No abstract

Most therapeutic proteins in clinical trials or on the market are, to a variable extent, immunogenic. Formation of antidrug antibodies poses a risk that should be assessed during drug development, as it possibly compromises drug safety and alters pharmacokinetics. The generation of these antibodies is critically dependent on the presence of T helper cell epitopes, which have classically been identified by in vitro methods using blood cells from human donors. As a novel development, in silico methods that identify T cell epitopes have been coming on line. These methods are relatively inexpensive and allow the mapping of epitopes from virtually all human leukocyte antigen molecules derived from a wide genetic background. In vitro assays remain important, but guided by in silico data they can focus on selected peptides and human leukocyte antigen haplotypes, thereby significantly reducing time and cost.

No abstract

All therapeutic proteins are potentially immunogenic. Antibodies formed against these drugs can decrease efficacy, leading to drastically increased therapeutic costs and in rare cases to serious and sometimes life threatening side-effects. Many efforts are therefore undertaken to develop therapeutic proteins with minimal immunogenicity. For this, immunogenicity prediction of candidate drugs during early drug development is essential. Several in silico, in vitro and in vivo models are used to predict immunogenicity of drug leads, to modify potentially immunogenic properties and to continue development of drug candidates with expected low immunogenicity. Despite the extensive use of these predictive models, their actual predictive value varies. Important reasons for this uncertainty are the limited/insufficient knowledge on the immune mechanisms underlying immunogenicity of therapeutic proteins, the fact that different predictive models explore different components of the immune system and the lack of an integrated clinical validation. In this review, we discuss the predictive models in use, summarize aspects of immunogenicity that these models predict and explore the merits and the limitations of each of the models.

No abstract