Accelerated Discovery of Carbamate Cbl-b Inhibitors Using Generative AI Models and Structure-Based Drug Design

  • J Med Chem. 2024 Aug 22;67(16):14210-14233. doi: 10.1021/acs.jmedchem.4c01034.
Taylor R Quinn  1 Kathryn A Giblin  2 Clare Thomson  2 Jeffrey A Boerth  1 Gayathri Bommakanti  1 Erin Braybrooke  2 Christina Chan  2 Alex J Chinn  1 Erin Code  3 Caifeng Cui  4 Yukai Fan  4 Neil P Grimster  1 Keishi Kohara  2 Michelle L Lamb  1 Lina Ma  4 Adelphe M Mfuh  1 Graeme R Robb  2 Kevin J Robbins  1 Marianne Schimpl  5 Haoran Tang  5 Jamie Ware  5 Gail L Wrigley  2 Lin Xue  4 Yun Zhang  1 Huimin Zhu  4 Samantha J Hughes  2
Affiliations
  • 1. Early TDE Discovery, Oncology R&D, AstraZeneca, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States.
  • 2. Early TDE Discovery, Oncology R&D, AstraZeneca, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K.
  • 3. Discovery Sciences, R&D, AstraZeneca, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States.
  • 4. Pharmaron Beijing Co., Ltd., 6 Taihe Road, BDA, Beijing 100176, P. R. China.
  • 5. Discovery Sciences, R&D, AstraZeneca, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K.
Abstract

Casitas B-lymphoma proto-oncogene-b (Cbl-b) is a RING finger E3 Ligase that has an important role in effector T cell function, acting as a negative regulator of T cell, natural killer (NK) cell, and B cell activation. A discovery effort toward Cbl-b inhibitors was pursued in which a generative AI design engine, REINVENT, was combined with a medicinal chemistry structure-based design to discover novel inhibitors of Cbl-b. Key to the success of this effort was the evolution of the "Design" phase of the Design-Make-Test-Analyze cycle to involve iterative rounds of an in silico structure-based drug design, strongly guided by physics-based affinity prediction and machine learning DMPK predictive models, prior to selection for synthesis. This led to the accelerated discovery of a potent series of carbamate Cbl-b inhibitors.

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