Abstract

The experimental use of antibodies that have not been validated for context-specific use frequently misdirects biomedical research. Experimental results that derive from the use of inadequately validated antibodies are estimated to waste over $1 billion annually in the United States alone and to consume millions of animal and human biological samples in experiments whose conclusions may be unreliable. Community validation frameworks, reporting standards, and independent characterisation initiatives have made important progress, and multi-stakeholder coordination efforts are emerging. However, the research community lacks a formally developed, consensus-based action plan that specifies what each stakeholder group should do, by when, and with what priority. We conducted a modified Delphi study with international experts representing academic researchers, scientific publishers, research funders, antibody manufacturers, and institutional research leaders to develop actionable recommendations for improving antibody validation, selection, and reporting practices. Thirty-two participants rated 33 proposed actions on effectiveness and feasibility using 9-point scales, with consensus assessed using the RAND/UCLA Appropriateness Method. Over two rounds, the panel achieved consensus on 15 items as both effective and feasible for implementation by 2030. These spanned institutional actions (training in antibody validation, integration into research integrity frameworks, support for local expertise networks), funder actions (dedicated validation budgets, grant application requirements, endorsement of community reporting standards), publisher actions (complete antibody reporting packages, clear validation standards), manufacturer actions (assignment of unique identifiers at source), and cross-stakeholder coordination (a shared roadmap for improvement). An additional 15 items were rated as effective but with uncertain feasibility, reflecting a consistent pattern in which the panel agreed on the value of proposed interventions but expressed reservations about realistic implementation timelines. One item was rejected by the panel with concerns around effectiveness and feasibility. Participants described four interconnected barriers to progress: diffuse ownership of the problem across stakeholders; market dynamics that inadequately reward antibody quality; difficulty justifying investment when returns are distributed across the research system; and coordination challenges among actors with different incentive structures. These barriers are addressable through coordinated action, and the findings complement existing technical and data-sharing initiatives by providing the structured, expert-informed policy framework to help translate awareness of the problem into concrete practice and policy changes.

Introduction

Antibodies are among the most widely used tools in biomedical research, enabling researchers to detect, quantify, and isolate specific proteins within complex biological samples. However, research antibodies do not always bind exclusively to their intended targets. Poor antibody specificity has repeatedly misdirected biomedical research across diverse fields, leading to incorrect conclusions about protein function, unreliable biomarker discovery, and misidentification of drug targets [[1]–[10]]. These failures represent not isolated occurrences but a persistent and systemic issue affecting the reliability of the published literature.

The scale of this problem is substantial. An estimated $28.2 billion per year is spent in the United States on preclinical research that is not reproducible, with issues relating to biological reagents likely being the biggest contributor [[11]]. Antibody-related failures alone account for an estimated $1 billion annually in the US in direct economic waste [[12],[13]], a figure that excludes the substantial opportunity cost of misdirected research effort. Beyond the economic dimension, there is a large and largely unquantified ethical cost: the waste of animals both in antibody production and in research studies that employ unsuitable antibodies, and the waste of donated human biological materials in experiments whose conclusions may be unreliable [[14]]. Recent estimates suggest that at least 4 million animal samples and 6 million human tissue samples have been consumed globally in experiments using antibodies that failed independent testing, without context-specific validation since December, 2000 [[14]].

How well an antibody performs in binding to its target varies between applications, protocols, and the cell or tissue types used for each experiment. Consequently, it is important that performance is assessed for each context of use, a process referred to as validation, and it is the responsibility of the researcher to carry out these studies for their specific protocol [[15]]. The complexity and scale of antibody use have contributed to a marketplace containing a high proportion of poorly performing antibodies [[1]]. Recent independent benchmarking by the YCharOS (Antibody Characterisation by Open Science) consortium quantified the likely scale of the problem: greater than 50% of 614 commercial antibodies against 65 neuroscience-related proteins failed characterisation experiments in three commonly used applications, and 88.4% of papers using poorly performing antibodies in immunofluorescence presented no relevant validation data [[1]]. Moreover, each protein target was linked to an average of approximately 12 published papers that presented data using poorly performing antibodies, perpetuating the use of these reagents and the associated issues of reproducibility.

Efforts to address this problem have operated on multiple fronts. Technical advances include the IWGAV five-pillar validation framework, which outlines complementary strategies (genetic, orthogonal, independent antibody, tagged protein, and immunocapture mass spectrometry), for establishing antibody selectivity [[15]], and the large-scale independent characterisation work of the YCharOS consortium, which has provided open, head-to-head comparisons of commercial antibodies using knockout cell lines as isogenic controls [[1]]. Reporting standards such as the Materials Design Analysis Reporting (MDAR) framework have sought to improve transparency at the point of publication [[16]], and tools including the Research Resource Identifier (RRID) initiative have enabled more precise tracking of antibody use across the literature [[17]]. Individual manufacturers have also invested in more rigorous characterisation, in some cases removing or relabelling products found to perform poorly [[1],[14]].

In parallel, multi-stakeholder coordination has begun to emerge. Kahn and colleagues [[12]] assembled a history of efforts to address the antibody characterisation crisis and proposed sector-specific actions for researchers, universities, journals, vendors, scientific societies, disease foundations, and funding agencies. The Only Good Antibodies (OGA) community, of which members of the present study team are founding contributors [[18]], has brought together stakeholders across the research ecosystem to work toward making best practice in antibody use more feasible, easy, and rewarding, drawing on the Centre for Open Science’s framework for culture change in research. These initiatives represent important progress. However, the field still lacks a formally developed, consensus-based action plan in which proposed interventions have been systematically rated for both effectiveness and feasibility by a diverse expert panel. Without such structured prioritisation, it is difficult for stakeholders to know which actions to implement first, where the strongest agreement exists, and where feasibility barriers require targeted attention.

The underlying reasons for the persistence of this problem are complex and multifaceted, encompassing the inherent challenges of producing and characterising high-quality antibodies, but also researcher awareness, education, and incentive structures [[14]]. Researchers identify time, cost, and lack of supervisor support as primary barriers to better validation practices, while strongly supporting structural solutions including open data sharing, dedicated validation funding, and publisher requirements [[14]]. Behavioural analysis drawing on the COM-B (Capabilities + Opportunities + Motivation Behaviour) model suggests that individual capability and motivation interact with the opportunity provided by the research environment, such that improvements to practice require changes not only in individual behaviour but in the systems and structures that shape it [[14],[18]]. This suggests that the problem requires coordinated action across the research ecosystem.

Having established the scale and drivers of the problem [[14]], and situated it within the broader landscape of ongoing technical and community efforts [[1],[12],[18]], this study sought to move from identifying what needs to change, to building structured agreement on how to change it. We convened a Delphi study to develop recommendations for improving antibody validation, selection, and reporting practices, and to analyse the obstacles to their implementation.

Final consensus outcomes

Across the two rounds, the panel evaluated 33 proposed actions, achieving consensus on 15 items as both effective (median ≥7) and feasible (median ≥7), without disagreement (). These 15 recommendations span all four stakeholder domains and represent actions that expert panellists agreed would meaningfully improve antibody practices and could realistically be implemented by 2030.

[Table 1] Consensus recommendations—items rated both effective and feasible with agreement.

Item | Full Recommendation | Median Effectiveness | Median Feasibility | Stakeholder Group targeted by recommendation Journal and Publisher Policies R1 | Authors should include the Research Resource Identifier (RRID) for each antibody used, where available. | 7.0 | 8.0 | Journals, Authors R2 | Authors should provide sufficient metadata to allow unambiguous identification of each antibody. This includes: Clone ID (for monoclonal antibodies), Catalogue number and lot number (for polyclonal antibodies), Vendor or source. | 8.0 | 8.0 | Journals, Authors R3 | Authors should report the dilution ratio at which each antibody is used. In addition, authors should state the antibody protein concentration where possible. Examples include dilution ratios such as 1:1000 or 1:500, or concentrations expressed in µg/mL. | 8.0 | 8.0 | Journals, Authors A1 | Journals should have a clearly stated standard for antibody validation and reporting. | 7.5 | 7.0 | Journals Research Funder Actions R10 | Funders should include a section in the application form where applicants must detail the steps they will take to validate the antibodies used. | 7.5 | 8.0 | Funders R11 | Applicants to biomedical funding schemes should include a specific line item within their budget for resources to conduct robust antibody validation (if relevant to their proposal). | 8.0 | 7.0 | Funders R12 | Funders should create or expand targeted schemes to support the development and validation of critical antibody-based research tools. These could include, for example: Characterisation of under-studied antibodies, Generation of KO/KD cell lines, Cross-platform benchmarking of antibody performance, Replacement of animal-derived antibodies. | 8.0 | 7.0 | Funders R13 | Funders should share information within the applicant guidance that antibody performance is an important limitation in many experimental methods. Applicants should be encouraged to address this in methodological or reproducibility sections, as this will be part of how these sections are evaluated by reviewers and panels. | 7.0 | 7.0 | Funders R14 | Funders should encourage grantees to deposit antibody validation data in open-access repositories, ideally linked to RRIDs or registry entries. This expectation could be embedded in end-of-grant reporting or incentivised through visibility or reproducibility initiatives. | 7.0 | 7.0 | Funders R15 | Funders should formally endorse community-developed reporting standards that promote antibody transparency and validation (e.g., IWGAV, MDAR). Such endorsements can help reinforce aligned expectations across funders and journals. | 7.0 | 7.0 | Funders Institutional and Educational Actions R19 | Training in antibody validation should be offered in relevant bioscience courses and programmes. This could include: Undergraduate education (e.g., in immunology, neuroscience, or molecular biology programmes), Postgraduate or doctoral training (e.g., through doctoral training centres), Postdoctoral or continuing professional development. Training should be field-appropriate, not universally required across all domains. | 8.0 | 7.0 | Institutions, Learned Societies R20 | Universities and research institutions should incorporate antibody validation expectations into research integrity and ethics review frameworks. This may include: Asking researchers to consider antibody validation as part of research ethics approval processes, Providing guidance or templates for documenting validation plans. | 7.0 | 7.0 | Institutions R21 | Universities, research institutions and learned societies should recognise and support the Antibody Champions Scheme. This UK-based initiative, co-funded by the Medical Research Council (MRC) and the National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), will be launched in 2026. It will be a voluntary scheme of local champions or experts in antibody validation. These individuals will serve as: Points of contact for advice on antibody selection, validation design, and data interpretation; Trainers or facilitators in antibody validation best practice workshops or research methods courses; Advocates for antibody good practice within departments or core facilities. Support could include protected time, acknowledgement in performance reviews, or funding for training and outreach. | 7.0 | 7.0 | Institutions, Learned Societies Cross-Stakeholder Coordination R22 | Stakeholders should work together to develop a shared roadmap for improving antibody validation practices by 2030. This would outline: Stakeholder-specific actions and responsibilities, Target timelines and milestones, Mechanisms for accountability and feedback. | 7.0 | 7.0 | All Stakeholders Manufacturer Actions A7 | Antibody manufacturers should be encouraged to assign RRIDs to their products at source. | 8.0 | 7.0 | Manufacturers

The panel rated 17 items as uncertain on at least one dimension (). In 15 of these 17 cases, items were rated as effective (median ≥7) but with uncertain feasibility (median 4–6), indicating that while panellists believed these actions would improve antibody practices, they had reservations about realistic implementation by 2030. This pattern—high confidence in effectiveness, uncertainty about feasibility—was the dominant finding for items not achieving consensus, and is a recurring theme in the stakeholder-specific recommendations presented below. One item was rejected by the panel, with concerns around both effectiveness and feasibility (). A summary of the ratings across all items is presented in. A consolidated summary grouping all items by stakeholder group and rating status is provided in.

[Table 2] Items with agreed uncertain effectiveness and/or feasibility.

Item | Full Recommendation | Median Eff | Median Feas | Pattern Effective but Feasibility Uncertain R4 | Journals should require authors to deposit antibody validation data in an open-access repository, linked to a persistent identifier (e.g., RRID, DOI). Repositories might include: Zenodo, institutional repositories, manufacturer portals, or The Antibody Registry (linked to Biomed Resource Watch). | 8.0 | 5.0 | Effective, Uncertain Feasibility R6 | Publishers should provide training resources for reviewers and editors to help them evaluate antibody validation information in submitted manuscripts. | 7.0 | 5.0 | Effective, Uncertain Feasibility R7 | Publishers and other stakeholders should support the development and use of automated tools to flag potential antibody validation issues in submitted manuscripts, e.g., for antibodies that have already been withdrawn from the market because of performance issues. | 8.0 | 5.0 | Effective, Uncertain Feasibility R8 | As part of the submission process, authors should describe how antibody specificity was assessed, making reference to the International Working Group on Antibody Validation (IWGAV) framework. A brief paragraph, checklist, or table should indicate which of the IWGAV pillars were used, if any (e.g., genetic, orthogonal, independent antibody, tagged protein, IP-MS). | 7.0 | 6.0 | Effective, Uncertain Feasibility R9 | Where antibodies are critical to key experimental conclusions, where technically feasible, authors should either: present validation data based on a genetic strategy (e.g., knockout or knockdown), or cite previously published genetic validation data. | 8.0 | 5.0 | Effective, Uncertain Feasibility R16 | Funders should directly engage with manufacturers to encourage them to: Publish antibody validation datasets, Adopt consistent metadata standards (e.g., RRID, clone ID, lot number). | 7.0 | 5.0 | Effective, Uncertain Feasibility R17 | Funders should collaborate with independent benchmarking initiatives such as YCharOS or equivalent open-science consortia. They should directly co-fund the validation of antibodies against key targets to create suites of validated tools that can be used by researchers to improve data quality and thereby increase return on investment for the funder. | 8.0 | 6.0 | Effective, Uncertain Feasibility R18 | Funders should promote industry participation in independent benchmarking initiatives by asking applicants to consider working with initiatives such as YCharOS or equivalent open-science consortia as part of their work. This could be achieved either by including the recommendation in the guidance notes or flagging to applicants at the panel review stage. | 7.0 | 6.0 | Effective, Uncertain Feasibility R23 | A coordinated, shared infrastructure should be developed (or expanded) to: Aggregate antibody validation data, Provide standardised formats and tools for sharing, Enable journals, funders, and institutions to access validation status or red flags. This may involve expanding existing platforms such as YCharOS, or building interoperability across initiatives like RRID, Benchsci, CiteAb, and Antibody Registry. | 8.0 | 6.0 | Effective, Uncertain Feasibility A2 | Detailed Protocols and Validation data should be included in the manuscript or supplemental data. | 8.0 | 6.0 | Effective, Uncertain Feasibility A3 | Journals should appoint a specialist editor that focuses on reproducibility. | 7.0 | 5.5 | Effective, Uncertain Feasibility A6 | Funders should mandate that if funds are used to develop an antibody as part of a research project, the reagent must be a recombinant antibody and must be made available to the wider community. | 7.0 | 6.0 | Effective, Uncertain Feasibility A8 | Manufacturers should perform a standard set of validation experiments on their reagent antibodies and make the data available. | 8.0 | 6.5 | Effective, Uncertain Feasibility A9 | Manufacturers should shift production to all recombinant antibodies. | 7.0 | 5.0 | Effective, Uncertain Feasibility A10 | A learned society for antibody validation should be established. | 7.0 | 5.0 | Effective, Uncertain Feasibility Uncertain on Both Dimensions A4 | Funders should maintain and provide researchers with a list of manufacturers who validate their antibodies. | 6.0 | 5.0 | Uncertain Both A5 | Funders should mandate the use of recombinant antibodies (or at least monoclonals) when available. Justification should be required if a polyclonal is used. | 6.0 | 5.0* | Uncertain Both

[Table 3] Rejected recommendation—item rated uncertain effectiveness and infeasible, with agreement.

Item | Full Recommendation | Median Effectiveness | Median Feasibility R5 | Journals should introduce a tiered scoring system or transparency badge to recognise papers that meet higher standards of antibody reporting and validation. | 4.0 | 3.0

[Fig 2] Effectiveness and feasibility ratings for all 33 proposed actions.Each point represents one proposed action, plotted by median effectiveness (x-axis) and median feasibility (y-axis) ratings on 9-point scales. Points are colour-coded by stakeholder group. Dashed lines indicate the boundary between ratings classified as effective/feasible (median ≥7) and uncertain (median 4–6), following our RAND/UCLA round-down rule for boundary medians of 6.5. The upper-right quadrant contains items rated as both effective and feasible with agreement (15 items). The lower-right quadrant contains items rated as effective but with uncertain feasibility (15 items). Item with panel disagreement (A5, IPR > IPRAS) is shown as open circle with dashed borders; all other items achieved agreement. One item (R5) was rejected by the panel (uncertain effectiveness, infeasible). Points at identical coordinates have been slightly offset for readability.

Consistent with the criteria set out in advance for progressing between rounds, the research team reviewed the Round 2 results to determine whether a further round was warranted. This decision was based on three convergent factors. First, a high proportion of items (15 of 33, 45.5%) had achieved clear consensus as both effective and feasible across the two rounds. Second, disagreement remained minimal, with disagreement being present on only one item in Round 2. Third, rating stability between rounds was high: among the 12 items re-rated in Round 2, median effectiveness ratings remained identical for 9 items (75.0%) and median feasibility ratings remained identical for 10 items (83.3%), with no item shifting by more than 1 point on either scale. The minimal rating changes between rounds indicated that further iteration would be unlikely to resolve the persistent pattern of items rated as effective but with uncertain feasibility. Full quantitative results for all items across both rounds are presented in and, with complete qualitative commentary provided in and.

Discussion

This Delphi study engaged 32 international experts from across the research ecosystem to develop a formally prioritised, consensus-based set of recommendations for addressing antibody validation failures in biomedical research. The panel achieved consensus on 15 items as both effective and feasible for implementation by 2030, spanning institutional, funder, manufacturer, and publisher actions as well as cross-stakeholder coordination. An additional 15 items were rated as effective but with uncertain feasibility, reflecting a consistent pattern in which experts were confident that proposed interventions would improve antibody practices but less certain about realistic implementation within the target timeframe. These findings complement existing technical solutions [[1]], data-sharing initiatives [[17],[18]], and emerging stakeholder coordination efforts [[12],[18]]. They represent the prioritised judgement of a single, predefined expert panel convened across the relevant areas of expertise, rather than a definitive or globally generalisable standard, and are offered to inform stakeholder decisions. Because feasibility in particular depends on local policy, infrastructure and funding environments, readers working in differently configured research systems are best served by treating the recommendations as expert-prioritised options to weigh against their own conditions rather than as conclusions established for their setting.

Barriers to implementation

From the panel’s free-text comments, we interpret four interconnected barriers that help explain both the persistence of the antibody validation problem and the challenges of implementing solutions. These are offered as our interpretation of the qualitative material rather than as formally coded findings.

First, diffuse ownership enables collective inaction. Antibody validation failures impose costs across the research system—wasted funding, unreliable literature, consumed biological materials—but no single stakeholder bears the full cost or has authority to mandate change unilaterally. Researchers lack incentives to validate when publication and funding systems do not reward the practice; publishers hesitate to impose requirements that could divert submissions to less demanding competitors; funders may view validation as outside their mandate; and manufacturers competing on price and catalogue breadth face limited incentives to invest in validation when researchers continue to purchase on the basis of previous use and supplier reputation [[14]]. Panel members described a pattern of mutual deflection, with responsibility for validation perceived as belonging to other stakeholder groups rather than one’s own (, R22). Several characterised the broader landscape as too fragmented for meaningful alignment to feel achievable. However, the panel’s endorsement of a shared roadmap (R22) and the interdependencies across recommendations point toward a countervailing dynamic: participants noted that journal requirements would create de facto mandates for manufacturers, that researcher demand would shift manufacturer behaviour, and that funder investment in infrastructure would enable practices that other stakeholders could then require.

Second, market dynamics create structural tensions. The research antibody market operates predominantly under a Research Use Only model that prioritises catalogue breadth and low unit cost over rigorous validation. Manufacturer representatives acknowledged significant economic barriers to comprehensive validation, describing the costs as prohibitive and warning that extensive validation programmes would reduce revenue without a corresponding market incentive (, A8). At the same time, one manufacturer described their company’s ongoing commitment to validation, illustrating that the barrier is structural rather than universal. Yet the apparent low cost of inadequately validated antibodies is illusory when downstream costs are considered: failed experiments, repeated purchasing, wasted samples, and unreliable findings impose far greater costs on the research system than better validation would. Creating market conditions in which quality is rewarded—through purchasing decisions informed by independent characterisation data, journal requirements that make validation data visible, and funder signals that prioritise quality—is necessary to shift the equilibrium.

Third, resource justification challenges persist despite clear return on investment. Participants noted competing demands on limited resources, with some questioning whether funders would prioritise antibody validation given the breadth of competing demands on their budgets, even while acknowledging the value of the proposed actions (, A10). The economic case is compelling at a system level—the estimated $1 billion annually in direct waste in the US alone [[12],[13]], plus far greater opportunity costs—but the return is diffuse and difficult for individual stakeholders to capture. A funder investing in validation infrastructure benefits the entire community, not just its own grantees; an institution investing in training produces graduates who carry improved practices elsewhere. The benefits are real but distributed across time and across the ecosystem, and compounded by the difficulty of demonstrating negative outcomes that were prevented.

Fourth, coordination challenges reflect the scale of the problem. With more than 22,000 human protein targets, millions of antibody products, and a global research community, the coordination challenge is immense. No single authority can mandate standards, and voluntary adoption requires alignment across actors with different incentives, timescales, and resources. The panel’s findings suggest, however, that complete alignment is not a prerequisite for progress. The 15 consensus recommendations represent actions individual stakeholders can implement without waiting for others, and participants emphasised sequencing over simultaneity: several argued that funders must first act as enablers—establishing resources, infrastructure, and training—before mandates can be effective (, A5), while others suggested that even imperfect initial standards would allow journals to demonstrate a credible commitment to improving antibody reporting practices (, A1).

These barriers are consistent with recent multi-stakeholder commentaries. Kahn and colleagues [[12]] noted that the origins of the antibody crisis are ‘many and varied’ and that ‘it is unrealistic to expect a complete solution in the near future’, while emphasising that significant progress is achievable through concerted action. In earlier work by members of this study team [[18]], we argued that behavioural and cultural change could be supported by engaging stakeholders at every level using established frameworks for research culture change. The Delphi findings complement these analyses by providing quantitative evidence of where expert agreement is strongest and where feasibility barriers remain.

Next steps and implementation

The findings lend themselves to a programme of targeted stakeholder consultation. To support this, we have prepared separate documents for each stakeholder group—publishers, funders, institutions, and manufacturers—presenting consensus recommendations alongside implementation options derived from the panel’s qualitative feedback (–). These present options rather than prescriptions, recognising that the optimal approach will vary across organisations, countries, and contexts. Items rated as effective with uncertain feasibility are not dismissed but treated as implementation challenges to be addressed through consultation. Interdependencies across stakeholder groups are made explicit throughout, supporting coordinated rather than siloed implementation.

Several elements of the recommended infrastructure already exist. The NC3Rs Antibody Champions programme provides a model for the local champions recommendation (R21) and delivers masterclasses for doctoral training aligned with R19. The OGA Academy offers structured online training [[18]], the OGA Antibody Database provides searchable characterisation data, and YCharOS continues to expand its open characterisation pipeline [[1]]. The eLife stakeholder roadmap [[12]] proposed actions that align closely with many consensus items, and the RRID initiative [[17]] provides identification infrastructure on which several publisher-focussed recommendations depend. These resources demonstrate that the recommendations can be implemented by building on proven approaches. Notably, several recommendations are interdependent: shared infrastructure for aggregating validation data (R23) would support automated manuscript screening (R7), informed grant review (R10), and evidence-based purchasing decisions, suggesting that investment in infrastructure could unlock a cascade of downstream improvements across stakeholder groups. As an early test of how these coordinating mechanisms might work in practice, a networked antibody champions model — embedding trained local leads within institutions to deliver training and support reagent-selection decisions — is currently being piloted across UK institutions, providing an initial operationalisation of the local-champion and roadmap recommendations (R21, R22).

To illustrate how the recommendations translate into practice, consider the consensus item asking journals to establish clear standards for antibody validation and reporting that authors must meet (A1). The consensus wording is deliberately non-prescriptive, but the panel’s qualitative feedback and the accompanying publisher consultation document () indicate what such standards could contain. A minimal baseline would adopt the validation and reporting language already present in the MDAR framework [[16]], which covers RRIDs, vendor, catalogue number, clone or lot number, and dilution. Journals could strengthen this by asking authors to indicate which IWGAV-aligned evidence [[15]] supports each antibody-dependent conclusion, and by requiring authors to state explicitly where context-specific validation is absent. Journals could also ask authors to supply the validation data underpinning antibody-dependent conclusions, either within the manuscript or its supplementary material or by deposition in an open repository (R4, A2). Where generating new validation data is impractical, authors could be permitted to cite previously published validation evidence obtained under closely matched experimental conditions, or to reference independent characterisation resources such as the knockout-controlled datasets generated by YCharOS [[1]] and aggregated in the OGA Antibody Database.

Limitations

Several limitations should be considered. Participants were identified through existing professional networks, and the panel may therefore share perspectives that do not fully represent the diversity of views across the global research community. The panel was weighted toward the United Kingdom and United States, and the biomedical research ecosystem in other countries may face different challenges. Some sectors (particularly research funders, n = 4) had few representatives, meaning feasibility assessments for funder-directed recommendations may not capture the full diversity of perspectives. The high retention rate (93.8%) and low disagreement suggest a relatively cohesive panel, which may underestimate the range of views from a broader consultation.

The Delphi method captures expert opinion at a specific point in time and does not constitute empirical evidence of effectiveness. The consensus recommendations represent the panel’s assessment of what is likely to be effective and feasible, but actual effectiveness will depend on implementation context. The panel reached consensus after two rounds, which limited iterative refinement where uncertainty persisted. However, rating stability between rounds suggests that additional rounds would have been unlikely to shift results substantially. The qualitative data were synthesised narratively rather than through formal thematic analysis, consistent with RAND/UCLA practice where free-text comments inform item interpretation rather than serving as independent qualitative data. Full qualitative commentary is provided in and.

Conclusions

This Delphi study provides cross-stakeholder feasibility ratings with an explicit implementation horizon of 2030 and also offers prioritisation across sectors. The rating of ‘effective but uncertain feasibility’ that characterises many of the proposed actions should not be interpreted as reason for inaction. It reflects an honest assessment that the pace and sequencing of change matter as much as the direction. The 15 consensus recommendations provide an evidence-based starting point for immediate action; the remaining items rated as effective represent a second wave whose feasibility is likely to increase as early implementation builds infrastructure, shifts incentives, and establishes new norms. This staged approach—enablement to expectation to mandate—was consistently endorsed by the panel and is reflected in the consultation documents (–). Together with existing technical and community initiatives, these findings provide a structured basis for coordinated multi-stakeholder action to address a persistent and costly source of unreliability in biomedical research. Despite the challenges outlined above, this study represents an important step toward building the cross-sector alignment necessary to improve antibody validation standards and research reproducibility over time.