DryLab and Empower: a successful combination for the optimisation of mAb subunit analysis

The development of liquid chromatography methods for monoclonal antibody characterisation has traditionally relied on trial-and-error approaches, which can be time-consuming and may not deliver optimal performance or robustness. Retention modelling offers a more efficient alternative by enabling chromatographic behaviour to be predicted from a limited set of experiments, without requiring prior knowledge of analyte structures.

In this application note, DryLab is combined with Empower 3 to streamline LC method development for mAb subunit analysis, supporting both RP-LC and HILIC separations. The study demonstrates how in silico optimisation and automation can accelerate method development while ensuring robustness.

Download the full technical application note below

Key highlights of the method

• DryLab retention modelling enabled RP-LC and HILIC method development using only four initial experiments per separation mode
• DryLab prediction accuracy is typically reported as greater than 98% for retention times
• The DryLab–Empower interface automates the creation and transfer of sample sets, method sets and instrument methods between the two platforms
• More than 6500 chromatograms were generated in silico within seconds during RP-LC robustness evaluation
• RP-LC and HILIC methods for nivolumab subunits were developed in less than one week using the retention modelling approach

Supporting your method development strategies

Retention modelling provides a structured approach to chromatographic method development, reducing experimental workload while supporting robust analytical performance. The integration between DryLab and Empower further simplifies data acquisition, processing and optimisation activities.

• Reduces reliance on traditional one-factor-at-a-time development approaches
• Enables in silico evaluation of chromatographic parameters such as gradient conditions, flow rate and column dimensions
• Facilitates optimisation of critical separations, including the resolution of oxidised and non-oxidised Fc/2 subunits
• Supports robustness assessment through extensive virtual testing of method parameters
• Simplifies knowledge management through automatically generated development reports
• Reduces manual intervention through automated exchanges between DryLab and Empower

This approach supports efficient and scientifically driven LC method development workflow.

Applications

• RP-LC separation of monoclonal antibody subunits generated after IdeS digestion and reduction
• Separation of oxidised and non-oxidised Fc/2 subunits using RP-LC
• HILIC analysis of mAb subunits for the separation of glycoforms that co-elute in RP-LC
• Product-specific optimisation of monoclonal antibody characterisation methods
• Robustness assessment and optimisation of protein chromatographic methods through retention modelling