Topological, hydrophobicity, and other descriptors on α-glucosidase inhibition: a QSAR study on xanthone derivatives

Bio-Guided Isolation of Two New Hypoglycemic Triterpenoid Saponins from Polygonum capitatum

Purpose

Diabetes is a common disease caused by a combination of genetic and environmental factors, which was the top three diseases threatening human health. Therefore, it is necessary to seek more efficient hypoglycemic drugs. The main objective of this study was to investigate the potential hypoglycemic effects of compounds from Polygonum capitatum.

Materials and Methods

Our experiments were divided into three steps: (1) α-amylase test and oral starch tolerance test (OSTT) for screening the biological extract part of P. capitatum; (2) chemical isolation and identification using various separation techniques, and spectrum methods; and (3) evaluation of α-amylase inhibitory activity of isolates and in silico analysis for mechanism investigation.

Results

The n-butanol fractioned part of P. capitatum was confirmed to be the biological part according to α-amylase test. Then, two new triterpenoid saponins were isolated from the n-butanol part, which were also the first isolated triterpenoid saponins from P. capitatum. The activities of compounds 1 and 2 against α-amylase were 51.9±2.8% and 38.1±2.2%, respectively, which was consistent with the molecular docking analysis. In which, 1 and 2 showed the binding affinity energy for α-amylase was −9.4 kcal/mol and −7.8 kcal/mol, respectively.

Conclusion

Two new triterpenoid saponins were firstly isolated from P. capitatum, and displays potency as a hypoglycemic agent through blocking α-amylase.

Ecotoxicological QSAR modeling of the acute toxicity of organic compounds to the freshwater crustacean Thamnocephalus platyurus

Growing interest in environmental toxicity assessment using Thamnocephalus platyurus as organism has led to an increased availability of acute toxicity data. Despite this growing interest in tests with this organism, however, to the best of our knowledge there are no computational models to predict the acute toxicity in T. platyurus. In view of the limited number of in silico models for this crustacean, we developed Quantitative Structure-Activity Relationship (QSAR) models for the prediction of acute toxicity towards T. platyurus, reflected by the 24h LC_50, using publicly available data according to the ISO 14380:2011 guideline. Two models were developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). We used partial least squares and gradient boosting machine techniques, which gave encouraging statistical quality in our data set.

A descriptor-based analysis to highlight the mechanistic rationale of mutagenicity

Cancer is a main concern for human health and there is a need of alternative methodologies to rapidly screen large quantitative of compounds that may represent a toxicological risk. Here a statistical analyses is performed on a benchmark database of experimental Ames data to identify chemical descriptors discriminating mutagens and non-mutagens. A total of 53 activating and deactivating modulators are identified, that flagged respectively a percentage of mutagen and non-mutagen up to 87%. Modulators are further combined to form synergistic cross-terms, accounting for the effect that combined properties may have on the final toxicity. Exclusion rules are defined as exception to the modulators. Synergistic cross-terms and exclusion rules improve the enrichment of mutagens/non-mutagens with respect of the original abundance in the dataset to values higher than 95%. The external predictivity of modulators and cross-terms reach balanced accuracy up to 0.775 that is analogous to other mutagenicity models from the literature, confirming the suitability of the rules to real-life screening of chemicals. Modulators are discussed for their mechanistic link to mutagenicity. This analysis confirms the key role of some properties (polarizability, shape, mass, presence of reactive functional groups or unsaturated planar systems) as driving elements for the initiation of the mutagenicity.

Prediction of Terpenoid Toxicity Based on a Quantitative Structure-Activity Relationship Model

Terpenoids, including monoterpenoids (C10), norisoprenoids (C13), and sesquiterpenoids (C15), constitute a large group of plant-derived naturally occurring secondary metabolites with highly diverse chemical structures. A quantitative structure-activity relationship (QSAR) model to predict terpenoid toxicity and to evaluate the influence of their chemical structures was developed in this study by assessing in real time the toxicity of 27 terpenoid standards using the Gram-negative bioluminescent Vibrio fischeri. Under the test conditions, at a concentration of 1 µM, the terpenoids showed a toxicity level lower than 5%, with the exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate, and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30% at concentrations of 50-100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene, and linalool oxide. Regarding the functional group, terpenoid toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. The CODESSA software was employed to develop QSAR models based on the correlation of terpenoid toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoid toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., maximum partial charge for a carbon (C) atom (Zefirov's partial charge (PC)) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with a training coefficient and test coefficient correlation higher than 0.810 and 0.535, respectively, and a square coefficient of cross-validation (Q2) higher than 0.689. According to the obtained data, the QSAR models are suitable and rapid tools to predict terpenoid toxicity in a diversity of food products.

A comprehensive review on xanthone derivatives as α-glucosidase inhibitors

α-Glucosidase plays an important role in carbohydrate metabolism and is therefore an attractive therapeutic target for the treatment of diabetes, obesity and other related complications. In the last two decades, considerable interest has been given to natural and synthetic xanthone derivatives in this field of research. Herein, a comprehensive review of the literature on xanthones as inhibitors of α-glucosidase activity, their mechanism of action, experimental procedures and structure-activity relationships have been reviewed for more than 280 analogs. With this overview we intend to motivate and challenge researchers (e.g. chemistry, biology, pharmaceutical and medicinal areas) for the design of novel xanthones as multipotent drugs and exploit the properties of this class of compounds in the management of diabetic complications.

Biological activity, quantitative structure-activity relationship analysis, and molecular docking of xanthone derivatives as anticancer drugs

Background

Xanthone derivatives have a wide range of pharmacological activities, such as those involving antibacterial, antiviral, antimalarial, anthelmintic, anti-inflammatory, antiprotozoal, and anticancer properties. Among these, we investigated the anticancer properties of xanthone. This research aimed to analyze the biological activity of ten novel xanthone derivatives, to investigate the most contributing-descriptors for their cytotoxic activities, and to examine the possible mechanism of actions of xanthone compound through molecular docking.

Materials and methods

The cytotoxic tests were carried out on WiDR and Vero cell lines, by a 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) assay method. The structural features required for xanthone’s anticancer activity were conducted by using the semi-empirical Austin Model-1 method, and continued with quantitative structure-activity relationship (QSAR) analysis using BuildQSAR program. The study of the possible mechanism of actions of the selected xanthone compound was done through molecular docking with PLANTS.

Results

The three novel xanthone derivatives (compounds 5, 7, and 8) exhibited cytotoxic activity with compound 5 showed the highest degree of cytotoxicity at concentration 9.23 µg/mL (37.8 µM). The following best equation model was obtained from the BuildQSAR calculation: log 1/IC₅₀ = −8.124 qC1 −35.088 qC2 −6.008 qC3 + 1.831 u + 0.540 logP −9.115 (n = 10, r = 0.976, s = 0.144, F = 15.920, Q2 = 0.651, SPRESS = 0.390). This equation model generated 15 proposed new xanthone compounds with better-predicted anticancer activities. A molecular docking study of compound 5 showed that xanthone formed binding interactions with some receptors involved in cancer pathology, including telomerase, tumor-promoting inflammation (COX-2), and cyclin-dependent kinase-2 (CDK2) inhibitor.

Conclusion

The results suggested that compound 5 showed the best cytotoxic activity among the xanthone derivatives tested. QSAR analysis showed that the descriptors contributed to xanthone’s cytotoxic activity were the net atomic charge at qC1, qC2, and qC3 positions, also dipole moment and logP. Compound 5 was suspected to be cytotoxic by its inhibition of telomerase, COX-2, and CDK2 receptors.

Classification study of solvation free energies of organic molecules using machine learning techniques

Classification models to predict the solvation free energies of organic molecules were developed using decision tree, random forest and support vector machine approaches and with MACCS fingerprints, MOE and PaDEL descriptors.