Integrative Multi-Kinase Approach for the Identification of Potent Antiplasmodial Hits - Lima, Marilia N. N.; Cassiano, Gustavo C.; Tomaz, Kaira C. P.; Silva, Arthur C.; Sousa, Bruna K. P.; Ferreira, Leticia T.; Tavella, Tatyana A.; Calit, Juliana; Bargieri, Daniel Y.; Neves, Bruno J.; Costa, Fabio T. M.; Andrade, Carolina Horta (Frontiers in Chemistry, 2019)
Malaria is a tropical infectious disease that affects over 219 million people worldwide. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new antimalarial drugs is a global health priority. Multi-target drug discovery is a promising and innovative strategy for drug discovery and it is currently regarded as one of the best strategies to face drug resistance. Aiming to identify new multi-target antimalarial drug candidates, we developed an integrative computational approach to select multi-kinase inhibitors (MKI) for Plasmodium falciparum calcium-dependent protein kinases 1 and 4 (CDPK1 and CDPK4) and protein kinase 6 (PK6). For this purpose, we developed and validated shape-based and machine learning models to prioritize compounds for experimental evaluation. Then, we applied the best models for virtual screening of a large commercial database of drug-like molecules. Ten computational hits were experimentally evaluated against asexual blood stages of both sensitive and multi-drug resistant P. falciparum strains. Among them, LabMol-171, LabMol-172, and LabMol-181 showed potent antiplasmodial activity at nanomolar concentrations (EC50 ≤ 700 nM) and selectivity indices greater than 15 folds. In addition, LabMol-171 and LabMol-181 showed good in vitro inhibition of P. berghei ookinete formation and therefore represent promising transmission-blocking scaffolds. Finally, docking studies with protein kinases CDPK1, CDPK4 and PK6 showed structural insights for further hit-to-lead optimization studies.