Methyleugenol Has an Antidepressant Effect in a Neuroendocrine Model: In Silico and In Vivo Evidence.

Major depressive disorder is a severe mood disorder characterized by different emotions and feelings. This study investigated the antidepressant activity of the phenylpropanoid methyleugenol (ME) in adult female mice exposed to a stress model induced by dexamethasone. The animals were randomly divided into groups containing eight animals and were pre-administered with dexamethasone (64 µg/kg subcutaneously). After 165 and 180 min, they were treated with ME (25, 50 and 100 mg/kg intraperitoneally) or imipramine (10 mg/kg intraperitoneally) after 45 min and 30 min, respectively; they were then submitted to tests which were filmed. The videos were analyzed blindly. In the tail suspension test, ME (50 mg/kg) increased latency and reduced immobility time. In the splash test, ME (50 mg/kg) decreased grooming latency and increased grooming time. In the open field, there was no statistical difference for the ME groups regarding the number of crosses, and ME (50 mg/kg) increased the number of rearing and time spent in the center. Regarding in silico studies, ME interacted with dopaminergic D1 and α1 adrenergic pathway receptors and with tryptophan hydroxylase inhibitor. In the in vivo evaluation of the pathways of action, the antidepressant potential of ME (50 mg/kg) was reversed by SCH23390 (4 mg/kg intraperitoneally) dopaminergic D1 receptor, Prazosin (1 mg/kg intraperitoneally) α1 adrenergic receptor, and PCPA (4 mg/kg intraperitoneally) tryptophan hydroxylase inhibitor. Our findings indicate that ME did not alter with the locomotor activity of the animals and shows antidepressant activity in female mice with the participation of the D1, α1 and serotonergic systems.

Kaurane-Type Diterpenoids as Potential Inhibitors of Dihydrofolate Reductase-Thymidylate Synthase in New World Leishmania Species.

The bifunctional enzyme Dihydrofolate reductase-thymidylate synthase (DHFR-TS) plays a crucial role in the survival of the Leishmania parasite, as folates are essential cofactors for purine and pyrimidine nucleotide biosynthesis. However, DHFR inhibitors are largely ineffective in controlling trypanosomatid infections, largely due to the presence of Pteridine reductase 1 (PTR1). Therefore, the search for structures with dual inhibitory activity against PTR1/DHFR-TS is crucial in the development of new anti-Leishmania chemotherapies. In this research, using the Leishmania major DHFR-TS recombinant protein, enzymatic inhibitory assays were performed on four kauranes and two derivatives that had been previously tested against LmPTR1. The structure 302 (6.3 µM) and its derivative 302a (4.5 µM) showed the lowest IC50 values among the evaluated molecules. To evaluate the mechanism of action of these structures, molecular docking calculations and molecular dynamics simulations were performed using a DHFR-TS hybrid model. Results showed that hydrogen bond interactions are critical for the inhibitory activity against LmDHFR-TS, as well as the presence of the p-hydroxyl group of the phenylpropanoid moiety of 302a. Finally, additional computational studies were performed on DHFR-TS structures from Leishmania species that cause cutaneous and mucocutaneous leishmaniasis in the New World (L. braziliensis, L. panamensis, and L. amazonensis) to explore the targeting potential of these kauranes in these species. It was demonstrated that structures 302 and 302a are multi-Leishmania species compounds with dual DHFR-TS/PTR1 inhibitory activity.

Machine-Learning- and Structure-Based Virtual Screening for Selecting Cinnamic Acid Derivatives as Leishmania major DHFR-TS Inhibitors.

The critical enzyme dihydrofolate reductase-thymidylate synthase in Leishmania major (LmDHFR-TS) serves a dual-purpose role and is essential for DNA synthesis, a cornerstone of the parasite's reproductive processes. Consequently, the development of inhibitors against LmDHFR-TS is crucial for the creation of novel anti-Leishmania chemotherapies. In this study, we employed an in-house database containing 314 secondary metabolites derived from cinnamic acid that occurred in the Asteraceae family. We conducted a combined ligand/structure-based virtual screening to identify potential inhibitors against LmDHFR-TS. Through consensus analysis of both approaches, we identified three compounds, i.e., lithospermic acid (237), diarctigenin (306), and isolappaol A (308), that exhibited a high probability of being inhibitors according to both approaches and were consequently classified as promising hits. Subsequently, we expanded the binding mode examination of these compounds within the active site of the test enzyme through molecular dynamics simulations, revealing a high degree of structural stability and minimal fluctuations in its tertiary structure. The in silico predictions were then validated through in vitro assays to examine the inhibitory capacity of the top-ranked naturally occurring compounds against LmDHFR-TS recombinant protein. The test compounds effectively inhibited the enzyme with IC50 values ranging from 6.1 to 10.1 µM. In contrast, other common cinnamic acid derivatives (i.e., flavonoid glycosides) from the Asteraceae family, such as hesperidin, isovitexin 4'-O-glucoside, and rutin, exhibited low activity against this target. The selective index (SI) for all tested compounds was determined using HsDHFR with moderate inhibitory effect. Among these hits, lignans 306 and 308 demonstrated the highest selectivity, displaying superior SI values compared to methotrexate, the reference inhibitor of DHFR-TS. Therefore, continued research into the anti-leishmanial potential of these C6C3-hybrid butyrolactone lignans may offer a brighter outlook for combating this neglected tropical disease.

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/.

Development of 2D, 3D-QSAR and Pharmacophore Modeling of Chalcones for the Inhibition of Monoamine Oxidase B.

BACKGROUND: Selective and reversible types of MAO-B inhibitors have emerged as promising candidates for the management of neurodegenerative diseases. Several functionalized chalcone derivatives were shown to have potential reversible MAO-B inhibitory activity, which have recently been reported from our laboratory. METHODS: With the experimental results of about 70 chalcone derivatives, we further developed a pharmacophore modelling, and 2D and 3D- QSAR analyses of these reported chalcones for MAOB inhibition. RESULTS: The 2D-QSAR model presented four variables (MATS7v, GATS 1i and 3i, and C-006) from 143 Dragon 7 molecular descriptors, with a r2 value of 0.76 and a Q2 cv for cross-validation equal to 0.72. An external validation also was performed using 11 chalcones, obtaining a Q2 ext value of 0.74. The second 3D-QSAR model using MLR (multiple linear regression) was built starting from 128 Volsurf+ molecular descriptors, being identified as 4 variables (Molecular descriptors): D3, CW1 and LgS11, and L2LGS. Adetermination coefficient (r2) value of 0.76 and a Q2 cv for cross-validation equal to 0.72 were obtained for this model. An external validation also was performed using 11 chalcones and a Q2 ext value of 0.74 was found. CONCLUSION: This report exhibited a good correlation and satisfactory agreement between experiment and theory.

Identification of Kaurane-Type Diterpenes as Inhibitors of Leishmania Pteridine Reductase I.

The current treatments against Leishmania parasites present high toxicity and multiple side effects, which makes the control and elimination of leishmaniasis challenging. Natural products constitute an interesting and diverse chemical space for the identification of new antileishmanial drugs. To identify new drug options, an in-house database of 360 kauranes (tetracyclic diterpenes) was generated, and a combined ligand- and structure-based virtual screening (VS) approach was performed to select potential inhibitors of Leishmania major (Lm) pteridine reductase I (PTR1). The best-ranked kauranes were employed to verify the validity of the VS approach through LmPTR1 enzyme inhibition assay. The half-maximal inhibitory concentration (IC50) values of selected bioactive compounds were examined using the random forest (RF) model (i.e., 2ß-hydroxy-menth-6-en-5ß-yl ent-kaurenoate (135) and 3α-cinnamoyloxy-ent-kaur-16-en-19-oic acid (302)) were below 10 µM. A compound similar to 302, 3α-p-coumaroyloxy-ent-kaur-16-en-19-oic acid (302a), was also synthesized and showed the highest activity against LmPTR1. Finally, molecular docking calculations and molecular dynamics simulations were performed for the VS-selected, most-active kauranes within the active sites of PTR1 hybrid models, generated from three Leishmania species that are known to cause cutaneous leishmaniasis in the new world (i.e., L. braziliensis, L. panamensis, and L. amazonensis) to explore the targeting potential of these kauranes to other species-dependent variants of this enzyme.

Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity at cholinergic synapses in various regions of the nervous system. The inhibition of acetylcholinesterase is frequently used to treat Alzheimer's disease. In this study, a merged BindingDB and ChEMBL dataset containing molecules with reported half-maximal inhibitory concentration (IC50) values for AChE (7032 molecules) was used to build machine learning classification models for selecting potential AChE inhibitors from the SistematX dataset (8593 secondary metabolites). A total of seven fivefold models with accuracy above 80% after cross-validation were obtained using three types of molecular descriptors (VolSurf, DRAGON 5.0, and bit-based fingerprints). A total of 521 secondary metabolites (6.1%) were classified as active in this stage. Subsequently, virtual screening was performed, and 25 secondary metabolites were identified as potential inhibitors of AChE. Separately, the crystal structure of AChE in complex with (-)-galantamine was used to perform molecular docking calculations with the entire SistematX dataset. Consensus analysis of both methodologies was performed. Only eight structures achieved combined probability values above 0.5. Finally, two sesquiterpene lactones, structures 15 and 24, were predicted to be able to cross the blood-brain barrier, which was confirmed in the VolSurf+ quantitative model, revealing these two structures as the most promising secondary metabolites for AChE inhibition among the 8593 molecules tested. A consensus analysis of classification models and molecular docking calculations identified four potential inhibitors of acetylcholinesterase from the SistematX dataset (8593 structures).

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.

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.

Selection of antileishmanial sesquiterpene lactones from SistematX database using a combined ligand-/structure-based virtual screening approach.

Leishmaniasis refers to a complex of diseases, caused by the intracellular parasitic protozoans belonging to the genus Leishmania. Among the three types of disease manifestations, the most severe type is visceral leishmaniasis, which is caused by Leishmania donovani, and is diagnosed in more than 20,000 cases annually, worldwide. Because the current therapeutic options for disease treatment are associated with several limitations, the identification of new potential leads/drugs remains necessary. In this study, a combined approach was used, based on two different virtual screening (VS) methods, which were designed to select promising antileishmanial agents from among the entire sesquiterpene lactone (SL) dataset registered in SistematX, a web interface for managing a secondary metabolite database that is accessible by multiple platforms on the Internet. Thus, a ChEMBL dataset, including 3159 and 1569 structures that were previously tested against L. donovani amastigotes and promastigotes in vitro, respectively, was used to develop two random forest models, which performed with greater than 74% accuracy in both the cross-validation and test sets. Subsequently, a ligand-based VS assay was performed against the 1306 SistematX-registered SLs. In parallel, the crystal structures of three L. donovani target proteins, N-myristoyltransferase, ornithine decarboxylase, and mitogen-activated protein kinase 3, and a homology model of pteridine reductase 1 were used to perform a structure-based VS, using molecular docking, of the entire SistematX SL dataset. The consensus analysis of these two VS approaches resulted in the normalization of probability scores and identified 13 promising, enzyme-targeting, antileishmanial SLs from SistematX that may act against L. donovani. A combined approach based on two different virtual screening methods (structure-based and ligand-based) was performed using an in-house dataset composed of 1306 sesquiterpene lactones to identify potential antileishmanial (Leishmania donovani) structures.

Virtual Screening Studies for Discovery of Novel Inhibitors of Inflammatory Process Targets.

Inflammation has been very evident in infectious diseases, but in recent times research has increasingly shown that a range of non-infectious diseases may present with inflammatory conditions. This fact becomes important as new anti-inflammatory drugs emerge with different targets for treatment of diseases. Virtual screening (VS) involves applying computational methods to discover new ligands for biological structures from the formation of large libraries composed of a large number of compounds. This review aims to report several studies employing a variety of VS: ligand-based and structure-based VS are being used more frequently in combination to decrease the probability of choosing false positive candidates. There are also studies that use only one approach. Docking is widely employed as structure-based VS methodology, however pharmacophore models based on the structure are becoming more prevalent. Molecular dynamics simulations, despite their computational cost, are still utilized to validate docking scores and analyze the stability of the complex ligand-structure. It is important to note that several studies employed several drug-like rules to screen structures, as well, decoys and PAINS to validate the models. Natural product databases, despite the lower number of the compounds compared to other databases that are available, are commonly referred to as a source of drug-like molecules. There is a literal explosion of software being released for a variety of purposes and several of them are free tools and/or web tools. Overall, VS studies are nowadays a normal part of medicinal chemistry to determine novel potential inhibitors for targets of inflammatory diseases.

SistematX, an Online Web-Based Cheminformatics Tool for Data Management of Secondary Metabolites.

The traditional work of a natural products researcher consists in large part of time-consuming experimental work, collecting biota to prepare and analyze extracts and to identify innovative metabolites. However, along this long scientific path, much information is lost or restricted to a specific niche. The large amounts of data already produced and the science of metabolomics reveal new questions: Are these compounds known or new? How fast can this information be obtained? To answer these and other relevant questions, an appropriate procedure to correctly store information on the data retrieved from the discovered metabolites is necessary. The SistematX (http://sistematx.ufpb.br) interface is implemented considering the following aspects: (a) the ability to search by structure, SMILES (Simplified Molecular-Input Line-Entry System) code, compound name and species; (b) the ability to save chemical structures found by searching; (c) compound data results include important characteristics for natural products chemistry; and (d) the user can find specific information for taxonomic rank (from family to species) of the plant from which the compound was isolated, the searched-for molecule, and the bibliographic reference and Global Positioning System (GPS) coordinates. The SistematX homepage allows the user to log into the data management area using a login name and password and gain access to administration pages. In this article, we introduced a modern and innovative web interface for the management of a secondary metabolite database. With its multiplatform design, it is able to be properly consulted via the internet and managed from any accredited computer. The interface provided by SistematX contains a wealth of useful information for the scientific community about natural products, highlighting the locations of species from which compounds are isolated.

Computer-Aided Drug Design Using Sesquiterpene Lactones as Sources of New Structures with Potential Activity against Infectious Neglected Diseases.

This review presents an survey to the biological importance of sesquiterpene lactones (SLs) in the fight against four infectious neglected tropical diseases (NTDs)-leishmaniasis, schistosomiasis, Chagas disease, and sleeping sickness-as alternatives to the current chemotherapies that display several problems such as low effectiveness, resistance, and high toxicity. Several studies have demonstrated the great potential of some SLs as therapeutic agents for these NTDs and the relationship between the protozoal activities with their chemical structure. Recently, Computer-Aided Drug Design (CADD) studies have helped increase the knowledge of SLs regarding their mechanisms, the discovery of new lead molecules, the identification of pharmacophore groups and increase the biological activity by employing in silico tools such as molecular docking, virtual screening and Quantitative-Structure Activity Relationship (QSAR) studies.