Marta Massano

Methods Validation App (MVA) is a desktop application designed to streamline methods validation in regulated laboratories. MVA is developed using Python’s powerful libraries for data handling and statistical analysis. It provides a user-friendly interface that enables analysts to efficiently evaluate key validation parameters. By automating critical processes such as calibration, limit of detection, precision, and accuracy, MVA minimizes human error, standardizes validation protocols, and accelerates evaluation. This tool is designed to guarantee adherence to regulatory requirements while concomitantly enhancing accessibility for individuals with and without expertise in analytical methods validation. As analytical techniques evolve, MVA assist laboratories in sustaining rigorous and dependable validation workflows. MVA is freely available at https://mva.databloom.eu.

Methods Validation App (MVA) is a desktop application designed to streamline methods validation in regulated laboratories. MVA is developed using Python’s powerful libraries for data handling and statistical analysis. It provides a user-friendly interface that enables analysts to efficiently evaluate key validation parameters. By automating critical processes such as calibration, limit of detection, precision, and accuracy, MVA minimizes human error, standardizes validation protocols, and accelerates evaluation. This tool is designed to guarantee adherence to regulatory requirements while concomitantly enhancing accessibility for individuals with and without expertise in analytical methods validation. As analytical techniques evolve, MVA assist laboratories in sustaining rigorous and dependable validation workflows. MVA is freely available at https://mva.databloom.eu.

Background: Bipolar Disorder (BD) is a complex psychiatric condition characterized by alternating episodes of mania and depression. Diagnosis relies exclusively on clinical evaluation, frequently resulting in misdiagnosis with other psychiatric disorders due to overlapping symptomatology. This diagnostic uncertainty necessitates the development of objective laboratory-based tools to complement clinical assessment. The absence of reliable biomarkers represents a critical gap that hinders accurate diagnosis and timely therapeutic intervention. This study addresses the need for objective metabolic signatures capable of distinguishing BD patients from healthy individuals through urinary metabolomics profiling.Results: Ultra High-Performance Liquid Chromatography coupled to High Resolution Mass Spectrometry (UHPLC-HRMS) was employed to analyze urine samples from 20 BD patients and 20 healthy controls in both positive and negative ionization modes. A rigorous untargeted metabolomics workflow incorporating blanks, internal standards, and pooled quality control samples ensured high data quality and reproducibility throughout the analytical sequence. Features were extracted and putatively annotated using Compound Discoverer® v3.3 software, followed by implementation of a structured filtering pipeline designed to address high-dimensional data and potential confounding effects from pharmacological treatments. Exploratory principal component analysis revealed clear group separation, while partial least squares discriminant analysis achieved excellent classification performance with accuracy exceeding 90%. The retained metabolic features demonstrated robust discriminative power, successfully distinguishing BD patients from healthy controls based on characteristic urinary metabolic profiles.Significance: This research demonstrates that a characteristic urinary metabolic fingerprint distinguishes BD patients from healthy individuals with high accuracy. The validated analytical and chemometric workflow provides a foundation for developing objective laboratory-based diagnostic tools that could complement clinical evaluation, potentially reducing misdiagnosis rates. These findings represent a significant step toward translating metabolomics research into clinically actionable biomarker strategies for psychiatric disorders.

Methoxpropamine (MXPr) is an arylcyclohexylamine dissociative drug structurally similar to 3‐methoxyeticyclidine, ketamine, and deschloroketamine, recently appeared in the European illegal market, and was classified within the new psychoactive substances (NPS). Our study investigated the metabolism of MXPr to elucidate the distribution of the parent drug and its metabolites in body fluids and fur of 16 mice. After the intraperitoneal administration of MXPr (1, 3, and 10 mg/kg), urine samples from eight male and eight female mice were collected every hour for six consecutive hours and then at 12‐ to 24‐h intervals. Additionally, plasma samples were collected 24 h after MXPr (1 and 3 mg/kg) administration. Urine and plasma were diluted 1:3 with acetonitrile/methanol (95:5) and directly injected into the UHPLC‐QTOF‐HRMS system. The phase‐I and phase‐II metabolites were preliminarily identified by means of the fragmentation patterns and the exact masses of both their precursor and fragment ions. Lastly, the mice fur was analyzed following an extraction procedure specific for the keratin matrix. Desmethyl‐MXPr‐glucoronide was identified in urine as the main metabolite, detected up to 24 h after administration. The presence of norMXPr in urine, plasma, and fur was also relevant, following a N‐dealkylation process of the parent drug. Other metabolites that were identified in fur and plasma included desmethyl‐MXPr and dihydro‐MXPr. Knowledge of the MXPr metabolites evolution is likely to support their introduction as target compounds in NPS toxicological screening analysis on real samples, both to confirm intake and extend the detection window of the dissociative drug MXPr in the biological matrices.

New psychoactive substances (NPS) have been introduced into the market in recent years, with new analytes reported every year. The use of these substances in women can occur at any stage of life, even in the childbearing age. Drug use during pregnancy presents significant risks for the mother and the fetus, so it is important to have tools that allow to detect prenatal exposure to these substances of abuse. Therefore, an analytical method for the determination of 137 NPS and other drugs of abuse in meconium by UHPLC-QTOF was developed and validated for semi-quantitative purpose. Linearity range, limit of detection (LOD), precision, matrix effect, selectivity, and specificity were evaluated. For all analytes, the calibration curves were studied in the ranges between 2, 10, or 50 ng/g and 750 or 1000 ng/g, (depending on the analyte) and the LOD ranged between 0.04 and 2.4 ng/g. The method was applied to 30 meconium specimens from cases in which fentanyl had been administered as epidural anesthesia at the time of delivery or cases in which the maternal hair was positive to other drug of abuse. Four meconium samples tested positive for fentanyl (range concentration = 440–750 ng/g) and two samples tested positive to acetylfentanyl (range concentration = 190–1400 ng/g).