The Regulatory Guidance embodied by ICHQ8 through (soon to be written) ICHQ12 tells us in no uncertain terms that we must protect the patient across the product lifecycle by assuring quality based on scientific knowledge, i.e., we must adequately characterize and optimize the manufacturing process for each lifecycle stage. However, there is much confusion over what adequate process characterization and process optimization entails – how much time and money at each stage, and therefore, how to achieve it.
All companies do some form of process optimization and characterization, whether consciously or unconsciously…they have to. All too often however, it is woefully inadequate. The level of process understanding and control is not commensurate with the process owner’s risk-tolerance at that lifecycle stage, often inevitably leading to nasty surprises.
As a champion of process optimization, I often hear practitioners lament that there is just not enough time – that they are not allowed enough time – to do the job right. The fact is, there will never be enough time and money available to understand everything about a process and eliminate all risk of failure.
The perception that there is not enough time and money stems from the use of sub-optimal technical methodology (wasted effort) and an inability to adequately manage risk across the organization (risk is managed in silos). This can be remedied by establishing a risk-based, data analysis driven, process optimization program, and applying it appropriately across the product lifecycle…setting the stage for more effective process development, clinical supply, validation, technology transfer, process improvement, faster regulatory approvals, lower cost of goods, and avoidance of costly recalls and regulatory action.
By definition, all process optimization approaches include risk management, experimentation, and data analysis. Many times unfortunately, approaches suffer from ill-defined critical to quality attributes; disjointed risk identification, ranking, communication and control; one-factor-at-a-time experimentation; insufficient sample sizing, lack of replication and other shortcomings that inevitably lead to those nasty surprises.
An effective process optimization approach on the other hand, systematically applies proven engineering, statistical, and risk management practices, to get the biggest bang for the time and money available, i.e., process knowledge is maximized and the risks imposed by the practical reality of budget and timeline are understood and accepted across the organization.
Process optimization is the “gas in the tank” that fuels success across the product lifecycle. To name one example, it’s all the “getting ready stuff” for Process Validation (imagine life with objective criteria for validation-readiness). An effective process optimization approach is designed to march in lock-step with the knowledge and risk demands unique to each lifecycle stage…and will pay dividends, over and over again.