Synthesis and biological evaluation of 1-benzyl-N-(2-(phenylamino)pyridin-3-yl)-1H-1,2,3-triazole-4-carboxamides as antimitotic agents.

A library of 1-benzyl-N-(2-(phenylamino)pyridin-3-yl)-1H-1,2,3-triazole-4-carboxamides (7a-al) have been designed, synthesized and screened for their anti-proliferative activity against some selected human cancer cell lines namely DU-145, A-549, MCF-7 and HeLa. Most of them have shown promising cytotoxicity against lung cancer cell line (A549), amongst them 7f was found to be the most potent anti-proliferative congener. Furthermore, 7f exhibited comparable tubulin polymerization inhibition (IC50 value 2.04 µM) to the standard E7010 (IC50 value 2.15 µM). Moreover, flow cytometric analysis revealed that this compound induced apoptosis via cell cycle arrest at G2/M phase in A549 cells. Induction of apoptosis was further observed by examining the mitochondrial membrane potential and was also confirmed by Hoechst staining as well as Annexin V-FITC assays. Furthermore, molecular docking studies indicated that compound 7f binds to the colchicine binding site of the ß-tubulin. Thus, 7f exhibits anti-proliferative properties by inhibiting the tubulin polymerization through the binding at the colchicine active site and by induction of apoptosis.

Synthesis and biological evaluation of imidazopyridinyl-1,3,4-oxadiazole conjugates as apoptosis inducers and topoisomerase IIα inhibitors.

A series of imidazopyridinyl-1,3,4-oxadiazole conjugates were synthesized and investigated for their cytotoxic activity and some compounds showed promising cytotoxic activity. Compound 8q (NSC: 763639) exhibited notable growth inhibition that satisfies threshold criteria at single dose (10µM) on all human cancer cell lines. This compound was further evaluated at five dose levels (0.01, 0.1, 1, 10 and 100µM) to obtain GI50 values ranging from 1.30 to 5.64µM. Flow cytometric analysis revealed that compound 8q arrests the A549 cells in sub G1 phase followed by induction of apoptosis which was further confirmed by Annexin-V-FITC, Hoechst nuclear staining, caspase 3 activation, measurement of mitochondrial membrane potential and ROS generation. Topo II mediated DNA relaxation assay results showed that conjugate 8q could significantly inhibit the activity of topo II. Moreover, molecular docking studies also indicated binding to the topoisomerase enzyme (PDBID 1ZXN).

Benzo[b]furan derivatives induces apoptosis by targeting the PI3K/Akt/mTOR signaling pathway in human breast cancer cells.

The PI3K/Akt/mTOR signaling pathway plays a key regulatory function in cell survival, proliferation, migration, metabolism and apoptosis. Aberrant activation of the PI3K/Akt/mTOR pathway is found in many types of cancer and thus plays a major role in breast cancer cell proliferation. In our previous studies, benzo[b]furan derivatives were evaluated for their anticancer activity and the lead compounds identified were 26 and 36. These observations prompted us to investigate the molecular mechanism and apoptotic pathway of these lead molecules against breast cancer cells. Benzo[b]furan derivatives (26 and 36) were evaluated for their antiproliferative activity against human breast cancer cell lines MCF-7 and MDA MB-231. These compounds (26 and 36) have shown potent efficiency against breast cancer cells (MCF-7) with IC50 values 0.057 and 0.051µM respectively. Cell cycle analysis revealed that these compounds induced cell cycle arrest at G2/M phase in MCF-7 cells. Western blot analysis revealed that these compounds inhibit the PI3K/Akt/mTOR signaling pathway and induced mitochondrial mediated apoptosis in human breast cancer cells (MCF-7).

Synthesis of 2-anilinopyridyl-triazole conjugates as antimitotic agents.

A series of 2-anilinopyridyl­triazole conjugates (6a­t) were prepared and evaluated for their cytotoxic activity against a panel of three human cancer cell lines. Among them compounds 6q, 6r and 6s showed significant cytotoxic activity with IC50 values ranging from 0.1 to 4.1 µM. Structure­activity relationships were elucidated with various substitutions on these conjugates. Flow cytometric analysis revealed that these compounds arrest the cell cycle at the G2/M phase and induce cell death by apoptosis. The tubulin polymerization assay and immunofluorescence analysis showed that these compounds (6q, 6r and 6s) effectively inhibited the microtubule assembly in human prostate cancer cells (DU-145). The docking studies showed that 6s interacts and binds efficiently with the tubulin protein at the colchicine binding site. This was further confirmed by the colchicine competitive binding assay. Moreover, compounds 6q, 6r and 6s possess anti-tubulin activity both in vitro and within cells as demonstrated by the ratio of soluble versus polymerized tubulin. Further the apoptotic effects of compounds were confirmed by Hoechst staining, caspase 3 activation, annexin-V FITC, mitochondrial membrane potential and DNA fragmentation analysis. Interestingly, these compounds did not affect the normal human embryonic kidney cells, HEK-293.

Synthesis and cytotoxic activity of 2-anilinopyridine-3-acrylamides as tubulin polymerization inhibitors.

In an attempt to develop potent anticancer agents, a series of 2-anilinonicotinyl-linked acrylamide conjugates were designed, synthesized, and evaluated for cytotoxic activity against various human cancer cell lines, anti-tubulin activity and cell-cycle effects. Among the series, compounds 6 d [(E)-N-(6-fluorobenzo[d]thiazol-2-yl)-3-(2-((3,4,5-trimethoxyphenyl)amino)pyridin-3-yl)acrylamide] and 6 p [(E)-3-(2-((4-methoxyphenyl)amino)pyridin-3-yl)-N-(6-nitrobenzo[d]thiazol-2-yl)acrylamide] showed promising cytotoxicity, specifically against the A549 human lung adenocarcinoma epithelial cell line, with GI50 values of 0.6±0.23 and 1.8±0.22 µM, respectively. Furthermore, cell-cycle perturbation studies by flow cytometry analysis indicated drastic cell-cycle effects in the G2 /M phase in this cell line followed by caspase-3 activation and apoptotic cell death. Molecular docking studies of the most potent compound, 6 d, revealed that this compound interacts with and binds efficiently in the active site of tubulin.

Machine learning study of classifiers trained with biophysiochemical properties of amino acids to predict fibril forming Peptide motifs.

It is important to understand the cause of amyloid illnesses by predicting the short protein fragments capable of forming amyloid-like fibril motifs aiding in the discovery of sequence-targeted anti-aggregation drugs. It is extremely desirable to design computational tools to provide affordable in silico predictions owing to the limitations of molecular techniques for their identification. In this research article, we tried to study, from a machine learning perspective, the performance of several machine learning classifiers that use heterogenous features based on biochemical and biophysical properties of amino acids to discriminate between amyloidogenic and non-amyloidogenic regions in peptides. Four conventional machine learning classifiers namely Support Vector Machine, Neural network, Decision tree and Random forest were trained and tested to find the best classifier that fits the problem domain well. Prior to classification, novel implementations of two biologically-inspired feature optimization techniques based on evolutionary algorithms and methodologies that mimic social life and a multivariate method based on projection are utilized in order to remove the unimportant and uninformative features. Among the dimenionality reduction algorithms considered under the study, prediction results show that algorithms based on evolutionary computation is the most effective. SVM best suits the problem domain in its fitment among the classifiers considered. The best classifier is also compared with an online predictor to evidence the equilibrium maintained between true positive rates and false positive rates in the proposed classifier. This exploratory study suggests that these methods are promising in providing amyloidogenity prediction and may be further extended for large-scale proteomic studies.

Exploiting heterogeneous features to improve in silico prediction of peptide status - amyloidogenic or non-amyloidogenic.

BACKGROUND: Prediction of short stretches in protein sequences capable of forming amyloid-like fibrils is important in understanding the underlying cause of amyloid illnesses thereby aiding in the discovery of sequence-targeted anti-aggregation pharmaceuticals. Due to the constraints of experimental molecular techniques in identifying such motif segments, it is highly desirable to develop computational methods to provide better and affordable in silico predictions. RESULTS: Accurate in silico prediction techniques of amyloidogenic peptide regions rely on the cooperation between informative features and classifier design. In this research article, we propose one such efficient fibril prediction implementation exploiting heterogeneous features based on bio-physio-chemical (BPC) properties, auto-correlation function of carefully selected amino acid indices and atomic composition within a protein fragment of amino acids in a window. In an attempt to get an optimal number of BPC features, an evolutionary Support Vector Machine (SVM) integrating a novel implementation of hybrid Genetic Algorithm termed Memetic Algorithm and SVM is utilized. Five prediction modules designed using Artificial Neural Network (ANN) models are trained with independent and integrated features in order to validate the fibril forming motifs. The results provide evidence that incorporating new feature namely auto-correlation function besides BPC, attempt to strengthen the sequence interaction effect in forming the feature vector thereby obtaining better prediction quality in terms of sensitivity, specificity, Mathews Correlation Coefficient and Area under the Receiver Operating Characteristics curve. CONCLUSION: A significant improvement in performance is observed by introducing features like auto-correlation function that maintains sequence order effect, in addition to the conventional BPC properties selected through a novel optimization strategy to predict the peptide status - amyloidogenic or non-amyloidogenic. The proposed approach achieves acceptable results, comparable to most online predictors. Besides, it compensates the lacuna in existing amyloid fibril prediction tools by maintaining equilibrium between sensitivity and specificity.