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Pyridine‐N‐Oxide Alkaloids from <i>Allium stipitatum</i> and Their Synthetic Disulfide Analogs as Potential Drug Candidates against <i>Mycobacterium tuberculosis</i>: A Molecular Docking, QSBAR, and ADMET Prediction Approach

Cedric Dzidzor Kodjo Amengor, Emmanuel Orman, Cynthia Amaning Danquah, Inemesit Okon Ben, Prince Danan Biniyam, Benjamin Kingsley Harley

2022BioMed Research International21 citationsDOIOpen Access PDF

Abstract

In this study, we consider pyridine‐N‐oxide alkaloids from Allium stipitatum and their synthetic disulfide analogs (PDAs) as candidates for next‐generational antimycobacterial agents, in light of growing resistance to existing conventional therapies. In silico studies involving molecular docking simulations of 12 PDAs were carried out against 7 Mycobacterium tuberculosis target proteins (MTs) to determine their theoretical binding affinities. Compounds A3, A6, and B9 demonstrated stronger binding affinities on similar MTs. Molecular descriptors (MDs) describing structural and physicochemical properties of the compounds were also calculated using ChemDes, explored using Pearson’s correlation analysis, and principal component analysis (PCA) in comparison with MDs from conventional antitubercular medicines. The PDAs possessed similar scores as isoniazid and pyrazinamide. The MDs were also used to conduct a quantitative structure‐binding affinity relationship (QSBAR) study by building good fit and significant models through principal component regression (PCR) and partial least squares regression (PLSR). Leave‐one‐out cross‐validation was adopted in the PLSR, resulting in good predictive models on all MTs (range of R 2 = 0.7541‐0.8992; range of Q 2 = 0.6183‐0.8162). Both PCR and PLSR models predicted the significant effects of ndonr, Hy, Mol wt, nhev, nring, ndb, Log P, W, Pol, ISIZ, TIAC, Getov, and UI on the binding of ligands to the MTs. In silico prediction of PDAs’ ADMET profiles was conducted with QikProp utility. The ADMET profiles of the compounds were favorable. The outcome of the current study strengthens the significance of these compounds as promising lead candidates for the treatment of multidrug‐resistant tuberculosis.

Topics & Concepts

In silicoAntimycobacterialMycobacterium tuberculosisPartial least squares regressionMolecular descriptorDocking (animal)ChemistryQuantitative structure–activity relationshipLooPrincipal component regressionStereochemistryPrincipal component analysisIsoniazidComputational biologyTuberculosisBiologyBiochemistryMathematicsVeterinary medicineGenePathologyMedicineStatisticsComputational Drug Discovery MethodsSynthesis and biological activityPharmacological Effects of Natural Compounds