In-silico and in-vitro screening of Asiatic acid and Asiaticoside A against Cathepsin S enzyme.

BACKGROUND: Atherosclerosis is a form of cardiovascular disease that affects the endothelium of the blood vessel. Series of events are involved in the pathophysiology of this disease which includes the breaking down of the connective tissue elastin and collagen responsible for the tensile strength of the arterial wall by proteolytic enzyme. One of these enzymes called Cathepsin S (CatS) is upregulated in the progression of the disease and its inhibition has been proposed to be a promising pharmacological target to improve the prognosis of the disease condition. Asiatic acid and asiaticoside A are both pentacyclic triterpenoids isolated from Centella asiatica. Their use in treating various cardiovascular diseases has been reported. METHODS: In this study through in silico and in vitro methods, the pharmacokinetic properties, residue interaction, and inhibitory activities of these compounds were checked against the CatS enzyme. The SwissADME online package and the ToxTree 3.01 version of the offline software were used to determine the physicochemical properties of the compounds. RESULT: Asiatic acid reported no violation of the Lipinski rule while asiaticoside A violated the rule with regards to its molecular structure and size. The molecular docking was done using Molecular Operating Environment (MOE) and the S-score of - 7.25988, - 7.08466, and - 4.147913 Kcal/mol were recorded for LY300328, asiaticoside A, and asiatic acid respectively. Asiaticoside A has a docking score value (- 7.08466Kcal/mol) close to the co-crystallize compound. Apart from the close docking score, the amino acid residue glycine69 and asparagine163 both interact with the co-crystallized compound and asiaticoside A. The in vitro result clearly shows the inhibitory effect of asiaticoside and asiatic acid. Asiaticoside A has an inhibitory value of about 40% and asiatic acid has an inhibitory value of about 20%. CONCLUSION: This clearly shows that asiaticoside will be a better drug candidate than asiatic acid in inhibiting the CatS enzyme for the purpose of improving the outcome of atherosclerosis. However, certain modifications need to be made to the structural make-up of asiaticoside A to improve its pharmacokinetics properties.

Cheminformatic Profiling and Hit Prioritization of Natural Products with Activities against Methicillin-Resistant Staphylococcus aureus (MRSA).

Several natural products (NPs) have displayed varying in vitro activities against methicillin-resistant Staphylococcus aureus (MRSA). However, few of these compounds have not been developed into potential antimicrobial drug candidates. This may be due to the high cost and tedious and time-consuming process of conducting the necessary preclinical tests on these compounds. In this study, cheminformatic profiling was performed on 111 anti-MRSA NPs (AMNPs), using a few orally administered conventional drugs for MRSA (CDs) as reference, to identify compounds with prospects to become drug candidates. This was followed by prioritizing these hits and identifying the liabilities among the AMNPs for possible optimization. Cheminformatic profiling revealed that most of the AMNPs were within the required drug-like region of the investigated properties. For example, more than 76% of the AMNPs showed compliance with the Lipinski, Veber, and Egan predictive rules for oral absorption and permeability. About 34% of the AMNPs showed the prospect to penetrate the blood-brain barrier (BBB), an advantage over the CDs, which are generally non-permeant of BBB. The analysis of toxicity revealed that 59% of the AMNPs might have negligible or no toxicity risks. Structure-activity relationship (SAR) analysis revealed chemical groups that may be determinants of the reported bioactivity of the compounds. A hit prioritization strategy using a novel "desirability scoring function" was able to identify AMNPs with the desired drug-likeness. Hit optimization strategies implemented on AMNPs with poor desirability scores led to the design of two compounds with improved desirability scores.

Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach.

In view of the vast number of natural products with potential antiplasmodial bioactivity and cost of conducting antiplasmodial bioactivity assays, it may be judicious to learn from previous antiplasmodial bioassays and predict bioactivity of these natural products before experimental bioassays. This study set out to harness antimalarial bioactivity data of natural products to build accurate predictive models, utilizing classical machine learning approaches, which can find potential antimalarial hits from new sets of natural products. Classical machine learning approaches were used to build four classifier models (Naïve Bayesian, Voted Perceptron, Random Forest and Sequence Minimization Optimization of Support Vector Machines) from bioactivity data of natural products with in-vitro antiplasmodial activity (NAA) using a combination of the molecular descriptors and two-dimensional molecular fingerprints of the compounds. Models were evaluated with an independent test dataset. Possible chemical features associated with reported antimalarial activities of the compounds were also extracted. From the results, Random Forest (accuracy 82.81%, Kappa statistics 0.65 and Area under Receiver Operating Characteristics curve 0.91) and Sequential Minimization Optimization (accuracy 85.93%, Kappa statistics 0.72 and Area under Receiver Operating Characteristics curve 0.86) showed good predictive performance for the NAA dataset. The amine chemical group (specifically alkyl amines and basic nitrogen) was confirmed to be essential for antimalarial activity in active NAA dataset. This study built and evaluated classifier models that were used to predict the antiplasmodial bioactivity class (active or inactive) of a set of natural products from interBioScreen chemical library.