Network pharmacology combined with molecular docking and experimental verification to elucidate functional mechanism of Fufang Zhenzhu Tiaozhi against type 2 diabetes mellitus

Fufang Zhenzhu Tiaozhi (FTZ) capsules have been prescribed for treating glucose and lipid metabolism disorders such as type 2 diabetes mellitus (T2DM). However, the underlying mechanism remains unknown. In this study, network pharmacology and experimental verification were combined to investigate the mechanisms of FTZ in treating T2DM. A total of 176 active ingredients and 1169 corresponding targets were screened using biological databases. 598 potential targets of T2DM were retrieved from GeneCards, PharmGKB, OMIM, Drugbank, and TTD. The Venn diagram was employed to identify the 194 intersection targets, which were employed to construct the "Herb-Compound-Target" interacting networks. These common targets were also used to prepare a protein-protein interaction (PPI) network to uncover potential targets. The four core targets were docked to their corresponding targets for binding analysis. Additionally, the top-ranked poses of ingredients and the positive compounds from each protein were evaluated for stability using molecular dynamics. Our results suggest that core active ingredients such as kaempferol, luteolin, and baicalein have high binding affinity and stability with AKT1, PTGS2 (also known as COX-2), DPP4, and PAPRG. GO and KEGG analyses indicated that the treatment T2DM by FTZ might be related to different pathway like AMPK and EGFR pathways. The experimental validation results proved that kaempferol, luteolin, and baicalein could significantly inhibit the activity of DPP4 and COX-2, kaempferol and luteolin were also able to activate AKT and AMPK signaling pathway. This study further validated previous findings and enhanced our understanding of the potential effects of FTZ on T2DM.Communicated by Ramaswamy H. Sarma.

Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine

BACKGROUND

Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs.

PURPOSE

This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development.

STUDY DESIGN

We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research.

METHODS

The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM.

RESULT

Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery.

CONCLUSIONS

The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.

Adjusted degradation of BRD4 S and BRD4 L based on fine structural modifications of the pyrrolopyridone scaffold

Novel pyrrolopyridone BET degraders were designed and synthesized based on the binding mode between the pyrrolopyridone BET inhibitor with the BRD4 protein. The potent degraders on MV-4-11 cells were discovered through structure-activity relationship study. Modification of warhead on pyrrolopyridone BET degraders significantly regulates BRD4 isoform (long and short) protein degradation, which induces differential cell cycle arrest and apoptosis on MV-4-11 cells. Docking study revealed that the fine structural modification of BET degraders may bind with the BD domain of BRD4 protein to engage various surface areas that bind with CRBN.