Impact Factor 3.782 | CiteScore 3.51
More on impact ›

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Chem. | doi: 10.3389/fchem.2019.00773

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

  • 1Universidade Federal de Goiás, Brazil
  • 2Campinas State University, Brazil
  • 3University of São Paulo, Brazil
  • 4Centro Universitário de Anápolis, Brazil

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.

Keywords: Malaria, Shape-based, machine learning, Virtual Screening, Plasmodium falciparum, Multi-target

Received: 16 Aug 2019; Accepted: 25 Oct 2019.

Copyright: © 2019 Lima, Cassiano, Tomaz, Silva, Ferreira, Tavella, Calit, Bargieri, Neves, Costa and Andrade. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Carolina H. Andrade, Universidade Federal de Goiás, Goiânia, 74001-970, Goiás, Brazil,