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

Abstract

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.

Homology Modeling, de Novo Design of Ligands, and Molecular Docking Identify Potential Inhibitors of Leishmania donovani 24-Sterol Methyltransferase

“…Target identification and validation are pivotal for rational drug designs ( Lionta et al., 2014 ). Contemporary strategies comprising experimental (metabolomic and transcriptomic approaches) and computational (structure- and ligand-based) approaches have led to the identification of numerous biological targets necessary for the survival of Leishmania parasites ( Mandal et al., 2009 ; Mavromoustakos et al., 2011 ; Rinschen et al., 2019 ; Kwofie et al., 2020 ). However, the incomplete knowledge on Leishmania biology and the limited studies on the exact functions of sterols in intracellular organelles have hampered the exploitation of effective sterol inhibitors against leishmaniasis.…”

Genomic Insight of Leishmania Parasite: In-Depth Review of Drug Resistance Mechanisms and Genetic Mutations

“…Many patients' incorrect antimonial therapy causes pharmacological stress on the parasite organisms, resulting in adaptation and, finally, persistence against Sb(V). 103,104 The gradual evolution of antimony resistance raises the notion that numerous mutations need to occur to produce a resistant phenotype. Several in vitro processes may clarify the reported antimonial resistance, albeit it should be noted that the in vitro response is not always translated into clinical resistance.…”

Artificial intelligence and machine learning in Leishmania drug discovery

“…Of these, nine research articles, ten reviews, and one editorial from 18 sources, the journal Current Topics in Medicinal Chemistry was the only one that submitted more than one document (three documents). Finally, all the documents found, after excluding the repeated ones, were as follows: [4,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. After reading the abstracts, the articles presented in Table 1 were selected.…”