Leishmaniasis is a neglected parasitic infectious disease caused by approximately more than 20 species of the obligate intracellular, protozoan parasite of the Leishmania genus (family Trypanosomatidae). With high morbidity, mortality and incidence rate in tropical, subtropical and southern Europe region of the world, leishmaniasis remains one of the most serious and devastating infectious disease whose pathology critically jeopardises human health, causing 70,000 to 1 million new cases and 70,000 deaths annually worldwide (WHO, 2023) https://www.who.int/news-room/fact-sheets/detail/leishmaniasis. Leishmaniasis generally manifests in 3 clinical forms which include cutaneous (causes skin lesions), mucocutaneous (affects mucous membrane of nose, throat and mouth) and visceral form (destructs visceral organs) (Schirmann et al., 2023).

The first-line treatment for leishmaniasis is pentavalent antimonials such as sodium stibogluconate (Pentostam) and meglumine antimonate (Glucantime). Second-line drugs such as Amphotericin B (AmB), Pentamidine, Paromomycin, and Miltefosine are suitable alternative treatments in cases of parasite resistance or limitations to antimonials (Croft and Olliaro, 2011; Sundar and Chakravarty, 2015). Despite their widespread use, the current medications are highly toxic, expensive, and have a wide range of adverse effects like fatigue, reversible renal failure, pancreas inflammation, pancytopenia, joint pain, diffuse muscle pain, joint stiffness, abnormal heart rhythms, and cardiotoxicity (Sundar et al., 2024). Hence, new medications with enhanced efficacy and safety profiles are required.

Currently, bioinformatics is a very good strategy that accelerates the discovery of new drugs and using in-silico techniques in drug development can prove to be very advantageous because of the lower costs, energy and time (Mak and Pichika, 2019). The selection of targets in various biological pathways is one of the most crucial steps in the drug discovery process. When selecting the targets in a parasite, it is crucial to choose a target which is either absent in the host or differs significantly from host homolog(s). Host homologs are molecules or proteins that are similar to the target in the host organism, while off-targets are molecules or proteins that the drug isn't supposed to affect but might interact with by mistake. (Battista et al., 2020). By selecting a target with substantial differences from the host homologs and minimizing the impact on off-targets, the drug can promote specific action against the parasite while minimizing side effects on the host. This selectivity is very important because it allows the drug to specifically target the parasite and disrupt its essential processes without interfering with the normal functioning of the host organism.

Since the host's immune system is dependent primarily on oxidative stress to fight infection, survival of parasite is primarily relies on the ability to resist this attack (Sorci and Faivre, 2009). Infective trypanosomatids lack catalase (Kraeva et al., 2017) and other traditional redox regulating systems (Ivens et al., 2005), therefore they rely on trypanothione reductase (TR), a unique variation of glutathione that is essential for maintaining thiol homeostasis. TR's closest human homolog is glutathione reductase (GR) which catalyses identical reaction on comparable substrates (Battista et al., 2020). Both enzymes reduce a disulfide bridge, which for GR (GSSG2→GSH) is intermolecular while for TR (TS2 → T (SH)2) is intramolecular (Fig. 1). Hence, TR can prove to be a potential drug target as it fulfils most of the requirements for an appropriate therapeutic target which includes-being important for parasite survival, absent in the host, where GR is present instead of TR and high druggability, hence can be effectively targeted (Omar and Khan, 2007). Hence, all this above makes TR an appealing drug target. So, we targeted TR with FDA-approved drugs and tried to explore novel inhibitors of this enzyme via computational methods.

Using in-silico methods, the study aimed to identify possible LdTR inhibitor candidates. For this, homology modelling was used to elucidate the three-dimensional (3D) structure of LdTR thereafter subjecting FDA-approved drugs to virtual screening via molecular docking. Next, molecular dynamics simulation and MM/PBSA analysis of the optimal lead-protein complexes were done to find possible novel LdTR inhibitors.