Review: application and opportunities for machine learning and artificial intelligence in preclinical immunogenicity risk assessment
Timothy Paul Hickling, Morten Nielsen, Pieter Meysman, Rachel H. Rose, Olga Obrezanova · 2026
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Description
Timothy Paul Hickling and colleagues review how machine learning is reshaping preclinical immunogenicity risk assessment in a 2026 article. Biologic drugs can provoke anti-drug antibodies that blunt treatment and complicate development, yet predicting that risk before human trials is currently impossible in full. The review grew out of an industry-academic workshop on what artificial intelligence can realistically add.
Machine learning has served this field for over twenty years, chiefly by predicting which protein fragments a key immune molecule will display, flagging likely T cell triggers in a drug candidate. Such predictions now guide candidate selection and protein redesign toward lower risk. They are candid about the bottleneck: inconsistent assays and measurements across companies make training data hard to merge. Modeling that links immune mechanisms to trial simulations partly bridges the gap. The review closes with the data standards the field needs next.
Catalog record
| CREATOR | Timothy Paul Hickling, Morten Nielsen, Pieter Meysman, Rachel H. Rose, Olga Obrezanova |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 55 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |