By Mike Long, Ph.D., ValSource, LLC
Utilizing language from the 2011 FDA process validation (PV) guidance, robust continued process verification (CPV) planning can guide an organization to:
Any discussion on CPV planning needs to start at stage 1, process design. (See Table 1 for definitions.) It does not matter if the planning is for a new product, a tech transfer, or a legacy product. The components of stage 1 are critical. Identifying the relative severities of critical quality attributes (CQAs) and important material and process parameters is critical. Without them — for example, in the case of a legacy product — creation AND execution of CPV plans will not be possible.
Table 1: Process Validation Life Cycle Stages
CPV — Setting Up The Program
When setting up an organization’s CPV/OPV program, it is important to understand the types of products the firm manufactures, the scale of these manufacturing operations, and the number of batches manufactured per year. The approach used for an orphan product of which one batch a year is made will not be the same as a solid oral dosage product with 100 batches a year at multiple sites.
When setting up a CPV program, many organizations initiate their planning on products that have yet to be approved or introduced to market. However, the organization’s efforts should be initiated on products currently being manufactured and marketed. The overall risk (to the firm and to patients) is naturally greater for these products. If possible, the efforts should be in parallel. But for midsize to large companies with a large number of products, efforts should be placed on legacy first if prioritization is required.
CPV Requires Risk-Based Approach And Risk-Based Thinking
It is not uncommon for organizations to simply think of CPV as a new fancy term for statistical process control (SPC). While tools such as SPC are critical for the implementation of a CPV program, reliance on only those tools will not create a robust program and may even cause unwarranted reactions.
How is this? Remember, quality risk management (QRM) is embedded in the application of the process validation life cycle. It is noted throughout the 2011 FDA guidance as well as the EMA’s recent update of annex 15. The manner in which an organization plans, executes, and reacts requires an application of risk. And risk is defined as the combination of the probability of occurrence of harm and the severity of that harm:
Risk = Severity x Probability of Occurrence
SPC and other statistical tools used in a CPV program only provide one component of risk — probability of occurrence. — They do not provide the relative severity component of the risk calculation. If an organization does not have a robust QRM or risk-based thinking culture in place, overreaction to signals may occur. An organization’s CPV policies and procedures need to have QRM principles embedded in them so resources can be applied in a risk-based manner.
Creating The Plan
As described in Table 1, there is short-term and long-term CPV. Each requires planning:
Initial planning components
An Important Note On Data Verification
The data used in the statistical analysis activities of the CPV PV program must be verified. While some organizations have the ability to electronically record lab results and transfer them into a statistical package to be analyzed, these organizations tend to be the exception. Most statistical analysis requires some manual entry into a statistical package whether it be entering it by hand or cutting and pasting. This manual movement of data and entry into stat packages has a risk of transcription error.
An organization needs to create processes and procedures for verification of the data used in the analysis. The approaches may differ company to company, but this is a critical step in assuring the continuous improvement decisions are not being made due to a transcription error. The person verifying the data used in the statistical analysis does not have to be a statistician. However, in conjunction with the data being verified as accurate, organizations should have a reviewer with statistical training engaged in the review and approval of a CPV report (and any other process validation life cycle document that has statistical analysis components to it).
Planning for CPV begins during development with the understanding of important product and process attributes to be monitored during the life span of the product. Robust processes and systems need to be implemented to execute the plan. Effective training on the life cycle, as well as statistical and risk-based thinking, is needed to realize the goal.
About The Author:
Mike Long, Ph.D., is currently senior director of consulting services at ValSource, LLC. He is a past member of the Parenteral Drug Association’s (PDA’s) Science Advisory Board and co-leader of the PDA’s Quality Risk Management interest group. Long is the current lead of the PDA Applied Statistics interest group and co-leader of the PDA’s new CPV task force. Long is a frequent industry speaker/writer who has instructed courses in data analysis at Tufts University’s graduate engineering management program and taught risk management and quality systems in Regis College’s graduate program in regulatory and clinical research management. Long is a master black belt who earned a bachelor’s degree from Worcester Polytechnic Institute, a master’s degree from Tufts University, and a doctorate from Northeastern University.