💡 Pfizer scientists used chromatographic separations to track column performance and predict test results.
⚙️ Models created from analytical data can reduce testing and improve bioprocess analysis across scales.
Introduction:
In the biopharmaceutical industry, the implementation of manufacturing intelligence principles, such as process analytical technologies (PATs) and modeling, lags behind the chemical processing industry. This is due to the red tape involved in implementing new technology in manufacturing and the complexity of the data produced in bioprocessing. Chromatographic separations, in particular, pose a challenge in analyzing and tracking the performance of columns. However, leveraging chromatogram shapes can offer a solution to these challenges.
- Chromatograms can be used to track the performance of columns and compare performance across different scales of bioprocessing.
- Analytical data from chromatograms can be used to create models that predict analytical lab results, reducing the need for extensive testing in manufacturing.
- Using principal component analysis (PCA) and multiple linear regression analysis, variables that impact the removal of impurities from biopharmaceutical products can be identified.
- Comparing chromatograms from small-scale runs to commercial scale runs allows for a more systematic analysis and removes the subjectiveness of visual comparison.
- Leveraging chromatogram shapes can improve process analysis and optimization in bioprocessing.
Conclusion:
Leveraging chromatogram shapes in bioprocessing can provide a more systematic and objective approach to analyzing and optimizing processes. By utilizing analytical data from chromatograms, models can be developed to predict lab results and reduce the need for extensive testing. This advancement in manufacturing intelligence principles can help close the gap between the biopharmaceutical industry and the chemical processing industry in terms of implementing new technologies. Overall, leveraging chromatogram shapes offers potential for improved efficiency and productivity in bioprocessing.
