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 generally is 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.

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.

Design of Inhibitors for Glyceraldehyde-3-phosphate Dehydrogenase (GAPDH) Enzyme of <i>Leishmania mexicana</i>

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.

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.

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.

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.

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.

Molecular description of α-keto-based inhibitors of cruzain with activity against Chagas disease combining 3D-QSAR studies and molecular dynamics

In this work, a group of α-keto-based inhibitors of the cruzain enzyme with anti-chagas activity was selected for a three-dimensional quantitative structure-activity relationship study (3D-QSAR) combined with molecular dynamics (MD). Firstly, statistical models based on Partial Least Square (PLS) regression were developed employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) descriptors. Validation parameters (q² and r² )for the models were, respectively, 0.910 and 0.997 (CoMFA) and 0.913 and 0.992 (CoMSIA). In addition, external validation for the models using a test group revealed r²_pred  = 0.728 (CoMFA) and 0.971 (CoMSIA). The most relevant aspect in this study was the generation of molecular fields in both favorable and unfavorable regions based on the models developed. These fields are important to interpret modifications necessary to enhance the biological activities of the inhibitors. This analysis was restricted considering the inhibitors in a fixed conformation, not interacting with their target, the cruzain enzyme. Then, MD was employed taking into account important variables such as time and temperature. MD helped describe the behavior of the inhibitors and their properties showed similar results as those generated by QSAR-3D study.

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.

Computational analyses of interactions between ALK-5 and bioactive ligands: insights for the design of potential anticancer agents

Activin Receptor-Like Kinase 5 (ALK-5) is related to some types of cancer, such as breast, lung, and pancreas. In this study, we have used molecular docking, molecular dynamics simulations, and free energy calculations in order to explore key interactions between ALK-5 and six bioactive ligands with different ranges of biological activity. The motivation of this work is the lack of crystal structure for inhibitor-protein complexes for this set of ligands. The understanding of the molecular structure and the protein-ligand interaction could give support for the development of new drugs against cancer. The results show that the calculated binding free energy using MM-GBSA, MM-PBSA, and SIE is correlated with experimental data with r² = 0.88, 0.80, and 0.94, respectively, which indicates that the calculated binding free energy is in excellent agreement with experimental data. In addition, the results demonstrate that H bonds with Lys232, Glu245, Tyr249, His283, Asp351, and one structural water molecule play an important role for the inhibition of ALK-5. Overall, we discussed the main interactions between ALK-5 and six inhibitors that may be used as starting points for designing new molecules to the treatment of cancer.

New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists

The treatment of neuropathic pain is very complex and there are few drugs approved for this purpose. Among the studied compounds in the literature, sigma-1 receptor antagonists have shown to be promising. In order to develop QSAR studies applied to the compounds of 1-arylpyrazole derivatives, multivariate analyses have been performed in this work using partial least square (PLS) and artificial neural network (ANN) methods. A PLS model has been obtained and validated with 45 compounds in the training set and 13 compounds in the test set (r2training = 0.761, q2 = 0.656, r2test = 0.746, MSEtest = 0.132 and MAEtest = 0.258). Additionally, multi-layer perceptron ANNs (MLP-ANNs) were employed in order to propose non-linear models trained by gradient descent with momentum backpropagation function. Based on MSEtest values, the best MLP-ANN models were combined in a MLP-ANN consensus model (MLP-ANN-CM; r2test = 0.824, MSEtest = 0.088 and MAEtest = 0.197). In the end, a general consensus model (GCM) has been obtained using PLS and MLP-ANN-CM models (r2test = 0.811, MSEtest = 0.100 and MAEtest = 0.218). Besides, the selected descriptors (GGI6, Mor23m, SRW06, H7m, MLOGP, and μ) revealed important features that should be considered when one is planning new compounds of the 1-arylpyrazole class. The multivariate models proposed in this work are definitely a powerful tool for the rational drug design of new compounds for neuropathic pain treatment. Graphical abstract Main scaffold of the 1-arylpyrazole derivatives and the selected descriptors.

Study on molecular structure, spectroscopic properties (FTIR and UV-Vis), NBO, QTAIM, HOMO-LUMO energies and docking studies of 5-fluorouracil, a substance used to treat cancer

Cancer cells can expand to other parts of body through blood system and nodes from a mechanism known as metastasis. Due to the large annual growth of cancer cases, various biological targets have been studied and related to this disorder. A very interesting target related to cancer is human epidermal growth factor receptor 2 (HER2). In this study, we analyzed the main intermolecular interactions between a drug used in the cancer treatment (5-fluorouracil) and HER2. Molecular modeling methods were also employed to assess the molecular structure, spectroscopic properties (FTIR and UV-Vis), NBO, QTAIM and HOMO-LUMO energies of 5-FU. From the docking simulations it was possible to analyze the interactions that occur between some residues in the binding site of HER2 and 5-FU. To validate the choice of basis set that was used in the NBO and QTAIM analyses, theoretical calculations were performed to obtain FT-IR and UV/Vis spectra, and the theoretical results are consistent with the experimental data, showing that the basis set chosen is suitable. For the maximum λ from the theoretical calculation (254.89nm) of UV/Vis, the electronic transition from HOMO to LUMO occurs at 4.89eV. From NBO analyses, we observed interactions between Asp863 and 5-FU, i.e. the orbitals with high transfer of electrons are LP O₁₅ (donor NBO) and BD* (π) N₁-H₁₀ (acceptor NBO), being that the value of this interaction is 7.72kcal/mol. Results from QTAIM indicate one main intermolecular H bond, which is necessary to stabilize the complex formed between the ligands and the biological target. Therefore, this study allowed a careful evaluation on the main structural, spectroscopic and electronic properties involved in the interaction between 5-FU and HER2, an important biological complex related to the cancer treatment.

The Role of QSAR and Virtual Screening Studies in Type 2 Diabetes Drug Discovery

BACKGROUND

Due to the increasing number of diabetes cases worldwide, there is an international concern to provide even more effective treatments to control this condition.

METHODS

This review brings together a selection of studies that helped to broaden the comprehension of various biological targets and associated mechanisms involved in type 2 diabetes mellitus.

RESULTS

Such studies demonstrated that QSAR techniques and virtual screenings have been successfully employed in drug design projects.

CONCLUSIONS

Therefore, the main goal of this review is to give the state-of-art for the development of new drugs for the treatment of type 2 diabetes mellitus and to evaluate how computational tools, such as virtual screening and 3D-QSAR, can aid the development of new drugs with reduced adverse side effects.

Understanding electrostatic and steric requirements related to hypertensive action of AT(1) antagonists using molecular modeling techniques

AT1 receptor is an interesting biological target involved in several important diseases, such as blood hypertension and cardiovascular pathologies. In this study we investigated the main electrostatic and steric features of a series of AT1 antagonists related to hypertensive activity using structure and ligand-based strategies (docking and CoMFA). The generated 3D model had good internal and external consistency and was used to predict the potency of an external test set. The predicted values of pIC50 are in good agreement with the experimental results of biological activity, indicating that the 3D model can be used to predict the biological property of untested compounds. The electrostatic and steric CoMFA maps showed molecular recognition patterns, which were analyzed with structure-based molecular modeling studies (docking). The most and the least potent compounds docked into the AT1 binding site were subjected to molecular dynamics simulations with the aim to verify the stability and the flexibility of the ligand-receptor interactions. These results provided valuable insights on the electronic/structural requirements to design novel AT1 antagonists.

Identifying structural features related to the biological activity of a series of AT1 antagonists from fragment-based drug design

Antagonists of AT1 receptor are interesting substances to develop drugs that can be used for the treatment of hypertension and other diseases that affect the cardiovascular system. This study investigates the main interactions between various AT1 antagonists and the biological target by applying fragment-based drug design (Hologram QSAR - HQSAR). The proposed HQSAR model yielded significant correlation coefficients (q(2) = 0.764 and r(2) = 0.914), indicating that the method is rigorous and reliable. All models were externally validated using a test set and the results showed good agreement between the experimental and predicted data (r(2) test = 0.740). Therefore, our model can positively contribute to understand the structural features involved in the main interactions between the AT1 antagonists and the residues of the binding site.

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.

Molecular features related to HIV integrase inhibition obtained from structure- and ligand-based approaches

Among several biological targets to treat AIDS, HIV integrase is a promising enzyme that can be employed to develop new anti-HIV agents. The aim of this work is to propose a mechanistic interpretation of HIV-1 integrase inhibition and to rationalize the molecular features related to the binding affinity of studied ligands. A set of 79 HIV-1 integrase inhibitors and its relationship with biological activity are investigated employing 2D and 3D QSAR models, docking analysis and DFT studies. Analyses of docking poses and frontier molecular orbitals revealed important features on the main ligand-receptor interactions. 2D and 3D models presenting good internal consistency, predictive power and stability were obtained in all cases. Significant correlation coefficients (r(2) = 0.908 and q(2)= 0.643 for 2D model; r(2)= 0.904 and q(2)= 0.719 for 3D model) were obtained, indicating the potential of these models for untested compounds. The generated holograms and contribution maps revealed important molecular requirements to HIV-1 IN inhibition and several evidences for molecular modifications. The final models along with information resulting from molecular orbitals, 2D contribution and 3D contour maps should be useful in the design of new inhibitors with increased potency and selectivity within the chemical diversity of the data.

Computational studies of TGF-βRI (ALK-5) inhibitors: analysis of the binding interactions between ligand-receptor using 2D and 3D techniques

ALK-5 (Activin-Like Kinase 5) is a biological receptor involved in a variety of pathological processes such as cancer and fibrosis. ALK-5 receptor propagates an intracellular signaling that forms a protein complex capable of reaching the nucleus and modulating the gene transcription. In the present study, comparative molecular field analysis (CoMFA) and hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of potent ALK-5 inhibitors. Significant correlation coefficients (CoMFA, r(2)=0.99 and q(2)=0.85; HQSAR, r(2)=0.92 and q(2)=0.72) were obtained, indicating the predictive potential of the 2D and 3D models for untested compounds. The models were then used to predict the potency of a test set, and the predicted values from the HQSAR and CoMFA models were in good agreement with the experimental results. The final QSAR models, along with the information obtained from 3D (steric and electrostatic) contour maps and 2D contribution maps, can be useful for the design of novel bioactive ligands.

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.

Molecular features related to the binding mode of PPARδ agonists from QSAR and docking analyses

Diabetes affects approximately 4% of world's population and metabolic syndrome has been directly related to obesity. There is a class of nuclear receptors, peroxisome proliferator-activated receptors (PPARs), which controls the metabolism of carbohydrates and lipids. It has been considered an attractive target to treat diabetes and metabolic syndrome. Accordingly, the primary objective of this study was to employ molecular modelling techniques to understand the factors involved in PPARδ activation. The QSAR models obtained showed good internal and external consistency and presented good validation coefficients (QSAR: q(2) = 0.83, r(2) = 0.87; HQSAR: q(2) = 0.73, r(2) = 0.90; CoMFA: q(2) = 0.88, r(2) = 0.94). The selected properties and the contour maps described the possible interactions between the PPARδ receptor and its agonists. From these findings, it is possible to propose molecular modifications to design new compounds with improved biological properties.

Hologram quantitative structure–activity relationships for a series of farnesoid X receptor activators

The farnesoid X receptor (FXR) is an attractive drug target for the development of novel therapeutic agents for the treatment of dyslipidemia and cholestasis. Hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of potent FXR activators originated from natural product-like libraries. A training set containing 82 compounds served to establish the models. The best HQSAR model was generated using atoms, bonds, connections, chirality, and donor and acceptor as fragment distinction and fragment size default (4-7) with six components. The model was used to predict the potency of 20 test set compounds that were not included in the training set, and the predicted values were in good agreement with the experimental results. The final HQSAR model and the information obtained from HQSAR 2D contribution maps should be useful for the design of novel FXR ligands having improved potency.