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.

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.

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.

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 neo- lignans 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 mM) and eight showed activity against intracellular amastigotes (IC50 values from 7 to 16 mM). Eighteen derivatives demonstrated no mammalian cytotoxicity up to 200 mM. The phenolic ac- etate derivative of natural dehydrodieugenol

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 r2pred  = 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.

On the relationship of anthranilic derivatives structure and the FXR (Farnesoid X receptor) agonist activity.

Farnesoid X receptor (FXR) is a nuclear receptor related to lipid and glucose homeostasis and is considered an important molecular target to treatment of metabolic diseases as diabetes, dyslipidemia, and liver cancer. Nowadays, there are several FXR agonists reported in the literature and some of it in clinical trials for liver disorders. Herein, a compound series was employed to generate QSAR models to better understand the structural basis for FXR activation by anthranilic acid derivatives (AADs). Furthermore, here we evaluate the inclusion of the standard deviation (SD) of EC50 values in QSAR models quality. Comparison between the use of experimental variance plus average values in model construction with the standard method of model generation that considers only the average values was performed. 2D and 3D QSAR models based on the AAD data set including SD values showed similar molecular interpretation maps and quality (Q2LOO, Q2(F2), and Q2(F3)), when compared to models based only on average values. SD-based models revealed more accurate predictions for the set of test compounds, with lower mean absolute error indices as well as more residuals near zero. Additionally, the visual interpretation of different QSAR approaches agrees with experimental data, highlighting key elements for understanding the biological activity of AADs. The approach using standard deviation values may offer new possibilities for generating more accurate QSAR models based on available experimental data.

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.

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.

In silico studies on the interaction between bioactive ligands and ALK5, a biological target related to the cancer treatment.

Studies have showed that there are many biological targets related to the cancer treatment, for example, TGF type I receptor (TGF-ßRI or ALK5). The ALK5 inhibition is a strategy to treat some types of cancer, such as breast, lung, and pancreas. Here, we performed CoMFA and CoMSIA studies for 70 ligands with ALK5 inhibition. The internal validation for both models (q(2)LOO = 0.887 and 0.822, respectively) showed their robustness, while the external validations showed their predictive power (CoMFA: r(2)test = 0.998; CoMSIA: r(2)test = 0.975). After all validations, CoMFA and CoMSIA maps indicated physicochemical evidences on the main factors involved in the interaction between bioactive ligands and ALK5. Therefore, these results suggest molecular modifications to design new ALK5 inhibitors.

Hologram quantitative structure-activity relationship and comparative molecular interaction field analysis of aminothiazole and thiazolesulfonamide as reversible LSD1 inhibitors.

BACKGROUND: LSD-1 is an enzyme that removes methyl groups from lysine residues of histone proteins. LSD-1 inhibition decreases cellular proliferation and therefore represents a therapeutic target for cancer treatment. MAO and LSD-1 are both flavin adenine dinucleotide-dependent MAOs, and the MAO inhibitor, tranylcypromine, is currently undergoing clinical trials for cancer treatment because it acts as an irreversible LSD-1 inhibitor. MATERIALS & METHODS: The present study investigated new reversible LSD-1 inhibitors, in order to develop novel selective anticancer agents. We constructed 2 and 3D quantitative structure-activity relationship models by using a series of 54 aminothiazole and thiazolesulfonamide derivatives. RESULTS: The models were validated internally and externally (q(2) , 0.691 and 0.701; r(2) , 0.894 and 0.937; r(2) test , 0.785 and 0.644, for 2 and 3D models, respectively). Fragment contribution maps, as well as steric and electrostatic contour maps were generated in order to obtain chemical information related to LSD-1 inhibition. CONCLUSION: The thiazolesulfonamide group was fundamental to the inhibition of LSD-1 by these compounds and that bulky and aromatic substituents at the thiazole ring were important for their steric and electrostatic interactions with the active site of LSD-1.

Benchmark studies of UV-vis spectra simulation for cinnamates with UV filter profile.

Skin cancer is a serious public health problem worldwide, being incident over all five continents and affecting an increasing number of people. As sunscreens are considered an important preventive measure, studies to develop better and safer sunscreens are crucial. Cinnamates are UVB filters with good efficiency and cost-benefit, therefore, their study could lead to the development of new UV filters. A benchmark to define the most suitable density functional theory (DFT) functional to predict UV-vis spectra for ethylhexyl methoxycinnamate was performed. Time-dependent DFT (TD-DFT) calculations were then carried out [B3LYP/6-311 + G(d,p) and B3P86/6-311 + G(d,p) in methanol environment] for seven cinammete derivatives implemented in the Gaussian 03 package. All DFT/TD-DFT simulations were performed after a conformational search with the Monte-Carlo method and MMFF94 force field. B3LYP and B3P86 functionals were better at reproducing closely the experimental spectra of ethylhexyl methoxycinnamate. Calculations of seven cinnamates showed a λmax of around 310 nm, corroborating literature reports. It was observed that the energy for the main electronic transition was around 3.95 eV and could be explained by electron delocalization on the aromatic ring and ester group, which is important to UV absorption. The methodology employed proved to be suitable for determination of the UV spectra of cinnamates and could be used as a tool for the development of novel UV filters.

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.

Computational studies of TGF-ßRI (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.

Artificial neural networks and the study of the psychoactivity of cannabinoid compounds.

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher's weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.