Chalcone Derivatives as Potential Inhibitors of P-Glycoprotein and NorA: An In Silico and In Vitro Study

Discovery of novel flavonoid derivatives as potential dual inhibitors against α-glucosidase and α-amylase: virtual screening, synthesis, and biological evaluation

Diabetes mellitus is one of the top ten causes of death worldwide, accounting for 6.7 million deaths in 2021, and is one of the most rapidly growing global health emergencies of this century. Although several classes of therapeutic drugs have been invented and applied in clinical practice, diabetes continues to pose a serious and growing threat to public health and places a tremendous burden on those affected and their families. The strategy of reducing carbohydrate digestibility by inhibiting the activities of α-glucosidase and α-amylase is regarded as a promising preventative treatment for type 2 diabetes. In this study, we investigated the dual inhibitory effect against two polysaccharide hydrolytic enzymes of flavonoid derivatives from an in-house chemical database. By combining molecular docking and structure-activity relationship analysis, twelve compounds with docking energies less than or equal to - 8.0 kcal mol-1 and containing required structural features for dual inhibition of the two enzymes were identified and subjected to chemical synthesis and in vitro evaluation. The obtained results showed that five compounds exhibited dual inhibitory effects on the target enzymes with better IC50 values than the approved positive control acarbose. Molecular dynamics simulations were performed to elucidate the binding of these flavonoids to the enzymes. The predicted pharmacokinetic and toxicological properties suggest that these compounds are viable for further development as type 2 diabetes drugs.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

The green chemistry of chalcones: Valuable sources of privileged core structures for drug discovery

The sustainable use of resources is essential in all production areas, including pharmaceuticals. However, the aspect of sustainability needs to be taken into consideration not only in the production phase, but during the whole medicinal chemistry drug discovery trajectory. The continuous progress in the fields of green chemistry and the use of artificial intelligence are contributing to the speed and effectiveness of a more sustainable drug discovery pipeline. In this light, here we review the most recent sustainable and green synthetic approaches used for the preparation and derivatization of chalcones, an important class of privileged structures and building blocks used for the preparation of new biologically active compounds with a broad spectrum of potential therapeutic applications. The literature here reported has been retrieved from the SciFinder database using the term “chalcone” as a keyword and filtering the results applying the concept: “green chemistry”, and from the Reaxys database using the keywords “chalcone” and “green”. For both databases the time-frame was 2017–2022. References were manually selected based on relevance.