A potential antiviral against COVID-19 obtained from Byrsonima coccolobifolia leaves extract.

In this study, we specifically focused on the crude methanolic leaf extract of Byrsonima coccolobifolia, investigating its antifungal potential against human pathogenic fungi and its antiviral activity against COVID-19. Through the use of high-performance liquid chromatography coupled with electrospray ionization ion trap tandem mass spectrometry, direct infusion electrospray ionization ion trap tandem mass spectrometry, and chromatographic dereplication procedures, we identified galloyl quinic acid derivatives, catechin derivatives, proanthocyanidins, and flavonoid glycosides. The broth dilution assay revealed that the methanolic leaf extract of B. coccolobifolia exhibits antifungal activity against Cryptococcus neoformans (IC50 = 4 µg/mL). Additionally, docking studies were conducted to elucidate the interactions between the identified compounds and the central residues at the binding site of biological targets associated with COVID-19. Furthermore, the extract demonstrated an in vitro half-maximum effective concentration (EC50 = 7 µg/mL) and exhibited significant selectivity (>90%) toward SARS-CoV-2.

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.

MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling.

QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset's preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.

Structural analysis of factors related to FAM3C/ILEI dimerization and identification of inhibitor candidates targeting cancer treatment.

FAM3 is a superfamily of four cytokines that maintain a single globular structure ß -ß -α of three classes: FAM3A, B, C and D. FAM3C was the first member of this family related to cancer and is functionally characterized as an essential factor for the epithelial-mesenchymal transition (EMT), leading to late delays in tumor progression. Due to its crucial role in EMT and metastasis, FAM3C has been termed an interleukin-like EMT (ILEI) inducer. There are several studies on the part of FAM3C in the progression of cancer and other diseases. However, little is known about its cellular receptors and possible inhibitors. In this study, based on in silico approaches, we performed structural analyses of factors related to FAM3C/ILEI dimerization. We also identified four possible inhibitor candidates, expected to be exciting prototypes and could be submitted to future biological tests targeting cancer treatment.

Identification of inhibitors as drug candidates against Chagas disease.

Chagas disease, after more than a century after its discovery, is still a major public health problem. It is estimated that approximately 10 million people worldwide are infected with T. cruzi. However, the situation is more critical in Latin America and other regions where the disease is endemic. The largest number of cases occurs in Brazil, Argentina, and Mexico as more than 100 million people in these regions are located in areas with a high risk of contamination by the vector. The need for new therapeutic alternatives is urgent, as the available drugs have severe limitations such as low efficacy and high toxicity. From this scenario, in this work, we employed the virtual screening technique using cruzain and BDF2 as key biological targets for the survival of the parasite. Our objective was to identify potential inhibitors of T. cruzi trypomastigotes, which could be considered drug candidates against Chagas disease. For this, we employed different in silico methodologies and the obtained results were corroborated using in vitro biological assays. For the VS studies, a database containing synthetic compounds was simulated at the binding site of cruzain and BDF2. In addition, pharmacophoric models were constructed in the initial phases of VS, as well as other advanced analyses (molecular dynamics simulations, calculations of binding free energy, and ADME prediction) were carried out and the results allowed the selection of potential inhibitors of T. cruzi. Based on the obtained data, 32 different compounds commercially available were subjected to biological tests against the trypomastigote form of T. cruzi. As result, 11 of those compounds displayed significant activity against T. cruzi and can be considered potential candidates for the treatment of Chagas disease.

The Potential of Secondary Metabolites from Plants as Drugs or Leads against Trypanosoma cruzi-An Update from 2012 to 2021.

BACKGROUND: Chagas disease (American Trypanosomiasis) is classified by the World Health Organization (WHO) as one of the seventeen neglected tropical diseases (NTD), affecting, mainly, several regions of Latin America. INTRODUCTION: However, immigration has expanded the range of this disease to other continents. Thousands of patients with Chagas disease die annually, yet no new therapeutics for Chagas disease have been approved, with only nifurtimox and benznidazole available. Treatment with these drugs presents several challenges, including protozoan resistance, toxicity, and low efficacy. Natural products, including the secondary metabolites found in plants, offer a myriad of complex structures that can be sourced directly or optimized for drug discovery. METHODS: Therefore, this review aims to assess the literature from the last 10 years (2012-2021) and present the anti-T. cruzi compounds isolated from plants in this period, as well as briefly discuss computational approaches and challenges in natural product drug discovery. Using this approach, more than 350 different metabolites were divided based on their biosynthetic pathway alkaloids, terpenoids, flavonoids, polyketides, and phenylpropanoids which displayed activity against different forms of this parasite epimastigote, trypomastigote and more important, the intracellular form, amastigote. CONCLUSION: In this aspect, there are several compounds with high potential which could be considered as a scaffold for the development of new drugs for the treatment of Chagas disease-for this, more advanced studies must be performed including pharmacokinetics (PK) and pharmacodynamics (PD) analysis as well as conduction of in vivo assays, these being important limitations in the discovery of new anti-T. cruzi compounds.

Enzyme-Catalyzed Spiroacetal Formation in Polyketide Antibiotic Biosynthesis.

A key step in the biosynthesis of numerous polyketides is the stereospecific formation of a spiroacetal (spiroketal). We report here that spiroacetal formation in the biosynthesis of the macrocyclic polyketides ossamycin and oligomycin involves catalysis by a novel spiroacetal cyclase. OssO from the ossamycin biosynthetic gene cluster (BGC) is homologous to OlmO, the product of an unannotated gene from the oligomycin BGC. The deletion of olmO abolished oligomycin production and led to the isolation of oligomycin-like metabolites lacking the spiroacetal structure. Purified OlmO catalyzed complete conversion of the major metabolite into oligomycin C. Crystal structures of OssO and OlmO reveal an unusual 10-strand ß-barrel. Three conserved polar residues are clustered together in the ß-barrel cavity, and site-specific mutation of any of these residues either abolished or substantially diminished OlmO activity, supporting a role for general acid/general base catalysis in spiroacetal formation.

Recent Advances in the Prediction of Pharmacokinetics Properties in Drug Design Studies: A Review.

This review presents the main aspects related to pharmacokinetic properties, which are essential for the efficacy and safety of drugs. This topic is very important because the analysis of pharmacokinetic aspects in the initial design stages of drug candidates can increase the chances of success for the entire process. In this scenario, experimental and in silico techniques have been widely used. Due to the difficulties encountered with the use of some experimental tests to determine pharmacokinetic properties, several in silico tools have been developed and have shown promising results. Therefore, in this review, we address the main free tools/servers that have been used in this area, as well as some cases of application. Finally, we present some studies that employ a multidisciplinary approach with synergy between in silico, in vitro, and in vivo techniques to assess ADME properties of bioactive substances, achieving successful results in drug discovery and design.

Virtual screening and in vitro assays of novel hits as promising DPP-4 inhibitors.

Diabetes is a metabolic disorder that presents hyperglycemia and vascular complications due to the non-production of insulin or its inappropriate use by the body. One of the strategies to treat diabetes is the inhibition of dipeptidyl peptidase-4 (DPP-4) and it is interesting to conduct virtual screening studies to search for new inhibitors of the DPP-4 enzyme. This study involves a virtual screening using the crystallographic structure of DPP-4 and a compound subset from the ZINC database. To filter this compound subset, we used some physicochemical properties, positioning at the three DPP-4 binding sites, molecular interactions, and ADME-Tox properties. The conformations of ligands obtained from AutoDock Vina were analyzed using a consensus with other algorithms (AutoDock and GOLD). The compounds selected from virtual screening were submitted to biological assays using the "DPPIV-Glo™ protease assay". Cytotoxicity tests were also performed. One promising compound (ZINC1572309) established interactions with important residues at the binding site. The results of the ADME-Tox prediction for ZINC1572309 were compared with a reference drug (sitagliptin). The cytotoxicity of sitagliptin and ZINC1572309 were evaluated using the XTT short-term cytotoxic assay, including normal and tumor cell lines to observe the cellular response to inhibitor treatment at different genetic bases. Both compounds (ZINC1572309 and the reference drug - sitagliptin) also inhibited DPP-4 activity, suggesting interesting biological effects of the selected compound at non-cytotoxic concentrations. Therefore, from in silico and in vitro studies, a potential hit as DPP-4 inhibitor was discovered and it can be structurally optimized to achieve suitable activity and pharmacokinetic profiles.

Topomer-CoMFA proposed as a tool to construct dual EGFR/HER-2 models.

Protein kinases (in this case, HER-2 and EGFR) are involved in cancer-related diseases. Some reports have shown unique CoMFA models using the sum of activities expressed as pIC50 (-log IC50), as the classical CoMFA technique would not be the best strategy to construct models for multitarget therapy considering that the molecular alignment will not be the same for different targets. An alternative for this problem is the use of Topomer-CoMFA, a variation of CoMFA, which does not require the alignment step in the generation of 3D models. In this study, we propose the combined use of the sum of activities and Topomer-CoMFA for the construction of a unique dual 3D model considering the inhibitory activities against EGFR and HER-2. For this, 88 compounds from the literature were divided into two groups: training (71) and test (17) sets. The biological activity of each compound, expressed as IC50 for EGFR and HER-2, was transformed into pIC50, summed, and used as the dependent variable in the Topomer-CoMFA analyses. The obtained model was considered statistically robust in the prediction of the dual activity of new compounds. Finally, based on the obtained model, we proposed structural modifications to some of the compounds used to improve the biological data. From the 3D model, we suggested new derivative compounds with improved biological activity for both targets. Therefore, the combination of the techniques proposed in this study proves to be a good strategy to construct better statistical models that can predict biological activities in multitarget systems.

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade.

Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.

The application of machine learning techniques to innovative antibacterial discovery and development.

INTRODUCTION: After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing prevalence of antibiotic drug resistance. However, the increasing amount of available data promotes the use of machine learning techniques (MLT) in drug discovery projects (e.g. construction of regression/classification models and ranking/virtual screening of compounds). AREAS COVERED: In this review, the authors cover some of the applications of MLT in medicinal chemistry, focusing on the development of new antibiotics, the prediction of resistance and its mechanisms. The aim of this review is to illustrate the main advantages and disadvantages and the major trends from studies over the past 5 years. EXPERT OPINION: The application of MLT to antibacterial drug discovery can aid the selection of new and potent lead compounds, with desirable pharmacokinetic and toxic profiles for further optimization. The increasing volume of available data along with the constant improvement in computational power and algorithms has meant that we are experiencing a transition in the way we face modern issues such as drug resistance, where our decisions are data-driven and experiments can be focused by data-suggested hypotheses.

Competition Between Phenothiazines and BH3 Peptide for the Binding Site of the Antiapoptotic BCL-2 Protein.

The study of proteins and mechanisms involved in the apoptosis and new knowledge about cancer's biology are essential for planning new drugs. Tumor cells develop several strategies to gain proliferative advantages, including molecular alterations to evade from apoptosis. Failures in apoptosis could contribute to cancer pathogenesis, since these defects can cause the accumulation of dividing cells and do not remove genetic variants that have malignant potential. The apoptosis mechanism is composed by proteins that are members of BCL-2 and cysteine-protease families. BH3-only peptides are the "natural" intracellular ligands of BCL-2 family proteins. On the other hand, studies have proved that phenothiazine compounds influence the induction of cellular death. To understand the characteristics of phenothiazines and their effects on tumoral cells and organelles involved in the apoptosis, as well as evaluating their pharmacologic potential, we have carried out computational simulation with the purpose of relating the structures of the phenothiazines with their biological activity. Since the tridimensional (3D) structure of the target protein is known, we have employed the molecular docking approach to study the interactions between compounds and the protein's active site. Hereafter, the molecular dynamics technique was used to verify the temporal evolution of the BCL-2 complexes with phenothiazinic compounds and the BH3 peptide, the stability and the mobility of these molecules in the BCL-2 binding site. From these results, the calculation of binding free energy between the compounds and the biological target was carried out. Thus, it was possible to verify that thioridazine and trifluoperazine tend to increase the stability of the BCL-2 protein and can compete for the binding site with the BH3 peptide.

Integrated Protocol to Design Potential Inhibitors of Dipeptidyl Peptidase- 4 (DPP-4).

BACKGROUND: A strategy for the treatment of type II diabetes mellitus is the inhibition of the enzyme known as dipeptidyl peptidase-4 (DPP-4). AIMS: This study aims to investigate the main interactions between DPP-4 and a set of inhibitors, as well as proposing potential candidates to inhibit this enzyme. METHODS: We performed molecular docking studies followed by the construction and validation of CoMFA and CoMSIA models. The information provided from these models was used to aid in the search for new candidates to inhibit DPP-4 and the design of new bioactive ligands from structural modifications in the most active molecule of the studied series. RESULTS: We were able to propose a set of analogues with biological activity predicted by the CoMFA and CoMSIA models, suggesting that our protocol can be used to guide the design of new DPP-4 inhibitors as drug candidates to treat diabetes. CONCLUSION: Once the integration of the techniques mentioned in this article was effective, our strategy can be applied to design possible new DPP-4 inhibitors as candidates to treat diabetes.

Advances with support vector machines for novel drug discovery.

INTRODUCTION: Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learning techniques. SVMs and their variations offer numerous drug discovery applications, which range from the classification of substances (as active or inactive) to the construction of regression models and the ranking/virtual screening of databased compounds. Areas covered: Herein, the authors consider some of the applications of SVMs in medicinal chemistry, illustrating their main advantages and disadvantages, as well as trends in their utilization, via the available published literature. The aim of this review is to provide an up-to-date review of the recent applications of SVMs in drug discovery as described by the literature, thereby highlighting their strengths, weaknesses, and future challenges. Expert opinion: Techniques based on SVMs are considered as powerful approaches in early drug discovery. The ability of SVMs to classify active or inactive compounds has enabled the prioritization of substances for virtual screening. Indeed, one of the main advantages of SVMs is related to their potential in the analysis of nonlinear problems. However, despite successes in employing SVMs, the challenges of improving accuracy remain.

Advances and Perspectives in Applying Deep Learning for Drug Design and Discovery.

Discovering (or planning) a new drug candidate involves many parameters, which makes this process slow, costly, and leading to failures at the end in some cases. In the last decades, we have witnessed a revolution in the computational area (hardware, software, large-scale computing, etc.), as well as an explosion in data generation (big data), which raises the need for more sophisticated algorithms to analyze this myriad of data. In this scenario, we can highlight the potentialities of artificial intelligence (AI) or computational intelligence (CI) as a powerful tool to analyze medicinal chemistry data. According to IEEE, computational intelligence involves the theory, the design, the application, and the development of biologically and linguistically motivated computational paradigms. In addition, CI encompasses three main methodologies: neural networks (NN), fuzzy systems, and evolutionary computation. In particular, artificial neural networks have been successfully applied in medicinal chemistry studies. A branch of the NN area that has attracted a lot of attention refers to deep learning (DL) due to its generalization power and ability to extract features from data. Therefore, in this mini-review we will briefly outline the present scope, advances, and challenges related to the use of DL in drug design and discovery, describing successful studies involving quantitative structure-activity relationships (QSAR) and virtual screening (VS) of databases containing thousands of compounds.

Studies on the Dual Activity of EGFR and HER-2 Inhibitors Using Structure-Based Drug Design Techniques.

HER-2 and EGFR are biological targets related to the development of cancer and the discovery and/or development of a dual inhibitor could be a good strategy to design an effective drug candidate. In this study, analyses of the chemical properties of a group of substances having affinity for both HER-2 and EGFR were carried out with the aim of understanding the main factors involved in the interaction between these inhibitors and the biological targets. Comparative analysis of molecular interaction fields (CoMFA) and comparative molecular similarity index analysis (CoMSIA) techniques were applied on 63 compounds. From CoMFA analyses, we found for both HER-2 (r² calibration = 0.98 and q²cv = 0.83) and EGFR (r² calibration = 0.98 and q²cv = 0.73) good predictive models. Good models for CoMSIA technique have also been found for HER-2 (r² calibration = 0.92 and q²cv = 0.74) and EGFR (r² calibration = 0.97 and q²cv = 0.72). The constructed models could indicate some important characteristics for the inhibition of the biological targets. New compounds were proposed as candidates to inhibit both proteins. Therefore, this study may guide future projects for the development of new drug candidates for the treatment of breast cancer.

Structural Dynamics of DPP-4 and Its Influence on the Projection of Bioactive Ligands.

Dipeptidyl peptidase-4 (DPP-4) is a target to treat type II diabetes mellitus. Therefore, it is important to understand the structural aspects of this enzyme and its interaction with drug candidates. This study involved molecular dynamics simulations, normal mode analysis, binding site detection and analysis of molecular interactions to understand the protein dynamics. We identified some DPP-4 functional motions contributing to the exposure of the binding sites and twist movements revealing how the two enzyme chains are interconnected in their bioactive form, which are defined as chains A (residues 40-767) and B (residues 40-767). By understanding the enzyme structure, its motions and the regions of its binding sites, it will be possible to contribute to the design of new DPP-4 inhibitors as drug candidates to treat diabetes.

Advances and Challenges in Drug Design of PPARδ Ligands.

BACKGROUND: Peroxisome proliferator-activated receptors (PPAR) are nuclear receptors activated by endogenous fatty acids and prostaglandins that are classified into three types: α, γ and δ, which have different functions and tissue distribution. PPAR modulators have been exploited to the treatment of important metabolic diseases, such as type 2 diabetes mellitus and metabolic syndrome, which are considered relevant epidemic diseases currently. Along the last decades, several studies have reported structural differences between the three PPAR subtypes associated with the discovery of selective ligands, dual and pan-agonists. Nowadays, there are several approved drugs that activate PPARα (fibrates) and PPARγ (glitazones), but up to now there is none clinically used drug targeting PPARδ. Additionally, several side-effects associated with the use of PPARα and γ agonists are reported by regulatory agencies, which do not indicate anymore their use as first-line drugs. OBJECTIVE: A significant new market has grown in the last years, focusing on the development of new PPARδ agonists as drug candidates to treat metabolic diseases and, in this sense, this study proposes to review the structural requirements to achieve selective PPARδ activation, as well to discuss the most relevant agonists in clinical trials, providing information on the current phase in the drug discovery and design targeting PPARδ. CONCLUSION: Several PPARδ ligands with high potency were reported in the literature and were designed or discovered by a combination of experimental and computational approaches. Furthermore, the reported importance of pockets and individual residues at PPARδ binding site as well as the importance of substituent and some physicochemical properties that could help to design of new classes of agonists.

MD simulations and multivariate studies for modeling the antileishmanial activity of peptides.

Leishmaniasis, a protozoan-caused disease, requires alternative treatments with minimized side-effects and less prone to resistance development. Antimicrobial peptides represent a possible choice to be developed. We report on the prospection of structural parameters of 23 helical antimicrobial and leishmanicidal peptides as a tool for modeling and predicting the activity of new peptides. This investigation is based on molecular dynamic simulations (MD) in mimetic membrane environment, as most of these peptides share the feature of interacting with phospholipid bilayers. To overcome the lack of experimental data on peptides' structures, we started simulations from designed 100% α-helices. This procedure was validated through comparisons with NMR data and the determination of the structure of Decoralin-amide. From physicochemical features and MD results, descriptors were raised and statistically related to the minimum inhibitory concentration against Leishmania by the multivariate data analysis technique. This statistical procedure confirmed five descriptors combined by different loadings in five principal components. The leishmanicidal activity depends on peptides' charge, backbone solvation, volume, and solvent-accessible surface area. The generated model possesses good predictability (q2  = 0.715, r2  = 0.898) and is indicative for the most and the least active peptides. This is a novel theoretical path for structure-activity studies combining computational methods that identify and prioritize the promising peptide candidates.