Modeling of Biopharmaceutical Processes—Part 1: Microbial and Mammalian Unit Operations

Process-modeling tools can ensure smooth technology transfer of microbial and mammalian processes from bench to commercial scale.
May 31, 2008


The scale-up of processes from bench to commercial scale is challenging because of uncertainties about the physiochemical environment experienced by the cells at large scale. Process modeling tools can be useful in facilitating the scale-up and technology transfer of microbial and mammalian processes by ensuring that the process requirements can be met by the facility capabilities.

Avecia Biologics Limited
Economics of scale and significant improvements made in the understanding of bioprocesses, design of equipment, and construction of facilities, have driven commercial production of biotech molecules to larger scales in the last decade. Microbial and mammalian commercial processes are routinely performed at 10,000 L or greater amongst the major biotech companies and contract manufacturing organizations. However, the scale-up of processes from bench to commercial scale is challenging, with uncertainties about the physicochemical environment experienced by the cells at large scale. Some of the scale-dependent bioreactor differences are not readily influenced. As such, these differences need to be fully understood to determine the potential impact on culture performance.

This article is the twelfth in the Elements of Biopharmaceutical Production series and focuses on process modeling tools that can be useful in facilitating the scale-up and technology transfer of microbial and mammalian processes. Issues addressed include mixing and gassing characteristics, pressure differences, and heat transfer rates (fermentation). The underlying principles are presented along with examples from existing biotech processes.


Anurag S. Rathore, PhD
Bioreactor mixing is required to deliver process requirements for mass transfer of dissolved oxygen and carbon dioxide (in cell culture), and to aid in convective heat transfer for control of temperature (in fermentation). In addition, mixing strives to achieve bioreactor liquid homogeneity with respect to cell density, gas dispersion, and chemical gradients (pH and nutrients). In the case of mammalian cells, however, excessive mixing by impeller agitation or gas sparging may result in cell damage because of mechanical hydrodynamic shear (e.g., microscale eddies) or interfacial shear by bubble bursting events.1 As a result, a balance between cellular requirements, cellular sensitivities, and bioreactor capability is key to defining a comparable operating space that ensures consistent process performance between scales.

The Impact of Mixing on pH Gradient

Gradients in pH are influenced by bulk mix times, the type and concentration of titrant used (acid or base), pH control strategy, pH probe location, and titrant addition location. To determine the impact of pH gradients on cell culture performance, it is important to understand the magnitude of the pH excursion with regard to deviation from set-point and the excursion duration. Once this is established, the potential impact on process performance can be evaluated and resolved as required. The magnitude and duration of the pH gradients can be measured directly by control simulations in buffer solutions at large scale.2,3 However, this can be challenging with regard to pH probe location and probe response time.

Figure 1. Comparison of transient pH profiles from the wet test and computational fluid dynamics (CFD) simulation
Computational fluid dynamics (CFD) can be used to model pH gradients with bolus titrant additions, and reasonable agreement with wet testing has been observed for base addition at 2,000 L (Figure 1). Initial discrepancies in the magnitude of the pH deviations over the first 22 seconds between wet testing and CFD are attributed to differences in measured and assumed locations for base addition and pH probe placement. Although CFD can be applied to assess bioreactor design capability and process scale-up, care should be taken to assess CFD predictions against the model assumptions and verified with wet testing.