Cheminformatics-driven discovery of hit compounds against Paracoccidioides spp.

Aims: The development of safe and effective therapies for treating paracoccidioidomycosis using computational strategies were employed to discover anti-Paracoccidioides compounds. Materials & methods: We 1) collected, curated and integrated the largest library of compounds tested against Paracoccidioides spp.; 2) employed a similarity search to virtually screen the ChemBridge database and select nine compounds for experimental evaluation; 3) performed an experimental evaluation to determine the minimum inhibitory concentration and minimum fungicidal concentration as well as cytotoxicity; and 4) employed computational tools to identify potential targets for the most active compounds. Seven compounds presented activity against Paracoccidioides spp. Conclusion: These compounds are new hits with a predicted mechanisms of action, making them potentially attractive to develop new compounds.

Multitask learning-driven identification of novel antitrypanosomal compounds.

Background: Chagas disease and human African trypanosomiasis cause substantial death and morbidity, particularly in low- and middle-income countries, making the need for novel drugs urgent. Methodology & results: Therefore, an explainable multitask pipeline to profile the activity of compounds against three trypanosomes (Trypanosoma brucei brucei, Trypanosoma brucei rhodesiense and Trypanosoma cruzi) were created. These models successfully discovered four new experimental hits (LC-3, LC-4, LC-6 and LC-15). Among them, LC-6 showed promising results, with IC50 values ranging 0.01-0.072 µM and selectivity indices >10,000. Conclusion: These results demonstrate that the multitask protocol offers predictivity and interpretability in the virtual screening of new antitrypanosomal compounds and has the potential to improve hit rates in Chagas and human African trypanosomiasis projects.

Selene-Ethylenelacticamides and N-Aryl-Propanamides as Broad-Spectrum Leishmanicidal Agents.

The World Health Organization classifies Leishmania as one of the 17 "neglected diseases" that burden tropical and sub-tropical climate regions with over half a million diagnosed cases each year. Despite this, currently available anti-leishmania drugs have high toxicity and the potential to be made obsolete by parasite drug resistance. We chose to analyze organoselenides for leishmanicidal potential given the reduced toxicity inherent to selenium and the displayed biological activity of organoselenides against Leishmania. Thus, the biological activities of 77 selenoesters and their N-aryl-propanamide derivatives were predicted using robust in silico models of Leishmania infantum, Leishmania amazonensis, Leishmania major, and Leishmania (Viannia) braziliensis. The models identified 28 compounds with >60% probability of demonstrating leishmanicidal activity against L. infantum, and likewise, 26 for L. amazonesis, 25 for L. braziliensis, and 23 for L. major. The in silico prediction of ADMET properties suggests high rates of oral absorption and good bioavailability for these compounds. In the in silico toxicity evaluation, only seven compounds showed signs of toxicity in up to one or two parameters. The methodology was corroborated with the ensuing experimental validation, which evaluated the inhibition of the Promastigote form of the Leishmania species under study. The activity of the molecules was determined by the IC50 value (µM); IC50 values < 20 µM indicated better inhibition profiles. Sixteen compounds were synthesized and tested for their activity. Eight molecules presented IC50 values < 20 µM for at least one of the Leishmania species under study, with compound NC34 presenting the strongest parasite inhibition profile. Furthermore, the methodology used was effective, as many of the compounds with the highest probability of activity were confirmed by the in vitro tests performed.

Four diterpenes identified in silico were isolated from Hyptidinae and demonstrated in vitro activity against Mycobacterium tuberculosis.

Plants of Hyptidinae subtribe (Lamiaceae - family), as Mesosphaerum sidifolium, are a source of bioactive molecules. In the search for new drug candidates, we perform chemical characterization of diterpenes isolated from the aerial parts of M. sidifolium was carried out with uni- and bidimensional NMR spectral data, and evaluate in silico through the construction of a predictive model followed by in vitro testing Mycobacterium tuberculosis and Mycobacterium smegmatis. Resulted in the isolation of four components: Pomiferin D (1), Salviol (2), Pomiferin E (3) and 2α-hydroxysugiol (4), as well as two phenolic compounds, rosmarinic and caffeic acids. In silico model identified 48 diterpenes likely to have biological activity against M. tuberculosis. The diterpenes isolated were tested in vitro against M. tuberculosis demonstrating MIC = 125 µM for 4 and 1, while 2 and 3 -MIC = 250 µM. These compounds did not show biological activity at these concentrations for M. smegmatis.

MolPredictX: Online Biological Activity Predictions by Machine Learning Models.

Here we report the development of MolPredictX, an innovate and freely accessible web interface for biological activity predictions of query molecules. MolPredictX utilizes in-house QSAR models to provide 27 qualitative predictions (active or inactive), and quantitative probabilities for bioactivity against parasitic (Trypanosoma and Leishmania), viral (Dengue, Sars-CoV and Hepatitis C), pathogenic yeast (Candida albicans), bacterial (Salmonella enterica and Escherichia coli), and Alzheimer disease enzymes. In this article, we introduce the methodology and usability of this webtool, highlighting its potential role in the development of new drugs against a variety of diseases. MolPredictX is undergoing continuous development and is freely available at https://www.molpredictx.ufpb.br/.

Natural Products from Annonaceae as Potential Antichagasic Agents.

Chagas disease, a neglected tropical disease, is endemic in 21 Latin American countries and particularly prevalent in Brazil. Chagas disease has drawn more attention in recent years due to its expansion into non-endemic areas. The aim of this work was to computationally identify and experimentally validate the natural products from an Annonaceae family as antichagasic agents. Through the ligand-based virtual screening, we identified 57 molecules with potential activity against the epimastigote form of T. cruzi. Then, 16 molecules were analyzed in the in vitro study, of which, six molecules displayed previously unknown antiepimastigote activity. We also evaluated these six molecules for trypanocidal activity. We observed that all six molecules have potential activity against the amastigote form, but no molecules were active against the trypomastigote form. 13-Epicupressic acid seems to be the most promising, as it was predicted as an active compound in the in silico study against the amastigote form of T. cruzi, in addition to having in vitro activity against the epimastigote form.

Recent Studies on Neglected Drug Design.

BACKGROUND: Neglected diseases require special attention when looking for new therapeutic alternatives, as these are diseases of extreme complexity and severity that affect populations belonging to lower social classes who lack access to basic rights, such as sanitation. INTRODUCTION: Among the alternatives available for obtaining new drugs is Medicinal Chemistry, which is responsible for the discovery, identification, invention, and preparation of prototypes. In this perspective, the present work aims to make a bibliographic review on the recent studies of Medicinal Chemistry applied to neglected diseases. METHODS: A literature review was carried out by searching the "Web of Sciences" database, including recent articles published on the Neglected Drug Design. RESULTS: In general, it was noticed that the most studied neglected diseases corresponded to Chagas disease and leishmaniasis, with studies on organic synthesis, optimization of structures, and molecular hybrids being the most used strategies. It is also worth mentioning the growing number of computationally developed studies, providing speed and optimization of costs in the procurement process. CONCLUSION: The CADD approach and organic synthesis studies, when applied in the area of Medicinal Chemistry, have proven to be important in the research and discovery of drugs for Neglected Diseases, both in terms of planning the experimental methodology used to obtain it and in the selection of compounds with higher activity potential.

Chemical safety assessment of transformation products of landfill leachate formed during the Fenton process.

Organic chemicals identified in raw landfill leachate (LL) and their transformation products (TPs), formed during Fenton treatment, were analyzed for chemical safety following REACH guidelines. The raw LL was located in the metropolitan region of Campina Grande, in northeast Brazil. We elucidated 197 unique chemical structures, including 154 compounds that were present in raw LL and 82 compounds that were detected in the treated LL, totaling 39 persistent compounds and 43 TPs. In silico models were developed to identify and prioritize the potential level of hazard/risk these compounds pose to the environment and society. The models revealed that the Fenton process improved the biodegradability of TPs. Still, a slight increase in ecotoxicological effects was observed among the compounds in treated LL compared with those present in raw LL. No differences were observed for aryl hydrocarbon receptor (AhR) and antioxidant response element (ARE) mutagenicity. Similar behavior among both raw and treated LL samples was observed for biodegradability; Tetrahymena pyriformis, Daphnia magna, Pimephales promelas and ARE, AhR, and Ames mutagenicity. Overall, our results suggest that raw and treated LL samples have similar activity profiles for all endpoints other than biodegradability.

The SistematX Web Portal of Natural Products: An Update.

Natural products and their secondary metabolites are promising starting points for the development of drug prototypes and new drugs, as many current treatments for numerous diseases are directly or indirectly related to such compounds. State-of-the-art, curated, integrated, and frequently updated databases of secondary metabolites are thus highly relevant to drug discovery. The SistematX Web Portal, introduced in 2018, is undergoing development to address this need and documents crucial information about plant secondary metabolites, including the exact location of the species from which the compounds were isolated. SistematX also allows registered users to log in to the data management area and gain access to administrative pages. This study reports recent updates and modifications to the SistematX Web Portal, including a batch download option, the generation and visualization of 1H and 13C nuclear magnetic resonance spectra, and the calculation of physicochemical (drug-like and lead-like) properties and biological activity profiles. The SistematX Web Portal is freely available at http://sistematx.ufpb.br.

Natural Products as Potential Agents against SARS-CoV and SARSCoV- 2.

BACKGROUND: Natural products are useful agents for the discovery of new lead- compounds and effective drugs to combat coronaviruses (CoV). OBJECTIVE: The present work provides an overview of natural substances, plant extracts, and essential oils as potential anti-SARS-CoV agents. In addition, this work evaluates their drug-like properties which are essential in the selection of compounds in order to accelerate the drug development process. METHODS: The search was carried out using PubMed, ScienceDirect and SciFinder. Articles addressing plant-based natural products as potential SARS-CoV or SARS-CoV-2 agents within the last seventeen years were analyzed and selected. The descriptors for Chemometrics analysis were obtained in alvaDesc and the principal component analysis (PCA) was carried out in SIMCA version 13.0. RESULTS: Based on in vitro assays and computational analyses, this review covers twentynine medicinal plant species and more than 300 isolated substances as potential anti-coronavirus agents. Among them, flavonoids and terpenes are the most promising compound classes. In silico analyses of drug-like properties corroborate these findings and indicate promising candidates for in vitro and in vivo studies to validate their activity. CONCLUSION: This paper highlights the role of ethnopharmacology in drug discovery and suggests the use of integrative (in silico/ in vitro) and chemocentric approaches to strengthen current studies and guide future research in the field of antiviral agents.

Selenium and Computational Studies.

BACKGROUND: Organocalcogens are a class of organic compounds obtained by the synthesis experiments to include S, Se, or Te. Among the elements that comprise this class, Se is characterized as an essential mineral and nutrient for humans. Se has been widely studied in many aspects. Organic synthesis of organoselenides is used for obtaining new potential drug candidates and may be highly beneficial from the use of computational approaches to reduce time and cost of the experiments. Thus, the goal of our study is to evaluate the computational approaches used in the organoselenides research from 1999 to 2019. METHODS: A literature review was performed by searching the database "Web of Sciences". RESULTS: Most of the theoretical studies included structural elucidation or structure-property analysis. We also found research regarding molecular docking approaches and Quantitative Structure-Activity Relationship (QSAR) studies. CONCLUSIONS: Computational studies have been widely applied to organoselenides. They demonstrated promising results and resulted in reduced the cost of research, increased efficacy, and, ultimately, novel organoselenides with desired properties.

Pred-Skin: A Web Portal for Accurate Prediction of Human Skin Sensitizers.

Safety assessment is an essential component of the regulatory acceptance of industrial chemicals. Previously, we have developed a model to predict the skin sensitization potential of chemicals for two assays, the human patch test and murine local lymph node assay, and implemented this model in a web portal. Here, we report on the substantially revised and expanded freely available web tool, Pred-Skin version 3.0. This up-to-date version of Pred-Skin incorporates multiple quantitative structure-activity relationship (QSAR) models developed with in vitro, in chemico, and mice and human in vivo data, integrated into a consensus naïve Bayes model that predicts human effects. Individual QSAR models were generated using skin sensitization data derived from human repeat insult patch tests, human maximization tests, and mouse local lymph node assays. In addition, data for three validated alternative methods, the direct peptide reactivity assay, KeratinoSens, and the human cell line activation test, were employed as well. Models were developed using open-source tools and rigorously validated according to the best practices of QSAR modeling. Predictions obtained from these models were then used to build a naïve Bayes model for predicting human skin sensitization with the following external prediction accuracy: correct classification rate (89%), sensitivity (94%), positive predicted value (91%), specificity (84%), and negative predicted value (89%). As an additional assessment of model performance, we identified 11 cosmetic ingredients known to cause skin sensitization but were not included in our training set, and nine of them were accurately predicted as sensitizers by our models. Pred-Skin can be used as a reliable alternative to animal tests for predicting human skin sensitization.

Computer-assisted discovery of compounds with insecticidal activity against Musca domestica and Mythimna separata.

Pesticides are used to control and combat insects and pests in the agricultural sector, households, and public health programs. The frequent and disorderly use of these pesticides may lead to variety of undesired effects. Therefore, natural products have many advantages over to synthetic compounds to be used as insecticides. The goal of this study was to find natural products with insecticidal potential against Musca domestica and Mythimna separata. To achieve this goal, we developed predictive QSAR models using MuDRA, PLS, and RF approaches and performed virtual screening of 117 natural products. As a result of QSAR modeling, we formulated the recommendations regarding physico-chemical characteristics for promising compounds active against Musca domestica and Mythimna separata. Homology models were successfully built for both species and molecular docking of QSAR hits vs known insecticides allowed us to prioritize twenty-two compounds against Musca domestica and six against Mythimna separata. Our results suggest that pimarane diterpenes, abietanes diterpenes, dimeric diterpenes and scopadulane diterpenes obtained from aerial parts of species of the genus Calceolaria (Calceolariaceae: Scrophulariaceae) can be considered as potential insecticidal.

Discovery of Alternative Chemotherapy Options for Leishmaniasis through Computational Studies of Asteraceae.

Leishmaniasis is a complex disease caused by over 20 Leishmania species that primarily affects populations with poor socioeconomic conditions. Currently available drugs for treating leishmaniasis include amphotericin B, paromomycin, and pentavalent antimonials, which have been associated with several limitations, such as low efficacy, the development of drug resistance, and high toxicity. Natural products are an interesting source of new drug candidates. The Asteraceae family includes more than 23 000 species worldwide. Secondary metabolites that can be found in species from this family have been widely explored as potential new treatments for leishmaniasis. Recently, computational tools have become more popular in medicinal chemistry to establish experimental designs, identify new drugs, and compare the molecular structures and activities of novel compounds. Herein, we review various studies that have used computational tools to examine various compounds identified in the Asteraceae family in the search for potential drug candidates against Leishmania.

Correction: QSAR without borders.

Correction for 'QSAR without borders' by Eugene N. Muratov et al., Chem. Soc. Rev., 2020, DOI: 10.1039/d0cs00098a.

Quantitative Structure-Activity Relationship Modeling and Docking of Monoterpenes with Insecticidal Activity Against Reticulitermes chinensis Snyder and Drosophila melanogaster.

The goal of this study was to perform in silico identification of bioinsecticidal potential of 42 monoterpenes against Drosophila melanogaster and Reticulitermes chinensis Snyder. Quantitative structure-activity relationship (QSAR) modeling was performed for both organisms, while docking and molecular dynamics were used only for Drosophila melanogaster. Neryl acetate has the lowest interaction energy (-87 kcal/mol) against active site of acetylcholinesterase, which is comparable to the ones of methiocarb and pirimicarb (-90 kcal/mol) and reported PDB binder 9-(3-iodobenzylamino)-1,2,3,4-tetrahydroacridine (-112.67 kcal/mol). Interaction stability was verified by molecular dynamics simulations and showed that the stability of ACHE active site complexes with three selected terpenes is comparable to the one of the pirimicarb and methiocarb. Overall, our results suggest that pulegone, citronellal, carvacrol, linalyl acetate, neryl acetate, citronellyl acetate, and geranyl acetate may be considered as a potential pesticide candidates.

Deep Learning-driven research for drug discovery: Tackling Malaria.

Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.