期刊
TOXICOLOGY AND APPLIED PHARMACOLOGY
卷 269, 期 2, 页码 195-204出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.taap.2013.03.010
关键词
Phospholipidosis; Drug safety; In silico toxicology
资金
- ORISE Research Participation Program at the Center for Drug Evaluation and Research (CDER)
Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure-activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset The results of our modeling for DIPL include rigorous external validation tests showing 80-81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above >= 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. Published by Elsevier Inc.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据