Dipeptidyl peptidase IV inhibition of phytocompounds from Artocarpus champeden (Lour.) Stokes: In silico molecular docking study and ADME-Tox prediction approach

The present study examines the potential activity prediction based on free binding energy (ΔG) and interaction confirmation of phytocompounds from Artocarpus champeden (Lour.) Stokes with macromolecule protein receptor of dipeptidyl peptidase IV (DPP-IV) using in silico molecular docking studies and physicochemical and pharmacokinetic properties (ADME-Tox) prediction approaches. The active subsites of the DPP-IV receptor macromolecule protein Protein Data Bank (ID: 1 × 70) were docked using Autodock v4.2.6 (100 docking runs). A grid box of 52 × 28 × 26 Å points spaced by 0.37 Å was centered on the active site of x = 40.926 Å; y = 50.522 Å; z = 35.031 Å. For ADME-Tox prediction, Swiss ADME online-based application programs were used. The results show that 12 pythocompounds from A. champeden have the potential as DPP-IV inhibitors based on ΔG value and interaction conformation. There are five pythocompounds with lower ΔG values and inhibition constants than the native ligand and seven pythocompounds with ΔG values and inhibition constants close to the native ligand. The 12 compounds form an interaction conformation at the active subsites of the DPP-IV receptor. At the same time, the results of the ADME-Tox prediction analysis showed that the 12 compounds had different physicochemical and pharmacokinetic properties.

In Vitro and In Silico ADME-Tox Profiling and Safety Significance of Multifunctional Monoamine Oxidase Inhibitors Targeting Neurodegenerative Diseases

Herein we report in vitro metabolic stability in human liver microsomes (HLMs), interactions with cytochrome P450 isoenzymes (CYP3A4, CYP2D6, and CYP2C9), and cytotoxicity analyses on HEK-293, HepG2, Huh7, and WTIIB cell lines of our most recent multitarget directed ligands PF9601N, ASS234, and contilisant. Based on these results, we conclude that (1) PF9601N and contilisant are metabolically stable in the HLM assay, in contrast to the very unstable ASS234; (2) CYP3A4 activity was decreased by PF9601N at all the tested concentrations and by ASS234 and contilisant only at the highest concentration; CYP2D6 activity was reduced by ASS234 at 1, 10, and 25 μM and by PF9601N at 10 and 25 μM, whereas contilisant increased its activity at the same concentrations; CYP2C9 was inhibited by the three compounds; (3) contilisant did not affect cell viability in the widest range of concentrations: up to 10 μM on HEK-293 cells, up to 30 μM on Huh7 cells, up to 50 μM on HepG2 cells, and up to 30 or 100 μM on WTIIB cells. Based on these results, we selected contilisant as a metabolically stable and nontoxic lead compound for further studies in Alzheimer’s disease therapy.

Large-scale evaluation of cytochrome P450 2C9 mediated drug interaction potential with machine learning-based consensus modeling

Cytochrome P450 (CYP) enzymes play an important role in the metabolism of xenobiotics. Since they are connected to drug interactions, screening for potential inhibitors is of utmost importance in drug discovery settings. Our study provides an extensive classification model for P450-drug interactions with one of the most prominent members, the 2C9 isoenzyme. Our model involved the largest set of 45,000 molecules ever used for developing prediction models. The models are based on three different types of descriptors, (a) typical one, two and three dimensional molecular descriptors, (b) chemical and pharmacophore fingerprints and (c) interaction fingerprints with docking scores. Two machine learning algorithms, the boosted tree and the multilayer feedforward of resilient backpropagation network were used and compared based on their performances. The models were validated both internally and using external validation sets. The results showed that the consensus voting technique with custom probability thresholds could provide promising results even in large-scale cases without any restrictions on the applicability domain. Our best model was capable to predict the 2C9 inhibitory activity with the area under the receiver operating characteristic curve (AUC) of 0.85 and 0.84 for the internal and the external test sets, respectively. The chemical space covered with the largest available dataset has reached its limit encompassing publicly available bioactivity data for the 2C9 isoenzyme.

Antitubercular activity of 1,2,3-triazolyl fatty acid derivatives

A collection of 1,2,3-triazoles unsaturated fatty acid mimics were efficiently synthesized by click chemistry. The 1,4-disubstituted analogs prepared covered different alkyl chain lengths and triazole positions. The compounds were subsequently tested against Mycobacterium tuberculosis, being most of them active with some of the analogs displaying activity at micromolar concentration. The most potent member of the series has the triazole moiety on the C-2 position with a carbon chain of eight or ten carbon atoms. The 1,5-isomers of the most active analog were significantly less active than the original isomer. The activity of the selected hit was assayed on several clinical MTB multi-drug resistant strains providing the same MIC.