Modeling Phospholipidosis Induction: Reliability and Warnings

Drug-induced
phospholipidosis (PLD) is characterized by accumulation
of phospholipids, the inducing drugs and lamellar inclusion bodies
in the lysosomes of affected tissues. These side effects must be considered
as early as possible during drug discovery, and, in fact, numerous
in silico models designed to predict PLD have been published. However,
the quality of any in silico model cannot be better than the quality
of the experimental data set used to build it. The present paper reports
an overview of the difficulties and errors encountered in the generation
of databases used for the published PLD models. A new database of
466 compounds was constructed from seven literature sources, containing
only publicly available compounds. A comparison of the PLD assignations
in selected databases proved useful in revealing some inconsistencies
and raised doubts about the previously assigned PLD+ and PLD–
classifications for some chemicals. Finally, a Partial Least Squares
Discriminant Analysis (PLS-DA) approach was also applied, revealing
further anomalies and clearly showing that metabolism as well as data
quality must be taken into account when generating accurate methods
for predicting the likelihood that a compound will induce PLD. A new
curated database of 331 compounds is proposed.