A structural feature-based computational approach for toxicology predictions.
BACKGROUND
Evaluation of pharmaceutical-related toxicities using quantitative structure-activity relationship (QSAR) software as decision support tools is becoming practical and is of keen interest to scientists in both product safety and discovery. QSARs can be used to predict preclinical and clinical endpoints, drug metabolism, pharmacokinetics and mechanisms responsible for toxicity. These in silico tools are of interest in supporting regulatory review processes, and priority setting in research and product development.


METHODS
A critical assessment of the current capabilities of a new technology, the Leadscope Model Applier, is presented. Possible strengths and limitations of this technology with emphasis on the chemoinformatics method are described, and supporting literature citations date back to 1983.


RESULTS
Insight will be gained into the Leadscope Model Applier technology for structural feature-based QSAR models and its potential capability for chemical inference if the training sets are transparently open. Currently, however, there is a lack of transparency due to the protection of the proprietary training set.


CONCLUSIONS
Further research and development is needed in the creation of more stringently validated models with greater transparency and better balance between sensitivity and specificity.
Valerio
Luis G
LG
Informatics and Computational Safety Analysis Staff, Science and Research Staff, US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002, USA. Luis.Valerio@fda.hhs.gov
Yang
Chihae
C
Arvidson
Kirk B
KB
Kruhlak
Naomi L
NL
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D021281:Quantitative Structure-Activity Relationship; D012984:Software