Advanced Process Control: Bridge the Gap
PAT is the missing link in the evolution of pharmaceutical process control, but industry must act on the opportunity to realize expected gains.
By David Radspinner, Ph.D., Thermo Electron, and Matt Tormollen, Pavilion Technologies
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.”
Pharmaceutical manufacturers have asserted process understanding and traditional control over their processes by:
- 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.
While these traditional “control levers” have produced products of acceptable quality, they do not continuously push for the highest quality product produced at the lowest cost. Continuous improvement requires understanding the critical sources of variability and working to reduce that variability, constantly. Advanced process control is designed to accomplish these goals. 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.”
A thirty-year focus on risk aversion has left a deep wound in the pharmaceutical industry. Manufacturers are still far more focused on avoiding mistakes than they are on continuously improving their processes. For many, quality is still measured more by the degree of error-free documentation than it is by fundamental process knowledge.