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

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

doi:10.3389/fchem.2019.00773

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

Front Chem. 2019 Nov 21:7:773. doi: 10.3389/fchem.2019.00773. eCollection 2019.

Affiliations

¹ LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil.
² Laboratory of Tropical Diseases-Prof. Dr. Luiz Jacintho da Silva, Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil.
³ Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
⁴ Laboratory of Cheminformatics, University Center of Anápolis/UniEVANGELICA, Anápolis, Brazil.

Abstract

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.

Keywords: Plasmodium falciparum; machine learning; malaria; multi-target; shape-based; virtual screening.