Science Inventory

Multivariate Models for Prediction of Human Skin Sensitization Hazard.

Citation:

Strickland, J., Q. Zang, M. Paris, D. Lehmann, D. Allen, N. Choksi, J. Matheson, A. Jacobs, W. Casey, AND N. Kleinstreuer. Multivariate Models for Prediction of Human Skin Sensitization Hazard. JOURNAL OF APPLIED TOXICOLOGY. John Wiley & Sons, Ltd., Indianapolis, IN, 37(3):347-360, (2017).

Impact/Purpose:

The Interagency Coordinating Committee on the Development of Alternative Methods (ICCVAM) is composed of representatives from 15 U.S. Federal regulatory and research agencies that use toxicological safety testing information. One of the top priorities of ICCVAM is the development and evaluation of non-animal approaches to identify potential skin sensitizers. This work describes an ongoing effort to an integrated decision strategy for predicting human skin sensitization hazard.

Description:

One of the lnteragency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays - the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens TM assay - six physicochemical properties and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches , logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h-CLAT and read-across; (2) DPRA, h-CLAT, read-across and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy 88%), any of the alternative methods alone (accuracy 63-79%) or test batteries combining data from the individual methods (accuracy 75%). These results suggest that computational methods are promising tools to identify effectively the potential human skin sensitizers without animal testing. Published 2016. This article has been contributed to by US Government employees and their work is in the public doma in in the USA.

URLs/Downloads:

http://dx.doi.org/10.1002/jat.3366   Exit

Record Details:

Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Product Published Date: 03/01/2017
Record Last Revised: 04/09/2018
OMB Category: Other
Record ID: 335253

Organization:

U.S. ENVIRONMENTAL PROTECTION AGENCY

OFFICE OF RESEARCH AND DEVELOPMENT

NATIONAL HEALTH AND ENVIRONMENTAL EFFECTS RESEARCH LABORATORY

ENVIRONMENTAL PUBLIC HEALTH DIVISION

CARDIOPULMONARY AND IMMUNOTOXICOLOGY BRANCH