Biopharmaceutical Process Control: Part Two: Process Modeling

Developing a model and using the same control system from development through manufacturing can improve process control

By Michael Boudreau and Gregory McMillan, Emerson Process Management

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Development of a commercial product from a cell culture or a recombinant microbe can be greatly enhanced if the same control system configuration environment, control toolset, historian, HMI, and alarm and event database are used from the bench top through pilot scale to production.

Online process models deployed on a control system common to development and production offer many advantages.

They can:

  • help control process and sensor non-linearities
  • allow for creation and testing of control strategies and batch recipes before clinical material is required if a process is represented by a high fidelity model.

In process development, high fidelity modeling can be used to determine the impact of operating conditions on yield and product quality. In production, on-line multivariate statistical modeling can help detect abnormal conditions and predict quality parameters at the end of the batch.

Where Part 1 of this article focused on developments in sensors for bioprocess control, this article will examine bioprocess modeling options, focusing on three types of models, all of which can run on a distributed control system (DCS):

  • First order plus dead time (FOPDT)
  • High fidelity
  • Multivariate statistics.

Insight into changes during batch operation may be gained through the identification of simple first order plus deadtime (FOPDT) models at different points in the batch. The article will also examine the structure of a high fidelity model, exploring its use in process development and plant design.

In addition, the article will examine how PLS (for Partial Least Squares, or, if you prefer,Projection to Latent Structures) can be used to detect deviations in quality parameters and how Principle Component Analysis (PCA) can be used to determine abnormal conditions.

FOPDT Modeling of Process Response

Application of the latest sensor technologies enables cell characteristics as well as substrate and metabolic byproduct concentrations to be measured in a bioreactor.

Autoclaveable auto-samplers transform automated multifunctional analyzers into at-line instruments. In both at-line and online types, the sample periods are short relative to response times of a cell culture, sothese sensors open the door to closedloop concentration control.

Strategies for control of glucose concentration in perfusion or fed batch bioreactors have been proposed [1] but their implementation has been frustrated by the need for direct and reliable measurement. As far back as the 1980’s, it was suggested that glucose/glutamine ratio control would enhance cell growth, cell viability and product quantity in monoclonal antibody production. [2] The nature of the input and output relationships for new sensor-based control loops can most effectively be characterized by the identification of first order plus dead time (FOPDT) models.

Nonlinear input/output relationships can be characterized in a piecewise linear manner by breaking the control range into linear segments and identifying a unique FOPDT model in each segment. Turbidity as a measure of cell growth is an example of a fixed nonlinearity that can be segmented over the range of the measured variable or signal. FOPDT models of the process gain and dynamics may change over the range of a measured variable. Once this relationship is known, then the tuning of the control may be automatically changed to compensate for the nonlinearity.

Process Development with High Fidelity Models

High fidelity models used for design, control and optimization of cell culture bioreactors describe the relationship between substrate utilization, cell growth and product formation. They are first principle models from a chemical engineering view point because they are based on mass and energy balances and they describe compressible and incompressible phases.

The pH is computed from a charge balance equation. Since high fidelity models are parameterized differential equations and depend on experimental data, they are also considered semi-empirical. In most cases, high fidelity models can extrapolate better than purely empirical models and thus can be used to examine process operation over a wider range.

Cell culture high fidelity models must account for viable and non-viable cells and recombinant microbe models must describe biomass segregation so they can be considered structured models. High fidelity models have been created for recombinant bacteria, fungi, yeast and mammalian cell cultures.

All the models are broken into three phases; a bulk liquid phase, a sparge gas phase and an overhead or sweeping gas phase. On a distributed control system, a practitioner can assemble a high fidelity model from a library of simpler models implemented in composite blocks. A composite block is a container for parameters and other control blocks. At the bottom level of the bioreactor simulations, composite blocks have input and output parameters and calculation blocks. At higher levels in the simulation, composite blocks still contain input and output parameters and nested composite blocks.

Each of the gas and liquid phases in a bioreactor high fidelity model is represented by a higher level composite block. Unsteady state component, mass and energy balance calculations are contained in each block. The phases also contain mixing and mass transfer composite blocks. A single kinetics block contains growth and product formation blocks and substrate utilization blocks.

These blocks in turn contain at the lowest level composite blocks representing Michaels Mention saturation kinetics combined with inhibition. This block architecture allows for the creation of a library and the flexibility to represent bacterial, fungal and cell culture bioreactors. The fermenter or bioreactor composite block is combined in a module with other composite blocks representing mass and energy balances of valves, pumps and flow meters to create a virtual process. In other words, composite blocks from a library are interconnected to match a piping and instrument diagram of an actual bioreactor. If a control system configuration already exists, it can be copied and mapped to this plant simulation module to create a virtual plant.

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