Nowhere is this wound more apparent than in the drug industry’s slow adoption of process control and automation — in particular, advanced process control technologies. Advanced Process Control (APC) software, widely used in the petrochemical and food industries, manipulates key process variables to achieve one or many objectives simultaneously; for example quality, yield, energy efficiency and waste reduction.
By providing a direct, real-time connection to any drug manufacturing process and APC platforms, Process Analytical Technology (PAT) opens up new possibilities for manufacturers.
While cultural issues remain a challenge, the basic infrastructure required to sustain PAT, both for IT and for automation, has not yet become widely deployed within the industry.
In fact, the degree of “evolution” or readiness for advanced control varies between drug companies, and even between different divisions or manufacturing plants within the same company. Some facilities may be highly automated, using manufacturing execution systems (MES), distributed control systems (DCS), electronic batch records (EBR), data historians, laboratory information management systems (LIMS), asset management systems, and enterprise wide resource planning (ERP) software. Others are limited to more primitive chart recorders, and operators must record data manually onto paper batch records.
These differences have a staggering impact on a manufacturer’s ability to analyze data and control processes, to take effective investigational corrective and preventative actions, and to continuously improve processes.
Nevertheless, the pharmaceutical industry is evolving toward more advanced process control as it embraces the concepts and technologies behind PAT. Many pharmaceutical companies are investing in sensors and analyzers (e.g. NIR, mass spectrometry, Raman) and evaluating, if not applying, them across a wide range of processes and locations from development scale for full production scale.
Control what matters – the end result
A key principle guiding the PAT initiative is the vision of achieving enhanced process understanding. According to the FDA’s PAT Guidance for Industry “a process is generally considered well understood when:
- all critical sources of variability are identified and explained;
- variability is managed by the process; and,
- product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions.”
- Developing manufacturing processes in the laboratory and the pilot plant that meet FDA standards for quality and control which may include process modeling to further enhance the success of tech transfer to the full scale manufacturing;
- Following procedural recipes for the manufacture of drugs with limited if any adjustment of the process;
- Conducting periodic lab samples to test quality;
- Monitoring process parameters that may or may not be directly related to critical quality attributes.
APC 101
APC is an intelligent and active software layer that sits above the distributed control system (DCS) or regulatory control layer in a traditional manufacturing automation hierarchy (Figure 1: APC in Relation to the Manufacturing Automation Hierarchy, below).
Regulatory control is a class of technology whose primary role is to maintain desired unit measurements such as mass and heat balances. Regulatory control does not continuously improve the process, but rather ensures that the hardware components within the system are not exceeding known process, equipment and safety limits.
In contrast, APC is designed to reduce changeability of key variables and continuously adjust the process to guarantee the desired end result. APC software solutions are developed by building a mathematical model of the manufacturing process.
Since many variables can affect a single process, a key part of developing an APC model is identifying and understanding the multiple critical variables that affect the desired end result. The model is built using all available knowledge of the process including human operators’ knowledge, operating data, and any known scientific principles, such as First Principle equations. The same process also identifies and explains the critical sources of variability. As described, the basic procedure of creating an APC solution delivers fundamental process understanding. The process model itself can be used in an off-line supervisory mode or an on-line measurement and control mode. In an off-line mode, a model-based APC solution can identify the best operating parameters to achieve desired outcomes. It can also be used on-line as a software-based analyzer to help provide and predict online quality or performance measurements.
This practice supports the third tenet of process understanding as described by the PAT Guidance Framework, enabling “product quality attributes to be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions.”
Software process analyzers provide more frequent, robust and often more cost-effective measurements than are available from traditional laboratory information systems. This online software based analyzer allows an operator or process engineer to monitor the real-time feedback and predictions of future process performance, and make manual adjustments to the process to ensure desired end quality.
This same model-based solution can also be deployed online to provide real-time control and optimization. The APC solution continuously monitors a number of key process variables minute-by-minute, predicts outcomes based on the established model of the process, feeds the real-time and predicted data back into the model, simulates the impact on the plant’s objectives and then issues commands back to the Distributed Control System (DCS) to make changes to the key variables that are driving results. This solution addresses the second tenet outlined by the PAT Guidance framework, “continuously reducing the variability caused by the process.” APC improves management and the optimization of a plant operation in four main areas:
- Enhanced ability to determine product quality in real-time, on-site without waiting for a lab sample or an end-of-the-run analysis. This capability is provided by the ability to predict quality accurately in-process and the improved process stability.
- Improved operations stability and consistency. The product and batch transitions are performed more quickly and consistently. This capability translates directly to improved operational efficiencies and transition losses are reduced.
- Ability to push process constraints. APC reduces process variability, enabling manufacturers to run closer to desired targets and push to constraints with more confidence. The result is more throughput, higher quality product or both.
- Increased operational efficiency. This is measured in terms of reduced raw materials and energy consumption.
A food industry success story
Fonterra Co-operative Group Ltd.’s dairy ingredients group, NZMP, began to implement APC at its facilities in 1995, to improve processing efficiency for milk proteins, powders, cheese and casein. The company realized an ROI of over 60%, and also saw its production rates improve by 5 to 15%, its product quality by 50%, energy efficiency by 5 to 12%, while product yields increased and variability fell, for all key control variables.
As a result of the performance gains, the company has installed APC at many of its 23 New Zealand-based operations and Fonterra’s majority-controlled overseas joint ventures.
Let us consider one specific implementation, a milk powder plant making nutritional, whole milk and skim milk powders. The plant has two evaporators and one spray dryer with the two falling-film evaporators feeding a Niro atomizing spray dryer processing 6.5 to 9 tons per hour.
The evaporators concentrate the milk to approximately 50% total solids (TS), with each evaporator using two effects. There is Mechanical Vapor Recompression (MVR) on the first effect and Thermal Vapor Recompression (TVR) on the second effect. After passing through the evaporators, the concentrate is dried in the dryer chamber and then in the static fluid bed (SFB) and the two vibrating fluid beds (VFB’s). The final moisture specification limit is typically set between 2.5 and 3.5%.
This APC project has been implemented in two phases. In the first phase, Fonterra’s on-line grading analysis system was installed to allow In-Process Testing (IPT) results to be measured and reported statistically. This allows operators to view product composition results relative to product specifications and budget goals. The on-line grading analysis system also provides a means to interface IPT results to control applications.
In the second phase, a Pavilion APC application was developed to minimize process variability by compensating for disturbances, with the intention of creating a performance-driven, “obedient” plant. Multivariable predictive control software was used to implement the evaporator and dryer application.
In addition, as part of the implementation, a software-based analyzer was deployed to predict moisture for continuous feedback to the control application. A wealth of historical data for dryer’s wide product mix allowed a model to be created that reflected moisture ranges for a variety of products with a high degree of accuracy. The analyzer, a Pavilion Soft Sensor, was enhanced by installing an application that biased the prediction hourly with IPT data from the online grading analysis system.
Control applications were developed for each evaporator feeding the dryer with the goal of reducing total solids (TS) variability by 50%. An audit conducted in April 2003 demonstrated that the APC solution had exceeded this objective, reducing the variation by approximately 73% for Evaporator 1 and 68% for Evaporator 2. The reduction in standard deviation of evaporator total solids will allow operations to increase total solids targets in the evaporators without violating viscosity limits. This allows increased water removal in the more thermally efficient evaporators. A higher concentrate total solids additionally allows increased dryer throughput. A 0.5% increase in total solids from the evaporators could lead to a 2% increase in dryer throughput.
Figure 2 and Figure 3 (below) show the final total solids from Evaporator 1, before and after APC control. Note that variation is noticeably improved when the APC controller is running.
Pharmaceutical manufacturing can achieve the same successes with APC that other industries have realized. The concepts, techniques and technology are transferable; but the industry needs to look at quality from a modern perspective.
Incentives and opportunities
Of course there is good news; actually great news. One of the main reasons (perceived or real) for pharma’s risk avoidance in manufacturing has been the regulatory environment. Yet now one of the key global regulatory agencies, the U.S. FDA, is dramatically changing its approach to quality in a positive way for everyone involved, especially the patients.
Critically, the FDA is encouraging companies to take a risk based approach to development, manufacturing and control. This is not so that it can reduce the amount of effort going into development, manufacturing and control, but redirect the efforts currently deployed to inefficient documentation efforts towards the most important aspects of manufacturing and control based upon risk management.
The incentives from the FDA come in the form of rapid approvals, more flexibility to adjust the process through continuous improvement initiatives, and reduced compliance oversight based upon modern risk management. The cost for these is simple; risk management, process knowledge, and control. These come from putting the technology in place, securing the right skills and experience (either internally or through partnerships) and focusing manufacturing and control from an integrated risk management approach.
Think big but act small
Evolution is by definition a gradual process, but can only happen when more individual facilities develop the infrastructure and change their cultures to focus on continuous improvement. When starting your journey to APC, think big but act small. Ask yourself how development, manufacturing and control will look ten years from now. How close to that vision are you now? What will it take to bridge the gaps between where you are today and where you want to go? If the gap is large, one may be overwhelmed, and react by procrastinating. Acting on a small scale, one can take quantifiable steps that will not only yield a positive return on investment, but won’t require huge capital outlays or structural changes.
In short, process control evolution is inevitable. But it must begin with you, within your facilities, divisions and companies.
About the Authors:
David A. Radspinner, Ph.D. joined Thermo Electron Corp. in May of 2005 as PAT Development Director and is located in Madison, Wis. He works on delivering PAT-related solutions for the pharmaceutical industry in areas of enhanced measurement systems, data management, process monitoring and control, process risk management, process mapping, and real-time release. He also helps customers define and implement their PAT strategy. Radspinner joined Thermo Electron from Sanofi-Aventis Pharmaceuticals, where he was the Drug Product PAT leader of their strategic PAT initiative. He developed a scientific, risk-based approach to PAT at Sanofi-Aventis on a full-scale production process for a high volume drug product leading to the first major comparability protocol filed and approved with the FDA.
Matt Tormollen joined Pavilion in January of 2003 and has more than 20 years of software industry experience. He is responsible for Pavilion’s worldwide market development, product management, communications and branding. Tormollen has been instrumental in supporting the company’s PAT solution offering, including multivariable data analysis, Soft Sensors, and advanced process control.
Figure 1: APC in Relation to the Manufacturing Automation Hierarchy
Figure 2: Fonterra Moisture Variability (before APC)
Figure 3: Fonterra Moisture Variability Reduction (with APC)
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