Everything is a Biomarker: Insights on Integrated Bioanalysis
Everything Is a Biomarker
A biomarker is a definition, not a category of assay. PK and immunogenicity both meet that definition, and that has real consequences for how we develop assays and interpret the data.
In most labs, “biomarker” is treated as a category of work. It is a service line, a box on the intake form, and a group that sits apart from the PK and immunogenicity teams. That makes sense for running a lab, but it does not track the underlying science, because, by its formal definition, almost everything we measure at the bench is a biomarker.
Here is the definition. The FDA-NIH BEST resource (Biomarkers, EndpointS, and other Tools) defines a biomarker as a measurable characteristic that indicates a normal or pathogenic biological process or a response to an exposure or intervention [1]. Read that against what we actually do. A drug concentration is a measurable characteristic that indicates exposure. An anti-drug antibody response is a measurable characteristic that indicates how the immune system responded to the therapeutic. A phosphorylation signal after ex vivo stimulation is a measurable characteristic that indicates target engagement. None of these is a different kind of science from the others. They are the same measurement problem asked three ways: quantify an analyte in a matrix well enough to support a decision.
Immunogenicity is a biomarker
Immunogenicity is where this gets concrete. The three-tiered ADA paradigm, screen, then confirm, then titer, has been the default for more than a decade, and as a screening cascade, it does its job. The trouble is that screening for the presence of antibodies is a different question from the one the clinical team is actually asking, which is whether those antibodies are affecting exposure, efficacy, or safety. An assay built to answer the first question will not necessarily answer the second, and we have tended to treat the tiered result as though it does.
Lauren Stevenson’s two 2026 Bioanalysis papers address this directly. “Immunogenicity Assays are Biomarker Assays: Is the 3-tiered paradigm fit-for-purpose?” [2] makes the case in its title: if immunogenicity assays are biomarker assays, they should be judged by the context-of-use rather than by a fixed procedure. “From Tiers to Truth” [3] is the how-to. Start with the clinical question, define the context-of-use, and let that determine the testing strategy, rather than defaulting to three tiers because that is what we have always run.
The follow-on work is where it gets interesting, because it goes in both directions. Mehta and colleagues [4] analyzed ADA data alongside PK and PD, and pre-existing and drug-sustaining antibodies showed real, quantifiable effects on exposure that a positive-or-negative tiered call would have flattened out. Valentine and colleagues [5], working on vidutolimod, went the other way: a risk-based assessment let them justify a simplified one-tiered, singlicate strategy. So, the context-of-use can ask for more than the three tiers provide, or it can ask for less. The tier count was never the point. Fitting the method to the decision is.
Context-of-use is the common thread
Once you stop sorting assays into PK, immunogenicity, and biomarker buckets and start treating them as the same kind of measurement, the question that matters changes. It is no longer which category an assay belongs to. It is what decision the result must support and what the assay needs to do to earn that support. That is the context-of-use, and it is not a new idea. Biomarker science has been working toward it for twenty years.
You can watch it develop in the literature. Fit-for-purpose method development appears in 2006. The GCC recommendations on biomarker method validation follow in 2012, then the multiplex ligand-binding recommendations in 2015. Parallelism is established as a foundation for biomarker assay development in 2018, and the thinking continues to build through the WRIB white papers, the 2019 C-Path Points to Consider, and the 2022 fit-for-purpose conference report. By 2026, Immunologix scientists are on the author lists for the AAPS J best-practices paper on parallelism [6] and for a context-of-use-guided transthyretin immunoassay framework [7].
The through-line is context-of-use: move away from generic validation templates and toward assays justified by the decisions they inform. The specific validation toolkit varies with the assay type, but that logic does not, and it holds for immunogenicity just as much as for a biomarker method.
Regulators are treating biomarkers as their own discipline
The regulators are converging on the same point, even if the written guidance has taken an awkward route to get there. ICH M10, finalized in 2022, was written for a single context-of-use: measuring drug and metabolite concentrations to support PK analysis. Precisely because its scope is that specific, M10 deliberately and appropriately excluded biomarkers [8]. That exclusion is itself a context-of-use judgment: biomarker measurement answers different questions, so it needs validation logic built for those questions rather than borrowed from PK.
The FDA’s own guidance, the final Bioanalytical Method Validation for Biomarkers, published 21 January 2025 [9], has caused some consternation, because it directs readers to start from M10, and M10 puts biomarkers out of scope. Some have read that as the FDA endorsing PK assay approaches for biomarker methods. That is not what we see in practice. In our direct experience, our regulatory colleagues readily engage in context-of-use-driven conversations across PK, biomarker, and immunogenicity assessments, addressing the program-specific needs of each.
Add it up, and the direction is integration. PK, immunogenicity, and biomarkers are one measurement science addressing different questions. They run on the same fit-for-purpose logic, and they tell you the most when you read them together rather than in separate lanes.
Integrated bioanalysis
That is the theme of this year’s Immunologix Bioanalysis Symposium. A drug level, an antibody titer or S/N response, and a pharmacodynamic readout are answers to related questions about the same molecule in the same patient. Read one at a time, they leave relevant information on the table. Read together, they tell you what the drug is actually doing in totality. That is what we mean by integrated bioanalysis, and it is why “everything is a biomarker” is a way of working rather than a tagline.
More details on the program and speakers will follow over the next few weeks.
The 3rd Annual Immunologix Bioanalysis Symposium is on October 1, 2026, at Le Méridien Boston Cambridge. Register Here to attend the event.
References
[1] FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource. Silver Spring (MD): Food and Drug Administration; Bethesda (MD): National Institutes of Health; 2016 (updated regularly). Available at: https://www.ncbi.nlm.nih.gov/books/NBK326791/
[2] Stevenson, L. Immunogenicity Assays are Biomarker Assays: Is the 3-tiered paradigm fit-for-purpose? An illustrative case study. Bioanalysis, May 2026. https://www.tandfonline.com/doi/full/10.1080/17576180.2026.2677736
[3] Stevenson, L. From Tiers to Truth: A biomarker-based framework for clinically relevant immunogenicity assessment. Bioanalysis, May 2026. https://www.tandfonline.com/doi/full/10.1080/17576180.2026.2672455
[4] Mehta, D., Wargin, W., Wallace-Teliz, S., Stevenson, L., Rigdon, G. Rethinking immunogenicity: an integrated approach reveals the PK/PD impact of pre-existing and drug-sustaining ADA. Bioanalysis, May 2026. https://www.tandfonline.com/doi/full/10.1080/17576180.2026.2667854
[5] Valentine, J.L., Shank, S., Fred Lucena, L.M., et al. Immunogenicity assessment for vidutolimod: a risk-driven approach for a simplified 1-tiered, singlicate antibody testing strategy. Frontiers in Immunology, May 2026. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2026.1751717/full
[6] King, L., Allinson, J., Amaravadi, L., et al. Best practices in the application of parallelism for biomarker assay validation. AAPS J, January 2026. https://link.springer.com/article/10.1208/s12248-025-01176-w
[7] Watanabe, T., Canfield, J., Anderson, E., Allinson, J., Viney, M., Mathews, J. Context-of-Use Guided Development and Validation of a Transthyretin Immunoassay: A Framework for Biomarker Assay Design. AAPS J, May 2026. https://link.springer.com/article/10.1208/s12248-026-01262-7
[8] International Council for Harmonisation. M10 Bioanalytical Method Validation and Study Sample Analysis. Final guidance, November 2022. https://www.fda.gov/media/162903/download
[9] U.S. Food and Drug Administration. Bioanalytical Method Validation for Biomarkers: Guidance for Industry. Center for Drug Evaluation and Research (CDER) and Center for Biologics Evaluation and Research (CBER), January 2025. Available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/bioanalytical-method-validation-biomarkers
