INTRODUCTION
Poly(ADP-ribose) polymerase 1 (PARP1) is a key mediator of DNA damage repair and an attractive therapeutic target for homologous recombination-deficient malignancies. The development of selective PARP1 inhibitors has been driven by the need to reduce the hematological toxicities associated with nonselective PARP inhibition.
AREA COVERED
This review summarizes patents and recent advances in selective PARP1 inhibitors reported from 2021 to the present. Particular emphasis is placed on the structural evolution of AZD5305-derived compounds and emerging quinazolinone- and isoquinolinone-based chemotypes. Key design strategies, including adenine-pocket optimization, linker remodeling, conformational restriction, and scaffold diversification, are discussed together with their impact on PARP1 selectivity and biological activity.
EXPERT OPINION
Selective PARP1 inhibition has become a major focus of innovation in the PARP field. Current patents indicate that adenine-pocket engagement, linker optimization, conformational control, and scaffold innovation are central to achieving high PARP1 selectivity and represent important directions for future intellectual property development. Despite significant progress, the disclosed chemical space remains relatively limited, highlighting opportunities for further scaffold diversification and differentiated patent strategies. These advances are expected to facilitate the development of next-generation PARP1-targeted therapeutics with improved safety profiles and broader clinical potential.
Jilong Duan, Yanjing Duan, Dongling Gu et al.· Expert Opinion on Therapeuti...· 0 citations
Drug repositioning holds promise for discovering new therapeutic applications for existing drugs, accelerating drug development and reducing associated costs. However, current methodologies encounter difficulties in managing diverse network representations, tackling cold start issues, and handling intrinsic attribute representations. Here we introduce a Unified Knowledge-Enhanced deep learning framework for Drug Repositioning (UKEDR), which integrates knowledge graph embedding, pre-training strategies, and recommendation systems to address these challenges. To overcome the cold start issue, UKEDR utilizes a semantic similarity-driven embedding approach. Our evaluations show that UKEDR performs better than various baselines, including classical machine learning, network-based, and deep learning approaches. In cold start scenarios, it demonstrates an improved capability in handling unseen nodes and generalizing to new compounds. The model also demonstrates strong robustness on imbalanced datasets and shows excellent generalization capabilities in specific drug-centric and disease-centric cold-start scenarios, validating its potential for real-world applications. Drug repositioning offers a promising avenue for accelerating drug development, yet existing methods struggle with network diversity, cold start issues, and intrinsic attribute representation. Here, the authors introduce UKEDR, a deep learning framework that integrates knowledge graph embedding and pre-training strategies to overcome the intractable cold start issue, achieving superior performance and interpretability in drug repurposing.
Kun Li, Jiacai Yi, Qing Ye et al.· Communications Chemistry· 1 citation
