Random Forest Model Predictions Afford Dual-Stage Antimalarial Agents

The need for novel antimalarials is apparent given the
continuing
disease burden worldwide, despite significant drug discovery advances
from the bench to the bedside. In particular, small-molecule agents
with potent efficacy against both the liver and blood stages of Plasmodium parasite infection are critical for clinical
settings as they would simultaneously prevent and treat malaria with
a reduced selection pressure for resistance. While experimental screens
for such dual-stage inhibitors have been conducted, the time and cost
of these efforts limit their scope. Here, we have focused on leveraging
machine learning approaches to discover novel antimalarials with such
properties. A random forest modeling approach was taken to predict
small molecules with in vitro efficacy versus liver-stage Plasmodium berghei parasites and a lack of human
liver cell cytotoxicity. Empirical validation of the model was achieved
with the realization of hits with liver-stage efficacy after prospective
scoring of a commercial diversity library and consideration of structural
diversity. A subset of these hits also demonstrated promising blood-stage Plasmodium falciparum efficacy. These 18 validated
dual-stage antimalarials represent novel starting points for drug
discovery and mechanism of action studies with significant potential
for seeding a new generation of therapies.