Evidence map›Paper›PMID 35091761›Full record

ArticleAnalytical and bioanalytical chemistry2022

Automated liquid handling robot for rapid lateral flow assay development.

Caitlin E Anderson, Toan Huynh, David J Gasperino, Luis F Alonzo, Jason L Cantera, Stephen P Harston, Helen V Hsieh, Rosemichelle Marzan, Shawn K McGuire, John R Williford and 7 more

Abstract readValidation Study
In one paragraph

Article in Analytical and bioanalytical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Caitlin E Anderson *Global Health Labs, Bellevue, WA, USA. caitlin.anderson@ghlabs.org.
Toan Huynh *Center for In Vitro Diagnostics, Intellectual Ventures, Bellevue, WA, USA.
David J GasperinoCenter for In Vitro Diagnostics, Intellectual Ventures, Bellevue, WA, USA.
Luis F AlonzoGlobal Health Labs, Bellevue, WA, USA.
Jason L CanteraPATH, Seattle, WA, USA.
Stephen P HarstonCenter for In Vitro Diagnostics, Intellectual Ventures, Bellevue, WA, USA.
Helen V HsiehCenter for In Vitro Diagnostics, Intellectual Ventures, Bellevue, WA, USA.
Rosemichelle MarzanCenter for In Vitro Diagnostics, Intellectual Ventures, Bellevue, WA, USA.
Shawn K McGuireCenter for In Vitro Diagnostics, Intellectual Ventures, Bellevue, WA, USA.
John R WillifordCenter for In Vitro Diagnostics, Intellectual Ventures, Bellevue, WA, USA.
Ciela I OncinaCenter for In Vitro Diagnostics, Intellectual Ventures, Bellevue, WA, USA.
Veronika A GlukhovaGlobal Health Labs, Bellevue, WA, USA.
Joshua D BishopGlobal Health Labs, Bellevue, WA, USA.
David M CateGlobal Health Labs, Bellevue, WA, USA.
Benjamin D GrantGlobal Health Labs, Bellevue, WA, USA.
Kevin P NicholsGlobal Health Labs, Bellevue, WA, USA.
Bernhard H WeiglGlobal Health Labs, Bellevue, WA, USA. bernhard.weigl@ghlabs.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The lateral flow assay (LFA) is one of the most popular technologies on the point-of-care diagnostics market due to its low cost and ease of use, with applications ranging from pregnancy to environmental toxins to infectious disease. While the use of these tests is relatively straightforward, significant development time and effort are required to create tests that are both sensitive and specific. Workflows to guide the LFA development process exist but moving from target selection to an LFA that is ready for field testing can be labor intensive, resource heavy, and time consuming. To reduce the cost and the duration of the LFA development process, we introduce a novel development platform centered on the flexibility, speed, and throughput of an automated robotic liquid handling system. The system comprises LFA-specific hardware and software that enable large optimization experiments with discrete and continuous variables such as antibody pair selection or reagent concentration. Initial validation of the platform was demonstrated during development of a malaria LFA but was readily expanded to encompass development of SARS-CoV-2 and Mycobacterium tuberculosis LFAs. The validity of the platform, where optimization experiments are run directly on LFAs rather than in solution, was based on a direct comparison between the robotic system and a more traditional ELISA-like method. By minimizing hands-on time, maximizing experiment size, and enabling improved reproducibility, the robotic system improved the quality and quantity of LFA assay development efforts.

Indexed as

Point-of-Care TestingCOVID-19COVID-19 Serological TestingEquipment DesignHumansImmunoassayMalariaMycobacterium tuberculosisPlasmodiumReproducibility of ResultsSARS-CoV-2Sensitivity and SpecificityTime FactorsTuberculosisHigh-throughput screeningLateral flow assayOptimization

Identifiers

PMID35091761
PMCPMC8799445

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.