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

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

Structure-Based Virtual Screening, Molecular Dynamics and Binding Free Energy Calculations of Hit Candidates as ALK-5 Inhibitors.

Activin-like kinase 5 (ALK-5) is involved in the physiopathology of several conditions, such as pancreatic carcinoma, cervical cancer and liver hepatoma. Cellular events that are landmarks of tumorigenesis, such as loss of cell polarity and acquisition of motile properties and mesenchymal phenotype, are associated to deregulated ALK-5 signaling. ALK-5 inhibitors, such as SB505154, GW6604, SD208, and LY2157299, have recently been reported to inhibit ALK-5 autophosphorylation and induce the transcription of matrix genes. Due to their ability to impair cell migration, invasion and metastasis, ALK-5 inhibitors have been explored as worthwhile hits as anticancer agents. This work reports the development of a structure-based virtual screening (SBVS) protocol aimed to prospect promising hits for further studies as novel ALK-5 inhibitors. From a lead-like subset of purchasable compounds, five molecules were identified as putative ALK-5 inhibitors. In addition, molecular dynamics and binding free energy calculations combined with pharmacokinetics and toxicity profiling demonstrated the suitability of these compounds to be further investigated as novel ALK-5 inhibitors.

Pattern recognition techniques applied to the study of leishmanial glyceraldehyde-3-phosphate dehydrogenase inhibition.

Chemometric pattern recognition techniques were employed in order to obtain Structure-Activity Relationship (SAR) models relating the structures of a series of adenosine compounds to the affinity for glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). A training set of 49 compounds was used to build the models and the best ones were obtained with one geometrical and four electronic descriptors. Classification models were externally validated by predictions for a test set of 14 compounds not used in the model building process. Results of good quality were obtained, as verified by the correct classifications achieved. Moreover, the results are in good agreement with previous SAR studies on these molecules, to such an extent that we can suggest that these findings may help in further investigations on ligands of LmGAPDH capable of improving treatment of leishmaniasis.

Monitoring Photo-Fenton and Photo-Electro-Fenton process of contaminants emerging concern by a gas diffusion electrode using Ca10-xFex-yWy(PO4)6(OH)2 nanoparticles as heterogeneous catalyst.

The catalytic performance of modified hydroxyapatite nanoparticles, Ca10-xFex-yWy(PO4)6(OH)2, was applied for the degradation of methylene blue (MB), fast green FCF (FG) and norfloxacin (NOR). XPS analysis pointed to the successful partial replacement of Ca by Fe. Under photo-electro-Fenton process, the catalyst Ca4FeII1·92W0·08FeIII4(PO4)6(OH)2 was combined with UVC radiation and electrogenerated H2O2 in a Printex L6 carbon-based gas diffusion electrode. The application of only 10 mA cm-2 resulted in 100% discoloration of MB and FG dyes in 50 min of treatment at pH 2.5, 7.0 and 9.0. The proposed treatment mechanism yielded maximum TOC removal of ∼80% and high mineralization current efficiency of ∼64%. Complete degradation of NOR was obtained in 40 min, and high mineralization of ∼86% was recorded after 240 min of treatment. Responses obtained from LC-ESI-MS/MS are in line with the theoretical Fukui indices and the ECOSAR data. The study enabled us to predict the main degradation route and the acute and chronic toxicity of the by-products formed during the contaminants degradation.

Identification of electronic and structural descriptors of adenosine analogues related to inhibition of leishmanial glyceraldehyde-3-phosphate dehydrogenase.

Quantitative structure-activity relationship (QSAR) studies were performed in order to identify molecular features responsible for the antileishmanial activity of 61 adenosine analogues acting as inhibitors of the enzyme glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). Density functional theory (DFT) was employed to calculate quantum-chemical descriptors, while several structural descriptors were generated with Dragon 5.4. Variable selection was undertaken with the ordered predictor selection (OPS) algorithm, which provided a set with the most relevant descriptors to perform PLS, PCR and MLR regressions. Reliable and predictive models were obtained, as attested by their high correlation coefficients, as well as the agreement between predicted and experimental values for an external test set. Additional validation procedures were carried out, demonstrating that robust models were developed, providing helpful tools for the optimization of the antileishmanial activity of adenosine compounds.

Understanding the molecular aspects of tetrahydrocannabinol and cannabidiol as antioxidants.

An antioxidant mechanism of tetrahydrocannabinol (THC) and cannabidiol (CBD) were compared with a simplified model of α-tocopherol, butylhydroxytoluene and hydroxytoluene in order to understand the antioxidant nature of THC and CBD molecules using DFT. The following electronic properties were evaluated: frontier orbitals nature, ionization potential, O-H bond dissociation energy (BDEOH), stabilization energy, and spin density distribution. An important factor that shows an influence in the antioxidant property of THC is the electron abstraction at the phenol position. Our data indicate that the decrease of the HOMO values and the highest ionization potential values are related to phenol, ether, and alkyl moieties. On the other hand, BDEOH in molecules with the cyclohexenyl group at ortho position of phenol are formed from lower energies than the molecules with an ether group at the meta position. In the light of our results, the properties calculated here predict that THC has a sightly higher antioxidant potential than CBD.

A PLS study on the psychotropic activity for a series of cannabinoid compounds.

INTRODUCTION: The use of the Cannabis sativa plant by man has been common for centuries due to its numerous therapeutic properties resulting from the compounds present in it, called cannabinoids. However, the use of these compounds as drugs is still limited due to the psychotropic effects caused by them. The proteins that act as receptors of cannabinoid compounds were identified and characterized, being called CB1 and CB2 receptors. There is a series of 50 cannabinoid compounds that was studied through quantum and chemometric methods in order to obtain a mathematical model that could relate the structure of these compounds to their psychotropic activity. That model proved to be effective by predicting the psychoactivity of the 50 compounds from the series and elucidating relevant characteristics that imply in psychoactivity. However, most of these 50 compounds do not have experimental data of biological activity with CB1 and CB2 receptors. OBJECTIVES: This study aims to generate QSAR models in order to predict the biological activity of the 50 cannabinoid compounds and then relate the predicted biological activity values to the already known psychoactivity. METHODS: Another series of cannabinoid compounds was selected to generate and validate QSAR models, aiming to predict the biological activity of the 50 cannabinoid compounds with both CB1 and CB2 receptors. RESULTS: The PLS-CB1 and PLS-CB2 QSAR models were generated and validated in this work, proving to be highly predictive, and the biological activities (pK ) of the 50 cannabinoid compounds were predicted by them. It is important to highlight compounds Ic14, Ic18, and Ic19 (psychotropic inactive) which presented higher predicted pK values than the main cannabinoid compounds (Δ9-THC and Δ8-THC). Also, compound Ic21 stood out as the highest value of the predicted biological activities in the interaction with the CB2 receptor. CONCLUSION: The generated PLS models and the predicted pKi values of the 50 cannabinoid compounds can provide valuable information in the drug design of new cannabinoid compounds that can interact with CB1 and CB2 receptors in a therapeutic way with no psychotropic effects.

A study of possible substitutes for the endocrine disruptor DEHP in two hormone receptors.

Bis(2-ethylhexyl) phthalate (DEHP) has been widely used for the production of plastics, and the compound has also been found to act as endocrine disruptor. Exposure to DEHP has been found to cause several hormonal problems, including decreased fertility. Due to the environmental and health risks posed by the use of DEHP, the present study employed molecular docking, molecular dynamics, and free energy analyses (MM-GBSA, MM-PBSA, and SIE) aiming at evaluating the action of DEHP and that of two other compounds (ATEC and DL9TH), tested as potential DEHP substitutes, on two hormone receptors (sex hormone-binding globulin - SHBG - and progesterone receptor - PR). The results obtained showed that ATEC may be a good substitute for DEHP in the production of plastics, such as PVC, considering that the compound recorded the greatest free energy values with respect to binding with SHBG (-31.36 kcal/mol obtained from MM-GBSA; -20.28 kcal/mol for MM-PBSA, and -7.40 for SIE) and PR (-36.40 kcal/mol for MM-GBSA; -27.00 kcal/mol for MM-PBSA, and -8.51 kcal/mol for SIE) - this shows that ATEC presented the least activity in the two hormone receptors. The findings of this study provide relevant insights on potential substitutes for DEHP and help shed light on the action of these new efficient substances, which have similar properties to DEHP (ATEC and DL9TH) yet do not act as endocrine disruptors.Communicated by Ramaswamy H. Sarma.

Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern.

INTRODUCTION: Modern drug discovery is generally accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed. AREAS COVERED: One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future. EXPERT OPINION: Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.

Evaluating Deep Learning models for predicting ALK-5 inhibition.

Computational methods have been widely used in drug design. The recent developments in machine learning techniques and the ever-growing chemical and biological databases are fertile ground for discoveries in this area. In this study, we evaluated the performance of Deep Learning models in comparison to Random Forest, and Support Vector Regression for predicting the biological activity (pIC50) of ALK-5 inhibitors as candidates to treat cancer. The generalization power of the models was assessed by internal and external validation procedures. A deep neural network model obtained the best performance in this comparative study, achieving a coefficient of determination of 0.658 on the external validation set with mean square error and mean absolute error of 0.373 and 0.450, respectively. Additionally, the relevance of the chemical descriptors for the prediction of biological activity was estimated using Permutation Importance. We can conclude that the forecast model obtained by the deep neural network is suitable for the problem and can be employed to predict the biological activity of new ALK-5 inhibitors.

Drug design of new 5-HT6R antagonists aided by artificial neural networks.

Alzheimer's Disease (AD) is the most frequent illness and cause of death amongst the age related-neurodegenerative disorders. The Alzheimer's Disease International (ADI) reported in 2019 that over 50 million people were living with dementia in the world and this number could potentially be around 152 million by 2050.5-hydroxtryptamine subtype 6 receptor (5-HT6R) has been identified as a potential anti-amnesic drug target and therefore, the administration of 5-HT6R antagonists can likely mitigate the memory loss and intellectual deterioration associated with AD. Herein, computational tools were applied to design new 5-HT6 antagonists and their biological activity values were predicted by our QSAR model obtained from Artificial Neural Networks (ANN). The proposed compounds here from the QSAR-ANN model presented significant biological activity values and some of them have achieved pKi above 9.00. Furthermore, our results suggest that the presence of halogen atoms (especially bromine) linked to the aromatic ring at para-position (HYD) contribute considerably to the increase of the biological activity values while bulky groups in the PI position do not culminate with the increase antagonist activity of compounds here analyzed. Finally, the ADME/Tox profile as well as the synthetic accessibility of new proposed compounds qualify them to go on further with experimental procedures and thenceforward their antagonist effects can be confirmed.

In silico Studies on the Interaction Between Bioactive Ligands and DPPIV: Insights on Potential Candidates for the Treatment of type 2 Diabetes Mellitus.

INTRODUCTION: The enzyme called dipeptidyl peptidase IV (DPP-IV) is related to the glycemic control associated with the stimulation of the pancreas to produce insulin. So, its inhibition is a good strategy for the treatment of type 2 diabetes mellitus. METHODS: In this study, we have employed molecular modeling strategies such as CoMFA, molecular docking, molecular dynamics, and binding free energy calculations of a set of DPP-IV inhibitors in order to understand the main characteristics related to the biological activity of these ligands against the enzyme. RESULTS: The models obtained from CoMFA presented significant values of internal (0.768) and external (0.988) validations. Important interactions with some residues, such as Glu205, Tyr666, Arg125, Ser630, Phe357 and Tyr662, were also identified. In addition, calculations of the electronic properties allowed relating the LUMO and HOMO energies with the biological activity of the compounds studied. The results obtained from the molecular dynamics simulations and the SIE calculations (ΔG) indicated that the inhibitor 40 increases the stability of the DPP-IV target. CONCLUSIONS: Therefore, from this study, it is possible to propose molecular modifications of these DPP-IV inhibitors in order to improve their potential to treat type 2 diabetes.

Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates.

Since the emergence of the new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been huge efforts of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19.

A partial least squares and artificial neural network study for a series of arylpiperazines as antidepressant agents.

Depression affects more than 300 million people around the world and can lead to suicide. About 30% of patients on treatment for depression drop out of therapy due to side effects or to latency time associated to therapeutic effects. 5-HT receptor, known as serotonin, is considered the key in depression treatment. Arylpiperazine compounds are responsible for several pharmacological effects and are considered as ligands in serotonin receptors, such as the subtype 5-HT2a. Here, in silico studies were developed using partial least squares (PLSs) and artificial neural networks (ANNs) to design new arylpiperazine compounds that could interact with the 5-HT2a receptor. First, molecular and electronic descriptors were calculated and posteriorly selected from correlation matrixes and genetic algorithm (GA). Then, the selected descriptors were used to construct PLS and ANN models that showed to be robust and predictive. Lastly, new arylpiperazine compounds were designed and their biological activity values were predicted by both PLS and ANN models. It is worth to highlight compounds G5 and G7 (predicted by the PLS model) and G3 and G15 (predicted by the ANN model), whose predicted pIC50 values were as high as the three highest values from the arylpiperazine original set studied here. Therefore, it can be asserted that the two models (PLS and ANN) proposed in this work are promising for the prediction of the biological activity of new arylpiperazine compounds and may significantly contribute to the design of new drugs for the treatment of depression.

Flavonoid Derivatives Targeting BCR-ABL Kinase: Semisynthesis, Molecular Dynamic Simulations and Enzymatic Inhibition.

BACKGROUND: Natural products have been universally approached in the research of novel trends useful to detail the essential paths of the life sciences and as a strategy for pharmacotherapeutics. OBJECTIVE: This work focuses on further modification to the 6-hydroxy-flavanone building block aiming to obtain improved BCR-ABL kinase inhibitors. METHODS: Ether derivatives were obtained from Williamson synthesis and triazole from Microwave- assisted click reaction. Chemical structures were finely characterized through IR, 1H and 13C NMR and HRMS. They were tested for their inhibitory activity against BCR-ABL kinase. RESULTS: Two inhibitors bearing a triazole ring as a pharmacophoric bridge demonstrated the strongest kinase inhibition at IC50 value of 364 nM (compound 3j) and 275 nM (compound 3k). CONCLUSION: 6-hydroxy-flavanone skeleton can be considered as a promising core for BCR-ABL kinase inhibitors.

Design of Inhibitors for Glyceraldehyde-3-phosphate Dehydrogenase (GAPDH) Enzyme of Leishmania mexicana.

BACKGROUND: Leishmaniosis is a neglected tropical disease and glyceraldehyde 3- phosphate dehydrogenase (GAPDH) is a key enzyme in the design of new drugs to fight this disease. OBJECTIVE: The present study aimed to evaluate potential inhibitors of GAPDH enzyme found in Leishmania mexicana (L. mexicana). METHODS: A search for novel antileishmanial molecules was carried out based on similarities from the pharmacophoric point of view related to the binding site of the crystallographic enzyme using the ZINCPharmer server. The molecules selected in this screening were subjected to molecular docking and molecular dynamics simulations. RESULTS: Consensual analysis of the docking energy values was performed, resulting in the selection of ten compounds. These ligand-receptor complexes were visually inspected in order to analyze the main interactions and subjected to toxicophoric evaluation, culminating in the selection of three compounds, which were subsequently submitted to molecular dynamics simulations. The docking results showed that the selected compounds interacted with GAPDH from L. mexicana, especially by hydrogen bonds with Cys166, Arg249, His194, Thr167, and Thr226. From the results obtained from molecular dynamics, it was observed that one of the loop regions, corresponding to the residues 195-222, can be related to the fitting of the substrate at the binding site, assisting in the positioning and the molecular recognition via residues responsible for the catalytic activity. CONCLUSION: The use of molecular modeling techniques enabled the identification of promising compounds as inhibitors of the GAPDH enzyme from L. mexicana, and the results obtained here can serve as a starting point to design new and more effective compounds than those currently available.

Dehydrodieugenol B derivatives as antiparasitic agents: Synthesis and biological activity against Trypanosoma cruzi.

Chagas disease is a neglected protozoan disease that affects more than eight million people in developing countries. Due to the limited number and toxicity profiles of therapies in current use, new drugs are urgently needed. In previous studies, we reported the isolation of two related antitrypanosomal neolignans from Nectandra leucantha (Lauraceae). In this work, a semi-synthetic library of twenty-three neolignan derivatives was prepared to explore synthetically accessible structure activity relationships (SAR) against Trypanosoma cruzi. Five compounds demonstrated activity against trypomastigotes (IC50 values from 8 to 64 µM) and eight showed activity against intracellular amastigotes (IC50 values from 7 to 16 µM). Eighteen derivatives demonstrated no mammalian cytotoxicity up to 200 µM. The phenolic acetate derivative of natural dehydrodieugenol B was effective against both parasite forms and eliminated 100% of amastigotes inside macrophages. This compound caused rapid and intense depolarization of the mitochondrial membrane potential, with decreased levels of intracellular reactive oxygen species being observed. Fluorescence assays demonstrated that this derivative affected neither the permeability nor the electric potential of the parasitic plasma membrane, an effect also corroborated by scanning electron microscopy studies. Structure-activity relationship studies (SARs) demonstrated that the presence of at least one allyl side chain on the biaryl ether core was important for antitrypanosomal activity, and that the free phenol is not essential. This set of neolignan derivatives represents a promising starting point for future Chagas disease drug discovery studies.

Two-dimensional QSAR studies on arylpiperazines as high-affinity 5-HT(1A) receptor ligands.

5-HT(1A) receptor plays an important role in the delayed onset of antidepressant action of a class of selective serotonin reuptake inhibitors. Moreover, 5-HT(1A) receptor levels have been shown to be altered in patients suffering from major depression. In this work, hologram quantitative structure-activity relationship (HQSAR) studies were performed on a series of arylpiperazine compounds presenting affinity to the 5-HT(1A) receptor. The models were constructed with a training set of 70 compounds. The most significant HQSAR model (q(2) = 0.81, r(2) = 0.96) was generated using atoms, bonds, connections, chirality, and donor and acceptor as fragment distinction, with fragment size of 6-9. Predictions for an external test set containing 20 compounds are in good agreement with experimental results showing the robustness of the model. Additionally, useful information can be obtained from the 2D contribution maps.

UDP-glucuronosyltransferases: Structure, Function and Drug Design Studies.

UDP-glucuronosyltransferases (UGTs) are important phase II metabolic enzymes responsible for approximately 40-70% of endo and xenobiotic reactions. It catalyzes the transfer of glucuronic acid to lipophilic substrates, converting them into hydrophilic compounds that are excreted. There are 22 active human UGTs that belong to 4 families. This review focuses on human UGTs, highlighting the most current issues in order to connect all information available and allowing a discussion on the challenges already solved and those in which we need to move forward. Although, several UGTs studies have been conducted, the most recent ones addressing drug-drug interactions and polymorphism issues, there are still bottlenecks to overcome. Tridimensional structure is difficult to obtain due to overexpression, purification, and crystallization problems as well as the action mechanism - since overlapping of substrate specificities renders impasses on the identification of which isoform is responsible for a particular drug metabolic pathway. For this reason, bioinformatic tools are gaining more space, since it is a faster and less expensive reliable methodology that complements in vitro and in vivo researches. Combinations of quantum and molecular methods have become increasingly common, leading to the incorporation of enzyme features comprising their structure, dynamics and chemical reactions. Breakthroughs related to the enzyme, not only enable the discovery of new drugs essential for the treatment of various diseases, but also provide an improved action of the existing drugs.

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges.

Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.