Computational Antibody Papers
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clinical trials
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2025
TitleKey points
- Authors study 171 Roche clinical studies representing 28 drugs for their ADA incidence.
- Authors demonstrate that ADA is highly context-specific with non-trivial inter-drug variation and factors such as disease or mode of action impacting the incidence.
- They train a random forest model on T-cell epitope predictions and a model combined with non-epitope features. The extended model, including non-epitope features performs better than the one that is solely sequence-based.
2025-01-21
Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies
- clinical trials
- databases
- Computational analysis of pK (clearance) of biologics based on a dataset collated for this publication.
- Authors collated a set of 64 therapeutic antibodies and their clearances.
- Here, they defined fast clearance as more than 5.4 mL/day/kg. 48 antibodies fel below this threshold and 16 above.
- They tested whether any single computationally calculated property (e.g. isoelectric point etc.) determines fast vs slow clearance.
- No single computational property was a good discriminator.
- THey constructed a random forest algorithm and showed that the poly specify reagent (PSR), which is an in vitro property and isoelectrip point, which can be computationally calculated are the strong discriminators according to the model.