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1 November 2022

Thiazolopyrimidine as a Promising Anticancer Pharmacophore: In Silico Drug Design, Molecular Docking and ADMET Prediction Studies †

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1
Department of Pharmaceutical Organic Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt
2
Faculty of Pharmacy, New Mansoura University, New Mansoura 7723730, Egypt
*
Author to whom correspondence should be addressed.
Presented at the 8th International Electronic Conference on Medicinal Chemistry, 1–30 November 2022; Available online: https://ecmc2022.sciforum.net/.
This article belongs to the Proceedings The 8th International Electronic Conference on Medicinal Chemistry

Abstract

Thiazolopyrimidines are well known to be designed to act as bio-isosteric analogues of purine nucleus. They proved to show a wide range of biological activities, such as anticancer, anti-inflammatory, antifungal, antiviral and antitubercular activity. In this study, a literature survey was thoroughly performed to elect the most active thiazolopyrimidine-containing scaffolds, acting as anticancer agents, to be subjected to extensive computational studies in order to explore the possible credible mode of their anticancer activity. First, drug-likeness was investigated for the most active derivatives, followed by molecular docking study against Cyclin-dependent kinases (CDK), Vascular endothelial growth factor receptor (VEGFR) and Phosphoinositide 3-kinases (PI3K) enzymes in order to assess their binding energy and propose the mode of action. Next, contact preference and surface mapping studies were carried out to explain the presence of remarkable affinity of certain analogues towards a specific enzyme, in addition to providing more information about their activity. Finally, physicochemical properties, Lipinski’s rule of five and ADMET prediction studies were applied to predict the best route of administration and to suggest the pharmacokinetics of the most promising candidates.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ECMC2022-13313/s1.

Author Contributions

Conceptualization, A.S.M. and M.A.M.M.; methodology, D.I.A.O. and A.S.M.; software, O.A.E.-K.; validation, O.A.E.-K.; A.S.M. and D.I.A.O.; formal analysis, O.A.E.-K.; A.S.M. and D.I.A.O.; investigation, O.A.E.-K.; A.S.M. and D.I.A.O.; resources, O.A.E.-K.; A.S.M. and D.I.A.O.; data curation, O.A.E.-K.; A.S.M. and D.I.A.O.; writing—original draft preparation, O.A.E.-K.; writing—review and editing, A.S.M.; D.I.A.O. and M.A.M.M.; visualization, O.A.E.-K.; A.S.M. and D.I.A.O.; supervision, A.S.M.; D.I.A.O. and M.A.M.M.; project administration, M.A.M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

All data are available at Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest.
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