How Do People Try to Beat Drugs Test? Effects of Synthetic Urine, Substituted Urine, Diluted Urine, and In Vitro Urinary Adulterants on Drugs of Abuse Testing

Fu, Shanlin

doi:10.1016/b978-0-12-815607-0.00026-5

Cited by 5 publications

(2 citation statements)

References 102 publications

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“…Due to this kind of drug testing's popularity, numerous regulations and executions have been created over the past three decades or more to regulate it, and they have been modified to be appropriate in both clinical and medicolegal frames (Fu, 2019).…”

Section: I-introductionmentioning

confidence: 99%

The impact of several urine adulterants on samples that test positive for Tetrahydrocannabinol using screening tests

Eid,

Ahmad,

Ghareeb

et al. 2023

Zagazig Journal of Forensic Medicine

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Adulteration, a process that involves manipulating a urine specimen with chemical adulterants to obtain a false negative test result. Aim: Assessment of effects of some adulterants on urinary drug testing of tetrahydrocannabinol (THC), comparing effects on two screening methods immunoassay and thin layer chromatography (TLC). Subjects and methods: urine samples first were test positive for THC by GC/MS in the toxicology lab at Beni-Suef University. THC positive urine samples were then adulterated by water, HCL bleach, 5% acetic acid, and Tetrahydrozoline eye drops at different concentrations. Finally urine samples were reevaluated for THC using 2 immunoassays, and TLC also, urinary PH, specific gravity, and creatinine and nitrites were assessed. Results:The PH of urine significantly decreased after being adulterated with 5% acetic acid at concentrations of 10 and 40, highly significant rise in the PH following 40% Tetrahydrozoline, 10% and 40% HCl bleach and after 300% water dilution. Neither 5% acetic acid nor Tetrahydrozoline at concentration 10 had any impact on urine creatinine levels. HCL-based bleach at concentration 40 and twofold water dilution caused the largest, most significant drop in specific gravity. Except for HCL-based bleach, which included nitrates in 100% of cases at a concentration of 40 and in 76.7% of cases at a concentration of 10, no nitrates were detected in urine samples before the addition of adulterants. Overall accuracy is the best for VIVA E immunoassay except in HCL bleach at 40% and water dilution by 300%, TLC is better. These results showed that VIVA E immunoassay overall accuracy is better than other methods comparing to GC/MS as gold standard for THC before adding adulterants. Conclusion: HCl bleach caused the most remarkable changed to urine parameters. VIVA E immunoassay overall accuracy is better than other methods comparing to GC/MS.

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Section: I-introductionmentioning

confidence: 99%

The impact of several urine adulterants on samples that test positive for Tetrahydrocannabinol using screening tests

Eid,

Ahmad,

Ghareeb

et al. 2023

Zagazig Journal of Forensic Medicine

View full text Add to dashboard Cite

show abstract

“…Urine manipulation includes all methods that try to avoid a positive drug test and aim for false negative results. The main techniques include substitution with drug-free or synthetic urine, in vitro and in vivo dilution, and chemical adulteration [3,4]. Next to common household products, also eye drops, alcohols, acids, bases, and other chemicals such as pyridinium chlorochromate (PCC), potassium nitrite (KNO 2 ), hydrogen peroxide (H 2 O 2 ), iodine (I 2 ), and sodium hypochlorite (NaOCl) are used [5].…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry

Streun

Steuer

Ebert

et al. 2021

Clinical Chemistry and Laboratory Medicine (CCLM)

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Objectives Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model. Methods Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach. Results Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted. Conclusions With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

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Urine Drug Screening in the Hospital Setting

Lee

Karri

Gupta

et al. 2020

Guide to the Inpatient Pain Consult

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How Do People Try to Beat Drugs Test? Effects of Synthetic Urine, Substituted Urine, Diluted Urine, and In Vitro Urinary Adulterants on Drugs of Abuse Testing

Cited by 5 publications

References 102 publications

The impact of several urine adulterants on samples that test positive for Tetrahydrocannabinol using screening tests

The impact of several urine adulterants on samples that test positive for Tetrahydrocannabinol using screening tests

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry

Urine Drug Screening in the Hospital Setting

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