Biomarker discovery is moving quickly. Proteomic profiling, AI-driven analytics, and more accessible multi-omics are helping teams nominate targets faster and connect signals to disease mechanisms. The symposium made one point especially clear, though: discovery only creates value when the biomarker can be measured reliably across real samples and real workflows.
Aviva sponsored this symposium, Advancing Biomarker Discovery for Disease Insights to spotlight both emerging discovery methods and the practical realities of translating biomarkers into dependable measurements. Here are some of our takeaways:
1) Discovery is accelerating - but translation still hinges on measurement
Across talks, the common friction point was reproducibility: signals that look compelling in early datasets can become difficult to confirm across cohorts, sites, and platforms. The limiting factor is often not biological relevance - it is whether the assay can deliver consistent performance in the intended sample type.
Practical takeaway
Define your measurement path early: sample type (CSF, serum, plasma, tissue), expected abundance, dynamic range, and the decision the biomarker must support.
2) Multi-modal data raises the bar for assay specificity and controls
As teams integrate proteomics, genomics, imaging, and orthogonal validation, false positives scale faster. Multi-modal workflows increase the need for reagent specificity, well-designed controls, and clearly understood failure modes, especially when moving from clean systems into complex matrices.
Practical takeaway
Plan orthogonal validation up front, and choose controls that test specificity in a meaningful context for your matrix and intended use.
3) “Actionable” biomarkers require assay readiness sooner than most teams expect
The most advanced programs treat assay planning as part of discovery, not a downstream cleanup step. Whether the endpoint is research use, clinical feasibility, or therapeutic monitoring, early assay readiness questions reduce rework later.
Practical takeaway
If your biomarker is moving toward translation, start asking now: sensitivity requirements, matrix effects, interference risk, scalability, and how performance will be verified over time.
What Aviva presented - building assay-ready antibody pairs for biomarker studies
In her session, April Livengood, PhD (Director of Strategic Business Development & Collaborations) focused on what matters most for antibody selection and assay performance in real sample matrices. (See us on LinkedIn)
Aviva’s workflow - from antibody discovery to matrix-tested assay candidates
Our recombinant immunoassay workflow starts with high-throughput antibody discovery and progresses through quantitative screening and functional pair testing, with decision points designed to preserve optionality and avoid single-clone failure.
Key elements include:
Example from our progranulin case study: Hundreds of recombinant clones were characterized (including affinity, specificity, and epitope diversity), top candidates were tested as ELISA pairs, and the optimized assay was evaluated in human serum and CSF.
How Aviva supports biomarker measurement
Aviva supports biomarker teams with both research-grade tools and custom capabilities that help bridge discovery and measurement - especially when programs are moving into complex matrices or higher-confidence workflows.
If you are evaluating a biomarker target and want to sanity check assay feasibility, please email us at info@avivasysbio.com or techsupport@avivasysbio.com and share:
We will point you to the most relevant next resource - including on-demand symposium materials, posters, case studies, or a short technical discussion with our team.