Evidence map›Paper›PMID 42395948›Full record

ArticleACS omega2026

Behind the Mask: Detection and Characterization of Benzalkonium Chloride Adulteration in Immunoassay-Based Urine Drug Testing for Tetrahydrocannabinol (THC).

Éabha Finn, Jennifer Huffman, Kristen J Skogerboe

Abstract read
In one paragraph

Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Éabha FinnDepartment of Chemistry, Seattle University, Seattle, Washington 98122, United States.
Jennifer HuffmanDepartment of Chemistry, Seattle University, Seattle, Washington 98122, United States.
Kristen J SkogerboeDepartment of Chemistry, Seattle University, Seattle, Washington 98122, United States.ORCID https://orcid.org/0009-0009-6805-5685

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urine drug testing for tetrahydrocannabinol (THC) is widely performed using lateral flow immunoassay (LFI) kits. However, these tests are vulnerable to adulteration by benzalkonium chloride (BAK), a cationic surfactant that is a common ingredient in consumer products, such as eyedrops and antiseptics, which can produce false-negative THC results. BAK is believed to act by forming micelles around the THC metabolite 11-nor-9-carboxy-tetrahydrocannabinol (THC-COOH), blocking detection. Because BAK is not routinely included in specimen validity testing panels, it remains a significant potential masking agent in urine drug screening. This study evaluated BAK-containing products and confirmed that readily available commercial formulations can effectively mask THC in the urine. For example, when 25 mL of an antiseptic liquid containing BAK at a nominal concentration of 1300 ppm was added to 75 mL of urine, the LFI test was found to flip from true positive to false negative. Consumer products containing lower concentrations of BAK, such as eye drops, typically mask THC detection when added to urine at an equal volume. To address the vulnerability of LFI-based THC testing to BAK addition, a rapid, low-cost, noninstrumental screening approach based on the standard addition method was developed to distinguish true negatives from BAK-induced false negatives. In blinded testing of 45 urine samples, consisting of 15 true positives, 15 true negatives, and 15 false negatives, the method correctly identified all of the adulterated samples. These findings highlight a practical strategy for improving the reliability of THC urine drug screening in settings where LFI kits are routinely used.

Identifiers

PMID42395948
PMCPMC13325391

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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.