Construction and Consensus Performance of (Q)SAR Models for Predicting Phospholipidosis Using a Dataset of 743
Amabel M Orogo1, Sydney S Choi1, Barbara L Minnier1
1U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.
Abstract:
Drug-induced phospholipidosis (PLD) continues to be a safety concern for pharmaceutical companies and regulatory agencies, prompting the FDA/CDER Phospholipidosis Working Group to develop a database of PLD findings that was recently expanded to contain a total of 743 compounds (385 positive and 358 negative). Three commercial (quantitative) structure-activity relationship [(Q)SAR)] software platforms [MC4PC, Leadscope Predictive Data Miner (LPDM), and Derek for Windows (DfW)] were used to build and/or test models with the database and evaluated individually and together for their ability to predict PLD induction. Models constructed with MC4PC showed improved sensitivity over previous models constructed with an earlier version of the database and software (61.2 % vs. 50.0 %), but lower specificity in cross-validation experiments (58.2 % vs. 91.9 %) due in part to the more balanced ratio of positives to negatives in the training set. A new model created with LPDM gave good cross-validation statistics (79.0 % sensitivity, 78.0 % specificity) and the single DfW structural alert for PLD was found to have high positive predictivity (83.3 %) but low sensitivity (10.4 %) when tested with the entire PLD database. Combining the predictions of MC4PC, LPDM and/or DfW resulted in increased sensitivity and coverage over using one software platform alone, although it did not enhance the overall prediction accuracy beyond that of the best performing individual software platform. The comparison across software platforms, however, facilitated the identification and analysis of chemicals that were consistently predicted incorrectly by all platforms. The prevalence of cationic amphiphilic drug (CAD) structural motifs in the database contributed heavily to many of the structural alerts and discriminating features in the models, but the subset of incorrectly predicted structures across all models underscores the need to account for mitigating features and/or additional filtering criteria to assess PLD, in particular for PLD-inducing non-CADs and non-PLD-inducing CADs. (Q)SAR tools may be used as part of an early screening battery or regulatory risk assessment approach to identify those compounds with the greatest chance of inducing PLD and potentially toxicity.
