Comparison of manual versus automated protein formulation development workflow on a Big Kahuna

Significance of the Topic

Formulation development for biopharmaceutical proteins is a critical yet time-consuming process that directly impacts drug stability and manufacturability.
Increasing regulatory demands require screening a large number of formulation conditions to ensure product robustness.
Automated platforms can accelerate development and improve data consistency while conserving valuable sample material.

Objectives and Study Overview

This study assessed the comparability of manual and automated protein formulation workflows using the Unchained Labs Big Kahuna system.
Two proprietary protein molecules (Molecule 1 and Molecule 2) were prepared in three or four formulations, respectively, then subjected to stress conditions (stirring, shaking or vortexing, and heating).
Both workflows were evaluated by enhanced visual inspection, dynamic light scattering (DLS), UV/Vis spectroscopy and size-exclusion chromatography (SEC).

Methodology

Manual workflow: Formulations were prepared at 200 mL scale, aliquoted into vials or tubes, stressed by magnetic stirring (24 h at 300–500 rpm), rotational agitation (24 h at 35 rpm) or heat (3 h at 48–57 °C), then analyzed individually.
Automated workflow: The dual-arm Big Kahuna system performed formulation dispensing, vortex-based agitation, heating, incubation in 96-well plates, sample dilution/aliquoting and plate transfers without interruption.
Analytical methods were virtually integrated into a shared database to streamline data collection and reporting.

Instrumentation

• Unchained Labs Big Kahuna automated formulation platform with HEPA enclosure
• Incubated plate shaker module
• DynaPro Plate Reader II for dynamic light scattering
• SpectraMax microplate UV/Vis reader
• UPLC system for size-exclusion chromatography

Key Results and Discussion

Both manual and automated workflows produced identical formulation stability rank orders.
Molecule 1: Formulation B was most stable, followed by C and A (B > C > A).
Molecule 2: Formulation C was most stable, followed by B≈B′ and A (C > B≈B′ > A).
Data from DLS, SEC and turbidity (A400) confirmed comparable aggregation profiles across workflows.
Testing in 96-well plates yielded similar trends to vial-based studies, enabling screening of over 300 formulations in a single day.

Benefits and Practical Applications of the Method

• Substantial time savings through automated sample handling and integrated analytics
• Reduced consumption of protein material by miniaturizing to microplate formats
• Improved repeatability and reduced operator variability
• Freed scientists to focus on experimental design, data interpretation and advanced characterization

Future Trends and Utilization Opportunities

Further integration of high-throughput platforms with machine learning to predict optimal excipient combinations.
Expansion to stability screening under additional stressors (e.g., freeze-thaw, light exposure).
Miniaturized continuous-flow and microfluidic formulation screening.
Application in personalized medicine development for rapid formulation of patient-specific biologics.

Conclusion

The Big Kahuna automated workflow delivers formulation stability results equivalent to traditional manual processes while significantly enhancing throughput, conserving material and improving data consistency.
This platform is well suited for accelerating biopharmaceutical formulation development and enabling higher-density screening campaigns.

References

• Burge R., Reynaud F., Jaume C. Comparison of manual versus automated protein formulation development workflow on a Big Kahuna. Unchained Labs Application Note, 2018.