f(x) prepacked and precalibrated chromatography columns give you faster and more reliable mechanistic modeling during downstream process development.
- Precalibrated column — supplied with a Results of Analysis that provides you with column-specific data for use in mechanistic modeling workflow. Ensures accurate model parameter values and reduces the time you need for column calibration.
- High-quality prepacked columns — packed with high-productivity BioProcess™ resin by Cytiva experts.
- Lab-scale process development column format — resin packed in Tricorn™ 10/200 column, which is tailored for experiments during process development and for process characterization.
f(x) columns for faster and more reliable mechanistic modeling results
To determine column parameters required for the model, you need to perform column calibration experiments. This takes time and requires expert knowledge to ensure that accurate parameter values are produced. With f(x) columns, your parameter values are instantly available, accurate, and include:Read more
- Bed height (cm)
- Particle size, d50v (µm)
- Total porosity, εtot
- Interstitial porosity, εinterstitial
- Ionic capacity, Λ, column specific (in μmol/mL resin backbone)
f(x) columns give you confidence about the column parameters used for your model, and free up your time to focus on generating molecule-specific data.
A suitable column format for your process development work
Tricorn™ 10/200 columns are high-quality, prepacked columns for robust and reproducible process development and validation. Because of the narrow 10 mm inner diameter combined with 20 cm bed height typically used in biomanufacturing, these columns are especially suited to scale-down studies.
About mechanistic modeling in chromatography
Mechanistic models use computer simulations to decrease the number of experiments needed during process development, and increase the design space investigated. These simulations are based on known physiochemical phenomena involved in chromatography and offer you the following advantages:
- Cost reduction and time saving — more data obtained from fewer experiments, which accelerates your process development.
- Improved robustness and efficiency to fulfill regulatory demands on quality by design.
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