Emerging Markers and Technologies in Urine Sample Validity for Drug Detection: A Brief Review of Detection Methods and Applications

Abstract

Urine sample validity testing is critical in medical diagnostics, forensic investigations, and workplace drug screening to ensure accurate and reliable results. The increasing sophistication of tampering methods, such as adulteration and substitution, necessitates the development of advanced testing techniques. This review explores the latest advancements in urine validity testing, including the integration of machine learning (ML) and artificial intelligence (AI) with high-resolution mass spectrometry (HRMS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). These technologies enhance the detection of novel chemical and biological markers, such as unique metabolite profiles, polyglycole patterns, and DNA methylation markers (e.g., TWIST1 and NID2). The identification of new biomarkers and the application of multi-modal analytical approaches provide comprehensive and sensitive detection of tampering. Future directions in the field focus on refining these technologies, ensuring ethical data handling, and expanding regulatory frameworks to protect privacy and enhance the reliability of testing protocols. The advancements discussed promise significant improvements in the detection and prevention of urine sample tampering, ensuring the integrity of testing processes across various applications.