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Jennifer Markarian is manufacturing editor of Pharmaceutical Technology.
Use of modeling software can help improve process understanding, and can be used in open- or closed-loop control.
Process modeling is becoming established as a method to design and optimize pharmaceutical processes using data from process analytical technology (PAT). Modeling software can be implemented to monitor the process and increase process understanding and, if desired, to control the process.
A process control model takes process data inputs, such as temperature and flow rate, from traditional sensors, as well as spectral and other data from PAT, and calculates a predicted output. In an open loop, process operators evaluate this predicted data and decide whether a change to the process is necessary. In closed-loop, advanced process control (APC), a process controller uses the model to make changes automatically, to keep the process in specification.
One use of process modeling and control is to evaluate the quality characteristics of incoming raw materials and determine how these characteristics will affect the final product. "A raw material could be within specification, yet result in an out-of-specification end product," notes Jakob Mohr Christensen, general manager at MKS Umetrics. "The modeling software can be used to determine how process parameters could be changed to account for variability in a raw material or, in some cases, to identify whether a given material should not be used at all. The software can be used to build quality control charts for the materials that go into the process."
Modeling software can be used to meet various goals, including improving the quality or economics of a product, or determining a design space. To build a process model, users identify the relevant variables and extract representative data, either from the existing process or, if the process is new, from designed experiments, explains Chris McCready, principal engineer at MKS Umetrics. With Umetrics’ offline SIMCA modeling software, users refine the model by analyzing data and reviewing it with process experts to compare the model to existing process knowledge. Next, they verify the model on the system using SIMCAonline software, while the process is running, and the controller is commissioned to monitor and/or control the process.
Currently, most of the pharmaceutical clients who use Umetrics' SIMCA-online process modeling software use an open-loop mode. "This 'advised future' mode is typically the first step of applying process control models to a pharmaceutical process, but the software can also be programmed to allow closed-loop control," explains McCready. "Although APC is a mature technology, a challenge is in applying these methods to pharmaceutical processes where the regulatory environment was written for release of batches. A few companies are building continuous processes with advanced control, while others are waiting to see."
Process control for continuous processes
Umetrics SIMCA modeling software uses finite impulse response (FIR) modeling to handle "data lagging," which is how the state of the process at a given time point is influenced by what was happening in the process at an earlier time. "Some variables have longer lag times than others," explains Lennart Eriksson, senior principal scientist and lecturer at MKS Umetrics. "The most appropriate lag sequence must be identified by testing each case as part of the overall process of refining the process model." FIR modeling in a continuous process can be compared to the method of "data unfolding" in a batch process, explained McCready in a presentation at IFPAC. The Umetrics software can be configured to either batch or continuous or even hybrids of the two. For example, in a biopharmaceutical perfusion process, the software is used to monitor the initial cell growth, which is a batch process, and then to monitor and control the steady-state, continuous perfusion process.
The topic of control systems for continuous processing will be discussed on the Innovation Stage at INTERPHEX 2015 (New York City) on Tuesday, April 21. Doug Hausner, Associate Director for Industrial Relations and Business Development at the Engineering Research Center for Structured Organic Particulate Systems, Rutgers University, will present "Meeting Regulatory Expectations for Continuous Processing Sensing and Control," and will review technology and regulatory expectations for implementing next-generation continuous processes in solid-dosage pharmaceutical manufacturing.
To hear Chris McCready explain the steps of implementing process control software, tune in to PharmTechTV.