Optimization Leading to a Potent and Selective Cbl-b Inactive-State Inhibitor That Demonstrated <i>In Vivo</i> Efficacy

In the preceding work in this issue (10.1021/acsmedchemlett.6c00103), we described our initial structure–activity relationship (SAR) optimization that led to a pan-Cbl inhibitor (6) that demonstrated efficacy in a mouse CT26 syngeneic model. Unfortunately, attempts to improve TGI with higher doses of 6 resulted in poor tolerability which we attributed to a lack of selectivity between Cbl-b and c-Cbl (∼2× by surface plasmon resonance (SPR)). Herein, we report our continued efforts that led to a breakthrough in achieving Cbl-b selectivity (up to 37×). The lead compound 33 demonstrated 14× selectivity against c-Cbl by SPR, was potent in a PBMC cell assay, and showed good oral exposure in mice. When tested in a CT26 model, 33 displayed improved tumor growth inhibition compared to our previously reported pan-Cbl inhibitor 6 (TGI 145% vs 82%). More importantly, 33 was better tolerated than 6, supporting our hypothesis that a selective Cbl-b inhibitor could be advantageous relative to a pan-Cbl inhibitor.

Authors

Institutions

Publication Details

Journal
ACS Medicinal Chemistry Letters
Published
2026-05-27
DOI
https://doi.org/10.1021/acsmedchemlett.6c00104
Primary Topic
Protein Kinase Regulation and GTPase Signaling
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization Leading to a Potent and Selective Cbl-b Inactive-State Inhibitor That Demonstrated In Vivo Efficacy

Dennis Leung, Michael J. Lambrecht, Robin Larouche‐Gauthier, Liz Mutter-Rottmayer et al.
ACS Medicinal Chemistry Letters
Protein Kinase Regulation and GTPase Signaling
article

Optimization Leading to a Potent and Selective Cbl-b Inactive-State Inhibitor That Demonstrated In Vivo Efficacy

Dennis Leung, Michael J. Lambrecht, Robin Larouche‐Gauthier, Liz Mutter-Rottmayer, Araz Jakalian, Madeleine Prangley, Pouyan Haghshenas, Hossein Ismaili, Samuel Aubert‐Nicol, Sascha Rutz, Peter M.U. Ung, Satoko Kakiuchi-Kiyota, Linda Bao, Mélissa Leblanc, Ponien Kou, Jodie Pang, Jean‐Philippe Leclerc, Liang Zhao, Haochu Huang, Qiuyue Wang, Chan Gao, Arun Yadav, Thomas Garner (18630951), Isabel Ishizuka, Jun Liang, Sophia Tan, Jeremy Murray, Xiaojing Wang, Georgette Castanedo, Lisa M. Barton, Hugo de Almeida, Peter Hsu, Christine Yu, Min Chen, Malcolm Huestis, Bingyan Zhu
article en

Abstract

In the preceding work in this issue (10.1021/acsmedchemlett.6c00103), we described our initial structure–activity relationship (SAR) optimization that led to a pan-Cbl inhibitor (6) that demonstrated efficacy in a mouse CT26 syngeneic model. Unfortunately, attempts to improve TGI with higher doses of 6 resulted in poor tolerability which we attributed to a lack of selectivity between Cbl-b and c-Cbl (∼2× by surface plasmon resonance (SPR)). Herein, we report our continued efforts that led to a breakthrough in achieving Cbl-b selectivity (up to 37×). The lead compound 33 demonstrated 14× selectivity against c-Cbl by SPR, was potent in a PBMC cell assay, and showed good oral exposure in mice. When tested in a CT26 model, 33 displayed improved tumor growth inhibition compared to our previously reported pan-Cbl inhibitor 6 (TGI 145% vs 82%). More importantly, 33 was better tolerated than 6, supporting our hypothesis that a selective Cbl-b inhibitor could be advantageous relative to a pan-Cbl inhibitor.

ACS Medicinal Chemistry Letters
WuXi AppTec (China) (CN), Paraza Pharma (Canada) (CA)
No poverty
Openalex Percentile: Top 10%
Protein Kinase Regulation and GTPase Signaling
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.