ReviewBiomedical chromatography : BMC2026
Analytical Quality by Design-Based Reverse-Phase High-Performance Liquid Chromatography Method Development for Biomarker: A Comprehensive Review of Quality Assessment and Analytical Trends.
Review in Biomedical chromatography : BMC, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
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Abstract
Biomarkers play a crucial role in disease diagnosis and therapeutic monitoring, necessitating the development of reliable and robust analytical methods. Reverse-phase high-performance liquid chromatography (RP-HPLC) is widely employed for biomarker analysis due to its versatility, sensitivity and reproducibility. However, conventional method development approaches are based on trial-and-error strategies, leading to limited robustness and poor reproducibility. The Analytical Quality by Design (AQbD) framework has emerged as a systematic approach to overcome these limitations. This review critically evaluates reported RP-HPLC methods for biomarker analysis in the context of AQbD principles. Key elements, including Analytical Target Profile (ATP), Critical Quality Attributes (CQAs), Critical Method Parameters (CMPs), risk assessment, Design of Experiments (DoE) and Method Operable Design Region (MODR), are discussed. Analysis of selected studies indicates that most lack comprehensive AQbD implementation, particularly in terms of multivariate optimization, structured risk assessment and design space establishment. Common limitations such as coelution, sensitivity and inadequate robustness are highlighted, along with AQbD-based strategies to address these challenges. Additionally, recent trends, including the integration of machine learning and the adoption of Green and White Analytical Chemistry principles, are discussed to promote sustainable method development. Overall, AQbD offers a robust framework for improving analytical performance for biomarker analysis.
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