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<p>Triple-negative breast cancer is an aggressive subtype characterized by the absence of estrogen, progesterone, and HER2 receptors, which renders it insensitive to most conventional therapies. Inhibition of PARP1 has been pointed out as a promising approach for BRCA1/2-mutated cancers due to a synthetic lethality mechanism. This study presents an integrated in-silico drug discovery workflow for the identification of new generation analogues of clinically approved drugs Olaparib and Talazoparib as potential PARP1 inhibitors. Structural analogues were retrieved from the ZINC database, and their affinity was screened by molecular docking. Drug-likeness and ADMET properties of docked analogues were further evaluated. Top candidates were then subjected to MD simulation and MM/GBSA binding free energy calculation to validate interaction stability and pharmacological potential. The combined computational results highlight several leads with a good binding profile, stability, and drug-like properties, thus representing promising therapeutic leads targeting PARP1 in BRCA-mutated TNBC. Overall, this study has underlined the usefulness of integrated in-silico approaches to accelerate the discovery of optimized PARP1 inhibitors for targeted cancer therapy.</p>

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In-silico discovery of novel PARP1 inhibitors for BRCA-mutated TNBC

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Shivani Yadav,
  • Prankur Awasthi,
  • Ritika Sinha,
  • Saba Hasan

摘要

Triple-negative breast cancer is an aggressive subtype characterized by the absence of estrogen, progesterone, and HER2 receptors, which renders it insensitive to most conventional therapies. Inhibition of PARP1 has been pointed out as a promising approach for BRCA1/2-mutated cancers due to a synthetic lethality mechanism. This study presents an integrated in-silico drug discovery workflow for the identification of new generation analogues of clinically approved drugs Olaparib and Talazoparib as potential PARP1 inhibitors. Structural analogues were retrieved from the ZINC database, and their affinity was screened by molecular docking. Drug-likeness and ADMET properties of docked analogues were further evaluated. Top candidates were then subjected to MD simulation and MM/GBSA binding free energy calculation to validate interaction stability and pharmacological potential. The combined computational results highlight several leads with a good binding profile, stability, and drug-like properties, thus representing promising therapeutic leads targeting PARP1 in BRCA-mutated TNBC. Overall, this study has underlined the usefulness of integrated in-silico approaches to accelerate the discovery of optimized PARP1 inhibitors for targeted cancer therapy.