Natural Products as Anti-Cancerous Therapeutic Molecules Targeted towards Topoisomerases

Swati Singh1, Veda P Pandey1, Kusum Yadav1

  • 1Department of Biochemistry & Institute for Development of Advanced Computing, ONGC Centre for Advanced Studies, University of Lucknow, Lucknow, U.P., India.

Insights

This review explores plant-derived compounds as anticancer agents targeting topoisomerases (Topo I and II). Computational methods aid in assessing their safety and efficacy for cancer treatment.

Area of Science:

  • Molecular Biology
  • Biochemistry
  • Pharmacology

Background:

  • Topoisomerases (Topo I and II) are crucial enzymes that manage DNA topology during essential cellular processes.
  • These enzymes are validated targets for cancer chemotherapy, as their inhibition disrupts cancer cell division.
  • Existing FDA-approved drugs like topotecan, irinotecan, and etoposide target topoisomerases.

Purpose of the Study:

  • To review the anticancer potential of plant-derived secondary metabolites against topoisomerases.
  • To highlight the role of computational approaches in drug design for these anticancer agents.

Main Methods:

  • Literature review of plant-derived compounds with topoisomerase inhibitory activity.
  • Discussion of computational methods including ADMET, molecular docking, molecular dynamics, and QSAR.
  • Analysis of established and novel topoisomerase inhibitors.

Main Results:

  • Plant-derived alkaloids, flavonoids, and terpenoids show promise as topoisomerase inhibitors.
  • Computational tools can predict and assess the safety, efficacy, and potency of these natural compounds.
  • Targeting topoisomerases leads to DNA damage and inhibits cancer cell proliferation.

Conclusions:

  • Plant-derived secondary metabolites represent a valuable source for developing novel anticancer therapeutics targeting topoisomerases.
  • Computer-aided drug design is instrumental in optimizing the development of these natural product-based drugs.
  • Further research into these compounds and computational modeling can accelerate the discovery of effective cancer treatments.

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