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

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.

Figure 1

Structures of the conventional antitubercular medicines and PDSA used in the study. The PDSA included; [A1] 2-(((methylthio)methyl)disulfanyl)pyridine-1-oxide; [A2] 2,2′-disulfanediylbis (pyridine-1-oxide); [A3] 2-(methyldisulfanyl)pyridine-1-oxide; [A4] 2-(methyldisulfanyl)pyridine; [A5] 2-(methyldisulfanyl)pyrimidine; [A6] 2-(methyldisulfanyl)quinoline; [A7] 1-methyl-2-phenyldisulfane [A8] 2-(methyldisulfanyl)thiophene; [B9] 3-(benzylthio)-5-(methyldisulfanyl)-4H-1,2,4-triazole-4-amine; [B10] 2-(methyldisulfanyl) thieno [2,3-d]pyrimidin-4-amine; [B11] 7-fluoro-2-methyl(disulfanyl) benzo [d]thiazole; [B12] 4-ethyl-5-(methyldisulfanyl)-4H-1,2,4-triazol-3-ol.

Figure 2

Correlation analysis (a) and principal component analysis (b) of the molecular descriptors of the ligands included in the study.

Figure 3

Summary of results from molecular docking studies. (a) Comparison of the docking outputs (B) with the natives (A) showing comparable binding pocket interactions. (b) Binding energies observed for the PDAs on the molecular targets considered. These were compared with that of the respective inhibitors (BTZ043, NAD, ZVT, TLMN, MX539, and I28) and isoniazid (INH). (c) 3D and 2D binding models of most effective ligands on selected molecular targets. The figure also shows the different amino acid units present at the respective binding sites.

Figure 4

Outcomes from partial least regression analysis in the quantitative structure-binding affinity relationship study. (a) PLS regression fits with cross-validation fits showing good prediction abilities of the models developed for each molecular target. (b) Standardized coefficients of the MDs in the PLS analysis. Red horizontal line is an imaginary line drawn to identify MDs with significant coefficients to the binding on the respective targets.

Figure 5

Predicted sites of metabolism reactivity of disulfide analogs. CYP3A4 reactivity site indicated by green circle.