Computational Methods to Discover Compounds for the Treatment of Chagas Disease

Introducing the NUATEI Consortium: A Mexican Research Program for the Identification of Natural and Synthetic Antimicrobial Compounds for Prevalent Infectious Diseases

The need for new drugs to treat human infections is a global health concern. Diseases like tuberculosis, trypanosomiasis, amoebiasis, and AIDS remain significant problems, especially in developing countries like Mexico. Despite existing treatments, issues such as resistance and adverse effects drive the search for new alternatives. Herein, we introduce the NUATEI research consortium, made up of experts from the Institute of Biomedical Research at UNAM, who identify and obtain natural and synthetic compounds and test their effects against human pathogens using in vitro and in vivo models. The consortium has evaluated hundreds of natural extracts and compounds against the pathogens causing tuberculosis, trypanosomiasis, amoebiasis, and AIDS, rendering promising results, including a patent with potential for preclinical studies. This paper presents the rationale behind the formation of this consortium, as well as its objectives and strategies, emphasizing the importance of natural and synthetic products as sources of antimicrobial compounds and the relevance of the diseases studied. Finally, we briefly describe the methods of the evaluation of the compounds in each biological model and the main achievements. The potential of the consortium to screen numerous compounds and identify new therapeutic agents is highlighted, demonstrating its significant contribution to addressing these infectious diseases.

Identification of novel thiadiazin derivatives as potentially selective inhibitors towards trypanothione reductase from Trypanosoma cruzi by molecular docking using the numerical index poses ratio Pr and the binding mode analysis

Abstract

Chagas disease is a serious health problem in Central and South America for which effective treatment is not currently available. This illness is caused by the protozoa Trypanosoma cruzi, a species that relies on a thiol-based metabolism to regulate oxidative stress. Trypanothione reductase enzyme plays a central role in the metabolic pathway of the parasite. In this work, a virtual screening of a library of novel thiadiazine derivatives against trypanothione reductase using molecular docking was performed. Four different series of hybrid ligands having in the structure one or two peptoid moieties (series I and II) or the tetrazole ring (series III and IV) were considered. An ad hoc numerical index called poses ratio was introduced to interpret the results of the docking analysis and to establish relevant structure-interaction relationships. In addition, six binding modes were found for the ligands with the highest populated conformational clusters after applying contact-based analysis. The most regular and relevant were binding modes I and II, found mainly for ligands from series I. A subsequent molecular docking on human glutathione reductase enzyme allowed to assess the possible cytotoxicity of the ligands towards human cells. A selective binding profile was found for ligands with interactions in the Hydrophobic cleft, the spermidine and the Z subsites inside the active site of trypanothione reductase. At the end of the study, new thiadiazine-based compounds were identified as plausible candidates to selectively inhibit the parasitic enzyme.

Graphic abstract

In silico repositioning of etidronate as a potential inhibitor of the Trypanosoma cruzi enolase

Enolase is a glycolytic enzyme that catalyzes the interconversion between 2-phosphoglycerate and phosphoenolpyruvate. In trypanosomatids, enolase was proposed as a key enzyme after in silico and in vivo analysis and it was validated as a protein essential for the survival of the parasite. Therefore, enolase constitutes an interesting enzyme target for the identification of drugs against Chagas disease. In this work, a combined virtual screening strategy was implemented, employing similarity virtual screening, molecular docking, and molecular dynamics. First, two known enolase inhibitors and the enzyme substrates were used as queries for the similarity screening on the Sweetlead database using five different algorithms. Compounds retrieved in the top 10 of at least three search algorithms were selected for further analysis, resulting in six compounds of medical use (etidronate, pamidronate, fosfomycin, acetohydroxamate, triclofos, and aminohydroxybutyrate). Molecular docking simulations and pose re-scoring predicted that binding with acetohydroxamate and triclofos would be weak, while fosfomycin and aminohydroxybutyrate predicted binding is experimentally implausible. Docking poses obtained for etidronate, pamidronate, and PEP were used for molecular dynamics calculations to describe their mode of binding. From the obtained results, we propose etidronate as a potential TcENO inhibitor and describe molecular motifs to be taken into account in the repurposing or design of drugs targeting this enzyme active site.

The significance of artificial intelligence in drug delivery system design

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.

Why hasn't there been more progress in new Chagas disease drug discovery?

Introduction: Chagas disease (CD) is a neglected disease caused by the protozoan parasite Trypanosoma cruzi. In terms of novel drug discovery, there has been no progress since the 1960s with the same two drugs, benznidazole and nifurtimox, still in use. The complex life cycle, genetic diversity of T. cruzi strains, different sensitivities to the available drugs, as well as little interest from pharmaceutical companies and inadequate methodologies for translating in vitro and in vivo findings to the discovery of new drugs have all contributed to the lack of progress.Areas covered: In this perspective, the authors give discussion to the relevant points connected to the lack of developments in CD drug discovery and provide their expert perspectives.Expert opinion: There are few drugs currently in the preclinical pipeline for the treatment of CD. Only three classes of compounds have been shown to achieve high cure rates in mouse models of infection: nitroimidazoles (fexinidazole), oxaborole DNDi-6148 and proteasome inhibitors (GNF6702). New biomarkers for Chagas' disease are urgently needed for the diagnosis and detection of cure/treatment efficacy. Efforts from academia and pharmaceutical companies are in progress and more intense interaction to accelerate the process of new drugs development is necessary.