PROTAC CRBN Degrader-1(University of Dundee)

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.

Protease-targeted chimeras (PROTACs) are a new technology that is receiving much attention in the treatment of diseases. The mechanism is to inhibit protein function by hijacking the ubiquitin E3 ligase for protein degradation. Heterogeneous bifunctional PROTACs contain a ligand for recruiting E3 ligase, a linker, and another ligand to bind to the target protein for degradation. A variety of small-molecule PROTACs (CRBN, VHL, IAPs, MDM2, DCAF15, DCAF16, and RNF114-based PROTACs) have been identified so far. In particular, CRBN-based PROTACs (e.g., ARV-110 and ARV-471) have received more attention for their promising therapeutic intervention. To date, CRBN-based PRTOACs have been extensively explored worldwide and have excelled not only in cancer diseases but also in cardiovascular diseases, immune diseases, neurodegenerative diseases, and viral infections. In this review, we will provide a comprehensive update on the latest research progress in CRBN-based PRTOACs area. Following the criteria, such as disease area and drug target class, we will present the degradants in alphabetical order by target. We also provide our own perspective on the future prospects and potential challenges facing PROTACs.