Immunogenicity testing plays a pivotal role in the development of biologics, biosimilars, vaccines, and advanced therapies. Detecting anti-drug antibodies (ADAs) ensures that safety and efficacy risks are properly understood before products reach patients. Yet running a reliable program is not simple—assays must be sensitive enough to detect low-level responses, selective enough to avoid false positives, and robust under real-world conditions. The sections below outline the most common challenges faced in immunogenicity testing methods and explain why solving them is critical for regulatory success and patient safety.
The Main Challenges in Immunogenicity Testing
While ADA testing follows a structured tiered approach (screening → confirmatory → characterization), each step brings technical, biological, and regulatory hurdles.
Drug interference masking ADA signals.
One of the biggest issues is excess circulating drug in patient samples. High drug concentrations form immune complexes with ADAs, preventing detection and leading to false negatives. To counter this, methods such as acid dissociation, affinity capture extraction (ACE), or bead-based extraction are used, but each must be validated for sensitivity and reproducibility. Selecting the right strategy becomes even more crucial for high-dose or long half-life biologics.
Choosing appropriate positive controls.
The performance of an ADA assay is directly tied to the positive control antibody used during validation. Ideally, this control reflects the therapeutic’s immunogenic profile, but often only surrogate polyclonal antibodies from immunized animals are available. These may not perfectly mimic patient responses, making it difficult to set cut-points and drug tolerance levels that truly reflect clinical risk. Careful characterization of positive controls is therefore essential.
Managing false positives from matrix interference.
Patient samples contain heterophile antibodies, rheumatoid factors, and other interfering substances that can generate non-specific binding. In bridging assays, soluble targets or multimers may also cause false-positive signals. Mitigation requires matrix testing across diverse donors, interference studies with common co-medications, and sometimes the use of target-blocking antibodies. Without these steps, assays risk over-reporting ADA incidence.
Balancing sensitivity and drug tolerance.
Assays must be sensitive enough to detect very low ADA levels while also tolerating the presence of circulating drug. Enhancing one often compromises the other: high sensitivity can increase susceptibility to false positives, while strong drug tolerance may reduce signal. Validating methods across multiple concentrations of both drug and ADA is necessary to find the optimal balance.
Neutralizing antibody (NAb) detection complexities.
Binding antibodies do not always neutralize drug activity. NAb assays—especially cell-based ones—are required to demonstrate functional impact. However, these assays are complex, variable, and often require specialized cell systems that mimic the drug’s mechanism of action. Competitive ligand-binding assays provide alternatives but may lack physiological relevance. Achieving consistency across NAb methods remains a major challenge.
Regulatory compliance and global differences.
Guidelines from the FDA, EMA, and NMPA all endorse a tiered approach but differ in detail. For example, NMPA requires cytokine-release testing for immune-cell–targeting therapies, while FDA/EMA place less emphasis on this. Designing validation plans that satisfy all regulators while remaining efficient is a constant challenge for global development programs.
Integration with DMPK and clinical data.
ADA results cannot be interpreted in isolation. They must be linked to pharmacokinetics, efficacy, and safety data to understand clinical consequences. Unexpected clearance changes, reduced drug exposure, or hypersensitivity reactions may all point to ADA involvement. Integrating immunogenicity testing with DMPK bioanalysis ensures a more complete understanding of drug behavior.
Conclusion
Immunogenicity testing is complex because it demands sensitivity, selectivity, and real-world robustness in equal measure. Challenges range from drug interference and positive control selection to managing false signals and validating neutralizing antibody assays. Regulatory requirements add another layer of complexity, especially in global programs. By integrating ADA data with DMPK and clinical outcomes, developers gain a clearer picture of true patient risk. Overcoming these hurdles ensures immunogenicity testing not only meets compliance standards but also guides safer, more effective therapies.