Associate Scientific Director Takeda Pharmaceutical Company Limited Burlington, Massachusetts
The immunogenicity caused by drugs can affect their effectiveness and pose significant challenges, including the potential for drug toxicity leading to adverse reactions of varying severity, some of which may be life-threatening. Due to the considerable genetic variation among individuals, it is difficult to identify those at risk and develop universal strategies to prevent or manage immunogenicity.
The preclinical evaluation and selection of a non-immunogenic drug candidate relies on in silico computational predictive tools and in vitro immune assays. The pre-clinical in vitro tests are crucial in identifying immunogenic epitopes that can trigger the production of anti-drug antibodies. However, the data collected during the early preclinical assessments cannot be directly translatable to clinical ADA assessment. This is because there are no suitable in vitro assays available that can accurately mimic the responses that happens in the human lymph nodes. Extensive research and documentation have shown that the human leukocyte antigen (HLA) plays a vital role in the immune system by processing and presenting immunogenic peptides or antigens to adaptive immune system, thereby facilitating antibody production. Therefore, we propose to identify the type of HLA alleles associated with higher drug-induced immunogenicity in patients' PBMCs exhibiting higher ADA response in early human studies. Then using computational tools determine the epitopes sequences of the drug binding to the identified HLA alleles. Using in vitro T cell and B cell assays, characterize the selected peptides that bind to HLA to mitigate the risks associated with drug molecules.
Learning Objectives:
Identify gaps hindering the transition from preclinical to clinical immunogenicity assessment due to disparities between preclinical and clinical models, and limitations in preclinical assays affecting accurate prediction of clinical immunogenicity.
Evaluate how HLA information can assist in explaining higher ADA responses in clinical subjects, in exclusion criteria, or in recommending pre-medication plans for HLA subjects, including immune suppressors or modulators.
Mitigate risks associated with drug molecules.
Enhance patient safety during clinical trials but also deepen our understanding of the complex interplay between patient factors and immune responses, leading to more informed regulatory submissions and clinical practices.
By integrating genetic data into clinical studies, we can improve the prediction and management of ADA formation, ultimately paving the way for safer and more effective therapies.
