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

Significance of the Topic

Formulation development for biopharmaceuticals is critical to ensure protein stability, efficacy and safety. Traditional manual workflows for stress testing and analytical characterization are time- and resource-intensive. Automating these processes can boost throughput, improve reproducibility and free scientists for design and interpretation tasks.

Objectives and Study Overview

This study compared a manual workflow versus the automated Unchained Labs Big Kahuna platform for formulation screening and forced-degradation testing of two proprietary protein drug molecules (Molecule 1 and Molecule 2). The goals were to assess whether automation delivers results equivalent to manual methods while increasing efficiency and throughput.

Methodology and Instrumentation

The manual process involved bench-scale preparation (~200 mL) of three formulations of Molecule 1 and four of Molecule 2 in serum vials and tubes, followed by stirring, agitation and heating stresses. Samples were analyzed by visual inspection, UV/Vis (A400, A280), dynamic light scattering (DLS) and size-exclusion chromatography (SEC).

The automated workflow employed a dual Big Kahuna system enclosed in a HEPA low-particulate chamber. Key modules included:

• Liquid handling arms for vial and plate transfers
• Deck stations for heating, cooling, vortex mixing and magnetic stirring
• Integrated UV/Vis plate reader and high-throughput DLS plate reader
• Virtual integration with a UPLC system for SEC via shared software control

Automation executed sample formulation, stressing (adapted agitation rates, vortexing in place of orbital shake), sample transfers to 96-well plates and analytical measurements in one uninterrupted workflow.

Main Results and Discussion

For Molecule 1, both manual and automated methods ranked formulation stability as B > C > A under all stress conditions. DLS monomer content, SEC aggregate levels and A400 turbidity confirmed matching rank orders. Heat transfer in 96-well plates was more efficient than vial heating, supporting microplate use.

For Molecule 2, the rank order from most to least stable was C > B≈B′ > A in both workflows. DLS metrics and SEC aggregate percentages were consistent. UV/Vis turbidity highlighted sensitivity to stirring stress but agreed on relative formulation robustness.

Benefits and Practical Applications

• Comparable data quality between manual and automated approaches
• Throughput increased to screening >300 formulations per day in 96-well format
• Reduced material consumption and manual handling errors
• Streamlined data integration across techniques for rapid decision-making

Future Trends and Opportunities

Further integration of high-throughput biophysical methods, advanced data analytics and machine learning-driven design of experiments will enhance formulation development. Miniaturization in microplate formats, combined with online monitoring tools, promises even greater efficiency and insights.

Conclusion

The Big Kahuna automated platform delivers formulation stability data equivalent to traditional manual workflows, while significantly increasing throughput and reproducibility. Adoption of such end-to-end automation can accelerate biopharmaceutical formulation screening and optimization without compromising data quality.

Reference

No external literature references were provided in the source document.