Identifying promising GSK3β inhibitors for cancer management: a computational pipeline combining virtual screening

Libo Hua1, Farah Anjum2, Alaa Shafie2

  • 1South China Research Center for Acupuncture and Moxibustion, Medical College of Acupuncture Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China.

PubMed

Insights

Computational screening identified two potent anticancer compounds, BMS-754807 and GSK429286A, targeting Glycogen synthase kinase-3 (GSK3β). These compounds show promise for developing safer and more effective cancer therapies.

Area of Science:

  • Biochemistry
  • Pharmacology
  • Computational Chemistry

Background:

  • Glycogen synthase kinase-3 (GSK3β) is a key kinase implicated in various cancers.
  • Existing GSK3β inhibitors often exhibit toxicity, necessitating the development of safer alternatives.
  • Targeting GSK3β presents a novel strategy for anticancer drug development.

Purpose of the Study:

  • To computationally screen a large library of anticancer compounds to identify novel GSK3β inhibitors.
  • To evaluate the binding affinity, stability, and drug-like properties of potential GSK3β inhibitors.

Main Methods:

  • Docking-based virtual screening of 4,222 anticancer compounds against the GSK3β binding pocket.
  • Physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) analysis.
  • 100 ns molecular dynamics simulations to assess binding stability.

Main Results:

  • Two compounds, BMS-754807 and GSK429286A, demonstrated high binding affinities to GSK3β (-11.9 and -9.8 kcal/mol, respectively), surpassing the positive control.
  • Molecular dynamics simulations confirmed stable and consistent interactions between the identified compounds and GSK3β.
  • The identified compounds are predicted to possess favorable drug-like properties.

Conclusions:

  • BMS-754807 and GSK429286A are promising candidates for GSK3β inhibition in cancer therapy.
  • Further experimental validation is recommended to assess their clinical potential.
  • This study highlights the utility of computational screening in identifying novel anticancer drug leads.