From Detection to Decision: Risk-Based Immunogenicity Testing

What is Immunogenicity?

Immunogenicity describes treatment‑induced B‑ and T‑cell responses to therapeutics and represents a major hurdle for drug development, due to the potential impact on patient safety and treatment efficacy. As a standalone endpoint, immunogenicity offers limited insight into a patient’s clinical profile; however, when viewed in combination with PK, PD, and safety data, its influence on the overall success of a treatment becomes clear.

The immunogenic response is driven by anti-drug antibodies (ADAs), which bind to one or more domains of a therapeutic. ADAs are further characterised as neutralising (nAbs) or non-neutralising antibodies. In both cases, ADAs may have an effect on mechanism of action by altering the clearance rate of the biotherapeutic and/or impairing its binding to targets. In severe cases, ADAs can result in hypersensitivity reactions and deficiency syndrome, where endogenous equivalents of a therapeutic are impacted.

While it is challenging to fully predict clinical immunogenicity, certain attributes of the drug, administration protocol and patient characteristics will be indicative of risk [1-3]. Beyond monoclonal antibodies, other modalities such as viral vectors, lipid-nanoparticles (LNPs), gene-editing proteins, and engineered cellular receptors (e.g., CAR-Ts) introduce distinct immunogenic pathways, further broadening the scope of assessment and mitigation [4]. The context of immunogenicity becomes even more complex when addressing multidomain therapeutics, including antibody-drug conjugates (ADCs) and other fusion‑based modalities, where the combined immunogenicity of individual domains, linker chemistry, and conjugation‑induced neoepitopes must all be considered.

Bioanalytical Approaches for Detecting and Characterising an ADA Response

Immunogenicity assessment relies on customised, validated bioanalytical methods aligned with regulatory guidelines [5-7], which were implemented in response to severe adverse events previously observed with high-risk molecules [8]. The goal was to ensure patient safety and to manage the overall bioanalytical scope through a three-tiered testing regimen, as follows: Screening assays classify samples against an empirically established cut‑point as “ADA‑reactive” or “non‑reactive”. Confirmatory assays apply a specificity cut‑point to discriminate “true positives” from non‑specific reactivity. ADA‑positive samples are then characterised for isotype, titre, and/or neutralising capacity.

NAbs are traditionally measured using cell‑based assays, which reflect biological mechanism in vitro. Ligand‑binding nAb assays offer greater operational simplicity and reproducibility but do not always fully model the applicable downstream effects; therefore, careful attention should be paid to platform selection.

A movement continues to build within the industry towards risk-based, context of use (CoU)-driven immunogenicity testing, with more focus on the clinical impact of ADAs and streamlined generation of meaningful datasets. Retrospective analyses of past programs have shown that low‑level or low‑titre ADA responses often lack clear clinical consequence, whereas higher‑magnitude or high‑titre responses can be associated with reduced efficacy, increased clearance, or altered safety, informing how much tiering and characterisation are needed in practice. As a result, immunogenicity assessment is shifting from a rigid, multi‑tier “tick‑box” exercise toward a more intentional, data‑driven strategy tailored to the specific risk profile and context of use [9-11].

The European Bioanalysis Forum (EBF) recommends consideration of one‑ or two-tier immunogenicity testing strategies that focus on screening signal‑to‑noise (S/N) as a continuous magnitude metric and deploy confirmation/characterisation only when deemed necessary. Recently, focus has shifted to screening S/N without a cut‑point, thereby treating immunogenicity as a biomarker to relate to other endpoints for a richer overall dataset [10-12]. Therefore, a risk‑based approach in collaboration with regulators is imperative, integrating these factors with assay strategies aligned to the specific CoU [1,5-7,10,13].

Consider a humanised monoclonal antibody administered intravenously in a first‑in‑human study. Based on the molecule’s human sequence, administration route, lack of strong aggregation signals in preclinical characterisation, and no major immunostimulatory features, the sponsor may justify an initial low‑risk ADA strategy, such as a screening assay as the primary clinical tool, with confirmation reserved if meaningful signals emerge. If clinical data later show an unexpected increase in drug clearance, loss of exposure‑response relationship, or infusion reactions in a subset of patients, the risk assessment should be revisited. At that point, the Sponsor may expand the ADA program to include confirmatory testing and/or additional characterization such as titre, isotype, epitope/domain specificity or nAb analysis. This staged approach reflects the FDA’s risk‑based, case‑by‑case framework and the EMA’s emphasis on integrating immunogenicity with PK, PD, safety, and clinical relevance.

Risk Factors

Immunogenicity risk factors can influence the incidence, magnitude and clinical impact of ADAs [14]. Factors that contribute most significantly to the formation of ADAs can be divided into three categories: patient-related, drug-related and trial-related. It is important to employ a tailored risk assessment to determine clinical sampling requirements, the potential need for pre-screening and/or prior deimmunisation, and the appropriate action plan for hypersensitivity reactions in terms of study discontinuation and patient monitoring [1,15]. Lastly, the appropriate bioanalytical testing strategy and timing to support the endpoint should also be established.

Major immunogenicity risk factors include:

• Structural therapeutic similarity to endogenous counterparts and non-human sequences
• Immunostimulatory therapeutics or immunostimulatory co-medications
• Subcutaneous and inhaled routes of administration where the immune system is highly active
• Pre-existing immunity to the therapeutic and/or excipients
• Episodic dosing

Putting It Together

1. Start with assessing risk: define CoU, therapeutic and patient-specific risks, and the clinical decisions that the immunogenicity data will inform
2. Choose a fit-for-purpose bioanalytical strategy that aligns with regulatory expectations:

• • Decide on a 1‑, 2‑, or 3‑tier strategy and the degree of development/validation required based on risk and analytical platform
  • Integrate PK/PD and safety data (and nAb, when needed) to determine the clinical relevance of a potential immunogenic response
  • Initiate further tiered testing, where need is identified

Conclusions

Long-term maintenance therapies employing biologic drugs and novel modalities are becoming increasingly popular for many conditions, due to their potency and targeted efficacy. However, their administration to patients is always accompanied by a degree of risk for an immunogenic response.

To assess the immunogenic potential of a biotherapeutic drug, and to correlate laboratory results with clinical events, it is vital to develop validated laboratory test methods that provide accurate assessment and characterisation of ADA responses. Across biologics and novel modalities, the immunogenicity testing strategy is converging on more semi-quantitative and clinically relevant outputs, focusing on patient impact rather than ADA incidence alone. This movement supports fit-for-purpose assay design and appropriate analytical tiers, rather than a one-size-fits-all approach borne from historical high-risk therapeutics.

It is important that Sponsors consult with applicable regulators (EMA/FDA/MHRA) early on when deciding on an immunogenicity testing strategy, to ensure that all parties, including the bioanalytical lab(s), are informed and are in agreement of the testing approach.

At Synexa, immunogenicity assessment is approached through a risk‑based, context‑of‑use framework. ADA and neutralising antibody data are generated in relation to therapeutic modality, clinical stage, and the decisions they are intended to inform, and considered alongside PK, PD, and safety data as programmes progress. Immunogenicity testing, therefore, extends beyond binary detection to become a clinically meaningful component of development strategy.

References

1. EMA Guideline on immunogenicity assessment of monoclonal antibodies intended for in vivo clinical use. EMA/CHMP/BMWP/86289/2010. May 2012.
2. Goodman et al. 2018. DOI: https://doi.org/10.4155/bio-2017-4971
3. Mora et al. 2023. DOI: https://doi.org/10.4155/bio-2024-0024
4. Tounekti et al. 2025. DOI: https://doi.org/10.1080/17576180.2025.2586976
5. FDA Guidance for Industry: Immunogenicity Testing of Therapeutic Protein Products — Developing and Validating Assays for Anti-Drug Antibody Detection. January 2019
6. FDA Guidance for Industry: Scientific Considerations in Demonstrating Biosimilarity to a Reference Product: Updated Recommendations for Assessing the Need for Comparative Efficacy Studies. October 2025
7. EMA Guideline on Immunogenicity Assessment of Therapeutic Proteins. EMEA/CHMP/BMWP/14327/2006. May 2017.
8. Tovey & Lallemand 2011. DOI: https://doi.org/10.1177/2042098611406318
9. Cowan et al. 2026. https://doi.org/10.1080/17576180.2026.2641928
10. Manning et al. 2022. DOI: https://doi.org/10.1208/s12248-022-00728-8
11. Goodman et al. 2024. DOI: https://doi.org/10.1080/17576180.2024.2376949
12. Kubiak 2025. https://doi.org/10.1208/s12248-025-01153-3
13. Cowan et al. 2025. DOI: https://doi.org/10.1080/17576180.2025.2487377
14. Pan et al 2023. DOI: https://doi.org/10.4155/bio-2023-0135
15. Tounekti et al. 2025. DOI: https://doi.org/10.1080/17576180.2024.2439229