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• W2888780169 abstract "Background: Plants of the Amaryllidaceae family have been under intense scrutiny for the presence of a couple of alkaloidal secondary metabolites with endued cytotoxic activity, such as pancratistatin (1), 7-deoxypancratistatin (2), narciclasine (3), 7-deoxynarciclasine (4), trans-dihydronarciclasine (5), and 7-deoxy-trans-dihydronarciclasine (6). Nevertheless, preclinical evaluation of these alkaloids has been put on hold because of the limited quantity of materials available from isolation. Aim: To explore the underlying cytotoxic molecular mechanisms of the Amaryllidaceae alkaloids (1–6) and to assess their absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles using chemoinformatic tools. Materials And Methods: AutoDock 4.0 software along with different in silico chemoinformatic tools, namely PharmMapper, Molinspiration, MetaPrint2D, and admetSAR servers, were used to assess the drugability of the Amaryllidaceae alkaloids (1–6). Results: Deoxycytidine kinase (dCK) (PDB: 1P60) was predicted as a potential target with fitting score of 5.574. In silico molecular docking of (1–6) into dCK revealed good interactions, where interesting hydrogen bonds were observed with the amino acid residues—Gly-28 and Ser-35—located in the highly conserved P-loop motif. This motif plays a special role in dCK function. Contrary to (1), in silico pharmacokinetic results have shown good absorption and permeation and thus good oral bioavailability for (2–6). Conclusion: The in silico docking data have proposed that the reported cytotoxic activity of the Amaryllidaceae alkaloids (1–6) could be mediated through dCK inhibition. In addition, the ADMET profile of these alkaloids is promising and thus (1–6) could be candidates for future drug development." @default.
• W2888780169 created "2018-09-07" @default.
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• W2888780169 date "2018-01-01" @default.
• W2888780169 modified "2023-10-14" @default.
• W2888780169 title "Potential deoxycytidine kinase inhibitory activity of amaryllidaceae alkaloids: An in silico approach" @default.
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• W2888780169 doi "https://doi.org/10.4103/jpbs.jpbs_44_18" @default.
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