Rapid Screening α-Glucosidase Inhibitors from Natural Products by At-Line Nanofractionation with Parallel Mass Spectrometry and Bioactivity Assessment.

In this study, a novel at-line nanofractionation screening platform was successfully developed for the rapid screening and identification of α-glucosidase inhibitors from natural products. A time-course bioassay based on high density well-plates was performed in parallel with high resolution mass spectrometry (MS), providing a straightforward and rapid procedure to simultaneously obtain chemical and biological information of active compounds. Through multiple nanofractionations into the same well-plate and comparisons of the orthogonal separation results of hydrophilic interaction liquid chromatography (HILIC) and reversed-phase liquid chromatography (RPLC), the α-glucosidase inhibitors can be accurately identified from co-eluates. The screening platform was comprehensively evaluated and validated, and was applied to the screenings of green tea polyphenols and Ginkgo folium flavonoids. After accurate peak shape and retention time matching between the bioactivity chromatograms and MS chromatograms, ten α-glucosidase inhibitors were successfully screened out and identified. The proposed screening method is rapid, effective and can avoid ignoring low abundant/active inhibitors.

Assessment of alpha glucosidase inhibitors produced from endophytic fungus Alternaria destruens as antimicrobial and antibiofilm agents.

Diabetes is considered as a major health concern worldwide and patients with diabetes are at high risk for infectious diseases. Therefore, α-glucosidase inhibitors possessing antibacterial activity along with the ability to inhibit biofilms would be better therapeutic agents for diabetic patients. In the present study, two fractions (AF1 and AF2) possessing α-glucosidase inhibitory activity were purified from an endophytic fungus Alternaria destruens (AKL-3) isolated from Calotropis gigantea. These were evaluated for their antimicrobial and antibiofilm potential against human pathogens. AF1 exhibited broad spectrum antimicrobial activity against all the tested pathogens. It also significantly inhibited biofilm formation and dispersed the preformed biofilm at sub-optimal concentrations. AF2 possessed lesser activity as compared to AF1. The active compounds were purified using semi preparative HPLC. Some of the active compounds were identified to be phenolic in nature. The active fractions were also determined to be non-mutagenic and non-cytotoxic in safety analysis. The study highlights the role of endophytic fungi as sources of α-glucosidase inhibitors with antimicrobial potential which can have application in management of diabetes.

Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies.

In this report are used two data sets involving the main antidiabetic enzyme targets α-amylase and α-glucosidase. The prediction of α-amylase and α-glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α-amylase and 1546 compounds in the case of α-glucosidase are selected to develop the tree model. In the case of CT-J48 have the better classification model performances for both targets with values above 80%-90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double-target approach for increasing the estimation of antidiabetic chemicals identification aimed by double-way workflow in virtual screening pipelines.