Integration of PAT in Biopharmaceutical Research: A Case Study - Pharmaceutical Technology

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Integration of PAT in Biopharmaceutical Research: A Case Study
This case study describes the implementation of process analytical technology on the cultivation process step of a whole-cell vaccine against whooping cough disease.


Pharmaceutical Technology
Volume 33, Issue 7

On-line measurements


Figure 2: The process analytical technology (PAT) information technology (IT) infrastructure consists of two bioreactors, each monitored by a base station (Reactors 1 and 2). The process data are collected from Siemens (Brussels, Belgium) PCS7, a process control system, as OPC (an industrial standard for data transfer between IT systems ) tags by SIPAT, a software system that allows an on-line check if a new process is running within the tested process design space. Near infrared (NIR) measurements are collected from a Bruker (Ettlingen, Germany) Matrix F Multiplex analyzer running Bruker OPUS software. One collector station controls both measurement channels in parallel. Operational data are stored in a local database and then pushed to the central SIPAT database running on the SIPAT server. Quality predictions are made in real time by the integrated Simca Q calculation engine on the server. The bioreactors are controlled by a batch server running Siemens Batch CC, a batch engine system.
To make use of the "timely measurements" (as stated in the PAT definition) to control the manufacturing process, the on-line process PAT data, including NIR, need to be readily available for process control models that feed back into the process. To allow this, the SIPAT software from NVI's project partner, Siemens (Brussels, Belgium), was used. This software gathers all data that are measured on-line (i.e., pH, dissolved oxygen, temperature, NIR, controller outputs, gas flows, etc.) and stores them in a central database with aligned timestamps. The applied IT architecture is schematized in Figure 2.

The SIPAT database is accessible in real-time for process models that can be run in the integrated Umetrics Simca (Malmö, Sweden) software. Output from these models flows back into SIPAT and can be used by the bioreactor control system to make adjustments to the process and to enable closed loop controls. This software allows the use of any process sensor to monitor and control any process step. In this case, the development of the SIPAT software allowed the integration of NIR data with the other process data, making a true PAT application and a real-time release strategy possible.

Design space

With the PAT initiative also came the concept of process design space. ICH defines "design space" as "the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality" (3, 12). This means that product quality is no longer assured at specific process settings, but rather that a range of settings should be explored. One of the best ways to explore the process design space is by using design of experiments (DoE) (13). In this way, several crucial process parameters are tested at a range of settings using a minimal number of experiments. The main benefit of this approach is that it allows one to investigate the interaction between crucial parameters at different settings. This approach was used to explore the process design space of the cultivation of B. pertussis by executing a series of designed experiments in which several critical process parameters were varied simultaneously. SIPAT was used to collect all of the experiment's variables (2).

Based on these experiments, a process model that describes the design space for the cultivation of B. pertussis was constructed for the tested ranges of the critical parameters. This process model can be executed on-line using the SIPAT software, which allows an on-line check if a new process is running within the tested process design space. Newly gathered data can be added to the process model so that it becomes more accurate over time, allowing the process to be optimized with time. Ultimately, this model can be validated so that it assures product quality on line, allowing real-time product release.


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