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.
Discovery and development of antimalarial drugs have long been dominated by single-target therapy. Continuous effort has been made to explore and identify different targets in malaria parasite crucial for the malaria treatment. The single-target drug therapy was initially successful, but it was later supplanted by combination therapy with multiple drugs to overcome drug resistance.
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.
The recent emergence in Southeast Asia of artemisinin resistance poses major threats to malaria control and elimination globally. Green nanotechnologies can constitute interesting tools for discovering anti-malarial medicines. This systematic review focused on the green synthesis of metal nanoparticles as potential source of new antiplasmodial drugs.