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Computational Chemistry Advances in the Development of PARP1 Inhibitors for Breast Cancer Therapy
Charmy Twala1, Penny Govender1, Krishna Govender1
1Department of Chemical Sciences, Faculty of Science, University of Johannesburg, Doornfontein Campus, Johannesburg 2028, South Africa.
Abstract:
Poly (ADP-ribose) polymerase 1 (PARP1) is an important enzyme that plays a central role in the DNA damage response, facilitating repair of single-stranded DNA breaks via the base excision repair (BER) pathway and thus genomic integrity. Its therapeutic relevance is compounded in breast cancer, particularly in BRCA1 or BRCA2 mutant cancers, where compromised homologous recombination repair (HRR) leaves a synthetic lethal dependency on PARP1-mediated repair. This review comprehensively discusses the recent advances in computational chemistry for the discovery of PARP1 inhibitors, focusing on their application in breast cancer therapy. Techniques such as molecular docking, molecular dynamics (MD) simulations, quantitative structure-activity relationship (QSAR) modeling, density functional theory (DFT), time-dependent DFT (TD-DFT), and machine learning (ML)-aided virtual screening have revolutionized the discovery of inhibitors. Some of the most prominent examples are Olaparib (IC50 = 5 nM), Rucaparib (IC50 = 7 nM), and Talazoparib (IC50 = 1 nM), which were optimized with docking scores between -9.0 to -9.3 kcal/mol and validated by in vitro and in vivo assays, achieving 60-80% inhibition of tumor growth in BRCA-mutated models and achieving up to 21-month improvement in progression-free survival in clinical trials of BRCA-mutated breast and ovarian cancer patients. These strategies enable site-specific hopping into the PARP1 nicotinamide-binding pocket to enhance inhibitor affinity and specificity and reduce off-target activity. Employing computation and experimental verification in a hybrid strategy have brought next-generation inhibitors to the clinic with accelerated development, higher efficacy, and personalized treatment for breast cancer patients. Future approaches, including AI-aided generative models and multi-omics integration, have the promise to further refine inhibitor design, paving the way for precision oncology.
Insights
Computational chemistry accelerates the discovery of Poly (ADP-ribose) polymerase 1 (PARP1) inhibitors for breast cancer. These advanced methods optimize drug design, leading to more effective and personalized treatments for BRCA-mutated cancers.
Area of Science:
- Computational chemistry and drug discovery
- Genomic integrity and DNA repair mechanisms
- Oncology and personalized medicine
Background:
- Poly (ADP-ribose) polymerase 1 (PARP1) is crucial for DNA repair, especially in BRCA-mutated breast cancers where it presents a synthetic lethal vulnerability.
- Targeting PARP1 offers a therapeutic strategy for breast cancer, particularly in cases with homologous recombination repair deficiencies.
Purpose of the Study:
- To review recent advances in computational chemistry for discovering PARP1 inhibitors.
- To highlight the application of these computational strategies in breast cancer therapy.
Main Methods:
- Utilized computational techniques including molecular docking, molecular dynamics (MD) simulations, quantitative structure-activity relationship (QSAR) modeling, density functional theory (DFT), and machine learning (ML)-aided virtual screening.
- Employed hybrid strategies combining computational predictions with experimental validation for inhibitor optimization.
Main Results:
- Computational methods identified potent PARP1 inhibitors like Olaparib, Rucaparib, and Talazoparib with high affinity (nM range) and optimized docking scores (-9.0 to -9.3 kcal/mol).
- These inhibitors demonstrated significant efficacy in preclinical models (60-80% tumor growth inhibition) and clinical trials (up to 21-month PFS improvement) for BRCA-mutated cancers.
- Computational strategies enabled precise targeting of the PARP1 nicotinamide-binding pocket, enhancing inhibitor specificity and reducing off-target effects.
Conclusions:
- Computational chemistry has revolutionized PARP1 inhibitor discovery, accelerating development and improving efficacy for breast cancer treatment.
- Hybrid computational-experimental approaches are crucial for developing next-generation inhibitors and enabling personalized oncology.
- Future directions include AI-driven generative models and multi-omics integration for further refinement of inhibitor design.
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