A Statistical Review of ICH Q10 Pharmaceutical Quality System

Applying the recommendations of ICH Q10 to statistical analysis can help prevent product recalls.
Aug 02, 2012
Volume 36, Issue 8

Lynn D. Torbeck
The International Conference on Harmonization ICH Q10 guideline, Pharmaceutical Quality System, and its two companion guidelines Q8 Pharmaceutical Development and Q9 Quality Risk Management, have been readily accepted if not fully implemented by the pharmaceutical industry over the past few years (1–3). Discussions of the statistical implications of Q8 and Q9 have appeared since theguidelines were harmonized (4). Little has been said, however, about the statistical content of the Q10 model, probably because it is perceived to be focused only on the management of the quality system. There are many Q10 recommendations that affect statistical issues facing the pharmaceutical industry, however, the guideline states that it is not "intended to create any new expectations beyond current regulatory requirements" (1).

Although no new statistics or sampling plans are explicitly required by Q10, it goes without saying that current regulatory requirements are, in fact, mandatory. In addition, cGMPs continue to improve over time and according to Q10, "Implementation of ICH Q10 throughout the product lifecycle should facilitate innovation and continual improvement and strengthen the link between pharmaceutical development and manufacturing activities" (1). That link should include the results of statistically designed experiments and related statistical and risk analysis.

While not explicitly requesting these approaches, ICH clearly implies that companies need to be proactive when it comes to corrective and preventive action (CAPA) programs. In today's environment, it is not sufficient to be reactive alone when problems occur. The Quality department must routinely seek out potential problems and prevent them before they result in rejects or recalls. For example, Q10 notes that companies should "Establish and Maintain a State of Control. To develop and use effective monitoring and control systems for process performance and product quality, thereby providing assurance of continued suitability and capability of processes" (1).

Having control over one's product and process is not a new expectation, although there is still confusion as to what a proper "state of control" means (4). It is not enough to ask for a state of control; the industry must provide and define additional modifiers. There are several ways in which a process can be in a state of control or, conversely, in a "state of out of control."

A process can be in control, for instance, for financial and accounting, for regulatory compliance, and for organizational and managerial control. These forms of control are usually assumed to be in place. There are two other states of control that are germane to statistics: engineering and statistical.

A process is said to be in a state of engineering control when the process can be changed and adjusted using control knobs and/or by setting the critical process parameters (independent variables) that affect the dependent responses (5). When in control, the product always meets its specifications even if inconsistent and erratic. Time plots with specification lines are used to monitor the process. A process is said to be out of engineering control when it fails to meet its specifications.

A process is said to be in a state of statistical control when the process has been designed, developed, and adjusted to produce product that, while still containing some variability in the critical quality attributes (dependent variables), is predictable in that variability over time. Statistical control charts are used to monitor the process. A process is said to be out of statistical control when it fails one or more of the eight Western Electric control chart rules (6). As Q10 notes, "The pharmaceutical quality system should include the following elements, process performance and product quality monitoring, corrective and preventive action, change management, and management review" (1).

Product quality monitoring can be interpreted as trending the critical quality attributes. Again, proactive CAPA is preferred to reactive CAPA. As Q10 highlights: "Advocate continual improvement" (1). This continual improvement should include proactive variability reduction.

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