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Computational Evaluation of Novel PARP-1 Inhibitors for Breast Cancer: Docking, Molecular Dynamics, MM/GBSA, DFT and
Charmy Twala1, Penny Govender1, Ephraim Marondedze1
1Department of Chemical Sciences, Faculty of Science, University of Johannesburg, Doornfontein Campus, Johannesburg 2094, South Africa.
Abstract:
Background/Objectives: Poly (ADP-ribose) polymerase (PARP1) has emerged as a promising therapeutic target in human breast cancer particularly in BRCA1/2 mutation carriers where a synthetic lethal interaction leads to massive tumor cell death upon specific inhibitors' administration. Current clinically approved PARP inhibitors (Talazoparib and Olaparib) show outstanding therapeutic capabilities but suffer from severe side effects. Most importantly, some of them can cause life-threatening cardiotoxicity through hERG off-target effects. Here, we performed an extensive study to identify lead compounds with improved binding modes and favorable predicted pharmacokinetics using an integrated computational strategy. Methods: An artificial intelligence-driven drug design (AIDDISON™ v2023) workflow was employed to search ultra-large chemical space libraries for active compounds, which were then optimized via computer-aided methods to form a PARP-Tailored Database (PTD). This database was then analyzed through a virtual screening workflow, molecular docking studies, molecular dynamics (MD) simulations, MM/GBSA binding free energy calculations, DFT analysis and ADME/Tox predictions using the Schrödinger suite (v2023-2), MobaXterm v25.2, Gaussian 16.0, ProTox-3 and Pred-hERG v5.0 respectively. Results: Three compounds (1a-1c) were identified as promising candidates. Among them 1a appeared to be the most active compound with a favorable docking score (-9.488 kcal/mol) that is not only higher than 1b and 1c but also higher than that of Talazoparib (-6.778 kcal/mol). MD simulations of 1a-1c in the active site revealed an average RMSD of ~2.5-3.6 Å which is better compared to the parent Talazoparib (5.6 Å). Interestingly, on the 250 ns extended MD study, 1a exhibited a slightly reduced RMSD between 2.4 and 3.2 Å, whereas Talazoparib retained higher fluctuations of ~5 Å to 6 Å. MM/GBSA binding energy analysis indicated 1a to have better predicted binding affinity (-67.820 kcal/mol), which is also better than Talazoparib (-63.734 kcal/mol). DFT calculations showed good electronic properties and in silico ADMET studies also indicated 1a to have good drug-likeness and lower predicted hepatotoxicity and cardiotoxicity risk. Conclusions: These findings identify compound 1a as a promising lead, while compounds 1b and 1c remain viable candidates for further optimization. However, experimental validation is critical to confirm the predicted biological activity and safety profiles.
Insights
This study identified compound 1a as a promising Poly (ADP-ribose) polymerase 1 (PARP1) inhibitor for breast cancer, showing improved binding and reduced cardiotoxicity compared to existing drugs.
Area of Science:
- Computational chemistry
- Medicinal chemistry
- Oncology
Background:
- Poly (ADP-ribose) polymerase 1 (PARP1) is a therapeutic target for breast cancer, especially in BRCA1/2 mutation carriers.
- Current PARP inhibitors like Talazoparib have efficacy but cause severe side effects, including cardiotoxicity via hERG off-target effects.
Purpose of the Study:
- To identify novel PARP1 inhibitors with improved binding modes and favorable pharmacokinetic profiles using computational methods.
- To mitigate off-target cardiotoxicity associated with existing PARP inhibitors.
Main Methods:
- Utilized an artificial intelligence-driven drug design workflow (AIDDISON™) and computer-aided methods to create a PARP-Tailored Database (PTD).
- Performed virtual screening, molecular docking, molecular dynamics (MD) simulations, MM/GBSA binding energy calculations, DFT analysis, and ADMET predictions using Schrödinger suite, Gaussian, ProTox-3, and Pred-hERG.
Main Results:
- Identified three lead compounds (1a-1c), with compound 1a showing superior docking score (-9.488 kcal/mol) and binding affinity (-67.820 kcal/mol) compared to Talazoparib.
- MD simulations indicated better stability for 1a (RMSD ~2.4-3.2 Å) versus Talazoparib (~5-6 Å).
- In silico ADMET studies predicted good drug-likeness and lower hepatotoxicity and cardiotoxicity risks for compound 1a.
Conclusions:
- Compound 1a is a promising lead PARP1 inhibitor candidate for breast cancer therapy.
- Compounds 1b and 1c warrant further optimization.
- Experimental validation is crucial to confirm the predicted efficacy and safety of these compounds.
