An overview of compound properties, multiparameter optimization, and computational drug design methods for PARP-1 inhibitor drugs

Exploration of poly (ADP-ribose) polymerase inhibitor resistance in the treatment of BRCA1/2-mutated cancer

Breast cancer susceptibility 1/2 (BRCA1/2) genes play a crucial role in DNA damage repair, yet mutations in these genes increase the susceptibility to tumorigenesis. Exploiting the synthetic lethality mechanism between BRCA1/2 mutations and poly(ADP-ribose) polymerase (PARP) inhibition has led to the development and clinical approval of PARP inhibitor (PARPi), representing a milestone in targeted therapy for BRCA1/2 mutant tumors. This approach has paved the way for leveraging synthetic lethality in tumor treatment strategies. Despite the initial success of PARPis, resistance to these agents diminishes their efficacy in BRCA1/2-mutant tumors. Investigations into PARPi resistance have identified replication fork stability and homologous recombination repair as key factors sensitive to PARPis. Additionally, studies suggest that replication gaps may also confer sensitivity to PARPis. Moreover, emerging evidence indicates a correlation between PARPi resistance and cisplatin resistance, suggesting a potential overlap in the mechanisms underlying resistance to both agents. Given these findings, it is imperative to explore the interplay between replication gaps and PARPi resistance, particularly in the context of platinum resistance. Understanding the impact of replication gaps on PARPi resistance may offer insights into novel therapeutic strategies to overcome resistance mechanisms and enhance the efficacy of targeted therapies in BRCA1/2-mutant tumors.

Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis

The poly (ADP-ribose) polymerase-1 (PARP-1) enzyme is an important target in the treatment of breast cancer. Currently, treatment options include the drugs Olaparib, Niraparib, Rucaparib, and Talazoparib; however, these drugs can cause severe side effects including hematological toxicity and cardiotoxicity. Although in silico models for the prediction of PARP-1 activity have been developed, the drawbacks of these models include low specificity, a narrow applicability domain, and a lack of interpretability. To address these issues, a comprehensive machine learning (ML)-based quantitative structure-activity relationship (QSAR) approach for the informed prediction of PARP-1 activity is presented. Classification models built using the Synthetic Minority Oversampling Technique (SMOTE) for data balancing gave robust and predictive models based on the K-nearest neighbor algorithm (accuracy 0.86, sensitivity 0.88, specificity 0.80). Regression models were built on structurally congeneric datasets, with the models for the phthalazinone class and fused cyclic compounds giving the best performance. In accordance with the Organization for Economic Cooperation and Development (OECD) guidelines, a mechanistic interpretation is proposed using the Shapley Additive Explanations (SHAP) to identify the important topological features to differentiate between PARP-1 actives and inactives. Moreover, an analysis of the PARP-1 dataset revealed the prevalence of activity cliffs, which possibly negatively impacts the model's predictive performance. Finally, a set of chemical transformation rules were extracted using the matched molecular pair analysis (MMPA) which provided mechanistic insights and can guide medicinal chemists in the design of novel PARP-1 inhibitors.

Novel PARP1 Inhibitors for Treating Cancer

Provided herein are novel PARP1 inhibitors, their pharmaceutical compositions, the use of such compounds in treating cancer, and processes for preparing such compounds.

Novel PARP1 Inhibitors for Treating Cancer

Provided herein are novel PARP1 inhibitors, pharmaceutical compositions, use of such compounds in treating cancer, and processes for preparing such compounds.