Automated platform buffer screening for multiple proteins on Big Tuna
Applications | 2019 | Unchained LabsInstrumentation
The selection of an optimal buffer system is vital to ensure the chemical, conformational, and colloidal stability of therapeutic proteins. Manual screening of buffer components, pH, ionic strength, and excipients is laborious and time-consuming, often limiting the speed of biologics development. Automated high-throughput screening can accelerate formulation design and reduce variability in sample handling.
This study demonstrates the use of the Big Tuna automated platform to perform a platform buffer screen on four monoclonal antibodies. Each antibody was evaluated in six buffer formulations based on a histidine pH 6.0 base with five different excipients. The goal was to complete buffer exchange to 96% target exchange and concentrate each formulation fivefold in a single experiment.
The platform employed Unfilter 24 filter plates and pressure-based ultrafiltration/diafiltration cycles to exchange buffers and concentrate samples. The protocol targeted 96% exchange with 66% volume removal per cycle and a final volume of 1.6 mL at 50 mg/mL. Cycle duration was adjusted in real time to maintain uniform flow and prevent membrane fouling. Protein concentrations were measured by A280 spectroscopy, and stability was assessed by dynamic light scattering.
The automated workflow achieved an average buffer exchange efficiency of 99.0%, ranging from 96.9% to >99.9% across all wells. Exchange cycles averaged 27 minutes, with a total exchange time of ~8.5 hours. Subsequent fivefold concentration required three cycles over ~1.5 hours. Final protein recoveries matched target concentrations (mean 50 mg/mL), and DLS analysis showed no significant changes in size or aggregation after processing.
The Big Tuna platform streamlines buffer screening by automating exchange and concentration steps, minimizing hands-on time and variability. It supports up to 96 samples per run and adapts pressure cycles in real time to accommodate different proteins and formulations. This approach enhances the throughput of formulation development and quality control workflows in biologics research.
Emerging opportunities include integration of inline analytical sensors for real-time quality assessment, expansion to microfluidic formats for ultra-low volumes, and application of machine learning algorithms to predict optimal buffer formulations. Such advances will further accelerate biologics development and reduce resource demands.
The Big Tuna automated system provides a robust, high-throughput solution for platform buffer screening and concentration of therapeutic proteins. It delivers consistent exchange efficiency, high protein recovery, and preserves sample stability, enabling rapid formulation optimization in biologics pipelines.
Sample Preparation
IndustriesProteomics
ManufacturerUnchained Labs
Summary
Importance of Platform Buffer Screening in Protein Formulation
The selection of an optimal buffer system is vital to ensure the chemical, conformational, and colloidal stability of therapeutic proteins. Manual screening of buffer components, pH, ionic strength, and excipients is laborious and time-consuming, often limiting the speed of biologics development. Automated high-throughput screening can accelerate formulation design and reduce variability in sample handling.
Study Objectives and Overview
This study demonstrates the use of the Big Tuna automated platform to perform a platform buffer screen on four monoclonal antibodies. Each antibody was evaluated in six buffer formulations based on a histidine pH 6.0 base with five different excipients. The goal was to complete buffer exchange to 96% target exchange and concentrate each formulation fivefold in a single experiment.
Methodology and Instrumentation
The platform employed Unfilter 24 filter plates and pressure-based ultrafiltration/diafiltration cycles to exchange buffers and concentrate samples. The protocol targeted 96% exchange with 66% volume removal per cycle and a final volume of 1.6 mL at 50 mg/mL. Cycle duration was adjusted in real time to maintain uniform flow and prevent membrane fouling. Protein concentrations were measured by A280 spectroscopy, and stability was assessed by dynamic light scattering.
Instrumentation Used
- Big Tuna automated buffer exchange system with Unfilter 24 plates
- Lunatic spectrophotometer for protein concentration (A280)
- Uncle analyzer for dynamic light scattering (DLS) measurements
Main Results and Discussion
The automated workflow achieved an average buffer exchange efficiency of 99.0%, ranging from 96.9% to >99.9% across all wells. Exchange cycles averaged 27 minutes, with a total exchange time of ~8.5 hours. Subsequent fivefold concentration required three cycles over ~1.5 hours. Final protein recoveries matched target concentrations (mean 50 mg/mL), and DLS analysis showed no significant changes in size or aggregation after processing.
Benefits and Practical Applications
The Big Tuna platform streamlines buffer screening by automating exchange and concentration steps, minimizing hands-on time and variability. It supports up to 96 samples per run and adapts pressure cycles in real time to accommodate different proteins and formulations. This approach enhances the throughput of formulation development and quality control workflows in biologics research.
Future Trends and Potential Applications
Emerging opportunities include integration of inline analytical sensors for real-time quality assessment, expansion to microfluidic formats for ultra-low volumes, and application of machine learning algorithms to predict optimal buffer formulations. Such advances will further accelerate biologics development and reduce resource demands.
Conclusion
The Big Tuna automated system provides a robust, high-throughput solution for platform buffer screening and concentration of therapeutic proteins. It delivers consistent exchange efficiency, high protein recovery, and preserves sample stability, enabling rapid formulation optimization in biologics pipelines.
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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