Considerations for Transitioning to Multi-Column Chromatography
Multi-column chromatography (MCC) can help scientists and manufacturers improve step efficiency while achieving their desired critical quality attributes (CQAs).
This article is posted on our Science Snippets Blog
It’s easy to change from single column chromatography (SCC) to multi column chromatography (MCC) because all of your critical process parameters (CPP) will stay the same. Each CPP has an impact on at least one CQA, and for the most part, your evaluation of the process parameters between MCC and SCC will not change.
That’s because MCC and SCC have the same loading, washing, elution, cleaning, and equilibration steps. Your molecule of interest will still experience the same chromatography process. In contrast to cycling a larger, single column in batch mode several times to increase resin utilization and productivity, MCC uses several columns simultaneously to execute the same chromatographic steps.
This means that instead of having one location (single column) where chromatographic steps are executed sequentially, you have several locations (multiple columns) where multiple chromatographic steps are executed at the same time. This way, you can place two columns in the loading step and improve capture by controlled overloading and subsequent break through capturing on the second column. Using multiple columns helps you ensure that each column in the loading zone achieves a higher resin utilization (>90% of static binding capacity) compared to a classic batch column (<70% of static binding capacity) – increasing your specific resin utilization and process productivity.
The result is that running your process steps in parallel does not change the process parameters impacting your product quality attributes – i.e., the CPPs stay the same. In some cases, you’ll need to adapt certain ranges or limits of critical and non-critical process parameters to account for higher loading densities, shorter contact times, or higher cycling numbers. Using these adapted limits and ranges, the resulting process intermediate will stay fully within the specifications you are expecting from SCC. With this method, you can keep all product CQA in their specified limits or ranges, and optimize the drug product’s therapeutic benefit for the patient.
When transitioning from SCC to MCC, it’s important to re-evaluate process parameters using risk assessments, prior scientific knowledge, and experimentation to determine their classification and acceptance ranges or limits.
Some examples of process parameters that we recommend reevaluating for MCC include operating binding capacity, cycle number, contact time, column dimensions, elution volume, and column wash volumes. You can investigate these parameters using your normal process development systems and studies. Additionally, you can perform scale-down proof-of-concept runs using a BioSMB PD system. This system can also support scale up to an MCC process unit like a BioSMB 80 or 350.
Here we’ve outlined an example for changing from a batch to continuous process while still processing the same amount of product in the same timeframe.
The above table shows side-by-side comparisons of a batch process translated to an MCC process on Resolute® BioSMB. This example yields a three-fold increase in productivity along with significant buffer savings. As mentioned, as long as you investigate and verify all core parameters, you can expect the same quality when transitioning from batch to MCC.
Finally, once you’ve investigated your process transfer and process parameters, it’s simple to program your phases with BioSMB control software because it’s designed to make recipe writing and phase execution intuitive. Using the process parameters you’ve defined, you can write complete MCC phases in less than five minutes.
For additional data analytics, you can use BioSMB analysis and reporting software. It conducts classical quantitative chromatogram peak analysis, and you can configurate it to represent cycle-by-cycle and column-by-column data trends throughout the process run. With this feature, you can identify trends in analogue signals over time specific to cycles or columns, compare past and present process runs for consistency, leverage predictive performance , and potentially find sources for a process deviations that occur mid-run.
In conclusion, it’s simple to transfer from a batch to MCC process by understanding the impact that CPPs have on CQA – which largely remain the same. Once you’ve built this understanding, either through assumptions or process development studies, you can streamline your translation and improve your efficiency and productivity with an intensified, next-generation chromatographic process.
Want to learn more about easing the transition between batch and multi-column chromatography? Read our white paper for more tips and details.
