By Jim Boushell, Senior Vice President, Biospecimens, DCN Dx
When I made the transition from commercial to research in the mid 90’s, the diagnostic industry was experiencing a boom in breakthrough science: PCR, proteomics, and next-generation technologies. Yet many promising products never made it out of the lab, for reasons unrelated to the science itself.
At the time, I didn’t stop to ask why. There was so much opportunity that I stayed focused on demand. My turning point came in a casual conversation with a family member: the issue was rooted in the specimen strategy, or lack of one.
Access to samples wasn’t the problem. The challenge was building diagnostic programs on incomplete or misaligned specimen sets, using convenience samples, remnants, or acquisition methods designed for ease rather than evidence. Those early decisions often determine whether a technology can meet regulatory requirements, demonstrate clinical utility, and perform under real use conditions.
Over three decades, I’ve learned that specimen strategy is not a procurement task. It’s a core component of development. At ProMedDx, we weren’t simply fulfilling demand; we were deploying site networks, standardized protocols, and operational infrastructure to support hundreds of diagnostic programs. The tests that ultimately changed patient care were built on the same principle: specimen strategy and development strategy are inseparable.
Common pitfalls and lessons learned
Remnant specimens
While inexpensive upfront, remnant specimens often fail to represent target populations or support regulatory claims, leading to costly delays and repeat studies. Prospective collections require more planning, but they deliver long-term certainty.
Where remnants tend to hurt teams is hidden variability: unknown collection devices and anticoagulants, time-to-processing drift, inconsistent storage history, limited metadata, and samples that do not map cleanly to the intended-use population. If your claims depend on stage, symptom status, comorbidities, or treatment effects, the “cheap” set can become the most expensive decision in the program.
Real-world validation
Devices intended for point-of-care use must be validated under actual operating conditions, not just in controlled labs. Performance data from expert operators rarely reflects real-world variability.
Operator technique, workflow timing, environmental conditions, and site-to-site differences show up fast in decentralized testing. If your specimen plan does not reflect those realities (collection context, transport time, temperature excursions, handling steps), you can end up with a performance story that is hard to reproduce and harder to defend.
Patient selection and regulatory alignment
Inclusion and exclusion criteria shape regulatory claims and reimbursement pathways. Misalignment early in development can create significant downstream challenges.
If the specimen set does not match the intended use, the clinical evidence can drift away from the label you want. The fix is rarely trivial: it usually means redesigning enrollment criteria, reopening sites, reworking comparators, or running additional studies to bridge gaps.
Adapting to change
COVID-19 accelerated the need for decentralized collection and flexible workflows. Success required applying proven strategies for reaching diverse and hard-to-access populations, reinforcing that adaptability depends on strong foundational protocols.
Teams that handled the transition well treated collection as an engineered workflow: clear site or at-home procedures, training, logistics, and documentation designed to survive real operational friction without compromising data integrity.
What differentiates successful diagnostics
It’s rarely about technology alone. Success comes from understanding diagnostics as a system: specimens, workflows, operators, and data, and making informed decisions early.
Time invested in getting specimen strategy right compounds forward. Time spent fixing missteps compounds backward.
A quick pressure test for a specimen plan
- Is the intended use and target population defined well enough to design the set (including key subgroups and likely confounders)?
- Do the specimens support the claims you want to make, not just the feasibility work you can do quickly?
- Is the comparator method or reference standard chosen and operationally feasible at sites?
- Is minimum metadata defined (collection device, matrix, processing time, storage history, reference method result), and is there a plan when it is missing?
- Are collection, handling, shipment, and storage workflows mapped end-to-end, including expected temperature and timing variation?
- Is the set sized to support the statistical plan and the regulatory pathway, not just “what we can get”?
Where DCN Dx fits
At DCN Dx, we built our biospecimen services to operate in the same program reality as assay development, clinical research, and regulatory planning. That means aligning specimen requirements, workflows, documentation, and data capture to the evidence package from the start, then executing collections with audit-ready traceability.
Our approach emphasizes:
- Starting with the regulatory endpoint in mind
- Designing protocols that reflect real-world use
- Building long-term site partnerships
- Engineering quality into every step
The competitive advantage
Specimen access is more available than it used to be. What sets programs apart is knowing which specimens to collect, how to collect them, and why they matter for your specific pathway. That insight comes from experience, not theory.
If you’re developing a diagnostic, ask: does your specimen strategy support your regulatory and commercial goals? Getting this right early is often the difference between a clean evidence story and a cycle of costly delays.
If need have a question about your specimen strategy, you can connect with us here. Learn more about our prospective collections service here.
