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Background <p>The increasing complexity of biological matrices and pharmaceutical formulations presents significant challenges to regulated bioanalytical workflows, particularly liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based quantification used in pharmacokinetic, bioequivalence, and therapeutic drug monitoring studies. Matrix-induced ion suppression or enhancement remains a major source of analytical variability. Recent advances in artificial intelligence (AI) and machine learning offer new opportunities for matrix-effect mitigation, signal optimization, and automated analytical decision-making.</p> Objective <p>To critically evaluate the role of AI-integrated bioanalytical workflows in mitigating matrix effects and enhancing analytical performance, with particular emphasis on LC–MS/MS applications, intelligent sample preparation, signal processing, chromatographic optimization, and process analytical technology (PAT).</p> Methods <p>A comprehensive review of recent literature was conducted focusing on AI-assisted bioanalytical methodologies. Studies involving artificial neural networks, support vector machines, random forests, convolutional neural networks, and deep learning models were examined for applications in peak integration, matrix-effect prediction, anomaly detection, chromatographic optimization, and real-time analytical monitoring. Regulatory and validation considerations were assessed in relation to ICH M10, FDA, EMA, and 21 CFR Part 11 requirements.</p> Results <p>AI-assisted analytical systems demonstrated considerable potential to improve matrix-effect prediction, signal interpretation, chromatographic performance, and workflow automation. The greatest benefits were observed in LC–MS/MS workflows, where matrix-induced variability substantially affects ionization efficiency and quantitative accuracy. Machine learning approaches enhanced peak integration, anomaly detection, retention-time prediction, and analytical optimization. However, challenges including model drift, overfitting, limited training datasets, reduced transferability across laboratories, and concerns regarding transparency, auditability, and reproducibility remain significant barriers to implementation.</p> Conclusion <p>AI-integrated bioanalytical platforms offer promising solutions for improving analytical consistency and mitigating matrix effects. Broader adoption will require explainable AI models, standardized benchmarking protocols, robust lifecycle management, and harmonized regulatory frameworks to ensure reliable and compliant implementation in pharmaceutical bioanalysis.</p>

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AI-Integrated LC–MS/MS Bioanalytical Platforms for Matrix-Effect Mitigation: Advances in Real-Time Monitoring, Sample Preparation, and Validation Challenges

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Annasaheb S. Gaikwad,
  • Sonali B. Pawar,
  • Maryada K. Rajguru

摘要

Background

The increasing complexity of biological matrices and pharmaceutical formulations presents significant challenges to regulated bioanalytical workflows, particularly liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based quantification used in pharmacokinetic, bioequivalence, and therapeutic drug monitoring studies. Matrix-induced ion suppression or enhancement remains a major source of analytical variability. Recent advances in artificial intelligence (AI) and machine learning offer new opportunities for matrix-effect mitigation, signal optimization, and automated analytical decision-making.

Objective

To critically evaluate the role of AI-integrated bioanalytical workflows in mitigating matrix effects and enhancing analytical performance, with particular emphasis on LC–MS/MS applications, intelligent sample preparation, signal processing, chromatographic optimization, and process analytical technology (PAT).

Methods

A comprehensive review of recent literature was conducted focusing on AI-assisted bioanalytical methodologies. Studies involving artificial neural networks, support vector machines, random forests, convolutional neural networks, and deep learning models were examined for applications in peak integration, matrix-effect prediction, anomaly detection, chromatographic optimization, and real-time analytical monitoring. Regulatory and validation considerations were assessed in relation to ICH M10, FDA, EMA, and 21 CFR Part 11 requirements.

Results

AI-assisted analytical systems demonstrated considerable potential to improve matrix-effect prediction, signal interpretation, chromatographic performance, and workflow automation. The greatest benefits were observed in LC–MS/MS workflows, where matrix-induced variability substantially affects ionization efficiency and quantitative accuracy. Machine learning approaches enhanced peak integration, anomaly detection, retention-time prediction, and analytical optimization. However, challenges including model drift, overfitting, limited training datasets, reduced transferability across laboratories, and concerns regarding transparency, auditability, and reproducibility remain significant barriers to implementation.

Conclusion

AI-integrated bioanalytical platforms offer promising solutions for improving analytical consistency and mitigating matrix effects. Broader adoption will require explainable AI models, standardized benchmarking protocols, robust lifecycle management, and harmonized regulatory frameworks to ensure reliable and compliant implementation in pharmaceutical bioanalysis.