Epitope characterization of anti-drug antibodies-a tool for discovery and health: an overview of the necessity of early epitope characterization to avoid anti-drug antibodies and promote patient health

Introduction: The market for monoclonal antibody (mAb) therapies is growing rapidly as the pharmaceutical industry expands its development across a broad spectrum of diseases. Unfortunately, as shown in the recent failure of bococizumab by Pfizer, these treatments often stimulate the formation of problematic anti-drug antibodies (ADAs). ADAs can cause side effects and limit efficacy for many patients. To increase efficacy and decrease safety concerns from ADAs, immunogenicity characterization is needed early in the drug development process. Here, we present emerging techniques that hold promise to improve ADA assays and their potential applications to pharmaceutical development and personalized medicine.Areas covered: This manuscript outlines the importance of epitope characterization to better understand immunogenicity and describes a strategy for using this information in treating patients taking mAb therapies.Expert opinion: We propose using high-information assays to characterize epitopes to help mAb therapy engineering and potentially improve individual patient outcomes. To understand this, we will discuss three different aspects of ADAs: (1) the problem of ADAs and what is currently being done about them, (2) the current state of epitope characterization and how it is being utilized, and (3) how early epitope characterization can advance drug discovery and improve outcomes for patients taking mAb therapies.

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.