Improved assay development of pharmaceutical modalities using feedback-controlled liquid chromatography optimization

Area of Science:

• Analytical Chemistry
• Biopharmaceutical Analysis
• Chromatography Method Development

Background:

• Developing analytical assays in the biopharmaceutical industry is often time-consuming and relies on manual trial-and-error.
• Existing methods lack efficiency, leading to extended development timelines and increased costs.

Purpose of the Study:

• To present an automated analytical workflow for streamlined method development and optimization.
• To demonstrate the application of an AI-based algorithm in chromatographic method development.
• To reduce manual user intervention and analyst time in assay development.

Main Methods:

• Implemented a feedback-controlled modeling approach for automated chromatographic method development.
• Utilized readily available Liquid Chromatography (LC) instrumentation and software.
• Focused on automatic optimization of mobile phase conditions and system control.

Main Results:

• Achieved streamlined development and optimization of chromatographic methods from start to finish.
• Significantly minimized the time requirement for analysts in method development.
• Demonstrated successful application to challenging multicomponent mixtures including small molecules, peptides, proteins, and vaccine products.

Conclusions:

• AI-based software and modern chromatography instrumentation accelerate the development of new separation assays.
• The automated workflow leads to substantial cost savings, improved method robustness, and faster analytical turnaround.
• This approach is applicable across various biopharmaceutical modalities.