Corosolic acid and its structural analogs: A systematic review of their biological activities and underlying mechanism of action.

BACKGROUND: The corosolic acid (CA), also known as plant insulin, is a pentacyclic triterpenoid extracted from plants such as Lagerstroemia speciosa. It has been shown to have anti-diabetic, anti-inflammatory and anti-tumor effects. Its structural analogs ursolic acid (UA), oleanolic acid (OA), maslinic acid (MA), asiatic acid (AA) and betulinic acid (BA) display similar individual pharmacological activities to those of CA. However, there is no systematic review documenting pharmacological activities of CA and its structural analogues. This study aims to fill this gap in literature. PURPOSE: This systematic review aims to summarize the medical applications of CA and its analogues. METHODS: A systematic review summarizes and compares the extraction techniques, pharmacokinetic parameters, and pharmacological effects of CA and its structural analogs. Hypoglycemic effect is one of the key inclusion criteria for searching Web of Science, PubMed, Embase and Cochrane databases up to October 2020 without language restrictions. 'corosolic acid', 'ursolic acid', 'oleanolic acid', 'maslinic acid', 'asiatic acid', 'betulinic acid', 'extraction', 'pharmacokinetic', 'pharmacological' were used to extract relevant literature. The PRISMA guidelines were followed. RESULTS: At the end of the searching process, 140 articles were selected for the systematic review. Information of CA and five of its structural analogs including UA, OA, MA, AA and BA were included in this review. CA and its structural analogs are pentacyclic triterpenes extracted from plants and they have low solubilities in water due to their rigid scaffold and hydrophobic properties. The introduction of water-soluble groups such as sugar or amino groups could increase the solubility of CA and its structural analogs. Their biological activities and underlying mechanism of action are reviewed and compared. CONCLUSION: CA and its structural analogs UA, OA, MA, AA and BA are demonstrated to show activities in lowering blood sugar, anti-inflammation and anti-tumor. Their oral absorption and bioavailability can be improved through structural modification and formulation design. CA and its structural analogs are promising natural product-based lead compounds for further development and mechanistic studies.

Synthesis and evaluation of 1-amino-6-halo-ß-carbolines as antimalarial and antiprion agents.

Malaria is one of the world's most devastating parasitic diseases, causing almost one million deaths each year. Growing resistance to classical antimalarial drugs, such as chloroquine, necessitates the discovery of new therapeutic agents for successful control of this global disease. Here, we report the synthesis of some 6-halo-ß-carbolines as analogues of the potent antimalarial natural product, manzamine A, retaining its heteroaromatic core whilst providing compounds with much improved synthetic accessibility. Two compounds displayed superior activity to chloroquine itself against a resistant Plasmodium falciparum strain, identifying them as promising leads for future development. Furthermore, in line with previous reports of similarities in antimalarial and antiprion effects of aminoaryl-based antimalarial agents, the 1-amino-ß-carboline libraries were also found to possess significant bioactivity against a prion-infected cell line.

Ligand-based virtual screening using Bayesian networks.

A Bayesian inference network (BIN) provides an interesting alternative to existing tools for similarity-based virtual screening. The BIN is particularly effective when the active molecules being sought have a high degree of structural homogeneity but has been found to perform less well with structurally heterogeneous sets of actives. In this paper, we introduce an alternative network model, called a Bayesian belief network (BBN), that seeks to overcome this limitation of the BIN approach. Simulated virtual screening experiments with the MDDR, WOMBAT and MUV data sets show that the BIN and BBN methods allow effective screening searches to be carried out. However, the results obtained are not obviously superior to those obtained using a much simpler approach that is based on the use of the Tanimoto coefficient and of the square roots of fragment occurrence frequencies.

SPR biosensor as a tool for screening prion protein binders as potential antiprion leads.

Prion diseases, also called transmissible spongiform encephalopathies (TSEs), are a group of neurodegenerative disorders affecting animals and humans. No effective treatments are currently available for the diseases, vCJD in particular. It is believed that the formation of protease-resistant insoluble prion protein (PrP(Sc)), which is the main component of amyloidal deposits, from the cellular prion protein (PrP(C)), is essential for the progression of the disease. Therefore, both PrP(Sc) and PrP(C) are currently being used as potential drug targets.This protocol details an optimised experimental protocol to conduct an affinity screening of compound libraries by the immobilisation of PrP(C) using an SPR-based instrument, Biacore 3000.

Synthesis and evaluation of a focused library of pyridine dicarbonitriles against prion disease.

We report the preparation and screening of a set of 55 pyridine dicarbonitriles as potential prion disease therapeutics. Use of microwave irradiation in an attempt to improve the synthesis typically led to only small enhancement in yields but gave cleaner reactions facilitating product isolation. The library was analysed for binding to human prion protein (huPrPC) by surface plasmon resonance and for inhibition of the formation of its partially protease resistant isoform PrPSc in mouse brain cells (SMB). A total of 26 compounds were found to bind to huPrPC whilst 12 showed discernable inhibition of PrPSc formation, five displaying EC(50)s in the range 2.5-9microwo compounds were found to reduce PrPSc levels to below 30% relative to an untreated control at 50nM.

Virtual screening using binary kernel discrimination: effect of noisy training data and the optimization of performance.

Binary kernel discrimination (BKD) uses a training set of compounds, for which structural and qualitative activity data are available, to produce a model that can then be applied to the structures of other compounds in order to predict their likely activity. Experiments with the MDL Drug Data Report database show that the optimal value of the smoothing parameter, and hence the predictive power of BKD, is crucially dependent on the number of false positives in the training set. It is also shown that the best results for BKD are achieved using one particular optimization method for the determination of the smoothing parameter that lies at the heart of the method and using the Jaccard/Tanimoto coefficient in the kernel function that is used to compute the similarity between a test set molecule and the members of the training set.