Outwitting an Old Neglected Nemesis: A Review on Leveraging Integrated Data-Driven Approaches to Aid in Unraveling of Leishmanicides of Therapeutic Potential

The global prevalence of leishmaniasis has increased with skyrocketed mortality in the past decade. The causative agent of leishmaniasis is Leishmania species, which infects populations in almost all the continents. Prevailing treatment regimens are consistently inefficient with reported side effects, toxicity and drug resistance. This review complements existing ones by discussing the current state of treatment options, therapeutic bottlenecks including chemoresistance and toxicity, as well as drug targets. It further highlights innovative applications of nanotherapeutics-based formulations, inhibitory potential of leishmanicides, anti-microbial peptides and organometallic compounds on leishmanial species. Moreover, it provides essential insights into recent machine learning-based models that have been used to predict novel leishmanicides and also discusses other new models that could be adopted to develop fast, efficient, robust and novel algorithms to aid in unraveling the next generation of anti-leishmanial drugs. A plethora of enriched functional genomic, proteomic, structural biology, high throughput bioassay and drug-related datasets are currently warehoused in both general and leishmania-specific databases. The warehoused datasets are essential inputs for training and testing algorithms to augment the prediction of biotherapeutic entities. In addition, we demonstrate how pharmacoinformatics techniques including ligand-, structure- and pharmacophore-based virtual screening approaches have been utilized to screen ligand libraries against both modeled and experimentally solved 3D structures of essential drug targets. In the era of data-driven decision-making, we believe that highlighting intricately linked topical issues relevant to leishmanial drug discovery offers a one-stop-shop opportunity to decipher critical literature with the potential to unlock implicit breakthroughs.