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<p>Checkpoint kinase 1 (CHK1) plays a critical role in DNA damage response and cell cycle regulation, making it an attractive target for cancer therapy. However, clinical translation of CHK1 inhibitors has been limited by selectivity issues, dose-limiting toxicities, and drug resistance mechanisms, necessitating the development of novel inhibitors with improved therapeutic profiles. We developed an integrated computational-experimental pipeline for CHK1 inhibitor discovery. Starting with 492,534 compounds from the Specs database, we applied PAINS filtering, molecular fingerprinting (ECFP4), and dimensionality reduction (UMAP with K-means clustering). Structure-based virtual screening included e-pharmacophore modeling, molecular docking (Schrödinger’s Glide), and 200-ns molecular dynamics simulations with MM-GBSA calculations. Lead compounds were evaluated for ADME properties and synthetic accessibility. The top candidate was validated in triple-negative breast cancer cell lines MDA-MB-231 and MDA-MB-468 using MTT assays. The pipeline identified 544 diverse compounds for analysis. Five compounds showed favorable CHK1 binding profiles. AO-022/43514723 emerged as the lead with a docking score of -9.205&#xa0;kcal/mol, forming stable interactions with hinge region residues GLU85 and CYS87. Molecular dynamics confirmed complex stability. Scaffold analysis revealed novel chemotype diversity distinct from existing clinical inhibitors. ADME profiling showed drug-like properties with acceptable synthetic accessibility (SA score = 2.93). In vitro validation demonstrated dose-dependent cytotoxicity with IC₅₀ values of 51.53&#xa0;μM (MDA-MB-231) and 64.02&#xa0;μM (MDA-MB-468), representing micromolar potency typical of early-stage computational hits requiring further optimization. This integrated approach successfully identified AO-022/43514723 as a structurally novel preliminary hit with favorable computational binding profiles and preliminary cellular activity. While direct CHK1 target engagement remains to be confirmed and potency optimization is required, this compound serves as a promising starting point for lead development. The workflow provides a generalizable framework for oncology drug discovery where conventional approaches face clinical challenges.</p> Graphical Abstract <p>Schematic representation of a computational-experimental pipeline for discovery of novel CHK1 inhibitors</p> <p></p>

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Integrated computational-experimental pipeline for CHK1 inhibitor discovery: structure-based identification of novel chemotypes with anticancer activity

  • 期刊论文

  • 发表日期：

    2025年10月20日

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  • Dhanushya Gopal,
  • Manasa Pacharla,
  • Nehal Arvind Kumar,
  • Prem Kumar,
  • Angelin Preetha Sathiyanathan,
  • Jaikanth Chandrasekaran,
  • Mohit Agrawal

摘要

Checkpoint kinase 1 (CHK1) plays a critical role in DNA damage response and cell cycle regulation, making it an attractive target for cancer therapy. However, clinical translation of CHK1 inhibitors has been limited by selectivity issues, dose-limiting toxicities, and drug resistance mechanisms, necessitating the development of novel inhibitors with improved therapeutic profiles. We developed an integrated computational-experimental pipeline for CHK1 inhibitor discovery. Starting with 492,534 compounds from the Specs database, we applied PAINS filtering, molecular fingerprinting (ECFP4), and dimensionality reduction (UMAP with K-means clustering). Structure-based virtual screening included e-pharmacophore modeling, molecular docking (Schrödinger’s Glide), and 200-ns molecular dynamics simulations with MM-GBSA calculations. Lead compounds were evaluated for ADME properties and synthetic accessibility. The top candidate was validated in triple-negative breast cancer cell lines MDA-MB-231 and MDA-MB-468 using MTT assays. The pipeline identified 544 diverse compounds for analysis. Five compounds showed favorable CHK1 binding profiles. AO-022/43514723 emerged as the lead with a docking score of -9.205 kcal/mol, forming stable interactions with hinge region residues GLU85 and CYS87. Molecular dynamics confirmed complex stability. Scaffold analysis revealed novel chemotype diversity distinct from existing clinical inhibitors. ADME profiling showed drug-like properties with acceptable synthetic accessibility (SA score = 2.93). In vitro validation demonstrated dose-dependent cytotoxicity with IC₅₀ values of 51.53 μM (MDA-MB-231) and 64.02 μM (MDA-MB-468), representing micromolar potency typical of early-stage computational hits requiring further optimization. This integrated approach successfully identified AO-022/43514723 as a structurally novel preliminary hit with favorable computational binding profiles and preliminary cellular activity. While direct CHK1 target engagement remains to be confirmed and potency optimization is required, this compound serves as a promising starting point for lead development. The workflow provides a generalizable framework for oncology drug discovery where conventional approaches face clinical challenges.

Graphical Abstract

Schematic representation of a computational-experimental pipeline for discovery of novel CHK1 inhibitors