A partial least squares regression study with antioxidant flavonoid compounds

Blackcurrant press cake by-product: Increased chemical bioaccessibility and reduced antioxidant protection after in vitro simulation of gastrointestinal digestion

This study describes the bioaccessibility in terms of total phenolic content (TPC) and antioxidant capacity before and after in vitro digestion from blackcurrant press cake extracts (BPC) and the bioactivity in cell culture, human erythrocytes as well as the in silico analysis. Chemical analysis of BPC presented an increase in TPC (270%) and anthocyanins (136%) after in vitro digestion, resulting in an improvement of antioxidant activity (DPPH 112%; FRAP: 153%). This behavior may be related to the highest activity of cyanidin-3-rutinoside, as confirmed by in silico analysis. The digested BPC did not exert cytotoxicity in cells and showed less antioxidant activity against the oxidative damage induced in endothelial cells and human erythrocytes compared to the non-digested extract. The results raise a question about the reliability we should place on results obtained only from crude samples, especially those that will be used to produce foods or nutraceuticals.

Exploring different computational approaches for effective diagnosis of breast cancer

Breast cancer has been identified as one among the top causes of female death worldwide. According to recent research, earlier detection plays an important role toward fortunate medicaments and thus, decreasing the mortality rate due to breast cancer among females. This review provides a fleeting summary involving traditional diagnostic procedures from the past and today, and also modern computational tools that have greatly aided in the identification of breast cancer. Computational techniques involving different algorithms such as Support vector machines, deep learning techniques and robotics are popular among the academicians for detection of breast cancer. They discovered that Convolutional neural network was a common option for categorization among such approaches. Deep learning techniques are evaluated using performance indicators such as accuracy, sensitivity, specificity, or measure. Furthermore, molecular docking, homology modeling and Molecular dynamics Simulation gives a road map for future discussions about developing improved early detection approaches that holds greater potential in increasing the survival rate of cancer patients. The different computational techniques can be a new dominion among researchers and combating the challenges associated with breast cancer.

5-Methoxy-1-methyl-2-{[4-(2-hydroxyphenyl)piperazin-1-yl]methyl}-1Hindole (KAD22) with Antioxidant Activity

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Compound KAD22 (5-methoxy-1-methyl-2-[4-(2-hydroxyphenyl)piperazin-1-yl]methyl-1H-indole) was designed as a potential dopamine D2 receptor agonist with antioxidant activity for possible treatment of Parkinson’s disease. The compound was obtained from 5-methoxy-1-methyl-1H-indole-2-carbaldehyde and 2-(piperazin-1-yl)phenol. KAD22 showed no affinity to dopamine D2 receptor but it is a potent antioxidant. Experimental and computational structural studies (conformational analysis, HOMO and LUMO orbitals, electrostatic potential map, non-covalent interaction plot, spectral properties, ligand-receptor interactions) of KAD22 were performed to address its biological activity.

In vitro and in silico studies of antioxidant activity of 2-thiazolylhydrazone derivatives

The antioxidant potential of a series of thiazolylhydrazone derivatives was investigated using three different methods namely DPPH, ABTS and FRAP assays. In general, the tested compounds showed higher or comparable activity to that of curcumin, used as positive control. Chemometric analyses demonstrated that the presence of hydrazone moiety is required for the activity of this class of compounds. From these results, compound 4 was identified as the most promising molecule and was then selected for further studies. The antiproliferative effect of compound 4 was evaluated, being active in three (T47D, MDA-MB-231 and SKMEL) of the six cancer cell lines tested, with IC₅₀ values ranging from 15.9 to 31.3 μM. Compound 4 exhibited no detectable cytotoxic effect on peripheral blood mononuclear cells (PBMC) when tested at a concentration of 100 μM, demonstrating good selectivity. From these results, it is possible to infer that there is a correlation between antioxidant capacity and anticancer effects.

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.

Understanding PPAR-δ affinity and selectivity using hologram quantitative structure-activity modeling, molecular docking and GRID calculations

AIM

Type 2 diabetes mellitus and metabolic syndrome are two diseases related to disorders of lipid and carbohydrate metabolism and insulin resistance. Peroxisome proliferator-activated receptors (PPARs) are a class of nuclear receptors that control the metabolism of lipids/carbohydrates and are considered targets for both diseases. PPAR affinity and selectivity are critical points to design drug candidates with appropriated pharmacodynamic/kinetic profiles.

MATERIALS & METHODS

Hologram quantitative structure-activity relationships studies were conducted, as well molecular docking and molecular interaction field calculations, in order to explain affinity and selectivity of selected compounds.

RESULTS

The constructed hologram quantitative structure-activity relationship models are robust and predictive (values of q² and r²_test above 0.70).

CONCLUSION

The quantitative structure-activity relationship models and docking/GRID analyses indicated that carboxyl group of indole-sulfonamide derivatives could interact at helix-3 region, being considered important point of PPAR-δ selectivity.

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.

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.

A chemometric study of the 5-HT1A receptor affinities presented by arylpiperazine compounds

Arylpiperazine compounds are promising 5-HT(1A) receptor ligands that can contribute for accelerating the onset of therapeutic effect of selective serotonin reuptake inhibitors. In the present work, the chemometric methods HCA, PCA, KNN, SIMCA and PLS were employed in order to obtain SAR and QSAR models relating the structures of arylpiperazine compounds to their 5-HT(1A) receptor affinities. A training set of 52 compounds was used to construct the models and the best ones were obtained with nine topological descriptors. The classification and regression models were externally validated by means of predictions for a test set of 14 compounds and have presented good quality, as verified by the correctness of classifications, in the case of pattern recognition studies, and by the high correlation coefficients (q(2)=0.76, r(2)=0.83) and small prediction errors for the PLS regression. Since the results are in good agreement with previous SAR studies, we can suggest that these findings can help in the search for 5-HT(1A) receptor ligands that are able to improve antidepressant treatment.