive tools for EPA, especially for hazard characterization and risk assessment of chemicals for which little or no test data is available. These tools will improve the ability to extrapolate the results of in vitro assays to the corresponding in vivo outcomes by increasing our knowledge of the critical parameters that influence the activity of chemicals in these systems. This research also addresses the Agency need for tools to prioritize and rank chemicals for testing in the current Endocrine Disruptor Screening Program (EDSP).

This research task takes an approach utilizing quantitative structure activity relationship (QSAR) principles for understanding the chemical parameters that influence the biological or toxicological activity of chemicals. This approach can be applied to understanding how a chemical’s structure is related to its physical-chemical properties and to the chemical’s biological activity.  In developing these relationships, this project supports EPA’s need to predict the potential hazard of existing and new chemicals with limited or no toxicity or effects data.

The results of this effort will be computational models including Expert Systems.  These Expert Systems are automated rule-based decision trees that can be used to predict which chemicals have the potential to disrupt endocrine systems. This is done by testing key chemicals within a chemical class to represent others, determining what is similar about the chemical structures and properties that explain their biological activity, and writing rules that help predict the activity of similar but untested chemicals that belong to the same category. The EPA program offices use these tools to decide which, of the hundreds of chemicals on Agency chemical lists, should be evaluated first because they are most likely to disrupt one of these endocrine-mediated pathways.

Learn more about MED's Estrogen Receptor (ER) Expert Systems for Chemical Priorization project.

MED Scientists:

Jeffrey Denny
Michael Hornung
Richard Kolanczyk
John Nichols
Patricia Schmieder
Jose Serrano
Mark Tapper


Jacobs, M.N., S.C. Laws, K. Willett, P.K. Schmieder, J. Odum, and T.F. Bovee. 2013. In vitro metabolism and bioavailability tests for endocrine active substances: What is needed next for regulatory purposes?  Altex 30:331-351.

Paul, K.B., Hedge, J.M.,  Macherla, C.,  Filer, D.,  Burgess, E.,  Simmons, S.O.,  Crofton, K.M.,  Hornung, M.W. 2013. Cross-species analysis of thyroperoxidase inhibition by xenobiotics demonstrates conservation of response between pig and rat. Toxicology, 312, 97-107.

Scholz, S., P. Renner, S. Belanger, F. Busquet, R. Davi, B. Demeneix, J.S. Denny, M. Leonard, M. McMaster,  D.L. Villeneuve, and M. Embry. 2013. Alternatives to in vivo tests to detect endocrine disrupting chemicals (EDCs) in fish and amphibians — screening for estrogen, androgen and thyroid hormone disruption. Critical Reviews in Toxicology 43:45-72.

Expected Products:




Sep 30, 2014

Chemical class-based Expert Systems (ES) used to prioritize endocrine disruption (e.g., estrogen receptor (ER) mediated) potential of food/non-food use pesticide inerts and antimicrobials. Michael Hornung

Sep 30, 2016

A thyroid hormone synthesis inhibition Expert System based upon a thyroid peroxidase inhibition Adverse Outcome Pathway (AOP) for prioritizing chemicals with endocrine disrupting potential. Michael Hornung

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