Since the finalization of the 2019 FDA Guidance, many companies have adopted the practice of conducting an immunogenicity risk assessment (IRA). The IRA considers a myriad of product- and patient-related factors that may impact a drug’s PK, PD, and safety. Since its inception, the industry has gained experience using the IRA to guide the development for immunogenicity testing in the clinic. This presentation will discuss the evolution of the IRA and case studies that highlight how it can support a sound assay development and sample analysis strategy for molecules with varying degrees of risk.

Unwanted immune responses to biologics occur due to factors from patients, products, and treatments. The purity of the product is important for understanding immunogenicity risks, with impurities and contaminants, such as host cell proteins, contributing to the overall risk profile. Uncertainty over acceptable product quality attributes can add time to development cycles and add risk to clinical trials. The presentation will discuss identifying and mitigating these risks.

The emergence of anti-drug antibodies can significantly impact drug safety and efficacy. Dendritic cells (DCs) play a vital role in priming CD4 T cells, which are essential for robust antibody production. Characterizing DC activities serves as a valuable tool to inform the immunogenicity risk associated with biotherapeutics. I will discuss our recent advancements and experiences in employing DC-based assays for immunogenicity risk assessment in the early drug discovery and development.

Assessing immunogenicity risk for antibody-drug conjugates (ADCs) presents unique challenges due to the impact of the drug conjugate on T cell proliferation. This presentation highlights an integrated approach combining in silico algorithms, in vitro MAPPs, and DC-PBMC proliferation assays to evaluate the immunogenicity risk of ADC therapeutics, illustrated through an ADC case study.

Effective mitigation of the immunogenicity of biologics necessitates a comprehensive understanding of its underlying mechanisms and multiple risk factors, enabling the identification of potential intervention points. Numerous academic and industrial organizations, along with US- and Europe-based scientific societies, have formed collaborations to address these issues. This presentation will provide an overview of ongoing and emerging initiatives within various frameworks.

• Anti-Drug Antibodies (ADAs): Mechanisms, prediction challenges, and clinical implications
• MHC binding, Treg epitopes, and ADA rates: Is there a predictive link?
• The data quality dilemma: Noise, bias, and the need for better training sets
• Cytokine-based assays: A more direct window into immunogenicity?
• Beyond binding: Are we ready for data-driven immunogenicity scoring?
  

• How to select and define the most appropriate assay/format (T cell assays, MAPPs assays, peptide assays)
• Which assay controls and references to include (HESI AAPS panel)
• The importance of the PBMC quality
• How many donors to includeInitiatives for harmonization and standardization (European Immunogenicity Platform, AAPS IRAM working group)
  

Preclinical and clinical data demonstrate that novel antigenic determinants efficiently recognized by mature T cells can emerge from a variety of non-mutational mechanisms, encompassing epitope mimicry, upregulation of cryptic epitopes, the usage of non-canonical initiation codons, alternative RNA splicing, and enzymatic and non-enzymatic post-translational protein modifications. Here, I will discuss the mechanisms underlying the formation of non-mutational neoantigens, as well as their implications in increasing MHC-restricted immunogenicity.

The translatability of immunogenicity data from Cynomolgus monkey models to humans has been debated. This study analyzes detailed immunogenicity data of 53 molecules and shows that Cyno monkeys provide valuable information on the incidence, magnitude, and clinical impact of ADA responses in humans. Despite species differences, Cyno immunogenicity data is crucial for immunogenicity risk assessment.

Drug immunogenicity mitigation by design consists of minimizing product-related risks, including CD4 T cell epitope sequence content. In silico and in vitro tools can guide removal of such liabilities, though data interpretation can be challenging because of the lack of assay standardization across laboratories. The presentation will describe the statistical methods used for a T cell assay qualification, aimed at enhancing confidence in producing high-quality decision-making data and robust inferences.

This seminar explores the use of statistical and mechanistic QSP models to evaluate immunogenicity for a nivolumab-ipilimumab combination therapy in melanoma. By analyzing clinical study data, these models reveal increased ADA occurrence with combination therapy but minimal effects on efficacy and safety. The findings enhance our understanding of the immunogenicity mechanism, provide critical guidance for optimizing dosing strategies and support clinical trial design.

The cellular and molecular mechanisms underlying the induction of immune tolerance using the intravenous infusion of biodegradable antigen-encapsulation PLGA nanoparticles and their similarity to natural tolerance induced by uptake of apoptotic debris will be discussed. In addition, the results of initial human Phase 1a/2 clinical translational studies in treatment of celiac disease and primary biliary cholangitis will be reviewed.

Immunogenicity risk assessment is a complex challenge requiring robust clinical data and predictive tools. The Immunogenicity Database Collaborative (IDC) addresses this need by curating a standardized dataset linking clinical immunogenicity outcomes to key risk factors. We present IDC V1 and demonstrate its utility in evaluating preclinical risk assessments, including in vitro assays and machine learning-based predictors. This effort supports improved benchmarking, risk modeling, and community collaboration across the biotherapeutics industry.

Immunogenicity of biotherapeutics arises from both drug- and patient-related factors, with CD4+ T cell/B cell epitopes playing a pivotal role. Numerous computational tools have been developed to predict these epitopes. I will discuss the performance of these tools through a blind assessment, offering critical insights into their strengths and limitations for advancing immunogenicity prediction in biotherapeutics.