Prasit Mandi

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.

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.

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.

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.

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.

QSAR modeling of aromatase inhibition by flavonoids using machine learning approaches

Aromatase is a member of the cytochrome P450 family responsible for catalyzing the rate-limiting conversion of androgens to estrogens. In the pursuit of robust aromatase inhibitors, quantitative structure-activity relationship (QSAR) and classification structure-activity relationship (CSAR) studies were performed on a non-redundant set of 63 flavonoids using multiple linear regression, artificial neural network, support vector machine and decision tree approaches. Easy-to-interpret descriptors providing comprehensive coverage on general characteristics of molecules (i.e., molecular size, flexibility, polarity, solubility, charge and electronic properties) were employed to describe the unique physicochemical properties of the investigated flavonoids. QSAR models provided good predictive performance as observed from their statistical parameters with Q values in the range of 0.8014 and 0.9870 for the cross-validation set and Q values in the range of 0.8966 and 0.9943 for the external test set. Furthermore, CSAR models developed with the J48 algorithm are able to accurately classify flavonoids as active and inactive as observed from the percentage of correctly classified instances in the range of 84.6 % and 100 %. The study presented herein represents the first large-scale QSAR study of aromatase inhibition on a large set of flavonoids. Such investigations provide an important insight on the origins of aromatase inhibitory properties of flavonoids as breast cancer 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, biological evaluation and in silico study of bis-thiourea derivatives as anticancer, antimalarial and antimicrobial agents

Two sets of bis-thioureas including a para series (8–14) and a meta series (4, 5, 15–19), were synthesized and evaluated for their anticancer, antimalarial and antimicrobial activities. Most of the synthesized bis-thioureas, except for analogs 8–11, displayed cytotoxicity against MOLT-3 cell line (IC50 = 1.55–32.32 µM). Derivatives 5, 14, 18 and 19 showed a broad spectrum of anticancer activity. Analogs (4, 5, 8, 13, 14, 18 and 19) exhibited higher inhibitory efficacy in HepG2 cells than the control drug, etoposide. Significantly, bis-trifluoromethyl analog 19 was the promising potent cytotoxic agent (IC50 = 1.50–18.82 µM) with the best safety index (1.64–20.60). Antimalarial activity results showed that trifluoromethyl derivative 18 was the most potent compound (IC50 = 1.92 µM, selective index = 6.86). Antimicrobial activity revealed that bis-thioureas 12, 18 and 19 exhibited selective activity against Gram-positive bacteria and fungi. Promisingly, the bis-trifluoromethyl derivative 19 was the most potent compound in the series and displayed higher potency, against most of the Gram-positive bacteria and fungi, than that of ampicillin, the reference drug. Among the tested strains of microorganisms, compound 19 inhibited the growth of Staphylococcus epidermidis ATCC 12228 and Micrococcus luteus ATCC 10240 with the lowest MIC of 1.47 µM. The findings demonstrated that trifluoromethyl group plays a crucial role in their biological activities. Furthermore, the molecular docking was performed to reveal possible binding modes of the compounds against target proteins.

Exploring the origins of structure–oxygen affinity relationship of human haemoglobin allosteric effector

A data set comprising 27 myo-inositol derivatives based on tetrakisphosphates and bispyrophosphates were used in the development of quantitative structure–activity relationship model for investigating its allosteric effector property against human haemoglobin (Hb). Three-dimensional structures of the investigated compounds were subjected to geometry optimisations at the density functional theory level. Physicochemical features of low-energy conformers were represented by quantum chemical and molecular descriptors. Feature selection by means of unsupervised forward selection and stepwise linear regression resulted in a set of four important descriptors. Multivariate analysis was performed using multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM). Robustness of the predictive performance of all methods was deduced from internal and external validation, which afforded values of 0.6306, 0.7484 and 0.8722 using MLR, ANN and SVM, respectively, for the former and values of 0.8332, 0.8847 and 0.9694, respectively, for the latter. The predictive model is anticipated to be useful for further guiding the rational design of robust allosteric effectors of human Hb.