Computational Antibody Papers

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2025
TitleKey points
    • 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.
    • Authors revisit computational calculations from sequence and structure to filter out clinical stage therapeutics as an alternative/refinement to the popular TAP metrics.
    • Authors explain how the FvCSP charge asymmetry calculated in TAP might not be the ideal formulation.
    • They introduce FV_CHML which as opposed to FvCSP is a difference between the net charges.
    • Of the several computational metrics employed they show that the FV_CHML metric captures most of the clinical stage therapeutics.
    • They analyse the effect of the isotype, demonstrating that for accurate pI calculations, constant region should be modeled and not only the Fv
    • They propose four descriptors that appear to show good degree of separation of natural vs clinical antibodies and some correlation with the experimental values: 1. Patch_cdr_hyd - hydrophobicity of CDRs, not the same as in TAP 2. ens_charge_Fv - in lieu of PPC and PNC from TAP 3. Cdr_len - these separate repertoire from clinical abs. 4. Fv_chml - in lieu of FvCSP from TAP