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Development of a phospholipidosis database and predictive quantitative structure-activity relationship (QSAR) models.

Naomi L Kruhlak¹, Sydney S Choi, Joseph F Contrera

¹U.S. Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, 10903 New Hampshire Avenue, Silver Spring, MD, 20993-0002.

Toxicology Mechanisms and Methods

|December 22, 2009

Summary

This study created a database of drug-induced phospholipidosis (PL) findings in animals. Computational toxicology models using this data can predict potential PL in new drug candidates.

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Area of Science:

Toxicology
Computational Chemistry
Drug Development

Background:

Drug-induced phospholipidosis (PL) is a lysosomal accumulation of phospholipids and drugs, often seen in preclinical studies.
PL can hinder or halt pharmaceutical development, necessitating predictive tools.

Purpose of the Study:

To construct a comprehensive database of PL findings across various animal species.
To utilize this database for training computational toxicology software (QSAR models) for predicting PL.

Main Methods:

Compiled PL data and chemical structures from literature, databases, and FDA reports for 583 compounds.
Developed Quantitative Structure-Activity Relationship (QSAR) models using MC4PC and MDL-QSAR software.
Assessed model performance via internal cross-validation, focusing on specificity and sensitivity.

Main Results:

The database included 190 positive and 393 negative PL findings.
Individual QSAR models showed varying predictive performance (e.g., MC4PC: 78% concordance, MDL-QSAR: 79% concordance).
Combining models significantly improved predictive performance, achieving 81% sensitivity and 79% specificity.

Conclusions:

QSAR models, trained on a curated PL database, can effectively predict potential drug-induced phospholipidosis.
Structural alerts identified by QSAR correlate with cationic amphiphilic drug (CAD) properties, supporting chemical structure-PL relationships.
These QSAR models offer a valuable tool for early screening of drug candidates to mitigate PL risks in pharmaceutical development.