drug discovery

Infections caused by protozoans remain a public health issue, especially in tropical countries. Serious adverse events, lack of efficacy at the different stages of the infection and routes of administration that have a negative impact on treatment adherence are some of the problems with currently available therapy against these diseases.

Malaria elimination can benefit from time and cost-efficient approaches for antimalarials such as drug repurposing. In this work, 796 DrugBank compounds were screened against 36 Plasmodium falciparum targets using QuickVina-W. Hits were selected after rescoring using GRaph Interaction Matching (GRIM) and ligand efficiency metrics: surface efficiency index (SEI), binding efficiency index (BEI) and lipophilic efficiency (LipE).

Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds.

Modelling and simulation are being increasingly utilized to support the discovery and development of new anti-malarial drugs. These approaches require reliable in vitro data for physicochemical properties, permeability, binding, intrinsic clearance and cytochrome P450 inhibition. This work was conducted to generate an in vitro data toolbox using standardized methods for a set of 45 anti-malarial drugs and to assess changes in physicochemical properties in relation to changing target product and candidate profiles.