🔬 The approach focuses on minimizing experiments and using predictive models for chromatographic behavior.
💡 By combining high-throughput screening, mathematical modeling, in silico and physical experiments, and process design, the approach speeds up process development.
✅ This approach has shown good predictive results and is suitable for a range of feed concentrations and chromatographic behavior.
🧪 The authors recommend high-throughput screening of resins and building an adsorption database for better results.
⏭️ The hybrid approach eliminates the need for laborious mathematical descriptions and reduces the cost of screening stages.
Introduction:
This article discusses a hybrid high-throughput process development (HTPD) approach that combines digital twins and experimental results. The goal of this approach is to speed up process development and allow for more screenings with fewer experiments. The article focuses on the application of this approach to the capture and first polishing steps for monoclonal antibodies (mAbs).
- The hybrid HTPD approach combines high-throughput screening, mathematical modeling of chromatography steps, in silico column experiments, physical column experiments, and final process design.
- The use of accurate mathematical models allows for faster screening of various operating conditions compared to experimental methods. This reduces the need for extensive screening stages and minimizes material and consumable costs.
- The team used transport-dispersive, solid-film linear driving force, and Langmuir models to describe chromatographic behavior and adsorption equilibrium.
- The hybrid approach showed good predictive results and validated its applicability for a range of feed concentrations and overall chromatographic behavior.
- High-throughput screening of resins is advisable to capture unexpected results and build an adsorption database, and the results showed that certain resins outperformed others.
Conclusion:
The hybrid high-throughput process development approach discussed in this article offers a promising method for speeding up process development and reducing costs for the capture and first polishing steps of monoclonal antibodies. By combining digital twins and experimental results, scientists can perform more screenings with fewer experiments and obtain accurate predictive models for chromatographic behavior.
