ArticleScientific reports2026
AI-assisted versus manual sustainability assessment of a high-throughput LC-MS/MS method for psychotropic and OTC drugs of abuse in human plasma.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
A rapid, sensitive, and selective LC-MSMS method was developed and validated for the quantification of dextromethorphan (DXM), pseudoephedrine (PSE), olanzapine (OLA), and fluoxetine (FLU) in human plasma. Mixture 1 (DXM/PSE) and mixture 2 (OLA/FLU) are fixed-dose combinations commonly misused at high doses for their euphoric effects. The proposed method employed a simple protein precipitation technique for sample preparation, using a cost-effective cross-over internal standard strategy; OLA for mixture 1 and DXM for mixture 2. Chromatographic separation was achieved on a Hypersil GOLD column (100 × 3 mm, 1.9 µm) using an isocratic mobile phase consisting of acetonitrile and 0.1% formic acid (70:30, v/v) at a flow rate of 0.3 mL/min. The short runtime of 2.5 min enables high-throughput analysis. Detection was performed in positive ionization mode using multiple reaction monitoring (MRM). The method exhibited linearity over concentration ranges of 0.05-25.0 ng/mL for DXM, 2.0-1000.0 ng/mL for PSE, 0.2-20.0 ng/mL for OLA, and 0.5-50.0 ng/mL for FLU with lower limits of quantification (LLOQs) of 0.05, 2.0, 0.2, and 0.5 ng/mL, respectively. The method was successfully validated in accordance with FDA and ICH bioanalytical method validation guidelines, demonstrating satisfactory selectivity, accuracy, and precision. The validated method demonstrated high extraction recovery (> 90%), limited, reproducible matrix effects (IS-normalized matrix factor CV ≤ 15%). This study represents a novel application of artificial intelligence (AI)-assisted evaluation, utilizing a universally accessible model to assess the greenness and whiteness of the proposed LC-MS/MS method through the Auto-AGREE and Auto-RGB 12 frameworks. The AI-generated assessments demonstrated high agreement with traditional metrics, highlighting the potential of AI tools to provide rapid and objective holistic sustainability evaluations for the global analytical community.
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