Computer-Aided Drug Discovery Approaches against the Tropical Infectious Diseases Malaria, Tuberculosis, Trypanosomiasis, and Leishmaniasis

pyGROMODS: a Python package for the generation of input files for molecular dynamic simulation with GROMACS

The pyGROMODS, an easy-to-use cross-platform python-based package, with a graphical user interface, for the generation of molecular dynamic (MD) input files and running MD simulation (MDS) of proteins, peptides, and protein-ligand complex using GROMACS, is here presented. Four routes, with underlining Python scripts, are implemented in pyGROMODS for the generation of MD input files. They are 'RLmulti' for processing multi-ligand protein complex, 'RLmany' for processing multiple ligands against a single protein target, 'RLsingle' for processing multiple pairs of proteins and ligands, and 'PPmore' for processing peptides or proteins without ligands or non-standard residues. In addition, using the package, the generated input files or appropriate input files from other sources can be uploaded to run MDS with GROMACS. The pyGROMODS is implemented with a unique ability to search the host machine systems for the installation of the required software, update and/or install required Python packages, allow the user to pre-define working directory, and generate unique workflow organization with well-defined folders and files in a well-organized manner. The pyGROMODS, which is released under the MIT License, is freely available for download via the GitHub (https://github.com/Dankem/pyGROMODS) and Zenodo (https://doi.org/10.5281/zenodo.7912747) repositories. The precompiled executables can also be downloaded from Zenodo (https://doi.org/10.5281/zenodo.8087090), and a video tutorial can be downloaded from https://youtu.be/I4OKc6uVx1M.Communicated by Ramaswamy H. Sarma.

Sulfonamide based pyrimidine derivatives combating Plasmodium parasite by inhibiting falcipains-2 and falcipains-3 as antimalarial agents

In this report, we present the design and synthesis of a novel series of pyrimidine-tethered spirochromane-based sulfonamide derivatives aimed at combating drug resistance in malaria. The antimalarial effectiveness of these compounds was assessed in vitro. Structural validation of the synthesized compounds was conducted using mass spectrometry and NMR spectroscopy. Strong antimalarial activity against CQ-sensitive (3D7) and CQ-resistant (W2) strains of Plasmodium falciparum was demonstrated by the majority of the compounds. Notably, compounds SZ14 and SZ9 demonstrated particularly potent effects, with compound SZ14 showing IC50 values of 2.84 μM and SZ9 3.22 μM, indicating single-digit micromolar activity. The compounds exhibiting strong antimalarial activity were assessed through enzymatic tests against the cysteine protease enzymes of P. falciparum, falcipain-2 and falcipain-3. The results indicated that SZ14 and SZ9 inhibited PfFP-2 (IC50 values: 4.1 and 5.4 μM, respectively), and PfFP-3 (IC50 values: 4.9 and 6.3 μM, respectively). To confirm the compounds' specificity towards the parasite, we investigated their cytotoxicity against Vero cell lines, revealing strong selectivity indices and no significant cytotoxic effects. Additionally, in vitro hemolysis testing showed these compounds to be non-toxic to normal human blood cells. Moreover, predicted in silico ADME parameters and physiochemical characteristics demonstrated the drug-likeness of the synthetic compounds. These collective findings suggest that sulfonamide derivatives based on pyrimidine-tethered oxospirochromane could serve as templates for the future development of potential antimalarial drugs.

Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope

The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.

In vitro and in silico guided identification of antimalarial phytoconstituent(s) in the root of Citrus maxima (Burm.) Merr

As a part of our continuous effort to find new therapeutic agents from natural sources, the hydroalcoholic (1:1) extract of Citrus maxima (Burm.) Merr. root was selected for the identification of possible antimalarial phytoconstituents. From the extract, three flavonoids including luteolin were isolated and evaluated for in vitro antimalarial activity against the chloroquine-sensitive (Pf3D7) and resistant (PfRKL-9) strains of Plasmodium falciparum. Among these, luteolin (CM3) showed the highest antimalarial activity with IC50 values of 2.315 ± 0.489 and 2.691 ± 0.454 µg/ml against the Pf3D7 and PfRKL-9 strains respectively. To assess the safety of luteolin (CM3), a cytotoxicity study against a normal human embryonic kidney cell line (HEK-293) was performed and the compound was found to be safe with a CC50 value of 222.3 ± 1.443 µg/ml. The docking study against 26 target proteins of P. falciparum revealed that luteolin (CM3) has a better binding affinity with two proteins, viz. P. falciparum lactate dehydrogenase (PfLDG) and P. falciparum enoyl-ACP reductase (PfEAR) in comparison to the co-crystallized ligands. Furthermore, the molecular dynamics simulation study of the protein-ligand complexes also supported the binding affinity and interactions of luteolin (CM3) at the active sites. Finally, the binding free energy calculation revealed that the luteolin formed a thermodynamically more stable complex with PfLDG (-50.955 ± 17.184 kJ/mol) than PfEAR (-24.856 ± 13.739 kJ/mol). Overall, in this study, we identified an antimalarial marker in the hydroalcoholic extract of C. maxima root which may act by inhibiting PfLDG.Communicated by Ramaswamy H. Sarma.

Synthesis of Benzocycloalkanone-Based Michael Acceptors and Biological Activities as Antimalarial and Antitrypanosomal Agents

A series of benzocycloalkanone derivatives have been prepared and evaluated as antimalarial and antitrypanosomal agents. The compounds were obtained by direct coupling of preformed 4-substituted benzaldehyde and indanone or tetralone substitutes through aldol condensation of Claisen-Schmidt using sodium hydroxide as a catalyst in ethanol at room temperature. Although designed to inhibit the formation of β-hematin in vitro, only three compounds, 10, 11, and 12, showed activities greater than 50% (75.16%, 63.02%, and 56.17%, respectively). The results of the in vivo antimalarial evaluation show that 10, 11, and 12 reduced parasitemia marginally, and an insignificant increase in the days of survival of the mice was observed. As trypanocidals, all compounds showed marginal activity as inhibitors of the proliferation of T. cruzi epimastigotes, except compound 33, with an activity of 51.08 ± 3.4% compared to the activity shown by the reference compound benznidazole 59.99 ± 2.9%. The compounds appear to have little cytotoxic effect against VERO cells in vitro; this new class of Michael acceptor agents clearly warrants further investigation.

Machine learning and drug discovery for neglected tropical diseases

Neglected tropical diseases affect millions of individuals and cause loss of productivity worldwide. They are common in developing countries without the financial resources for research and drug development. With increased availability of data from high throughput screening, machine learning has been introduced into the drug discovery process. Models can be trained to predict biological activities of compounds before working in the lab. In this study, we use three publicly available, high-throughput screening datasets to train machine learning models to predict biological activities related to inhibition of species that cause leishmaniasis, American trypanosomiasis (Chagas disease), and African trypanosomiasis (sleeping sickness). We compare machine learning models (tree based models, naive Bayes classifiers, and neural networks), featurizing methods (circular fingerprints, MACCS fingerprints, and RDKit descriptors), and techniques to deal with the imbalanced data (oversampling, undersampling, class weight/sample weight).

Identification of thrombopoiesis inducer based on a hybrid deep neural network model

Thrombocytopenia is a common haematological problem worldwide. Currently, there are no relatively safe and effective agents for the treatment of thrombocytopenia. To address this challenge, we propose a computational method that enables the discovery of novel drug candidates with haematopoietic activities. Based on different types of molecular representations, three deep learning (DL) algorithms, namely recurrent neural networks (RNNs), deep neural networks (DNNs), and hybrid neural networks (RNNs+DNNs), were used to develop classification models to distinguish between active and inactive compounds. The evaluation results illustrated that the hybrid DL model exhibited the best prediction performance, with an accuracy of 97.8 % and Matthews correlation coefficient of 0.958 on the test dataset. Subsequently, we performed drug discovery screening based on the hybrid DL model and identified a compound from the FDA-approved drug library that was structurally divergent from conventional drugs and showed a potential therapeutic action against thrombocytopenia. The novel drug candidate wedelolactone significantly promoted megakaryocyte differentiation in vitro and increased platelet levels and megakaryocyte differentiation in irradiated mice with no systemic toxicity. Overall, our work demonstrates how artificial intelligence can be used to discover novel drugs against thrombocytopenia.

A QSAR Study for Antileishmanial 2-Phenyl-2,3-dihydrobenzofurans †

Leishmaniasis, a parasitic disease that represents a threat to the life of millions of people around the globe, is currently lacking effective treatments. We have previously reported on the antileishmanial activity of a series of synthetic 2-phenyl-2,3-dihydrobenzofurans and some qualitative structure-activity relationships within this set of neolignan analogues. Therefore, in the present study, various quantitative structure-activity relationship (QSAR) models were created to explain and predict the antileishmanial activity of these compounds. Comparing the performance of QSAR models based on molecular descriptors and multiple linear regression, random forest, and support vector regression with models based on 3D molecular structures and their interaction fields (MIFs) with partial least squares regression, it turned out that the latter (i.e., 3D-QSAR models) were clearly superior to the former. MIF analysis for the best-performing and statistically most robust 3D-QSAR model revealed the most important structural features required for antileishmanial activity. Thus, this model can guide decision-making during further development by predicting the activity of potentially new leishmanicidal dihydrobenzofurans before synthesis.

Menthol carbonates as potent antiparasitic agents: synthesis and in vitro studies along with computer-aided approaches

Introduction

Despite the number of deaths and the significant economic and social costs associated with Chagas, Leishmaniasis and Malaria diseases worldwide, available drugs are limited and have serious side effects and high toxicity for the patient. Therefore, there is an urgent need for safe, low-cost, and effective treatments. Natural products are an important source of bioactive compounds and there is current interest in finding natural bioactive molecules that can be used for treating these parasitic diseases. In the present study we proposed to evaluate the in vitro antiparasitic activity of new menthol derivatives against Trypanosoma cruzi, Leishmania braziliensis and Plasmodium falciparum; moreover, we propose to explore their mode of action through in silico approaches.

Material and methods

A series of carbonate prodrugs (1–9) were synthesized from menthol with different aliphatic alcohols. Spectroscopic techniques were used to confirm the structures of the synthesized compounds. The cytotoxicity of the compounds was assessed using U-937 cells. In vitro trypanocidal, leishmanicidal and antiplasmodial activity were evaluated using a T. cruzi, L. braziliensis and P. falciparum organism, respectively. In addition, in silico studies were also performed through molecular dynamics simulations and MM-PBSA analysis.

Results

The assay revealed that most of the compounds were highly active against intracellular amastigotes of T. cruzi and L. braziliensis, and had moderate activity against the total forms of P. falciparum. Compound 2 was one of the drugs that showed a high selectivity index (SI) for the three organisms evaluated. The prediction of the ADME properties suggests that all the compounds have drug-like molecular properties and the probability to be lead candidates. Finally, molecular dynamics simulations, and MM-PBSA studies indicate that menthol at the substrate binding site of TcDHODH, LbDHODH and PfDHODH is structurally stable in the same order as the natural substrate; also, interactions of menthol with residues involved in the inhibition of TcDHODH and PfDHODH proteins were predicted.

Conclusions

The present study demonstrates that menthol prodrugs are promising antiparasitic agents; however, the mechanisms of action proposed in this study need to be experimentally verified by future enzymatic assays.

The New Era of Drug Discovery: The Power of Computer-aided Drug Design (CADD)

Abstract:

Drug design and discovery is a process that requires high financial costs and is timeconsuming. For many years, this process focused on empirical pharmacology. However, over the years, the target-based approach allowed a significant discovery in this field, initiating the rational design era. In view, to decrease the time and financial cost, rational drug design is benefited by increasing computer engineering and software development, and computer-aided drug design (CADD) emerges as a promising alternative. Since the 1970s, this approach has been able to identify many important and revolutionary compounds, like protease inhibitors, antibiotics, and others. Many anticancer compounds identified through this approach have shown their importance, being CADD essential in any drug discovery campaign. Thus, this perspective will present the prominent successful cases utilizing this approach and entering into the next stage of drug design. We believe that drug discovery will follow the progress in bioinformatics, using high-performance computing with molecular dynamics protocols faster and more effectively. In addition, artificial intelligence and machine learning will be the next process in the rational design of new drugs. Here, we hope that this paper generates new ideas and instigates research groups worldwide to use these methods and stimulate progress in drug design.

Development of Novel Anti-Leishmanials: The Case for Structure-Based Approaches

The neglected tropical disease (NTD) leishmaniasis is the collective name given to a diverse group of illnesses caused by ~20 species belonging to the genus Leishmania, a majority of which are vector borne and associated with complex life cycles that cause immense health, social, and economic burdens locally, but individually are not a major global health priority. Therapeutic approaches against leishmaniasis have various inadequacies including drug resistance and a lack of effective control and eradication of the disease spread. Therefore, the development of a rationale-driven, target based approaches towards novel therapeutics against leishmaniasis is an emergent need. The utilization of Artificial Intelligence/Machine Learning methods, which have made significant advances in drug discovery applications, would benefit the discovery process. In this review, following a summary of the disease epidemiology and available therapies, we consider three important leishmanial metabolic pathways that can be attractive targets for a structure-based drug discovery approach towards the development of novel anti-leishmanials. The folate biosynthesis pathway is critical, as Leishmania is auxotrophic for folates that are essential in many metabolic pathways. Leishmania can not synthesize purines de novo, and salvage them from the host, making the purine salvage pathway an attractive target for novel therapeutics. Leishmania also possesses an organelle glycosome, evolutionarily related to peroxisomes of higher eukaryotes, which is essential for the survival of the parasite. Research towards therapeutics is underway against enzymes from the first two pathways, while the third is as yet unexplored.

Applied Machine Learning Toward Drug Discovery Enhancement: Leishmaniases as a Case Study

Drug discovery (DD) research is a complex field with a high attrition rate. Machine learning (ML) approaches combined to chemoinformatics are of valuable input to this field. We, herein, focused on implementing multiple ML algorithms that shall learn from different molecular fingerprints (FPs) of 65 057 molecules that have been identified as active or inactive against Leishmania major promastigotes. We sought to build a classifier able to predict whether a given molecule has the potential of being anti-leishmanial or not. Using the RDkit library, we calculated 5 molecular FPs of the molecules. Then, we implemented 4 ML algorithms that we trained and tested for their ability to classify the molecules into active/inactive classes based on their chemical structure, encoded by the molecular FPs. Best performers were random forest (RF) and support vector machine (SVM), while atom-pair and topology torsion FPs were the best embedding functions. Both models were further assessed on different stratification levels of the dataset and showed stable performances. At last, we used them to predict the potential of molecules within the Food and Drug Administration (FDA)-approved drugs collection to present anti-Leishmania effects. We ranked these drugs according to their anti-Leishmanial probability and obtained in total seven anti-Leishmania agents, previously described in the literature, within the top 10 of each model. This validates the robustness of the approach, the algorithms, and FPs choices as well as the importance of the dataset size and content. We further engaged these molecules into reverse docking experiments on 3D crystal structures of seven well-studied Leishmania drug targets and could predict the molecular targets for 4 drugs. The results bring novel insights into anti-Leishmania compounds.

Computer-Aided Drug Design of Anti-inflammatory Agents Targeting Microsomal Prostaglandin E2 Synthase-1 (mPGES-1)

Inflammation is a natural process in response to external stimuli associated with organism protection. However, this reaction could be exaggerated, leading to severe damages related to physiopathological processes, such as rheumatoid arthritis, cancer, diabetes, allergies, infections, among others. Inflammation is mainly characterized by pain, increased temperature, flushing, and edema, which can be controlled using anti-inflammatory drugs. In this context, prostaglandin E2 (PGE2) inhibition has been targeted for designing new compounds with anti-inflammatory properties. It is a bioactive lipid overproduced during an inflammatory process, in which its increased production is carried out mainly by COX-1, COX-2, and microsomal prostaglandin E2 synthase-1 (mPGES-1). Recently, studies have demonstrated that mPGES-1 inhibition is a safe strategy to develop anti-inflammatory agents, which could protect against pain, acute inflammation, arthritis, autoimmune diseases, and different types of cancers. To decrease production costs and increase the probability of discovering active substances, computer-aided drug design (CADD) approaches have been increasingly used for designing new inhibitors. Thus, this review will cover all aspects involving high-throughput virtual screening, molecular docking, dynamics, fragment-based drug design, quantitative structure-activity relationship in seeking new promising mPGES-1 inhibitors.

Repurposing FDA-approved Drugs Targeting SARS-CoV2 3CLpro: a study by applying Virtual Screening, Molecular Dynamics, MM-PBSA Calculations and Covalent Docking

Background:

Since the end of 2019, the etiologic agent SAR-CoV-2 responsible for one of the most significant epidemics in history has caused severe global economic, social, and health damages. The drug repurposing approach and application of Structure-based Drug Discovery (SBDD) using in silico techniques are increasingly frequent, leading to the identification of several molecules that may represent promising potential.

Method:

In this context, here we use in silico methods of virtual screening (VS), pharmacophore modeling (PM), and fragment-based drug design (FBDD), in addition to molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM -PBSA) calculations, and covalent docking (CD) for the identification of potential treatments against SARS-CoV-2. We initially validated the docking protocol followed by VS in 1,613 FDA-approved drugs obtained from the ZINC database. Thus, we identified 15 top hits, of which three of them were selected for further simulations. In parallel, for the compounds with a fit score value ≤ of 30, we performed the FBDD protocol, where we designed 12 compounds

Result:

By applying a PM protocol in the ZINC database, we identified three promising drug candidates. Then, the 9 top hits were evaluated in simulations of MD, MM-PBSA, and CD. Subsequently, MD showed that all identified hits showed stability at the active site without significant changes in the protein's structural integrity, as evidenced by the RMSD, RMSF, Rg, SASA graphics. They also showed interactions with the catalytic dyad (His41 and Cys145) and other essential residues for activity (Glu166 and Gln189) and high affinity for MM-PBSA, with possible covalent inhibition mechanism.

Conclution:

Finally, our protocol helped identify potential compounds wherein ZINC896717 (Zafirlukast), ZINC1546066 (Erlotinib), and ZINC1554274 (Rilpivirine) were more promising and could be explored in vitro, in vivo, and clinical trials to prove their potential as antiviral agents.

Heat Shock Proteins as Targets for Novel Antimalarial Drug Discovery

Plasmodium falciparum, the parasitic agent that is responsible for a severe and dangerous form of human malaria, has a history of long years of cohabitation with human beings with attendant negative consequences. While there have been some gains in the fight against malaria through the application of various control measures and the use of chemotherapeutic agents, and despite the global decline in malaria cases and associated deaths, the continual search for new and effective therapeutic agents is key to achieving sustainable development goals. An important parasite survival strategy, which is also of serious concern to the scientific community, is the rate at which the parasites continually develop resistance to drugs. Among the key players in the parasite's ability to develop resistance, maintain cellular integrity, and survives within an unusual environment of the red blood cells are the molecular chaperones of the heat shock proteins (HSP) family. HSPs constitute a novel avenue for antimalarial drug discovery and by exploring their ubiquitous nature and multifunctional activities, they may be suitable targets for the discovery of multi-targets antimalarial drugs, needed to fight incessant drug resistance. In this chapter, features of selected families of plasmodial HSPs that can be exploited in drug discovery are presented. Also, known applications of HSPs in small molecule screening, their potential usefulness in high throughput drug screening, as well as possible challenges are highlighted.

In silico methods and tools for drug discovery

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.

Molecular Modeling Targeting Transmembrane Serine Protease 2 (TMPRSS2) as an Alternative Drug Target Against Coronaviruses

Since November 2019, the new Coronavirus disease (COVID-19) caused by the etiological agent SARS-CoV-2 has been responsible for several cases worldwide, becoming pandemic in March 2020. Pharmaceutical industries and academics have joined their efforts to discover new therapies to control the disease, since there are no specific drugs to combat this emerging virus. Thus, several targets have been explored, among them the transmembrane protease serine 2 (TMPRSS2) has gained greater interest in the scientific community. In this context, this review will describe the importance of TMPRSS2 protease and the significant advances in virtual screening focused on discovering new inhibitors. In this review, it was observed that molecular modeling methods could be powerful tools in identifying new molecules against SARS-CoV-2. Thus, this review could be used to guide researchers worldwide to explore the biological and clinical potential of compounds that could be promising drug candidates against SARS-CoV-2, acting by inhibition of TMPRSS2 protein.

Fragment tailoring strategy to design novel chemical entities as potential binders of novel corona virus main protease

The recent pandemic of severe acute respiratory syndrome-coronavirus2 (SARS-CoV-2) infection (COVID-19) has put the world on serious alert. The main protease of SARS-CoV-2 (SARS-CoV-2-MPro) cleaves the long polyprotein chains to release functional proteins required for replication of the virus and thus is a potential drug target to design new chemical entities in order to inhibit the viral replication in human cells. The current study employs state of art computational methods to design novel molecules by linking molecular fragments which specifically bind to different constituent sub-pockets of the SARS-CoV-2-MPro binding site. A huge library of 191678 fragments was screened against the binding cavity of SARS-CoV-2-MPro and high affinity fragments binding to adjacent sub-pockets were tailored to generate new molecules. These newly formed molecules were further subjected to molecular docking, ADMET filters and MM-GBSA binding energy calculations to select 17 best molecules (named as MP-In1 to MP-In17), which showed comparable binding affinities and interactions with the key binding site residues as the reference ligand. The complexes of these 17 molecules and the reference molecule with SARS-CoV-2-MPro, were subjected to molecular dynamics simulations, which assessed the stabilities of their binding with SARS-CoV-2-MPro. Fifteen molecules were found to form stable complexes with SARS-CoV-2-MPro. These novel chemical entities designed specifically according to the pharmacophoric requirements of SARS-CoV-2-MPro binding pockets showed good synthetic feasibility and returned no exact match when searched against chemical databases. Considering their interactions, binding efficiencies and novel chemotypes, they can be further evaluated as potential starting points for SARS-CoV-2 drug discovery.

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.

Artificial Intelligence Applied to the Rapid Identification of New Antimalarial Candidates with Dual-Stage Activity

Increasing reports of multidrug-resistant malaria parasites urge the discovery of new effective drugs with different chemical scaffolds. Protein kinases play a key role in many cellular processes such as signal transduction and cell division, making them interesting targets in many diseases. Protein kinase 7 (PK7) is an orphan kinase from the Plasmodium genus, essential for the sporogonic cycle of these parasites. Here, we applied a robust and integrative artificial intelligence-assisted virtual-screening (VS) approach using shape-based and machine learning models to identify new potential PK7 inhibitors with in vitro antiplasmodial activity. Eight virtual hits were experimentally evaluated, and compound LabMol-167 inhibited ookinete conversion of Plasmodium berghei and blood stages of Plasmodium falciparum at nanomolar concentrations with low cytotoxicity in mammalian cells. As PK7 does not have an essential role in the Plasmodium blood stage and our virtual screening strategy aimed for both PK7 and blood-stage inhibition, we conducted an in silico target fishing approach and propose that this compound might also inhibit P. falciparum PK5, acting as a possible dual-target inhibitor. Finally, docking studies of LabMol-167 with P. falciparum PK7 and PK5 proteins highlighted key interactions for further hit-to lead optimization.

3-aryl-indolinones derivatives as antiplasmodial agents: synthesis, biological activity and computational analysis

Malaria is an infectious illness, affecting vulnerable populations in Third World countries. Inspired by natural products, indole alkaloids have been used as a nucleus to design new antimalarial drugs. So, eighteen oxindole derivatives, aza analogues were obtained with moderate to excellent yields. Also, the saturated derivatives of oxindole and aza derivatives via H₂/Pd/C reduction were obtained in good yields, leading to racemic mixtures of each compound. Next, the inhibitory activity against P. falciparum of 18 compounds were tested, founding six compounds with IC₅₀ < 20 µM. The most active of these compounds was 8c; however, their unsaturated derivative 7c was inactive. Then, a structure-activity relationship analysis was done, founding that focused LUMO lobe on the specific molecular zone is related to inhibitory activity against P. falciparum. Finally, we found a potential inhibition of lactate dehydrogenase by oxindole derivatives, using molecular docking virtual screening.

Virtual Screening Based on QSAR and Molecular Docking of Possible Inhibitors Targeting Chagas CYP51

Chagas is a neglected tropical disease caused by the parasite Trypanosoma cruzi with no effective treatment in all its forms. There is a need to find more effective therapeutic alternatives with reduced toxicity. In this contribution, multiple linear regression models were used to identify the molecular descriptors that best describe the inhibitory activity of 52 fenarimol analogues against Trypanosoma cruzi. The topological, physicochemical, thermodynamic, electronic, and charge descriptors were evaluated to cover a wide range of properties that frequently encode biological activity. A model with high predictive value was obtained based on geometrical descriptors and descriptors encoding hydrophobicity and London dispersion forces as necessary for the inhibition of Trypanosoma cruzi-CYP51. Docking methodology was implemented to evaluate molecular interactions in silico. The virtual screening results in this study can be used for rational design of new analogues with improved activity against Chagas disease.

Antiplasmodial activity of sulfonylhydrazones: in vitro and in silico approaches

Malaria is still a life-threatening public health issue, and the upsurge of resistant strains requires continuous generation of active molecules. In this work, 35 sulfonylhydrazone derivatives were synthesized and evaluated against Plasmodium falciparum chloroquine-sensitive (3D7) and resistant (W2) strains. The most promising compound, 5b, had an IC₅₀ of 0.22 μM against W2 and was less cytotoxic and 26-fold more selective than chloroquine. The structure-activity relationship model, statistical analysis and molecular modeling studies suggested that antiplasmodial activity was related to hydrogen bond acceptor count, molecular weight and partition coefficient of octanol/water and displacement of frontier orbitals to the heteroaromatic ring beside the imine bond. This study demonstrates that the synthesized molecules with a simple scaffold allow the hit-to-lead process for new antimalarials to commence.

Colombian Contributions Fighting Leishmaniasis: A Systematic Review on Antileishmanials Combined with Chemoinformatics Analysis

Leishmaniasis is a parasitic morbid/fatal disease caused by Leishmania protozoa. Twelve million people worldwide are appraised to be currently infected, including ca. two million infections each year, and 350 million people in 88 countries are at risk of becoming infected. In Colombia, cutaneous leishmaniasis (CL) is a public health problem in some tropical areas. Therapeutics is based on traditional antileishmanial drugs, but this practice has several drawbacks for patients. Thus, the search for new antileishmanial agents is a serious need, but the lack of adequately funded research programs on drug discovery has hampered its progress. Some Colombian researchers have conducted different research projects focused on the assessment of the antileishmanial activity of naturally occurring and synthetic compounds against promastigotes and/or amastigotes. Results of such studies have separately demonstrated important hits and reasonable potential, but a holistic view of them is lacking. Hence, we present the outcome from a systematic review of the literature (under PRISMA guidelines) on those Colombian studies investigating antileishmanials during the last thirty-two years. In order to combine the general efforts aiming at finding a lead against Leishmania panamensis (one of the most studied and incident parasites in Colombia causing CL) and to recognize structural features of representative compounds, fingerprint-based analyses using conventional machine learning algorithms and clustering methods are shown. Abstraction from such a meta-description led to describe some function-determining molecular features and simplify the clustering of plausible isofunctional hits. This systematic review indicated that the Colombian efforts for the antileishmanials discovery are increasingly intensified, though improvements in the followed pathways must be definitively pursued. In this context, a brief discussion about scope, strengths and limitations of such advances and relationships is addressed.

Discovery of potent inhibitors for SARS-CoV-2's main protease by ligand-based/structure-based virtual screening, MD simulations, and binding energy calculations

COVID-19 has caused lockdowns all over the world in early 2020, as a global pandemic. Both theoretical and experimental efforts are seeking to find an effective treatment to suppress the virus. In silico drug design can play a vital role in identifying promising drug candidates against COVID-19. Herein, we focused on the main protease of SARS-CoV-2 that has crucial biological functions in the virus. We performed a ligand-based virtual screening followed by a docking screening for testing approved drugs and bioactive compounds listed in the DrugBank and ChEMBL databases. The top 8 docking results were advanced to all-atom MD simulations to study the relative stability of the protein-ligand interactions. MD simulations support that the catalytic residue, His41, has a neutral side chain with a protonated delta position. An absolute binding energy (ΔG) of -42 kJ mol-1 for the protein-ligand (Mpro-N3) complex has been calculated using the potential-of-mean-force (geometrical) approach. Furthermore, the relative binding energies were computed for the top docking results. Our results suggest several promising approved and bioactive inhibitors of SARS-CoV-2 Mpro as follows: a bioactive compound, ChEMBL275592, which has the best MM/GBSA binding energy; the second-best compound, montelukast, is an approved drug used in the treatment of asthma and allergic rhinitis; the third-best compound, ChEMBL288347, is a bioactive compound. Bromocriptine and saquinavir are other approved drugs that also demonstrate stability in the active site of Mpro, albeit their relative binding energies are low compared to the N3 inhibitor. This study provides useful insights into de novo protein design and novel inhibitor development, which could reduce the cost and time required for the discovery of a potent drug to combat SARS-CoV-2.

High-throughput virtual screening of drug databanks for potential inhibitors of SARS-CoV-2 spike glycoprotein

COVID-19, which is caused by a novel coronavirus known as SARS-CoV-2, has spread rapidly around the world, and it has infected more than 29 million individuals as recorded on 16 September 2020. Much effort has been made to stop the virus from spreading, and there are currently no approved pharmaceutical products to treat COVID-19. Here, we apply an in silico approach to investigate more than 3800 FDA approved drugs on the viral RBD S₁-ACE2 interface as a target. The compounds were investigated through flexible ligand docking, ADME property calculations and protein–ligand interaction maps. Molecular dynamics (MD) simulations were also performed on eleven compounds to study the stability and the interactions of the protein–ligand complexes. The MD simulations show that bagrosin, chidamide, ebastine, indacaterol, regorafenib, salazosulfadimidine, silodosin and tasosartan are relatively stable near the C terminal domain (CTD1) of the S₁ subunit of the viral S protein. The relative MMGBSA binding energies show that silodosin has the best binding to the target. The constant velocity steered molecular dynamics (SMD) simulations show that silodosin preferentially interacts with the RBD S₁ and has potential to act as an interfering compound between viral spike–host ACE2 interactions.

Communicated by Ramaswamy H. Sarma

Anti-staphylococcal activity and mode of action of thioridazine photoproducts

Antibiotic resistance became an increasing risk for population health threatening our ability to fight infectious diseases. The objective of this study was to evaluate the activity of laser irradiated thioridazine (TZ) against clinically-relevant bacteria in view to fight antibiotic resistance. TZ in ultrapure water solutions was irradiated (1–240 min) with 266 nm pulsed laser radiation. Irradiated solutions were characterized by UV–Vis and FTIR absorption spectroscopy, thin layer chromatography, laser-induced fluorescence, and dynamic surface tension measurements. Molecular docking studies were made to evaluate the molecular mechanisms of photoproducts action against Staphylococcus aureus and MRSA. More general, solutions were evaluated for their antimicrobial and efflux inhibitory activity against a panel of bacteria of clinical relevance. We observed an enhanced antimicrobial activity of TZ photoproducts against Gram-positive bacteria. This was higher than ciprofloxacin effects for methicillin- and ciprofloxacin-resistant Staphylococcus aureus. Molecular docking showed the Penicillin-binding proteins PBP3 and PBP2a inhibition by sulforidazine as a possible mechanism of action against Staphylococcus aureus and MRSA strains, respectively. Irradiated TZ reveals possible advantages in the treatment of infectious diseases produced by antibiotic-resistant Gram-positive bacteria. TZ repurposing and its photoproducts, obtained by laser irradiation, show accelerated and low-costs of development if compared to chemical synthesis.

Validation of ligands targeting metacaspase-2 (MCA2) from Trypanosoma brucei brucei and their application to MCA5 from T. congolense as possible trypanocides

Metacaspases (MCAs) are ideal drug and diagnostic targets for animal and human African trypanosomiasis, as these cysteine peptidases are absent from the metazoan kingdom and have been implicated in the parasite cell cycle and cell death. Tsetse fly-transmitted trypanosomes that live free in the bloodstream and/or cerebrospinal fluid of the mammalian host cause animal and human African trypanosomiasis (nagana or sleeping sickness respectively). Chemotherapy and chemoprophylaxis are the main forms of control, but in contrast to human trypanocides, the veterinary drugs are old and drug resistance is on the increase. A peptidomimetic library targeting the MCA2 from Trypanosoma brucei brucei has ligands with low IC₅₀ values, some of which were antiparasitic. This study validates the inhibitory activity of these ligands using the protein structure solved by X-ray diffraction after the ligand library was published. Water molecules were shown to be important in substrate binding and strategies to improve the efficacy of these ligands are highlighted. These ligands appear to be pan-specific as they were docked into the active site of the homology modelled MCA5 of animal infective Trypanosoma congolense with similar binding energies and conformations.

A Comprehensive QSAR Study on Antileishmanial and Antitrypanosomal Cinnamate Ester Analogues

Parasitic infections like leishmaniasis and trypanosomiasis remain as a worldwide concern to public health. Improvement of the currently available drug discovery pipelines for those diseases is therefore mandatory. We have recently reported on the antileishmanial and antitrypanosomal activity of a set of cinnamate esters where we identified several compounds with interesting activity against L. donovani and T. brucei rhodesiense. For a better understanding of such compounds' anti-infective activity, analyses of the underlying structure-activity relationships, especially from a quantitative point of view, would be a prerequisite for rational further development of such compounds. Thus, quantitative structure-activity relationships (QSAR) modeling for the mentioned set of compounds and their antileishmanial and antitrypanosomal activity was performed using a genetic algorithm as main variable selection tool and multiple linear regression as statistical analysis. Changes in the composition of the training/test sets were evaluated (two randomly selected and one by Kennard-Stone algorithm). The effect of the size of the models (number of descriptors) was also investigated. The quality of all resulting models was assessed by a variety of validation parameters. The models were ranked by newly introduced scoring functions accounting for the fulfillment of each of the validation criteria evaluated. The test sets were effectively within the applicability domain of the best models, which demonstrated high robustness. Detailed analysis of the molecular descriptors involved in those models revealed strong dependence of activity on the number and type of polar atoms, which affect the hydrophobic/hydrophilic properties causing a prominent influence on the investigated biological activities.

Recent Advances in the Discovery of Novel Antiprotozoal Agents

Parasitic diseases have serious health, social, and economic impacts, especially in the tropical regions of the world. Diseases caused by protozoan parasites are responsible for considerable mortality and morbidity, affecting more than 500 million people worldwide. Globally, the burden of protozoan diseases is increasing and is been exacerbated because of a lack of effective medication due to the drug resistance and toxicity of current antiprotozoal agents. These limitations have prompted many researchers to search for new drugs against protozoan parasites. In this review, we have compiled the latest information (2012-2017) on the structures and pharmacological activities of newly developed organic compounds against five major protozoan diseases, giardiasis, leishmaniasis, malaria, trichomoniasis, and trypanosomiasis, with the aim of showing recent advances in the discovery of new antiprotozoal drugs.

Therapeutic Interventions for Countering Leishmaniasis and Chagas's Disease: From Traditional Sources to Nanotechnological Systems

The incidence of neglected diseases in tropical countries, such as Leishmaniasis and Chagas's disease, is attributed to a set of biological and ecological factors associated with the socioeconomic context of developing countries and with a significant burden to health care systems. Both Leishmaniasis and Chagas's disease are caused by different protozoa and develop diverse symptoms, which depend on the specific species infecting man. Currently available drugs to treat these disorders have limited therapeutic outcomes, frequently due to microorganisms' drug resistance. In recent years, significant efforts have been made towards the development of innovative drug delivery systems aiming to improve bioavailability and pharmacokinetic profiles of classical drug therapy. This paper discusses the key facts of Leishmaniasis and Chagas's disease, the currently available pharmacological therapies and the new drug delivery systems for conventional drugs.

Computational Approaches as Rational Decision Support Systems for Discovering Next-Generation Antitubercular Agents: Mini-Review

Tuberculosis, malaria, dengue, chikungunya, leishmaniasis etc. are a large group of neglected tropical diseases that prevail in tropical and subtropical countries, affecting one billion people every year. Minimal funding and grants for research on these scientific problems challenge many researchers to find a different way to reduce the extensive time and cost involved in the drug discovery cycle of these problems. Computer-aided drug design techniques have already been proved successful in the discovery of new molecules rationally by reducing the time and cost involved in the development of drugs. In the current minireview, we are highlighting on the molecular modeling studies published during 2010-2018 for target specific antitubercular agents. This review includes the studies of Structure-Based (SB) and Ligand-Based (LB) modeling and those involving Machine Learning (ML) techniques against different antitubercular targets such as dihydrofolate reductase (DHFR), enoyl Acyl Carrier Protein (ACP) reductase (InhA), catalase-peroxidase (KatG), enzyme antigen 85C, protein tyrosine phosphatases (PtpA and PtpB), dUTPase, thioredoxin reductase (MtTrxR), etc. The information presented in this review will help the researchers to get acquainted with the recent progress in the modeling studies of antitubercular agents.

High Throughput and Computational Repurposing for Neglected Diseases

Purpose

Neglected tropical diseases (NTDs) represent are a heterogeneous group of communicable diseases that are found within the poorest populations of the world. There are 23 NTDs that have been prioritized by the World Health Organization, which are endemic in 149 countries and affect more than 1.4 billion people, costing these developing economies billions of dollars annually. The NTDs result from four different causative pathogens: protozoa, bacteria, helminth and virus. The majority of the diseases lack effective treatments. Therefore, new therapeutics for NTDs are desperately needed.

Methods

We describe various high throughput screening and computational approaches that have been performed in recent years. We have collated the molecules identified in these studies and calculated molecular properties.

Results

Numerous global repurposing efforts have yielded some promising compounds for various neglected tropical diseases. These compounds when analyzed as one would expect appear drug-like. Several large datasets are also now in the public domain and this enables machine learning models to be constructed that then facilitate the discovery of new molecules for these pathogens.

Conclusions

In the space of a few years many groups have either performed experimental or computational repurposing high throughput screens against neglected diseases. These have identified compounds which in many cases are already approved drugs. Such approaches perhaps offer a more efficient way to develop treatments which are generally not a focus for global pharmaceutical companies because of the economics or the lack of a viable market. Other diseases could perhaps benefit from these repurposing approaches.

Electronic supplementary material

The online version of this article (10.1007/s11095-018-2558-3) contains supplementary material, which is available to authorized users.

Chemoinformatics Strategies for Leishmaniasis Drug Discovery

Leishmaniasis is a fatal neglected tropical disease (NTD) that is caused by more than 20 species of Leishmania parasites. The disease kills approximately 20,000 people each year and more than 1 billion are susceptible to infection. Although counting on a few compounds, the therapeutic arsenal faces some drawbacks such as drug resistance, toxicity issues, high treatment costs, and accessibility problems, which highlight the need for novel treatment options. Worldwide efforts have been made to that aim and, as well as in other therapeutic areas, chemoinformatics have contributed significantly to leishmaniasis drug discovery. Breakthrough advances in the comprehension of the parasites’ molecular biology have enabled the design of high-affinity ligands for a number of macromolecular targets. In addition, the use of chemoinformatics has allowed highly accurate predictions of biological activity and physicochemical and pharmacokinetics properties of novel antileishmanial compounds. This review puts into perspective the current context of leishmaniasis drug discovery and focuses on the use of chemoinformatics to develop better therapies for this life-threatening condition.

Cinnamoyl-Oxaborole Amides: Synthesis and Their in Vitro Biological Activity

Due to the increased interest in their application in the treatment of infectious diseases, boron-containing compounds have received a significant coverage in the literature. Herein, a small set of novel cinnamoly-oxaborole amides were synthesized and screened against nagana Trypanosoma brucei brucei for antitrypanosomal activity. Compound 5g emerged as a new hit with an in vitro IC50 value of 0.086 μM against T. b. brucei without obvious inhibitory activity against HeLa cell lines. The same series was also screened against other human pathogens, including Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), for which moderate to weak activity (10 to >125 μM) was observed. Similarly, these compounds exhibited moderate activity against the human protozoal pathogen Trichomonas vaginalis with no observed effect on common microbiome bacterial species. The cross-species inhibitory activity presents the possibility of these compounds serving as broad-spectrum antibiotics for these prevalent three human pathogens.

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

Neglected disease drug discovery is generally poorly funded compared with major diseases and hence there is an increasing focus on collaboration and precompetitive efforts such as public-private partnerships (PPPs). The More Medicines for Tuberculosis (MM4TB) project is one such collaboration funded by the EU with the goal of discovering new drugs for tuberculosis. Collaborative Drug Discovery has provided a commercial web-based platform called CDD Vault which is a hosted collaborative solution for securely sharing diverse chemistry and biology data. Using CDD Vault alongside other commercial and free cheminformatics tools has enabled support of this and other large collaborative projects, aiding drug discovery efforts and fostering collaboration. We will describe CDD's efforts in assisting with the MM4TB project.

Development of Simplified Heterocyclic Acetogenin Analogues as Potent and Selective Trypanosoma brucei Inhibitors

Neglected tropical diseases caused by parasitic infections are an ongoing and increasing concern. They are a burden to human and animal health, having the most devastating effect on the world's poorest countries. Building upon our previously reported triazole analogues, in this study we describe the synthesis and biological testing of other novel heterocyclic acetogenin-inspired derivatives, namely 3,5-isoxazoles, furoxans, and furazans. Several of these compounds maintain low-micromolar levels of inhibition against Trypanosoma brucei, whilst having no observable inhibitory effect on mammalian cells, leading to the possibility of novel lead compounds for selective treatment.