IMDNA helps research teams determine whether a proposed molecular or protein assay is scientifically and analytically feasible before committing resources to full development. Feasibility work can evaluate the biological target, analyte form, expected abundance, specimen matrix, technology, critical reagents, controls, workflow constraints, multiplex potential, and preliminary analytical behavior.
Programs can support qPCR/RT-qPCR, ELISA, multiplex bead-based immunoassays, multiparameter flow cytometry, and cell-based functional assays, as well as integrated biomarker strategies that connect nucleic-acid, soluble-protein, cellular-phenotype, and functional-response measurements. The objective is not to declare an assay “validated” at the feasibility stage, but to generate enough evidence to make an informed go, modify, split, or stop decision and define the most defensible development path.
Evaluate whether DNA, RNA, protein, antibody, antigen, soluble biomarker, or another measurable endpoint actually answers the research question.
Assess expected abundance, specificity, dynamic range, matrix effects, reagent availability, and platform constraints.
Evaluate specimen collection, processing, stability, extraction, dilution, interference, sample volume, and throughput.
Build and challenge an initial assay configuration before committing to extensive optimization or validation.
Advance, redesign, change technology, split a multiplex, change matrix, or discontinue low-value approaches based on evidence.
The NIH/NCATS Assay Guidance Manual describes assay development as an iterative process in which the assay format, reagents, sensitivity, dynamic range, stability, artifacts, interference, instrumentation, and performance statistics are considered in relation to the intended purpose. Feasibility therefore sits before formal validation: it is the structured process of determining whether the proposed biology and analytical system can realistically support the intended measurement.
Is the proposed analyte mechanistically connected to the research question? Is it present in the selected tissue or biofluid? Is the analyte expected to change on the timescale, in the population, or under the experimental conditions being studied?
Can the analyte be measured with sufficient specificity, sensitivity, range, precision, and signal-to-background using the proposed technology and available reagents?
Will the intended specimen support the measurement? Matrix constituents can inhibit PCR, alter antibody binding, affect recovery, change apparent concentration, or destabilize the analyte.
Can the workflow meet practical requirements for sample volume, turnaround, multiplexing, instrumentation, operator complexity, reagent stability, throughput, cost, and transfer to the intended laboratory environment?
A well-designed feasibility study converts an idea into explicit technical questions that can be answered experimentally.
Define exactly what decision, comparison, biological mechanism, or research endpoint the assay is expected to support.
Clarify whether the relevant measurement is genomic DNA, RNA, splice variant, fusion transcript, protein, active enzyme, antigen, antibody, cytokine, or another molecular form.
Evaluate tissue, whole blood, plasma, serum, PBMCs, saliva, urine, swab, FFPE, cell supernatant, bone marrow, environmental matrix, or another specimen.
Estimate whether endogenous concentrations are likely to fall within a usable quantitative or detection range and whether diseased/treated samples may differ substantially from controls.
Consider paralogs, isoforms, homologous organisms, related variants, cross-reactive proteins, heterophilic antibodies, or other potential analytical confounders.
Select qPCR/RT-qPCR for targeted nucleic acids, ELISA for focused proteins, or multiplex immunoassay for multianalyte soluble-protein research when appropriate.
Assess primer/probe designability, antibody specificity, recombinant standards, calibrators, control materials, reference samples, and lot continuity.
Identify PCR inhibition, extraction loss, matrix binding, proteolysis, hemolysis, lipemia, heterophilic effects, cross-talk, nonspecific signal, or competing targets.
Evaluate target abundance differences, spectral channels, oligonucleotide interactions, shared chemistry, assay ranges, antibody cross-reactivity, and matrix compatibility.
Map each major failure mode to a control: extraction/process, inhibition, no-template, no-RT, positive, negative, calibrator, endogenous, exogenous, or QC materials.
Set preliminary expectations for specificity, sensitivity, range, precision, robustness, recovery, parallelism, or other characteristics according to intended use.
Define failure criteria before testing so that the feasibility study can support a real decision rather than becoming open-ended optimization.
The feasibility questions are different for nucleic-acid assays and ligand-binding assays. The platform should follow the analyte—not the other way around.
Key questions: Can a specific amplicon be designed? Is the target sufficiently conserved or unique? Is RNA quality adequate? Will the expected target abundance fall within the useful range?
Scientific basis: MIQE 2.0 and ISO 20395 emphasize assay design, specificity, efficiency, dynamic range, controls, detection/quantification limits, normalization, and robustness as core qPCR performance considerations.
Key questions: Is the protein detectable in the intended matrix? Are suitable capture/detection reagents available? Does endogenous analyte behave similarly to the calibrator?
Scientific basis: fit-for-purpose biomarker literature emphasizes matching method-development rigor to intended use and evaluating matrix behavior before relying on an assay for study decisions.
Key questions: Can all analytes be measured in one sample dilution and matrix without losing meaningful sensitivity or range?
Scientific basis: multiplex LBA guidance specifically recommends feasibility testing in the intended matrix and recognizes that one assay condition may not be optimal for every analyte in a multiplex.
Key questions: Are the cellular populations, surface or intracellular markers, activation states, and functional phenotypes measurable reproducibly in the intended specimen and instrument configuration?
Scientific basis: MIFlowCyt defines minimum information needed to interpret flow-cytometry experiments, while ICSH/ICCS guidance addresses intended use, specimen and processing conditions, analytical sensitivity/specificity, imprecision, and other performance considerations for cell-based fluorescence assays.
Key questions: Does the cellular model reproduce the biological mechanism of interest, and can the treatment-induced or stimulus-induced response be measured with adequate dynamic range, reproducibility, and biological relevance?
Scientific basis: the NIH/NCATS Assay Guidance Manual includes dedicated guidance for in-vitro cell-based assays, including viability, cytotoxicity, apoptosis, signaling, image-based assays, cellular models, assay artifacts, plate effects, and reproducibility.
Good assay design begins with information. The stronger the inputs, the faster weak concepts can be eliminated and viable approaches can move into formal development.
This distinction is essential. Feasibility generates evidence that a design is worth developing. Validation establishes, with a defined study plan and acceptance criteria, that a method performs adequately for its intended use.
| Stage | Primary Question | Typical Evidence |
|---|---|---|
| Concept Review | Does the proposed analyte and technology make biological sense? | Literature, sequence/protein biology, expected abundance, sample availability, known limitations. |
| Feasibility | Can the concept generate a usable analytical signal in the intended matrix? | Prototype data, specificity screen, range/sensitivity estimate, matrix behavior, reagent feasibility, preliminary precision. |
| Optimization | Can performance be improved and stabilized? | Concentration titration, buffer/chemistry changes, cycling or incubation optimization, multiplex balancing, control refinement. |
| Analytical Validation | Does the finalized method meet predefined performance requirements? | Structured studies of specificity/selectivity, range, precision, sensitivity, robustness, matrix effects, stability, controls, and other intended-use parameters. |
| Biological Verification | Does the assay behave meaningfully in representative biological samples? | Independent samples, appropriate comparator groups, longitudinal materials, orthogonal measurements, or biological replication. |
| Technology Transfer | Can another laboratory or workflow reproduce the method? | Transfer protocol, SOP, training, instrument verification, QC materials, predefined comparison criteria. |
The NIH Assay Guidance Manual describes assay development and validation as a cycle. IMDNA applies the same principle to research assay feasibility: define the question, identify the major risks, test them efficiently, and use the evidence to determine the next development step.
| Phase | Feasibility Activity | Decision Enabled |
|---|---|---|
| 1. Intended-use definition | Clarify analyte, specimen, biological comparison, expected output, and how the data will be used. | Prevents development of an assay that technically works but does not answer the research question. |
| 2. Scientific landscape review | Evaluate published biology, sequence/protein structure, target abundance, known interferences, existing methods, and evidence gaps. | Identify high-risk assumptions before reagent spend. |
| 3. Technology selection | Choose molecular, soluble-protein, flow-cytometric, or cell-based functional platform according to the analyte, biological question, and required performance. | Avoid forcing an analyte onto an unsuitable technology. |
| 4. Critical reagent assessment | Review primer/probe designability, antibody pairs, standards, controls, reference materials, extraction reagents, and instrument compatibility. | Determine whether the basic assay architecture is buildable. |
| 5. Prototype development | Build the smallest useful assay capable of testing the critical scientific assumptions. | Generate early evidence without over-investing in optimization. |
| 6. Matrix challenge | Test representative biological matrix, dilution behavior, inhibition, extraction recovery, nonspecific signal, and stability. | Determine whether buffer-only performance translates to real samples. |
| 7. Preliminary performance characterization | Estimate specificity/selectivity, sensitivity, quantitative range, precision, signal-to-background, and robustness parameters relevant to the method. | Determine whether the prototype has enough performance margin to justify development. |
| 8. Feasibility decision | Summarize technical risks, evidence, unresolved questions, and recommended path. | Go forward, redesign, change matrix, change technology, split multiplex, narrow claims, or stop. |
A feasibility study is most useful when it ends with a decision rather than a collection of preliminary experiments.
Early proof-of-concept data are valuable, but they should not be interpreted as evidence of a fully validated method.
Many assay-development failures originate from an incorrect assumption made before optimization: the wrong analyte, wrong specimen, inadequate reagent specificity, unrealistic sensitivity requirement, incompatible multiplex ranges, or an assay format that does not reflect the biology.
Therefore: feasibility is a scientific de-risking stage—not a shortened validation study.
A structured feasibility program converts an idea into a technically justified assay-development plan.
The exact package is tailored to the project, but the goal is to leave the feasibility phase with a clear evidence-based path forward.
Target biology, analyte rationale, intended use, relevant literature, assumptions, and major scientific risks.
Recommended technology, target configuration, matrix, workflow, controls, calibration strategy, and instrument requirements.
Primer/probe, antibody, antigen, standard, calibrator, control, extraction, buffer, or master-mix requirements.
Prototype assay results addressing the most important technical risks identified at project initiation.
Early estimates of specificity/selectivity, useful range, sensitivity, precision, matrix behavior, or multiplex compatibility as appropriate.
Known limitations, unresolved technical questions, sample constraints, reagent risks, interference risks, and mitigation options.
A documented feasibility conclusion tied to the intended research use.
Recommended optimization experiments, analytical validation parameters, biological verification strategy, and transfer requirements.
Project-specific design notes, experimental records, preliminary SOP elements, data summaries, and development recommendations.
IMDNA can support feasibility from an early biological concept or enter a project after a preliminary assay has already been attempted. The work can focus on one critical risk or provide a complete pre-development assessment.
Whether your project begins with a biomarker hypothesis, pathogen target, gene-expression signature, mutation, fusion transcript, cytokine panel, protein biomarker, new sample type, multiplex concept, or partially developed assay, IMDNA can evaluate the scientific and analytical feasibility and define a practical development path around the intended research use.