In-Silico Identification of the Best Compound Against <i>Leishmania infantum</i>: High Throughput Screening of All FDA Approved Drugs

Abstract

Amaç: Mevcut in-silico araştırması, Leishmania infantum lipofosfoglikan (LPG) ve γ-glutamilsistein sentetazına (γ-GCS) karşı umut verici ilaçlar bulmak amacıyla 20000 Gıda ve İlaç İdaresi onaylı ilaç bileşiklerinin taranması için tasarlandı ve uygulandı. Yöntemler: Her iki hedefin protein sekansı alındıktan sonra, 3D yapıları tahmin edildi ve doğrulandı. Moleküler yerleştirme iki varsayılan hedef (LPG ve γ-GCS) arasında yapıldı ve ligand-reseptör etkileşimlerini tahmin etmek için AutoDock 4.2 programı kullanılarak onaylanmış bileşikler seçildi. Bulgular: Yirmi bin ilaç bileşiği üzerinde deney yapıldıktan sonra, γ-GCS reseptörü için iki ve LPG reseptörü için beş olmak üzere toplam yedi ligand, bağlanma afiniteleri ve enerjilerine göre yeni, güçlü anti-leishmanial ilaçlar olarak belirlenmiştir. Bunlardan 5 ligand 8,5 kcal/mol'e kadar daha negatif Δ Gbinding ile LPG reseptörüne iyi bağlanma kapasitesi gösteren sitotoksik ve anti-kanser özelliklere sahipti. Bunlardan 2 ligand, 7,8 kcal/mol'e kadar daha negatif Δ Gbinding ile glutamil reseptörüne iyi bir bağlanma kapasitesi gösterdi. Sonuç: En yeni yazılım tabanlı yöntemler, yeni ilaç keşfi için organizmalarda biyolojik hedeflere özgü yeni peptid şablonlarını taramak ve tahmin etmek için güçlü araçlardır. Bununla birlikte, in vitro ve in vivo tekniklerin kullanımı, öngörülen ligandların öz Objective: Current in-silico research was designed and administered for the screening of 20000 Food and Drug Administrationapproved drug compounds with the goal of finding promising drugs against lipophosphoglycan (LPG) and γ-glutamylcysteine synthetase (γ-GCS) of Leishmania infantum. Methods: After the protein sequence of both targets was taken, the 3D structures of protein of interest were predicted and validated. Molecular docking was done among the two putative targets (LPG and γ-GCS) and approved compounds were selected using AutoDock 4.2 program to predict ligand-receptor interactions. Results: After docking experiment was done on 20000 drug compounds, a total number of seven ligands, two for γ-GCS receptor and five for LPG receptor, were assigned as novel, potent anti-leishmanial drugs based on their binding affinity and energy. Of those, five ligands possessed cytotoxic and anti-cancer characteristics and showed good binding capacity to LPG receptor with Δ Gbinding up to 8.5 kcal/mol more negative; while two compounds showed good binding capacity to glutamyl receptor with Δ Gbinding up to 7.8 kcal/mol more negative. Conclusion: The latest software-based methods are powerful tools for scanning and predicting new peptide templates specific to biological targets in organisms for new drug discovery. However, the use of in vitro and in vivo techniques is a requirement for better evaluation of the potential of projected ligands with the help of in-silico approaches, identifying molecular mechanism of action of the more active compounds is possible. This can help in defining the most likely molecular target, so that the subsequent optimization using in vitro and in vivo techniques ...

Challenges and Tools for In Vitro Leishmania Exploratory Screening in the Drug Development Process: An Updated Review

“…This approach aims to identify structure-activity relationships (SARs) in order to design optimized compounds that could be screened in silico [138,139]. In this way, a growing body of work was published on structure-based computational approaches and pharmacophore-based virtual screenings, such as the in silico high throughput screening and the molecular docking published by Saki et al in 2019 [140]: this work identified five potent antileishmanial ligands for lipophosphoglycan (LPS) receptor, and two for γ-glutamylcystein synthase (γ-GCS) receptor in L. infantum among 20,000 FDA approved drugs. In 2021, similar computational approaches notably led to identifying phytochemical inhibitors of squalene synthase [141] and selective inhibitors of dihydrofolate reductase [142] in L. donovani.…”

“…Allosteric modulators of superoxide dismutase in L. chagasi were also identified [143]. However, virtual screening appears ever more efficient with the development of methodological tools [140,144], but several potential biases were reported [145]. For this reason, although this approach is very interesting, fast, and a cost-efficient alternative to high-throughput screening, the results should be tested and validated experimentally [146][147][148] afterward.…”