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Monoclonal antibody engineering and machine learning applications in drug design have come a long way. What does this mean for mAbLab? 

HISTORY

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Orthoclone OKT3 is licensed and approved for preventing renal transplant rejection, marking the first approved mAb in history. This sets precedent for industry validity of mAbs, spurring further R&D.

Researchers  Köhler and Milstein create the first mAb and publish the first paper on hybridoma tech. This becomes the standard for mAb development for decades to come.

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First FDA-approved mAb, Adalimumab treats various conditions (arthritis, Crohn's, psoriasis, etc). Becomes the most sold drug in 2019, concreting mAbs in R&D. 

The FDA approves Rituxan, the first cancer-fighting mAb. Used to treat leukemias and lymphomas, this drug binds to receptors on abnormal WBCs and destroy them.

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Researchers at UCB develop first ML-produced mAb for psoriasis,  Bimekizumab, which outperforms conventional alternatives. 

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Machine learning seeks to integrate itself into the fundamentals of monoclonal antibody engineering as researchers in the status quo seek to develop in silico modeling of ML-based mAb conformation, binding affinity, and efficacy. Moreover, the development of ML architectures like transformers provides parallelism that allows applications to be stronger as more research is conducted. 

© 2024 by Shashank Kamal & Abhinav Gutha

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Submitted to ExploraVision 2024

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