An assessment of process robustness can be useful in risk assessment, reduction, potentially be used to support future manufacturing and process optimization. Robustness cannot be tested into a product; rather it must be incorporated into the design and development of the product. Performance of the product and process must be monitored throughout scale up, introduction and routine manufacturing to ensure robustness is maintained.
Principles Of Process RobustnessDefinition of Robustness –
“The ability of a process to demonstrate acceptable quality and performance while tolerating variability.”Process performance and variability may be managed through the choice of manufacturing technology. Well designed processes reduce the potential for human mistakes, thereby contributing to increased robustness. During product and process development both the inputs and outputs of the process are studied to determine the critical parameters and attributes for the process, the tolerances for those parameters and how best to control them. Critical Quality Attributes, Process Parameters, Process Capability, Manufacturing and Process Control Technologies and Quality System Infrastructure are referred as Manufacturing Science underlying a product and process. Principles of process robustness are as follows –
(A) Critical Quality Attributes (CQAs) – The identified measured attributes that are deemed critical to ensure the quality requirements – intended purity, efficacy and safety of an intermediate or final product, termed as Critical Quality Attributes.
(B) Critical Process Parameters (CPPs) – Is a process input that, when varied beyond a limited range has a direct and significant influence on a Critical Quality Attribute. It is important to distinguish between parameters that affect critical quality attributes and parameters that affect efficiency, yield, worker safety or other business objectives. Most processes are required to report an overall yield from bulk to semi-finished or finished product. It is important to have an understanding of the impact of raw materials, manufacturing equipment control, degree of automation or prescriptive procedure necessary to assure adequate control.
(C) Normal Operating Range (NOR) and Proven Acceptable Range (PAR) – In developing the manufacturing science a body of experimental data is obtained and the initially selected parameter tolerances are confirmed or adjusted to reflect the data. This becomes the Proven Acceptable Range for the parameter, and within the PAR an operating range is set based on the Normal Operating Range for the given parameter. In a robust process, critical process parameters have been identified and characterized so the process can be controlled within defined limits for those CPPs. A process that operates consistently in a narrow NOR demonstrates low process variability and good process control. The ability to operate in NOR is a function of the process equipment, defined process controls and process capability.
(D) Variability: Source and Control – Typical sources of variability includes process equipment capabilities, calibration limits, testing method variability, raw materials, human factors for non automated processes, sampling variability and environment factors within the plant facility.
(E) Setting Tolerance Limits – Upper and lower tolerances around a midpoint within the PAR of a parameter should be established to provide acceptable attributes. The defined limits should be practical and selected to accommodate the expected variability of parameters while confirming to the quality attribute acceptance criteria.
Development Of A Robust ProcessA systematic team-based approach to development is one way to gain process understanding and to ensure that a robust process is developed. The following are the steps for the development of a robust process –
(1) Form the Team – Development of a robust process should involve a team of technical experts from R&D, technology transfer, manufacturing, statistical science and other appropriate disciplines. This team approach to jointly develop the dosage form eliminates the virtual walls between functions, improves collaboration and allows early alignment around technical decisions leading to a more robust product. This team should be formed before optimization and scale-up.
(2) Define the Process – A typical process consists of a series of unit operations. Before the team can proceed with development of a robust process they must agree on the unit operations they are studying and define the process parameters and attributes. Defining the process is to list all possible product attributes and agree on potential Critical Quality Attributes. The final step in defining the process is determining process parameters. Categorizations of parameters to consider are materials, methods, machines, people, measurement and environment.
(3) Prioritize Experiments – It is recommended that the team initially use a structured analysis method such as a prioritization matrix to identify and prioritize both process parameters and attributes for further study. A ranking of parameters of importance is calculated by considering the expected impact of a parameter on attributes as well as the relative importance of the attributes.
(4) Analyze Measurement Capability – The analysis of a process cannot be meaningful unless the measuring instrument used to collect data is both repeatable and reproducible. Analysis should be performed to assess the capability of the measurement system for both parameters and attributes. Measurement tools and techniques should be of the appropriate precision over the range of interest for each parameter and attribute.
(5) Identify Functional Relationship Between Parameters and Attributes – The functional relationships can be identified through many different ways, including computational approaches, simulations or experimental approaches. Design of Experiments is the recommended approach because of the ability to find and quantitate interaction effects of different parameters. Properly designed experiments can help maximize scientific insights while minimizing resources because of the following –
- The time spent on planning experiments in advance can reduce the need for additional experiments.
- Fewer studies are required and each study is more comprehensive.
- Multiple factors are varied simultaneously.
ConclusionThe pharmaceutical manufacturers should implement robust manufacturing processes that reliably produce pharmaceuticals of high quality and that accommodate process change to support continuous process improvement. Creating a system that facilitates increased process understanding and leads to process robustness benefits the manufacturer through quality improvements and cost reduction. The goal of a well characterized product development effort is to transfer a robust process which can demonstrate, with a high level of assurance, to consistently produce product meeting pre-determined quality criteria when operated within the defined boundaries.
- PQRI Workgroup Members. Process Robustness – A PQRI White Paper. Pharmaceutical Engineering The Official Magazine of ISPE November/December 2006; Available from: http://www.06ND-online_Glodek-PQRI.pdf
- Taguchi G., Y. Wu., A. Wu. Taguchi Methods for Robust Design. American Society of Mechanical Engineers 2000.
- Johnson D. B., Bogle I. D. L. A Methodology for the Robust Evaluation of Pharmaceutical Processes under Uncertainty. Chem. Papers 54 (6a) 398-405 (2000).
- Innovation and Continuous Improvement in Pharmaceutical Manufacturing (Pharmaceutical CGMPs for the 21st Century) The PAT Team and Manufacturing Science Working Group Report: A Summary of Learning, Contributions and Proposed Next Steps for moving towards the “desired State” of Pharmaceutical Manufacturing in the 21st Century. Available from: http://www.2004-4080b1_01_manufSciWP.pdf.