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PreprintCC BY-NC-NDChemRxiv

Deployability-First Benchmarking of LFIA Innovation: A Framework and COVID-19 Literature Audit

  • Sergey N. Krylov1,2
  • Xingzhi Che1,2
  • Jeffrey Azzi1,2
  • Svetlana M. Krylova1,2
  • Nima Tabatabaei2,3,4
  • Laura Anfossi5,6
  • Ángel Maquieira Catalá7,8
  • Boris B. Dzantiev9,10
  • Anatoly V. Zherdev9,10
  • Michael Glen Becker11,12
  • Nicole Weckman13,14
  • Norberto A. Guzman15
2 more authors
  • Chuanlai Xu16,17
  • Sonu Gandhi18
18 affiliations
  1. 1Department of Chemistry, York University, 4700 Keele Street, Toronto, Ontario, Canada, M3J 1P3
  2. 2Centre for Research on Biomolecular Interactions, York University, 4700 Keele Street, Toronto, Ontario, Canada, M3J 1P3
  3. 3Department of Mechanical Engineering
  4. 4York University
  5. 5Department of Chemistry
  6. 6University of Turin
  7. 7Department of Chemistry and Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico
  8. 8Universitat Politècnica de València
  9. 9Research Center of Biotechnology
  10. 10Russian Academy of Sciences
  11. 11National Microbiology Laboratory
  12. 12Public Health Agency of Canada
  13. 13Institute for Studies in Transdisciplinary Engineering Education & Practice and Department of Chemical Engineering and Applied Chemistry
  14. 14University of Toronto
  15. 15Princeton Biochemicals, Inc.
  16. 16International Joint Research Laboratory for Biointerface and Biodetection, and School of Food Science and Technology
  17. 17Jiangnan University
  18. 18BRIC-National Institute of Animal Biotechnology (NIAB
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Abstract

Lateral flow immunoassays (LFIAs) are operationally simple, enabling low-barrier, decentralized testing. Their reported performance should therefore be interpreted in relation to the deployability of the required workflow. LFIA innovations often seek improved detection performance, commonly expressed as lower limits of detection (LODs), but workflows can differ substantially in burden and testing conditions. To guide experimental planning, interpretation of results, and formulation of performance claims, we developed a deployability-first benchmarking framework. At the planning stage, the intended deployment setting guides selection of a compatible workflow, while the performance question guides the type of evidence needed to address it. The framework distinguishes low-burden, enhanced lowburden, and higher-burden workflows. Once results are available, the implemented workflow, sample context, measured endpoint, LOD definition, and any comparator help determine what conclusions and comparisons are supported and how the claim should be framed. We applied these principles retrospectively to 96 COVID-19 LFIA innovation articles. Nearly two-thirds of workflows supporting the selected primary performance claims were higher-burden, requiring instrumentation, trained users, or laboratory conditions. Direct like-with-like benchmarking was often limited by differences in workflow burden, sample context, endpoint, LOD definition, readout mode, and comparator conditions. We operationalized result interpretation and claim framing as a claim-level checklist. Applied retrospectively to the same 96 selected primary claims, about 40% were supported as written, whereas about 60% required narrowing or reframing. COVID-19 served as a literature test case; the framework and checklist contain no COVID-19-specific criteria and were designed for medical and non-medical LFIA settings.

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Cite

BibTeXRIS
BibTeX
@misc{krylov2026deployabilityfirst,
  title = {{Deployability-First Benchmarking of LFIA Innovation: A Framework and COVID-19 Literature Audit}},
  author = {Sergey N. Krylov and Xingzhi Che and Jeffrey Azzi and Svetlana M. Krylova and Nima Tabatabaei and Laura Anfossi and Ángel Maquieira Catalá and Boris B. Dzantiev and Anatoly V. Zherdev and Michael Glen Becker and Nicole Weckman and Norberto A. Guzman and Chuanlai Xu and Sonu Gandhi},
  howpublished = {ChemRxiv},
  year = {2026},
  month = sep,
  doi = {10.26434/chemrxiv.15008543/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008543/v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - Deployability-First Benchmarking of LFIA Innovation: A Framework and COVID-19 Literature Audit
AU  - Sergey N. Krylov
AU  - Xingzhi Che
AU  - Jeffrey Azzi
AU  - Svetlana M. Krylova
AU  - Nima Tabatabaei
AU  - Laura Anfossi
AU  - Ángel Maquieira Catalá
AU  - Boris B. Dzantiev
AU  - Anatoly V. Zherdev
AU  - Michael Glen Becker
AU  - Nicole Weckman
AU  - Norberto A. Guzman
AU  - Chuanlai Xu
AU  - Sonu Gandhi
PY  - 2026
DA  - 2026/09/09
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15008543/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008543/v1
AB  - Lateral flow immunoassays (LFIAs) are operationally simple, enabling low-barrier, decentralized testing. Their reported performance should therefore be interpreted in relation to the deployability of the required workflow. LFIA innovations often seek improved detection performance, commonly expressed as lower limits of detection (LODs), but workflows can differ substantially in burden and testing conditions. To guide experimental planning, interpretation of results, and formulation of performance claims, we developed a deployability-first benchmarking framework. At the planning stage, the intended deployment setting guides selection of a compatible workflow, while the performance question guides the type of evidence needed to address it. The framework distinguishes low-burden, enhanced lowburden, and higher-burden workflows. Once results are available, the implemented workflow, sample context, measured endpoint, LOD definition, and any comparator help determine what conclusions and comparisons are supported and how the claim should be framed. We applied these principles retrospectively to 96 COVID-19 LFIA innovation articles. Nearly two-thirds of workflows supporting the selected primary performance claims were higher-burden, requiring instrumentation, trained users, or laboratory conditions. Direct like-with-like benchmarking was often limited by differences in workflow burden, sample context, endpoint, LOD definition, readout mode, and comparator conditions. We operationalized result interpretation and claim framing as a claim-level checklist. Applied retrospectively to the same 96 selected primary claims, about 40% were supported as written, whereas about 60% required narrowing or reframing. COVID-19 served as a literature test case; the framework and checklist contain no COVID-19-specific criteria and were designed for medical and non-medical LFIA settings.
ER  -