Data_Sheet_2_Integrative Multi-Kinase Approach for the Identification of Potent Antiplasmodial (5.35 MB)

Data_Sheet_2_Integrative Multi-Kinase Approach for the Identification of Potent Antiplasmodial

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posted on 21.11.2019 by Marilia N. N. Lima, Gustavo C. Cassiano, Kaira C. P. Tomaz, Arthur C. Silva, Bruna K. P. Sousa, Leticia T. Ferreira, Tatyana A. Tavella, Juliana Calit, Daniel Y. Bargieri, Bruno J. Neves, Fabio T. M. Costa, Carolina Horta Andrade

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 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 >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.