How Does the Quality of Phospholipidosis Data Influence the Predictivity of Structural Alerts?

The ability of drugs to induce phospholipidosis
(PLD) is linked
directly to their molecular substructures: hydrophobic, cyclic moieties
with hydrophilic, peripheral amine groups. These structural properties
can be captured and coded into SMILES arbitrary target specification
(SMARTS) patterns. Such structural alerts, which are capable of identifying
potential PLD inducers, should ideally be developed on a relatively
large but reliable data set. We had previously developed a model based
on SMARTS patterns consisting of 32 structural fragments using information
from 450 chemicals. In the present study, additional PLD structural
alerts have been developed based on a newer and larger data set combining
two data sets published recently by the United States Food and Drug
Administration (US FDA). To assess the predictive performance of the
updated SMARTS model, two publicly available data sets were considered.
These data sets were constructed using different criteria and hence
represent different standards for overall quality. In the first data
set high quality was assured as all negative chemicals were confirmed
by the gold standard method for the detection of PLDtransmission
electron microscopy (EM). The second data set was constructed from
seven previously published data sets and then curated by removing
compounds where conflicting results were found for PLD activity. Evaluation
of the updated SMARTS model showed a strong, positive correlation
between predictive performance of the alerts and the quality of the
data set used for the assessment. The results of this study confirm
the importance of using high quality data for modeling and evaluation,
especially in the case of PLD, where species, tissue, and dose dependence
of results are additional confounding factors.