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New dehydroabietic acid (DHA) derivatives with anticancer activity against HepG2 cancer cell lines as a potential drug targeting EGFR kinase domain. CoMFA study and virtual ligand-based screening.

Abstract
Liver cancer has become the third type of cancer that causes death; this is why the design of new chemotherapeutic drugs against this disease is a major need. With this idea, a series of Dehydroabietic Acid-Based Acylhydrazones have been used to generate a CoMFA model to design new anticancer agents. In this study, we employed a Comparative Molecular Field Analysis studies, we performed those methods on Dehydroabietic Acid-Based Acylhydrazones against HepG2 human cancer cell line. The statistical results are encouraging with Q2 equal to 0.527 and R2 equal to 0.962. The predictive ability of this model was determined using a test set of Dehydroabietic Acid-Based Acylhydrazones that gave an acceptable predictive correlation (R2test) value of 0.614. The developed model guides to design five new molecules with enhanced activity as potential drugs. On the other hand to determine a potential target to these ligands we have established a virtual screening using reverse docking with the most active molecule and 42 antiproliferative targets. Based on the affinity of complex ligand-Target, the intracellular domain of EGFR shows high stability. This suggests that our designed molecules can inhibit the target EGFR which is an important target on targeted therapy of many types of cancer.Communicated by Ramaswamy H. Sarma.
AuthorsHanane Zaki, Assia Belhassan, Mohamed Benlyas, Tahar Lakhlifi, Mohammed Bouachrine
JournalJournal of biomolecular structure & dynamics (J Biomol Struct Dyn) Vol. 39 Issue 8 Pg. 2993-3003 (May 2021) ISSN: 1538-0254 [Electronic] England
PMID32319344 (Publication Type: Journal Article)
Chemical References
  • Abietanes
  • Ligands
  • dehydroabietic acid
  • EGFR protein, human
  • ErbB Receptors
Topics
  • Abietanes
  • Drug Delivery Systems
  • Early Detection of Cancer
  • ErbB Receptors
  • Hep G2 Cells
  • Humans
  • Ligands
  • Molecular Docking Simulation
  • Neoplasms
  • Quantitative Structure-Activity Relationship

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