Integrative Multi-kinase Approach for the Identification of Potent Antiplasmodial Hits

Malaria is a serious endemic disease caused by parasites genus Plasmodium responsible for around half a million deaths a year1. The spread of parasitic resistance to available drugs has made malaria control difficult. Multi-kinase inhibition assign a synergistic effect able for increasing the effectiveness of the kinase inhibitors, avoiding the emergence of parasite resistance2. The main goal of this work was identification of new multi-kinase drug candidates by using integrated strategies in Medicinal Chemistry. We developed and validated shape-based and machine learning models to prioritize multi-kinase compounds for CDPK1, CDPK4 and PK6. At the end, a virtual screening campaign using the best models and commercial database was able to selected10 virtual hits for experimental evaluation. The compounds LabMol-171, -172 and -181 highlighted as promisor antiplasmodial activity (EC50 ~500 nM) with good selectivity (>15 folds). Besides that, LabMol-171 and -181 exhibit considerable inhibition of ookinete formation representing a promising transmission blocker.