作者: Prasad, G. V. Siva ; Pramanik, Atreyi ; Gupta, Gaurav ; Kumar, Sachin ; Bhat, Asif Ahmad ; Ali, Haider ; Sharma, Naveen ; Thapa, Riya ; Kaur, Irwanjot ; Renuka Jyothi, S.

ABSTRACTCereblon (CRBN), a member of the E3 ubiquitin ligase complex, has gained significant attention as a therapeutic target in cancer. CRBN regulates the degradation of various proteins in cancer progression, including transcription factors and signaling molecules. PROTACs (proteolysis‐targeting chimeras) are a novel approach that uses the cell's degradation system to remove disease‐causing proteins selectively. CRBN‐dependent PROTACs work by tagging harmful proteins for destruction through the ubiquitin–proteasome system. This strategy offers several advantages over traditional protein inhibition methods, including the potential to overcome drug resistance. Recent progress in developing CRBN‐based PROTACs has shown promising preclinical results in both hematologic malignancies and solid tumors. Additionally, CRBN‐based PROTACs have enhanced our understanding of CRBN's role in cancer, potentially serving as biomarkers for patient stratification and predicting therapeutic responses. In this review, we delineate the mechanisms of action for CRBN‐dependent PROTACs (CRBN‐PROTACs), summarize recent advances in preclinical and clinical applications, and provide our perspective on future development.

2024-01-01·Oncology Research

Development of PROTACS degrading KRAS and SOS1

Review

作者: Eggerstorfer, Marie-Therese ; Hamilton, Gerhard ; Stickler, Sandra

The Kirsten rat sarcoma virus-son of sevenless 1 (KRAS-SOS1) axis drives tumor growth preferentially in pancreatic, colon, and lung cancer. Now, KRAS G12C mutated tumors can be successfully treated with inhibitors that covalently block the cysteine of the switch II binding pocket of KRAS. However, the range of other KRAS mutations is not amenable to treatment and the G12C-directed agents Sotorasib and Adragrasib show a response rate of only approximately 40%, lasting for a mean period of 8 months. One approach to increase the efficacy of inhibitors is their inclusion into proteolysis-targeting chimeras (PROTACs), which degrade the proteins of interest and exhibit much higher antitumor activity through multiple cycles of activity. Accordingly, PROTACs have been developed based on KRAS- or SOS1-directed inhibitors coupled to either von Hippel-Lindau (VHL) or Cereblon (CRBN) ligands that invoke the proteasomal degradation. Several of these PROTACs show increased activity in vitro and in vivo compared to their cognate inhibitors but their toxicity in normal tissues is not clear. The CRBN PROTACs containing thalidomide derivatives cannot be tested in experimental animals. Resistance to such PROTACS arises through downregulation or inactivation of CRBN or factors of the functional VHL E3 ubiquitin ligase. Although highly active KRAS and SOS1 PROTACs have been formulated their clinical application remains difficult.

2023-02-14·ACS Omega

Predictive Modeling of PROTAC Cell Permeability with Machine Learning

Article

作者: Meibom, Daniel ; Poongavanam, Vasanthanathan ; Lehmann, Lutz ; Kihlberg, Jan ; Kölling, Florian ; Giese, Anja ; Göller, Andreas H.

Approaches for predicting proteolysis targeting chimera (PROTAC) cell permeability are of major interest to reduce resource-demanding synthesis and testing of low-permeable PROTACs. We report a comprehensive investigation of the scope and limitations of machine learning-based binary classification models developed using 17 simple descriptors for large and structurally diverse sets of cereblon (CRBN) and von Hippel-Lindau (VHL) PROTACs. For the VHL PROTAC set, kappa nearest neighbor and random forest models performed best and predicted the permeability of a blinded test set with >80% accuracy (k ≥ 0.57). Models retrained by combining the original training and the blinded test set performed equally well for a second blinded VHL set. However, models for CRBN PROTACs were less successful, mainly due to the imbalanced nature of the CRBN datasets. All descriptors contributed to the models, but size and lipophilicity were the most important. We conclude that properly trained machine learning models can be integrated as effective filters in the PROTAC design process.

100 项与 PROTAC CRBN Degrader-1(University of Dundee) 相关的药物交易

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