OVERVIEW OF PRE-CLINICAL TECHNIQUES FOR PREDICTING THE IMMUNOGENICITY OF THERAPEUTICS IN DRUG DEVELOPEMENT

Immunogenicity testing is a vital component of drug development as it leads to drugs that are safer and more effective. This review provides an overview of the pre-clinical models that can be used to predict the immunogenic potential of novel protein therapeutics prior to administration in humans. Tools important for the prediction of the immunogenicity of protein therapeutics include animal models, in vitro cell assays, and in silico techniques. Animal models including rodents, transgenic mice, and non-human primates are reviewed. Among the immunoinformatics tools commonly used to predict immunogenicity include the Structural Epitope Database, Immune Epitope Database and Analysis Resource (IEDB), The MHCBN database, Dana-Farber Repository for Machine Learning in Immunology, and TEPITOPE. Identifiation and subsequent removal or inhibition of epitopes and MHC agretopes minimizes immunogenicity. Strategies for minimization of immunogenicity in biotherapeutics including epitope and MHC agretope removal, improvement of solubility, derivatization with polyethylene glycol (PEG), and use of chimeric antibodies are also discussed. Immunogenicity testing is an important part of the drug development process as it leads to drugs that are safer and more effective. Animal models including rodents, transgenic mice, and non-human primates; in vitro cell assays; and immunoinformatics tools are used to identify epitopes and MHC agretopes which are then eliminated or inhibited so as to minimize immunogenicity.

Keywords
immunogenicity; immunogenicity testing; biotherapeutics; animal models; immunoinformatics