Automated LC-MS/MS Data Reporting Workflow Using mzML and R for Urine Drug Screening and Quantitative Confirmation

Monitoring medication compliance in mentally disordered offenders is crucial for effective rehabilitation and reducing recidivism. While LC-MS/MS is established as a primary technique for forensic urine drug testing, manual data evaluation using vendor-specific software reduces efficiency and introduces subjectivity. To address this, we developed a fully automated, vendor-neutral data reporting workflow using open-source mzML and R for urine drug screening and quantitative confirmation. The initial screening step automated the detection of 59 psychotropic drugs and metabolites by applying rigorous logic-based criteria, including absolute intensity thresholds, coelution tolerances, and qualifier-to-quantifier ratio limits. The subsequent confirmation process executed quantitative analysis using internal standard normalization and weighted linear regression for quetiapine and its primary active metabolite, norquetiapine. Following validation using forensic urine samples from probationers, the automated system demonstrated robust analytical reliability, successfully meeting established international bioanalytical guidelines. By ensuring compatibility across different platforms, this standardized approach enhanced laboratory throughput, overcame vendor-dependent limitations, and fortified evidentiary integrity. Furthermore, this open-source workflow demonstrates high versatility, streamlining data evaluation from various instrument vendors and facilitating its broad application in diverse bioanalytical studies.