Investigation of Separation Conditions and Prep Purification of GLP-1 Receptor Agonist Semaglutide

Significance of the Topic

GLP-1 receptor agonists such as semaglutide play an important role in the treatment of type 2 diabetes and obesity. Achieving high-purity fractions quickly and reproducibly is critical for pharmaceutical development and manufacturing. Automation and AI-driven optimization streamline chromatographic method development, saving time and reducing expertise requirements.

Objectives and Study Overview

The study demonstrates an end-to-end workflow for preparative purification of semaglutide and its impurities by combining:

• AI-based gradient optimization on analytical HPLC
• Automated scale-up to semi-preparative LC
• Rapid purity confirmation using MALDI-TOF MS

Methodology and Instrumentation

An AI algorithm in LabSolutions MD automatically refined multi-step gradient profiles to meet predefined resolution and elution time criteria for the target peak (semaglutide). Once optimal analytical conditions were found, the method-transfer feature scaled flow rate and column dimensions for preparative fractionation. Collected fractions were then assessed by MALDI-8030 TOF MS to confirm purity.

Used Instrumentation

• LabSolutions MD software for automated gradient optimization and method transfer
• Nexera XR analytical LC system with Shim-pack Scepter C18-120 column (4.6 × 150 mm, 5 µm)
• Nexera Prep preparative LC system with Shim-pack Scepter C18-120 column (20 × 150 mm, 5 µm)
• LCMS-2050 mass spectrometer for peak tracking by molecular weight
• MALDI-8030 benchtop MALDI-TOF MS for rapid purity confirmation

Key Results and Discussion

Initial linear gradients failed to achieve resolution > 2.0 between semaglutide and adjacent impurities. After three AI-driven correction cycles, a multi-step gradient with isocratic holds met the resolution criterion (Rs = 8.0). Scaling up to 20 mL/min preparative flow preserved chromatographic performance, enabling efficient fraction collection. MALDI-TOF analysis of the collected fraction showed a single [M+H]+ peak at m/z 4114.5 with no co-eluting impurities, confirming high purity.

Benefits and Practical Applications

• Significant time savings through AI-driven gradient optimization
• Reduced manual effort in method development and scale-up
• Reliable peak identification by molecular weight tracking
• Rapid purity assessment by MALDI requiring only seconds per sample
• Enhanced reproducibility and throughput in pharmaceutical workflows

Future Trends and Potential Applications

Further integration of AI analytics and advanced MS detection promises fully automated end-to-end workflows. Potential expansions include real-time feedback control, expanded automation for other peptide and protein therapeutics, and broader adoption in QA/QC environments for rapid batch release.

Conclusion

This investigation illustrates a seamless automated workflow for preparative purification of semaglutide, leveraging AI-guided HPLC optimization, method transfer to preparative scale, and MALDI-TOF confirmation. The approach accelerates method development, reduces human error, and ensures high-purity fractions for pharmaceutical R&D and production.

References

No formal literature citations were provided in the source document.