title: In silico modeling to predict drug-induced phospholipidosis.
abstract: 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.
authors.affiliation: Science and Research Staff, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002, USA.
authors.forename: Sydney S
authors.initials: SS
authors.lastname: Choi
authors.forename: Jae S
authors.initials: JS
authors.lastname: Kim
authors.forename: Luis G
authors.initials: LG
authors.lastname: Valerio
authors.forename: Nakissa
authors.initials: N
authors.lastname: Sadrieh
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D064420:Drug-Related Side Effects and Adverse Reactions; D008064:Lipidoses / Q000139:chemically induced* / Q000145:classification; D008954:Models, Biological; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results
chemical_list: D004364:Pharmaceutical Preparations