Computer Aided Drug Design Methodologies with Natural Products in the Drug Research Against Alzheimer's Disease

The protective effect of natural medicines in rheumatoid arthritis via inhibit angiogenesis

Rheumatoid arthritis is a chronic immunological disease leading to the progressive bone and joint destruction. Angiogenesis, accompanied by synovial hyperplasia and inflammation underlies joint destruction. Delaying or even blocking synovial angiogenesis has emerged as an important target of RA treatment. Natural medicines has a long history of treating RA, and numerous reports have suggested that natural medicines have a strong inhibitory activity on synovial angiogenesis, thereby improving the progression of RA. Natural medicines could regulate the following signaling pathways: HIF/VEGF/ANG, PI3K/Akt pathway, MAPKs pathway, NF-κB pathway, PPARγ pathway, JAK2/STAT3 pathway, etc., thereby inhibiting angiogenesis. Tripterygium wilfordii Hook. f. (TwHF), sinomenine, and total glucoside of Paeonia lactiflora Pall. Are currently the most representative of all natural products worthy of development and utilization. In this paper, the main factors affecting angiogenesis were discussed and different types of natural medicines that inhibit angiogenesis were systematically summarized. Their specific anti-angiogenesis mechanisms are also reviewed which aiming to provide new perspective and options for the management of RA by targeting angiogenesis.

Deciphering the multi-functional role of Indian propolis for the management of Alzheimer's disease by integrating LC-MS/MS, network pharmacology, molecular docking, and in-vitro studies

The conventional one-drug-one-disease theory has lost its sheen in multigenic diseases such as Alzheimer's disease (AD). Propolis, a honeybee-derived product has ethnopharmacological evidence of antioxidant, anti-inflammatory, antimicrobial and neuroprotective properties. However, the chemical composition is complex and highly variable geographically. So, to leverage the potential of propolis as an effective treatment modality, it is essential to understand the role of each phytochemical in the AD pathophysiology. Therefore, the present study was aimed at investigating the anti-Alzheimer effect of bioactive in Indian propolis (IP) by combining LC-MS/MS fingerprinting, with network-based analysis and experimental validation. First, phytoconstituents in IP extract were identified using an in-house LC-MS/MS method. The drug likeness and toxicity were assessed, followed by identification of AD targets. The constituent-target-gene network was then constructed along with protein-protein interactions, gene pathway, ontology, and enrichment analysis. LC-MS/MS analysis identified 16 known metabolites with druggable properties except for luteolin-5-methyl ether. The network pharmacology-based analysis revealed that the hit propolis constituents were majorly flavonoids, whereas the main AD-associated targets were MAOB, ESR1, BACE1, AChE, CDK5, GSK3β, and PTGS2. A total of 18 gene pathways were identified to be associated, with the pathways related to AD among the topmost enriched. Molecular docking analysis against top AD targets resulted in suitable binding interactions at the active site of target proteins. Further, the protective role of IP in AD was confirmed with cell-line studies on PC-12, in situ AChE inhibition, and antioxidant assays.

Molecular Docking and Network Pharmacology Interaction Analysis of Gingko Biloba (EGB761) Extract with Dual Target Inhibitory Mechanism in Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is the most common type of neurodegenerative dementia affecting people in their later years of life. The AD prevalence rate has significantly increased due to a lack of early detection technology and low therapeutic efficacy. Despite recent scientific advances, some aspects of AD pathological targets still require special attention. Certain traditionally consumed phytocompounds have been used for thousands of years to treat such pathologies. The standard extract of Gingko biloba (EGB761) is a combination of 13 macro phyto-compounds and various other micro phytocompounds that have shown greater therapeutic potential against the pathology of AD.

OBJECTIVE

Strong physiological evidence of cognitive health preservation has been observed in elderly people who keep an active lifestyle. According to some theories, consuming certain medicinal extracts helps build cognitive reserve. We outline the research employing EGB761 as a dual target for AD.

METHODS

This study investigates various inhibitory targets against AD using computational approaches such as molecular docking, network pharmacology, ADMET (full form), and bioactivity prediction of the selected compounds.

RESULTS

After interaction studies were done for all the phytoconstituents of EGB761, it was concluded that all four of the phytocompounds (kaempferol, isorhamnetin, quercetin, and ginkgotoxin) showed the maximum inhibitory activity against acetylcholinesterase (AChE) and GSK3β.

CONCLUSION

The highly active phytocompounds of EGB761, especially quercetin, kaempferol, and isorhamnetin, have better activity against AChE and GSK3β than its reported synthetic drug, according to molecular docking and network pharmacology research. These compounds may act on multiple targets in the protein network of AD. The AChE theory was primarily responsible for EGB761's therapeutic efficacy in treating AD.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.