There is a shift in antimalarial drug discovery from phenotypic screening toward target-based approaches, as more potential drug targets are being validated in Plasmodium species. Given the high attrition rate and high cost of drug discovery, it is important to select the targets most likely to deliver progressible drug candidates. In this paper, we describe the criteria that we consider important for selecting targets for antimalarial drug discovery.
Malaria is a tropical threatening disease caused by Plasmodium parasites, resulting in 409,000 deaths in 2019. The delay of mortality and morbidity has been compounded by the widespread of drug resistant parasites from Southeast Asia since two decades. The emergence of artemisinin-resistant Plasmodium in Africa, where most cases are accounted, highlights the urgent need for new medicines.
Microorganisms in nature are highly diverse biological resources, which can be explored for drug discovery. Some countries including Brazil, Columbia, Indonesia, China, and Mexico, which are blessed with geographical uniqueness with diverse climates display remarkable megabiodiversity, potentially provide microorganismal resources for such exploitation.
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.
The Malaria Drug Accelerator (MalDA) is a consortium of 15 leading scientific laboratories. The aim of MalDA is to improve and accelerate the early antimalarial drug discovery process by identifying new, essential, druggable targets.
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).
Malarial parasites employ actin dynamics for motility, and any disruption to these dynamics renders the parasites unable to effectively establish infection. Therefore, actin presents a potential target for malarial drug discovery, and naturally occurring actin inhibitors such as latrunculins are a promising starting point.
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.