Antiphospholipid Antibody Assays in 2021: Looking for a Predictive Value in Addition to a Diagnostic One

Antiphospholipid antibodies (aPL) are mandatory for the diagnosis but are also a risk factor for the antiphospholipid syndrome (APS) clinical manifestations. Lupus anticoagulant (LA), anticardiolipin (aCL), and anti-beta2 glycoprotein I (β2GPI) assays are the formal laboratory classification/diagnostic criteria. Additional nonclassification assays have been suggested; among them, antiphosphatidylserine-prothrombin (aPS/PT) and antidomain 1 β2GPI antibodies are the most promising ones although not yet formally accepted. aPL represent the example of a laboratory test that moved from dichotomous to quantitative results consistent with the idea that reporting quantitative data offers more diagnostic/prognostic information for both vascular and obstetric manifestations. Although the general rule is that the higher the aPL titer, the higher the test likelihood ratio, there is growing evidence that this is not the case for persistent low titers and obstetric events. LA displays the highest diagnostic/prognostic power, although some isolated LAs are apparently not associated with APS manifestations. Moreover, isotype characterization is also critical since IgG aPL are more diagnostic/prognostic than IgA or IgM. aPL are directed against two main autoantigens: β2GPI and PT. However, anti-β2GPI antibodies are more associated with the APS clinical spectrum. In addition, there is evidence that anti-β2GPI domain 1 antibodies display a stronger diagnostic/prognostic value. This finding supports the view that antigen and even epitope characterization represents a further step for improving the assay value. The strategy to improve aPL laboratory characterization is a lesson that can be translated to other autoantibody assays in order to improve our diagnostic and prognostic power.

Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition

Vaccines and immunotherapies depend on the ability of antibodies to sensitively and specifically recognize particular antigens and specific epitopes on those antigens. As such, detailed characterization of antibody–antigen binding provides important information to guide development. Due to the time and expense required, high-resolution structural characterization techniques are typically used sparingly and late in a development process. Here, we show that antibody–antigen binding can be characterized early in a process for whole panels of antibodies by combining experimental and computational analyses of competition between monoclonal antibodies for binding to an antigen. Experimental “epitope binning” of monoclonal antibodies uses high-throughput surface plasmon resonance to reveal which antibodies compete, while a new complementary computational analysis that we call “dock binning” evaluates antibody–antigen docking models to identify why and where they might compete, in terms of possible binding sites on the antigen. Experimental and computational characterization of the identified antigenic hotspots then enables the refinement of the competitors and their associated epitope binding regions on the antigen. While not performed at atomic resolution, this approach allows for the group-level identification of functionally related monoclonal antibodies (i.e., communities) and identification of their general binding regions on the antigen. By leveraging extensive epitope characterization data that can be readily generated both experimentally and computationally, researchers can gain broad insights into the basis for antibody–antigen recognition in wide-ranging vaccine and immunotherapy discovery and development programs.