EPA Science Inventory

Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering

Citation:

Martin, T. Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, 27(1):17-30, (2016).

Description:

In this study, hierarchical clustering classification models were developed to predict in vitro and in vivo oestrogen receptor (ER) activity. Classification models were developed for binding, agonist, and antagonist in vitro ER activity and for mouse in vivo uterotrophic ER binding. In vitro classification models yielded balanced accuracies ranging from 0.65 to 0.85 for the external prediction set. In vivo ER classification models yielded balanced accuracies ranging from 0.72 to 0.83. If used as additional biological descriptors for in vivo models, in vitro scores were found to increase the prediction accuracy of in vivo ER models. If in vitro activity was used directly as a surrogate for in vivo activity, the results were poor (balanced accuracy ranged from 0.49 to 0.72). Under-sampling negative compounds in the training set was found to increase the coverage (fraction of chemicals which can be predicted) and increase prediction sensitivity.

Purpose/Objective:

The purpose of this paper is to develop in silico models for in vitro estrogen receptor activity for use in alternatives assessment tools such as the Sustainability Dashboard

URLs/Downloads:

http://dx.doi.org/10.1080/1062936X.2015.1125945   Exit

Record Details:

Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Start Date: 01/19/2016
Completion Date: 01/19/2016
Record Last Revised: 08/31/2016
Record Created: 08/31/2016
Record Released: 08/31/2016
OMB Category: Other
Record ID: 325850

Organization:

U.S. ENVIRONMENTAL PROTECTION AGENCY

OFFICE OF RESEARCH AND DEVELOPMENT

NATIONAL RISK MANAGEMENT RESEARCH LABORATORY

SUSTAINABLE TECHNOLOGY DIVISION

CLEAN PROCESSES BRANCH