Investigations on Anticancer and Antimalarial Activities of Indole-Sulfonamide Derivatives and In Silico Studies.

A library of 44 indole-sulfonamide derivatives (1-44) were investigated for their cytotoxic activities against four cancer cell lines (i.e., HuCCA-1, HepG2, A549, and MOLT-3) and antimalarial effect. Most of the studied indoles exhibit anticancer activity against the MOLT-3 cell line, whereas only hydroxyl-containing bisindoles displayed anticancer activities against the other tested cancer cells as well as antimalarial effect. The most promising anticancer compounds were noted to be CF3, Cl, and NO2 derivatives of hydroxyl-bearing bisindoles (30, 31, and 36), while the most promising antimalarial compound was an OCH3 derivative of non-hydroxyl-containing bisindole 11. Five quantitative structure-activity relationship (QSAR) models were successfully constructed, providing acceptable predictive performance (training set: R = 0.6186-0.9488, RMSE = 0.0938-0.2432; validation set: R = 0.4242-0.9252, RMSE = 0.1100-0.2785). QSAR modeling revealed that mass, charge, polarizability, van der Waals volume, and electronegativity are key properties governing activities of the compounds. QSAR models were further applied to guide the rational design of an additional set of 22 compounds (P1-P22) in which their activities were predicted. The prediction revealed a set of promising virtually constructed compounds (P1, P3, P9, P10, and P16) for further synthesis and development as anticancer and antimalarial agents. Molecular docking was also performed to reveal possible modes of bindings and interactions between the studied compounds and target proteins. Taken together, insightful structure-activity relationship information obtained herein would be beneficial for future screening, design, and structural optimization of the related compounds.

Synthesis, molecular docking, and QSAR study of sulfonamide-based indoles as aromatase inhibitors.

Thirty four of indoles bearing sulfonamides (11-44) were synthesized and evaluated for their anti-aromatase activities. Interestingly, all indole derivatives inhibited the aromatase with IC50 range of 0.7-15.3 µM. Indoles (27-36) exerted higher aromatase inhibitory activity than that of ketoconazole. The phenoxy analogs 28 and 34 with methoxy group were shown to be the most potent compounds with sub-micromolar IC50 values (i.e., 0.7 and 0.8 µM, respectively) without affecting to the normal cell line. Molecular docking demonstrated that the indoles 28, 30 and 34 could occupy the same binding site on the aromatase pocket and share several binding residues with those of the natural substrate (androstenedione), which suggested the competitive binding could be the mode of inhibition of the compounds. The most potent analog 28 could mimic H-bond interactions of the natural androstenedione with MET374 and ASP309 residues on the aromatase. QSAR model also revealed that the para-phenoxy indole (28) affords the higher value of electronegativity descriptor MATS6e as well as the higher inhibitory activity compared with that of the ortho-phenoxy compound (34). The study highlighted a series of promising indoles to be potentially developed as novel aromatase inhibitors for therapeutics.

Exploring the Chemical Space of P-Glycoprotein Interacting Compounds.

BACKGROUND: P-glycoprotein (Pgp) is well known for its clinical importance in the pharmacokinetics of drugs and its role in multidrug resistance. The promiscuity of Pgp that arises from its ability to extrude a wide range of lipophilic and structurally unrelated compounds from cells, render the classification and understanding of its interacting compounds a great challenge. METHOD: In this study, a data set of Pgp-interacting compounds including 1463 inhibitors, 1085 noninhibitors, 308 substrates and 126 non-substrates was retrieved and subjected to a combination of analyses, including exploration of chemical space, statistical analysis of descriptor values and molecular fragment analysis, to obtain insight into distinct physicochemical properties and important chemical substructures which may govern the biological activity of investigated compounds toward Pgp. Statistical analysis of descriptor values and molecular fragment analysis indicated that particular size, shape, functional groups and molecular fragments may govern the classification of Pgp-interacting compounds by influencing their physicochemical properties and their interaction with Pgp. Overall, the interacting compounds (i.e., substrates and inhibitors) are larger in size, more flexible, more lipophilic, and less charged than non-interacting compounds (i.e., non-substrates and non-inhibitors). CONCLUSION: The fragment analysis suggested that methyl and amino groups may be involved in Pgp inhibition and/or transport. The 2-methoxyphenol fragment was noted to be a potential substructure for designing Pgp inhibitors, whereas the 2-sulfanylidene-1-[3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2- yl]-1,2-dihydropyridine-3-carbonitrile substructure was implied for avoiding transport by Pgp. Hence, this study could provide a comprehensive understanding of this drug transporter, which could benefit an early ADMET screening as well as drug design and development.

Synthesis and molecular docking of 1,2,3-triazole-based sulfonamides as aromatase inhibitors.

A series of 1,4-disubstituted-1,2,3-triazoles (13-35) containing sulfonamide moiety were synthesized and evaluated for their aromatase inhibitory effects. Most triazoles with open-chain sulfonamide showed significant aromatase inhibitory activity (IC50=1.3-9.4µM). Interestingly, the meta analog of triazole-benzene-sulfonamide (34) bearing 6,7-dimethoxy substituents on the isoquinoline ring displayed the most potent aromatase inhibitory activity (IC50=0.2µM) without affecting normal cell. Molecular docking of these triazoles against aromatase revealed that the compounds could snugly occupy the active site of the enzyme through hydrophobic, π-π stacking, and hydrogen bonding interactions. The potent compound 34 was able to form hydrogen bonds with Met374 and Ser478 which were suggested to be the essential residues for the promising inhibition. The study provides compound 34 as a potential lead molecule of anti-aromatase agent for further development.

AutoWeka: toward an automated data mining software for QSAR and QSPR studies.

UNLABELLED: In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predictive modeling. The latter is based on the concept of quantitative structure-activity/property relationship (QSAR/QSPR) when applied in modeling the biological activity and chemical properties, respectively, of compounds. Data mining is a powerful technology underlying QSAR/QSPR as it harnesses knowledge from large volumes of high-dimensional data via multivariate analysis. Although extremely useful, the technicalities of data mining may overwhelm potential users, especially those in the life sciences. Herein, we aim to lower the barriers to access and utilization of data mining software for QSAR/QSPR studies. AutoWeka is an automated data mining software tool that is powered by the widely used machine learning package Weka. The software provides a user-friendly graphical interface along with an automated parameter search capability. It employs two robust and popular machine learning methods: artificial neural networks and support vector machines. This chapter describes the practical usage of AutoWeka and relevant tools in the development of predictive QSAR/QSPR models. AVAILABILITY: The software is freely available at http://www.mt.mahidol.ac.th/autoweka.

Synthesis, biological evaluation and molecular docking of novel chalcone-coumarin hybrids as anticancer and antimalarial agents.

A new series of chalcone-coumarin derivatives (9-19) linked by the 1,2,3-triazole ring were synthesized through the azide/alkyne dipolar cycloaddition. Hybrid molecules were evaluated for their cytotoxic activity against four cancer cell lines (e.g., HuCCA-1, HepG2, A549 and MOLT-3) and antimalarial activity toward Plasmodium falciparum. Most of the synthesized hybrids, except for analogs 10 and 16, displayed cytotoxicity against MOLT-3 cell line without affecting normal cells. Analogs (10, 11, 16 and 18) exhibited higher inhibitory efficacy than the control drug, etoposide, in HepG2 cells. Significantly, the high cytotoxic potency of the hybrid 11 was shown to be non-toxic to normal cells. Interestingly, the chalcone-coumarin 18 was the most potent antimalarial compound affording IC50 value of 1.60 µM. Molecular docking suggested that the cytotoxicity of reported hybrids could be possibly due to their dual inhibition of α- and ß-tubulins at GTP and colchicine binding sites, respectively. Furthermore, falcipain-2 was identified to be a plausible target site of the hybrids given their antimalarial potency.

Design, synthesis and molecular docking studies of novel N-benzenesulfonyl-1,2,3,4-tetrahydroisoquinoline-based triazoles with potential anticancer activity.

A novel series of N-benzenesulfonyl-1,2,3,4-tetrahydroisoquinolines (14-33) containing triazole moiety were designed and synthesized through rational cycloadditions using the modified Pictet-Spengler reaction and the Click chemistry. Antiproliferative activity against four cancer cell lines (e.g., HuCCA-1, HepG2, A549 and MOLT-3) revealed that many substituted triazole analogs of benzoates (20, 29) and benzaldehydes (30, 32) exhibited anticancer activity against all of the tested cancer cell lines in which the ester analog 20 was shown to be the most potent compound against HuCCA-1 (IC50 = 0.63 µM) and A549 (IC50 = 0.57 µM) cell lines. Triazoles bearing phenyl (15, 24), tolyl (26, 27), acetophenone (19), benzoate (20, 29), benzaldehyde (21, 30) and naphthalenyl (25) substituents showed stronger anticancer activity against HepG2 cells than that of the etoposide. Interestingly, the p-tolyl analog (27) displayed the most potent inhibitory activity (IC50 = 0.56 µM) against HepG2 cells without affecting normal cells. Of the investigated tetrahydroisoquinoline-triazoles, the promising compounds 20 and 27 were selected for molecular docking against AKR1C3, which was identified to be a plausible target site.

Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection.

This study explores the chemical space and quantitative structure-activity relationship (QSAR) of a set of 60 sulfonylpyridazinones with aldose reductase inhibitory activity. The physicochemical properties of the investigated compounds were described by a total of 3230 descriptors comprising of 6 quantum chemical descriptors and 3224 molecular descriptors. A subset of 5 descriptors was selected from the aforementioned pool by means of Monte Carlo (MC) feature selection coupled to multiple linear regression (MLR). Predictive QSAR models were then constructed by MLR, support vector machine and artificial neural network, which afforded good predictive performance as deduced from internal and external validation. The investigated models are capable of accounting for the origins of aldose reductase inhibitory activity and could be utilized in predicting this property in screening for novel and robust compounds.

Exploring the chemical space of aromatase inhibitors.

Aromatase, a rate-limiting enzyme catalyzing the conversion of androgen to estrogen, is overexpressed in human breast cancer tissue. Aromatase inhibitors (AIs) have been used for the treatment of estrogen-dependent breast cancer in post-menopausal women by blocking the biosynthesis of estrogen. The undesirable side effects in current AIs have called for continued pursuit for novel candidates with aromatase inhibitory properties. This study explores the chemical space of all known AIs as a function of their physicochemical properties by means of univariate (i.e., statistical and histogram analysis) and multivariate (i.e., decision tree and principal component analysis) approaches in order to understand the origins of aromatase inhibitory activity. Such a non-redundant set of AIs spans a total of 973 compounds encompassing both steroidal and non-steroidal inhibitors. Substructure analysis of the molecular fragments provided pertinent information on the structural features important for ligands providing high and low aromatase inhibition. Analyses were performed on data sets stratified according to their structural scaffolds (i.e., steroids and non-steroids) and bioactivities (i.e., actives and inactives). These analyses have uncover a set of rules characteristic to active and inactive AIs as well as revealing the constituents giving rise to potent aromatase inhibition.

QSAR study of anti-prion activity of 2-aminothiazoles.

2-aminothiazoles is a class of compounds capable of treating life-threatening prion diseases. QSAR studies on a set of forty-seven 2-aminothiazole derivatives possessing anti-prion activity were performed using multivariate analysis, which comprised of multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM). The results indicated that MLR afforded reasonable performance with a correlation coefficient (r) and root mean squared error (RMSE) of 0.9073 and 0.2977, respectively, as obtained from leave-one-out cross-validation (LOO-CV). More sophisticated learning methods such as SVM provided models with the highest accuracy with r and RMSE of 0.9471 and 0.2264, respectively, while ANN gave reasonable performance with r and RMSE of 0.9023 and 0.3043, respectively, as obtained LOO-CV calculations. Descriptor analysis from the regression coefficients of the MLR model suggested that compounds should be asymmetrical molecule with low propensity to form hydrogen bonds and high frequency of N content at topological distance 02 in order to provide good activities. Insights from QSAR studies is anticipated to be useful in the design of novel derivatives based on the 2-aminothiazole scaffold as potent therapeutic agents against prion diseases.