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<p>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&#xa0;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 (&gt; 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.</p>

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AI-assisted versus manual sustainability assessment of a high-throughput LC–MS/MS method for psychotropic and OTC drugs of abuse in human plasma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Hend Z. Yamani,
  • Khaled Hesham,
  • Shereen M. Tawakkol,
  • Lobna A. Hussein,
  • Nesma M. Fahmy

摘要

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.