Discovery of new potent hits against intracellular Trypanosoma cruzi by QSAR-based virtual screening

Andrographolide: A Diterpenoid from Cymbopogon schoenanthus Identified as a New Hit Compound against Trypanosoma cruzi Using Machine Learning and Experimental Approaches

American Trypanosomiasis, also known as Chagas disease, is caused by the protozoan Trypanosoma cruzi and exhibits limited options for treatment. Natural products offer various structurally complex metabolites with biological activities, including those with anti-T. cruzi potential. The discovery and development of prototypes based on natural products frequently display multiple phases that could be facilitated by machine learning techniques to provide a fast and efficient method for selecting new hit candidates. Using Random Forest and k-Nearest Neighbors, two models were constructed to predict the biological activity of natural products from plants against intracellular amastigotes of T. cruzi. The diterpenoid andrographolide was identified from a virtual screening as a promising hit compound. Hereafter, it was isolated from Cymbopogon schoenanthus and chemically characterized by spectral data analysis. Andrographolide was evaluated against trypomastigote and amastigote forms of T. cruzi, showing IC50 values of 29.4 and 2.9 μM, respectively, while the standard drug benznidazole displayed IC50 values of 17.7 and 5.0 μM, respectively. Additionally, the isolated compound exhibited a reduced cytotoxicity (CC50 = 92.8 μM) against mammalian cells and afforded a selectivity index (SI) of 32, similar to that of benznidazole (SI = 39). From the in silico analyses, we can conclude that andrographolide fulfills many requirements implemented by DNDi to be a hit compound. Therefore, this work successfully obtained machine learning models capable of predicting the activity of compounds against intracellular forms of T. cruzi.

Artificial intelligence in molecular de novo design: Integration with experiment

In this mini review, we capture the latest progress of applying artificial intelligence (AI) techniques based on deep learning architectures to molecular de novo design with a focus on integration with experimental validation. We will cover the progress and experimental validation of novel generative algorithms, the validation of QSAR models and how AI-based molecular de novo design is starting to become connected with chemistry automation. While progress has been made in the last few years, it is still early days. The experimental validations conducted thus far should be considered proof-of-principle, providing confidence that the field is moving in the right direction.

Targeting trypanosomes: how chemogenomics and artificial intelligence can guide drug discovery

Trypanosomatids are protozoan parasites that cause human and animal neglected diseases. Despite global efforts, effective treatments are still much needed. Phenotypic screens have provided several chemical leads for drug discovery, but the mechanism of action for many of these chemicals is currently unknown. Recently, chemogenomic screens assessing the susceptibility or resistance of parasites carrying genome-wide modifications started to define the mechanism of action of drugs at large scale. In this review, we discuss how genomics is being used for drug discovery in trypanosomatids, how integration of chemical and genomics data from these and other organisms has guided prioritisations of candidate therapeutic targets and additional chemical starting points, and how these data can fuel the expansion of drug discovery pipelines into the era of artificial intelligence.

Inhibition of SARS-CoV-2 main protease: a repurposing study that targets the dimer interface of the protein

Coronavirus disease-2019 (COVID-19) was firstly reported in Wuhan, China, towards the end of 2019, and emerged as a pandemic. The spread and lethality rates of the COVID-19 have ignited studies that focus on the development of therapeutics for either treatment or prophylaxis purposes. In parallel, drug repurposing studies have also come into prominence. Herein, we aimed at having a holistic understanding of conformational and dynamical changes induced by an experimentally characterized inhibitor on main protease (Mpro) which would enable the discovery of novel inhibitors. To this end, we performed molecular dynamics simulations using crystal structures of apo and α-ketoamide 13b-bound Mpro homodimer. Analysis of trajectories pertaining to apo Mpro revealed a new target site, which is located at the homodimer interface, next to the catalytic dyad. Thereafter, we performed ensemble-based virtual screening by exploiting the ZINC and DrugBank databases and identified three candidate molecules, namely eluxadoline, diosmin, and ZINC02948810 that could invoke local and global conformational rearrangements which were also elicited by α-ketoamide 13b on the catalytic dyad of Mpro. Furthermore, ZINC23881687 stably interacted with catalytically important residues Glu166 and Ser1 and the target site throughout the simulation. However, it gave positive binding energy, presumably, due to displaying higher flexibility that might dominate the entropic term, which is not included in the MM-PBSA method. Finally, ZINC20425029, whose mode of action was different, modulated dynamical properties of catalytically important residue, Ala285. As such, this study presents valuable findings that might be used in the development of novel therapeutics against Mpro.Communicated by Ramaswamy H. Sarma.

Development of 3D-QSAR and pharmacophoric models to design new anti-Trypanosoma cruzi agents based on 2-aryloxynaphthoquinone scaffold

In this work we have collected a set of 30 trypanosomicidal naphthoquinones and developed pharmacophoric and 3D-QSAR models as tools for the design of new potential anti-Chagasic compounds. Firstly, qualitative information was obtained from SAR and pharmacophoric models identifying some fragments around the 2-aryloxynaphthoquinone scaffold important for the antiparasitic activity. Then, 3D-QSAR CoMFA and CoMSIA models were developed. The models showed adequate statistical parameters where the steric, electrostatic, and hydrophobic features explain the trypanosomicidal effect. Therefore, to validate our models, we carried out the design, synthesis, and biological evaluation on T. cruzi epimastigotes of five new compounds (33a-e). According to CoMFA model, three out of five compounds showed pIC₅₀ values within one logarithmic unit of deviation. The two compounds that did not fit the predictions were those with high lipophilicity, which agreed with the SAR and pharmacophore models. Docking and molecular dynamic studies were performed on T. cruzi trypanothione reductase, in a proposed binding site for this type of naphthoquinone. Interestingly, 33a-e showed the same interaction pattern as a naphthoquinone inhibitor (2). Finally, predicted drug-likeness properties indicated that 33a-e have optimal oral bioavailability. Thus, this study provides new in silico models for obtaining novel trypanosomicidal compounds.

Past and future of trypanosomatids high-throughput phenotypic screening

Diseases caused by trypanosomatid parasites affect millions of people mainly living in developing countries. Novel drugs are highly needed since there are no vaccines and available treatment has several limitations, such as resistance, low efficacy, and high toxicity. The drug discovery process is often analogous to finding a needle in the haystack. In the last decades a so-called rational drug design paradigm, heavily dependent on computational approaches, has promised to deliver new drugs in a more cost-effective way. Paradoxically however, the mainstay of these computational methods is data-driven, meaning they need activity data for new compounds to be generated and available in databases. Therefore, high-throughput screening (HTS) of compounds still is a much-needed exercise in drug discovery to fuel other rational approaches. In trypanosomatids, due to the scarcity of validated molecular targets and biological complexity of these parasites, phenotypic screening has become an essential tool for the discovery of new bioactive compounds. In this article we discuss the perspectives of phenotypic HTS for trypanosomatid drug discovery with emphasis on the role of image-based, high-content methods. We also propose an ideal cascade of assays for the identification of new drug candidates for clinical development using leishmaniasis as an example.

A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening

Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set (e.g.,QCV2) or the test set (e.g., specificity, selectivity or QF1/F2/F32). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.

Discovery of New Hits as Antitrypanosomal Agents by In Silico and In Vitro Assays Using Neolignan-Inspired Natural Products from Nectandra leucantha

In the present study, the phytochemical study of the n-hexane extract from flowers of Nectandra leucantha (Lauraceae) afforded six known neolignans (1–6) as well as one new metabolite (7), which were characterized by analysis of NMR, IR, UV, and ESI-HRMS data. The new compound 7 exhibited potent activity against the clinically relevant intracellular forms of T. cruzi (amastigotes), with an IC₅₀ value of 4.3 μM and no observed mammalian cytotoxicity in fibroblasts (CC₅₀ > 200 μM). Based on the results obtained and our previous antitrypanosomal data of 50 natural and semi-synthetic related neolignans, 2D and 3D molecular modeling techniques were employed to help the design of new neolignan-based compounds with higher activity. The results obtained from the models were important to understand the main structural features related to the biological response of the neolignans and to aid in the design of new neolignan-based compounds with better biological activity. Therefore, the results acquired from phytochemical, biological, and in silico studies showed that the integration of experimental and computational techniques consists of a powerful tool for the discovery of new prototypes for development of new drugs to treat CD.

Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project

Neglected tropical diseases (NTDs) are parasitic and bacterial infections that are widespread, especially in the tropics, and cause health problems for about one billion people over 149 countries worldwide. However, in terms of therapeutic agents, for example, nifurtimox and benznidazole were developed in the 1960s to treat Chagas disease, but new drugs are desirable because of their side effects. Drug discovery takes 12 to 14 years and costs $2.6 billon dollars, and hence, computer aided drug discovery (CADD) technology is expected to reduce the time and cost. This paper describes our methods and results based on CADD, mainly for NTDs. An overview of databases, molecular simulation and pharmacophore modeling, contest-based drug discovery, and machine learning and their results are presented herein.

Chagas Disease Chemotherapy: What Do We Know So Far?

Chagas disease is a Neglected Tropical Disease (NTD), and although endemic in Latin America, affects around 6-7 million people infected worldwide. The treatment of Chagas disease is based on benznidazole and nifurtimox, which are the only available drugs. However, they are not effective during the chronic phase and cause several side effects. Furthermore, BZ promotes cure in 80% of the patients in the acute phase, but the cure rate drops to 20% in adults in the chronic phase of the disease. In this review, we present several studies published in the last six years, which describes the antiparasitic potential of distinct drugs, from the synthesis of new compounds aiming to target the parasite, as well as the repositioning and the combination of drugs. We highlight several compounds for having shown results that are equivalent or superior to BZ, which means that they should be further studied, either in vitro or in vivo. Furthermore, we stand out the differences in the effects of BZ on the same strain of T. cruzi, which might be related to methodological differences such as parasite and cell ratios, host cell type and the time of adding the drug. In addition, we discuss the wide variety of strains and also the cell types used as a host cell, which makes it difficult to compare the trypanocidal effect of the compounds.

Chagas Disease: Current View of an Ancient and Global Chemotherapy Challenge

Chagas disease is a neglected tropical disease and a global public health issue. In terms of treatment, no progress has been made since the 1960s, when benznidazole and nifurtimox, two obsolete drugs still prescribed, were used to treat this disease. Hence, currently, there are no effective treatments available to tackle Chagas disease. Over the past 20 years, there has been an increasing interest in the disease. However, parasite genetic diversity, drug resistance, tropism, and complex life cycle, along with the limited understanding of the disease and inadequate methodologies and strategies, have resulted in the absence of new insights in drugs development and disappointing outcomes in clinical trials so far. In summary, new drugs are urgently needed. This Review considers the relevant aspects related to the lack of drugs for Chagas disease, resumes the advances in tools for drug discovery, and discusses the main features to be taken into account to develop new effective drugs.

COVID-19: CADD to the rescue

The recent outbreak of the deadly COVID-19 disease, being caused by the novel coronavirus (SARS-CoV-2), has put the world on red alert as it keeps spreading and recording more fatalities. Research efforts are being carried out to curtail the disease from spreading as it has been declared as of global health emergency. Hence, there is an exigent need to identify and design drugs that are capable of curing the infection and hinder its continual spread across the globe. Herein, a computer-aided drug design tool known as the virtual screening method was used to screen a database of 44 million compounds to find compounds that have the potential to inhibit the surface glycoprotein responsible for virus entry and binding. The consensus scoring approach selected three compounds with promising physicochemical properties and favorable molecular interactions with the target protein. These selected compounds can undergo lead optimization to be further developed as drugs that can be used in treating the COVID-19 disease.

High throughput virtual screening reveals SARS-CoV-2 multi-target binding natural compounds to lead instant therapy for COVID-19 treatment

The present-day world is severely suffering from the recently emerged SARS-CoV-2. The lack of prescribed drugs for the deadly virus has stressed the likely need to identify novel inhibitors to alleviate and stop the pandemic. In the present high throughput virtual screening study, we used in silico techniques like receptor-ligand docking, Molecular dynamic (MD), and ADME properties to screen natural compounds. It has been documented that many natural compounds display antiviral activities, including anti–SARS-CoV effect. The present study deals with compounds of Natural Product Activity and Species Source (NPASS) database with known biological activity that probably impedes the activity of six essential enzymes of the virus. Promising drug-like compounds were identified, demonstrating better docking score and binding energy for each druggable targets. After an extensive screening analysis, three novel multi-target natural compounds were predicted to subdue the activity of three/more major drug targets simultaneously. Concerning the utility of natural compounds in the formulation of many therapies, we propose these compounds as excellent lead candidates for the development of therapeutic drugs against SARS-CoV-2.

Current trends in the pharmacological management of Chagas disease

Chagas disease (CD) is a tropical neglected illness, affecting mainly populations of low socioeconomic status in Latin America. An estimated 6 to 8 million people worldwide are infected with Trypanosoma cruzi, the etiological agent of CD. Despite being one of the main global health problems, this disease continues without effective treatment during the chronic phase of the infection. The limitation of therapeutic strategies has been one of the biggest challenges on the fight against CD. Nifurtimox and benznidazole, developed in the 1970s, are still the only commercial options with established efficacy on CD. However, the efficacy of these drugs have a proven efficacy only during early infection and the benefits in the chronic phase are questionable. Consequently, there is a growing need for new pharmacological alternatives, either by optimization of existing drugs or by the formulation of new compounds. In the present study, a literature review of the currently adopted therapy, its concomitant combination with other drugs, and potential future treatments for CD was performed, considering articles published from 2012. The revised articles were selected according to the protocol of treatment: evaluation of drug association, drug repositioning and research of new drugs. As a result of the present revision, it was possible to conclude that the use of benznidazole in combination with other compounds showed better results when compared with its use as a single therapy. The search of new drugs has been the strategy most used in pursuing more effective forms of treatment for CD. However, studies have still focused on basic research, that is, they are still in a pre-clinical stage, using methodologies based on in vitro or in animal studies.

QSAR studies on benzothiophene derivatives as Plasmodium falciparum N-myristoyltransferase inhibitors: Molecular insights into affinity and selectivity

Malaria is an infectious disease caused by protozoan parasites of the genus Plasmodium and transmitted by Anopheles spp. mosquitos. Due to the emerging resistance to currently available drugs, great efforts must be invested in discovering new molecular targets and drugs. N-myristoyltransferase (NMT) is an essential enzyme to parasites and has been validated as a chemically tractable target for the discovery of new drug candidates against malaria. In this work, 2D and 3D quantitative structure-activity relationship (QSAR) studies were conducted on a series of benzothiophene derivatives as P. falciparum NMT (PfNMT) and human NMT (HsNMT) inhibitors to shed light on the molecular requirements for inhibitor affinity and selectivity. A combination of Quantitative Structure-activity Relationship (QSAR) methods, including the hologram quantitative structure-activity relationship (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) models, were used, and the impacts of the molecular alignment strategies (maximum common substructure and flexible ligand alignment) and atomic partial charge methods (Gasteiger-Hückel, MMFF94, AM1-BCC, CHELPG, and Mulliken) on the quality and reliability of the models were assessed. The best models exhibited internal consistency and could reasonably predict the inhibitory activity against both PfNMT (HQSAR: q² /r² /r² _pred = 0.83/0.98/0.81; CoMFA: q² /r² /r² _pred = 0.78/0.97/0.86; CoMSIA: q² /r² /r² _pred = 0.74/0.95/0.82) and HsNMT (HQSAR: q² /r² /r² _pred = 0.79/0.93/0.74; CoMFA: q² /r² /r² _pred = 0.82/0.98/0.60; CoMSIA: q² /r² /r² _pred = 0.62/0.95/0.56). The results enabled the identification of the polar interactions (electrostatic and hydrogen-bonding properties) as the major molecular features that affected the inhibitory activity and selectivity. These findings should be useful for the design of PfNMT inhibitors with high affinities and selectivities as antimalarial lead candidates.

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.