Discovery of potent and noncovalent KRASG12D inhibitors: Structure-based virtual screening and biological evaluation

Yuting Wang1, Hai Zhang2, Jindong Li3

  • 1Department of Pharmaceutical Analysis, China Pharmaceutical University, Nanjing, China.

Insights

Researchers identified four novel KRASG12D inhibitors for pancreatic cancer treatment. Hit compound 3 showed significant anti-tumor effects in preclinical models, offering a promising foundation for new cancer therapies.

Area of Science:

  • Oncology
  • Medicinal Chemistry
  • Drug Discovery

Background:

  • KRASG12D mutations are prevalent in pancreatic cancer, driving tumor growth.
  • Targeting KRASG12D represents a significant therapeutic opportunity for pancreatic ductal adenocarcinoma.
  • Developing effective KRASG12D inhibitors is crucial for advancing pancreatic cancer treatment.

Purpose of the Study:

  • To identify novel, noncovalent inhibitors targeting the KRASG12D mutation.
  • To evaluate the potency and efficacy of identified inhibitors against pancreatic cancer.
  • To establish a lead compound for further drug development against KRASG12D-driven cancers.

Main Methods:

  • Structure-based virtual screening was employed to identify potential KRASG12D inhibitors.
  • In vitro biological assays were conducted to assess compound affinity and cellular activity.
  • In vivo studies in tumor-bearing mice evaluated the anti-tumor efficacy of lead compounds.

Main Results:

  • Four potent, noncovalent KRASG12D inhibitors (hits 1-4) were identified.
  • Compounds demonstrated sub-nanomolar affinities for KRASG12D and dose-dependent inhibition of pancreatic cancer cells.
  • Hit compound 3 exhibited significant inhibition of tumor growth in preclinical mouse models.

Conclusions:

  • Hit compound 3 is a promising candidate for further development as a KRASG12D inhibitor.
  • This study provides a strong foundation for developing new treatments for KRASG12D-driven pancreatic cancer.
  • The identified compounds represent valuable starting points for hit-to-lead optimization in oncology drug discovery.