Diabetes mellitus is a multifactorial global health disorder that is rising at an alarming rate. Cardiovascular diseases, kidney damage and neuropathy are the main cause of high mortality rates among individuals with diabetes. One effective therapeutic approach for controlling hyperglycemia associated with type-2 diabetes is to target alpha-amylase and alpha-glucosidase, enzymes that catalyzes starch hydrolysis in the intestine. At present, approved inhibitors for these enzymes are restricted to acarbose, miglitol and voglibose. Although these inhibitors retard glucose absorption, undesirable gastrointestinal side effects impede their application. Therefore, research efforts continue to seek novel inhibitors with improved efficacy and minimal side effects. Natural products of plant origin have been a valuable source of therapeutic agents with lesser toxicity and side effects. The anti-diabetic potential through alpha-glucosidase inhibition of plant-derived molecules are summarized in this review. Eight molecules (Taxumariene F, Akebonoic acid, Morusin, Rhaponticin, Procyanidin A2, Alaternin, Mulberrofuran K and Psoralidin) were selected as promising drug candidates and their pharmacokinetic properties and toxicity were discussed where available.

/pdf/a-review-of-alpha-glucosidase-inhibitors-from-plants-as-9ow8o1o38u.pdf

A review of alpha-glucosidase inhibitors from plants as potential candidates for the treatment of type-2 diabetes.

Taxanes in cancer treatment: Activity, chemoresistance and its overcoming

Docking is commonly applied to drug design efforts, especially high-throughput virtual screenings of small molecules, to identify new compounds that bind to a given target. Despite great advances and successful applications in recent years, a number of issues remain unsolved. Most of the challenges and problems faced when running docking experiments are independent of the specific software used, and can be ascribed to either improper input preparation or to the simplified approaches applied to achieve high-throughput speed. Being aware of approximations and limitations of such methods is essential to prevent errors, deal with misleading results, and increase the success rate of virtual screening campaigns. In this review, best practices and most common issues of docking and virtual screening will be discussed, covering the journey from the design of the virtual experiment to the hit identification.

https://www.mdpi.com/1420-3049/20/10/18732/pdf

Charting a Path to Success in Virtual Screening.

Encyclopedia of Chemical Physics and Physical Chemistry

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules. Reviewed the niche area of phytomolecule-based anti-cancer drug discovery with respect to current trends including machine learning. 

https://link.springer.com/content/pdf/10.1007/s11030-022-10590-7.pdf

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Taxanes are naturally occurring compounds which belong to a powerful group of chemotherapeutic drugs with anticancer properties. Their current use, clinical efficacy, and unique mechanism of action indicate their potentiality for cancer drug discovery and development thereby promising to reduce the high economy associated with cancer worldwide. Extensive research has been carried out on taxanes with the aim to combat issues of drug resistance, side effects, limited natural supply, and also to increase the therapeutic index of these molecules. These efforts have led to the isolation of many naturally occurring compounds belonging to this family (more than 350 different kinds), and the synthesis of semisynthetic analogs of the naturally existing molecules (>500), and has also led to the characterization of many (>1000) of them. A web-based database system on clinically exploitable taxanes, providing a link between the structure and the pharmacological property of these molecules could help to reduce the druggability gap for these molecules.

/pdf/taxkb-a-knowledge-base-for-new-taxane-related-drug-discovery-1la5977z1u.pdf

TaxKB: a knowledge base for new taxane-related drug discovery

Eighty semisynthetic taxanes were used to develop a quantitative structure activity relationship (QSAR) and also dock with the microtubule assembly and develop a relationship between binding energy and activity. These two equations could be used to predict activity of new taxanes. The data collected from literature were divided into training (61) and test (19) sets and the former were used to develop the equation. In the QSAR, topological, hydrophobic, and structural descriptors are found to be the major contributors of activity. The statistical parameters r
 2 (0.72), $$ r_{\text{adj}}^{2} $$
 (0.69), q
 2 (0.65), and F ratio (23.26) were found to be satisfactory and the predictions were with in the 99% confidence interval. The docking studies of taxanes (using training set compounds) using microtubule (PDB: 1JFF) shows that the binding energy is negatively correlated with the antimicrotubule activity (r = −0.66). Hydrogen bond is found to be predominant factor for the binding of the taxanes with the microtubule. The relationship between binding energy and activity (r
 2 (0.72), $$ r_{\text{adj}}^{2} $$
 (0.69), q
 2 (0.65), and F ratio (23.26)) was able to predict the data in the test set within the 99% confidence limits. In addition, these theoretical studies will give additional inputs to the medicinal chemists to design newer and potent taxane derivatives as antimitotic agents.

Computational approaches to enhance activity of taxanes as antimitotic agent

Cytotoxic activity of 47 semi-synthetic Taxoteres against four different cancer cell lines, such as ovarian cancer (A121), human colon adenocarcinoma (HT29), human breast cancer (MCF7) and drug-resistant human breast cancer (MCF7R), was used to develop a quantitative structure–cytotoxicity relationship (QSCR). The data were divided into training (38) and test sets (9), in which the former was used to develop the QSCR and the later was used for the prediction of three of the cell lines namely A121, HT29 and MCF7. In the case of MCF7R, the training set contained 36 compounds and the test set contained nine compounds. The statistical measures such as squared correlation coefficient (r 2 = 0.73–0.81), adjusted squared correlation coefficient (r 2 adj = 0.72–0.80), cross-correlation coefficient (q 2 = 0.62–0.75) and F-ratio (17.49–27.49) were found to be satisfactory. Other statistical diagnoses such as n/p ratio, fraction of the variance in terms of r 2 and quality factor (Q) were also satisfactory. Thermodyn...

Quantitative structure–cytotoxicity relationships (QSCR) for semi-synthetic Taxoteres against cancer cell lines

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TaxKB: a knowledge base for new taxane-related drug discovery

Computational approaches to enhance activity of taxanes as antimitotic agent

Quantitative structure–cytotoxicity relationships (QSCR) for semi-synthetic Taxoteres against cancer cell lines