In silico modeling to predict drug-induced phospholipidosis

Toxicol Appl Pharmacol. 2013 Jun 1;269(2):195-204. doi: 10.1016/j.taap.2013.03.010. Epub 2013 Mar 27.


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.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Artificial Intelligence
  • Computer Simulation*
  • Drug-Related Side Effects and Adverse Reactions*
  • Lipidoses / chemically induced*
  • Lipidoses / classification
  • Models, Biological*
  • Molecular Structure
  • Pharmaceutical Preparations / chemistry
  • Quantitative Structure-Activity Relationship
  • Reproducibility of Results


  • Pharmaceutical Preparations