Method-at-a-Glance (MAG) as a Graphical Approach for Method Development in Reversed-Phase Liquid Chromatography of
Mahmoud Hamed1,2, Reham Kannouma3, Alaa Bedair3
1Pharmaceutical Chemistry Department, Faculty of Pharmacy, Misr International University, Km 28 Ismailia Road, Cairo 44971, Egypt.
Analytical Chemistry
|March 23, 2026
Summary
Method-at-a-Glance (MAG) offers a visual framework to streamline reversed-phase high-performance liquid chromatography (RP-HPLC) method development. This approach leverages existing literature to guide rational starting conditions, reducing experimental trial-and-error.
Area of Science:
- Analytical Chemistry
- Chromatography
- Pharmaceutical Analysis
Background:
- Reversed-phase high-performance liquid chromatography (RP-HPLC) is crucial in pharmaceutical and biomedical analysis.
- Current RP-HPLC method development relies heavily on trial-and-error, fragmented knowledge, or complex chemometric tools.
- Existing chromatographic expertise is not easily transferable for rapid, rational decision-making.
Purpose of the Study:
- Introduce Method-at-a-Glance (MAG), a novel graphical framework for RP-HPLC method development.
- Reimagine RP-HPLC method development as a visually guided process using collective chromatographic knowledge.
- Facilitate rapid identification of rational starting conditions and reduce preliminary experimentation.
Main Methods:
- Developed a conceptual, literature-encoded graphical framework (MAG).
- Abstracted and mapped reported chromatographic conditions for structurally related pharmaceutical classes.
- Synthesized existing literature into a transferable visual intelligence tool.
Main Results:
- MAG enables the rapid identification of rational starting conditions for RP-HPLC.
- Significantly reduces the scope of preliminary experimentation in method development.
- Provides a visually guided process grounded in collective chromatographic knowledge.
Conclusions:
- MAG accelerates RP-HPLC method development and improves reproducibility.
- Reduces the experimental burden and supports evolving laboratory needs.
- Highlights opportunities for AI-assisted chromatography and sustainable analytical design.
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