In Silico Evaluation of Antidiabetic Activity and ADMET Prediction of Compounds from Musa acuminata Colla Peel

Abstract

Diabetes is a severe chronic disease that affects 422 million adults worldwide in 2014. It was the fifth leading cause of mortality in the Philippines in 2019. The primary cause of death of diabetic patients is due to cardiovascular disease. α-Glucosidase inhibitors (AGIs) are known for their cardiovascular benefits because they prevent the postprandial glucose level from increasing, which plays a significant role in the development of cardiovascular diseases. The synthesis of AGIs is complex and requires a lot of steps. Thus, there is a need to explore and discover AGIs especially from plants, which are known sources of bioactive compounds. Drug discovery entails a complex, costly, time-consuming, and risky process. Computer-aided drug discovery/ design (CADD) methods such as molecular docking and ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction have been developed to identify the promising compounds that will be tested in in vitro and in vivo experiments. This shortens the research process and helps reduce the expense and risk of failure for drug discovery. In this study, molecular docking was conducted to predict the α-glucosidase inhibitory activity of compounds from Musa acuminata Colla peel against human intestinal α-glucosidase. Out of 87 compounds, only 11 compounds were found to have better or comparable binding affinity with the standard, acarbose (-8.8 kcal/mol) -namely, sesamin (-9.8 kcal/mol), asarinin (-9.7 kcal/mol), quercetin-7-rutinoside (-9.4 kcal/mol), kaempferol-3-rutinoside (-9.4 kcal/mol), (-)-epicatechin (-9.0 kcal/ mol), (+)-catechin (-8.9 kcal/mol), myricetin-3-rutinoside (-8.9 kcal/mol), quercetin (-8.9 kcal/ mol), kaempferol-3-rhamnoside-7-glucoside (-8.9 kcal/mol), stigmasterol (-8.9 kcal/mol), and β-sitosterol (-8.8 Kcal/mol). The prediction of ADMET properties and drug-likeness revealed how the best binding compounds may behave inside the body. Some of the compounds were found to be safe and have good absorption, distribution, metabolism, and excretion properties. They showed promising potentials that may lead to their development as drugs.

Evaluating the Potential Blood Coagulant Activity ofCaenothus americanus Compounds: Computational Analysis using Docking, Physicochemical, and ADMET Studies

“…Developing a new drug without toxic properties is crucial. Hence, the potential toxicity of C. americanus compounds has been evaluated using ADMETlab, with eleven computational toxicity parameters, in various animal and human organs (66, 69). Human ether-a-go-go related gene (HERG) is involved in the fatal arrhythmia known as torsade de pointes or the long QT syndrome, encoding a potassium ion (K+) channel (70).…”

“…The median lethal dose parameter, LD50, is used to measure acute toxicity in mice, which determines the relative toxicity of a compounds (66). The prediction of drug decomposition using ADMETlab indicated none of the compounds caused oral toxicity in rats.…”

“…The steady-state volume of distribution (VDss) is a crucial pharmacokinetic parameter that describes the distribution of an injected amount of drug to create a concentration equivalent to that in blood plasma (58). In other hand, the VD is the volume of plasma or blood in which the compound appears to be dissolved at a steady state or equilibrium (60,66).…”

Molecular Docking Simulation of Antidiabetic Molecules of Libas (Spondias pinnata) Fruit and Prediction of their Pharmacokinetic Properties

“…Human intestinal absorption or HIA is important in the apparent e cacy of the drug. HIA is also related to oral bioavailability [33]. ADMET Lab 2.0 said that an absorbance of less than 30% is considered to be poor absorption.…”

“…These inhibitors may cause drug-drug interactions when indicated with drugs that are metabolized by these enzymes. The effects of these drug-drug interactions may be a reduction in clearance of the drug which may cause toxicity, or decreased pharmacologic effect [33].…”