bioRxivBiochemistryPreprint
Indazolone-Based Molecular Glue Degraders as a Tunable Platform for Reprogramming Cereblon Substrate Specificity
Hui‐Jun Nie, Jiamin Wang, Hao Xu, Yin-Jue Zhou, Guang‐Liang YiniD, Xiaomim Xu, Gaoya Xu, Beijing CheniD, Xian Li, Xiaobei HuiD, Yubo ZhouiD, Jia Li, Chen XiaohuaiD
Abstract
Molecular glue degraders (MGDs) represent a transformative modality in drug discovery, with the cereblon (CRBN)-MGD axis offering a strategic gateway to systematically address the historically undruggable proteome. However, the limited chemical diversity of existing CRBN-mediated MGDs has constrained the full exploration of the degradable landscape. Here, we introduce an innovative indazolone-based platform that extends beyond canonical isoindolinone-glutarimide scaffolds, significantly expanding the accessible chemical space for CRBN modulation. Guided by mechanistic insights into CRBN-MGD complex conformational plasticity, we rationally designed indazolone architectures as novel CRBN ligands, developing diverse MGDs with potent ligase binding and exceptional substrate programmability. This platform enables fine-tuned control over neo-substrate recognition, supporting diverse degradation profiles ranging from the broad targeting of critical proteins (IKZF1/3, ZFP91, and LIMD1) to the exquisitely selective degradation of CK1 and IKZF2. Beyond expanding the chemical landscape for intractable targets, our indazolone-based MGDs exhibit favorable pharmacokinetic properties, offering robust promise for therapeutic development. Collectively, the remarkable tunability and exceptional neo-substrate programmability of the indazolone-based platform provide a highly transformative blueprint for next-generation MGD discovery, enabling systematic exploration of the vast CRBN-accessible proteome. Furthermore, as superior CRBN ligands, this platform holds immense potential for PROTAC discovery to systematically address previously intractable targets.
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
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