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A database of human predictive patch test data for skin sensitization
Judy Strickland1, Jaleh Abedini2, David G Allen2
1Inotiv, Inc, Morrisville, NC, USA. judy.strickland@inotivco.com.
Archives of Toxicology
|August 24, 2023
Summary
A comprehensive database of human predictive patch tests (HPPTs) was created to evaluate non-animal skin sensitization methods. This resource aids in developing new approaches for toxicological assessments, prioritizing human health relevance.
Area of Science:
- Toxicology
- Dermatology
- Computational Chemistry
Background:
- Evaluating non-animal testing methods requires reliable reference data.
- Human reference data is crucial for regulatory assessments to avoid interspecies variability.
- Existing data is often fragmented, necessitating consolidation for robust evaluation.
Purpose of the Study:
- To create a curated database of human predictive patch tests (HPPTs) for skin sensitization.
- To support the validation and development of non-animal alternative methods for skin sensitization assessment.
- To provide a publicly accessible resource for toxicological research and regulatory science.
Main Methods:
- Collected data from 2277 HPPTs across 1555 publications.
- Recorded protocol details, outcomes, and developed a reliability scoring system.
- Traced and deduplicated results for 1366 unique substances, characterizing physicochemical properties and chemical structures.
Main Results:
- A comprehensive database of human skin sensitization data was established.
- The database includes detailed information on test protocols, reliability scores, and substance characteristics.
- Public accessibility via the National Toxicology Program (NTP) and Integrated Chemical Environment (ICE) was ensured.
Conclusions:
- The developed database serves as a critical resource for evaluating non-animal skin sensitization tests.
- It facilitates the development of New Approach Methodologies (NAMs) for skin sensitization.
- Enhances the scientific basis for regulatory acceptance of alternative toxicological methods.
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