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Through the International Conference on Harmonisation (ICH) process, regulatory bodies in the EU, US and Japan have been moving steadily towards a sciencebased approach to drug development that will revolutionize the way pharmaceutical companies validate processes and ensure product quality. Concepts such as PAT, quality by design (QbD) and design space (DS), which figure prominently in ICH Q8 and ICH Q9,^1,2 encourage greater scientific understanding of processes and products, and hold out the promise of a lighter regulatory burden for companies that adopt such principles. Although the regulatory agencies have provided some helpful direction about how to put these principles into practice, they have not laid out a stepbystep guide. In the absence of such guidance, many companies have been slow to take advantage of the opportunities that these changes offer.

That hesitation is understandable. Consider the uncertainty surrounding validation of manufacturing processes — a key milestone in the drug approval process. The industry knows that the accepted approach to validation — the successful processing of three consecutive batches — is antiquated. Further, FDA, for example, now says that it never meant the threebatch guideline as a hard and fast rule. As recently as 2003, in the pages of this publication, a review of the literature on validation uncovered wide variations among experts in their understanding of the term and the regulatory requirements associated with it.³ However, by following a proven approach to sciencebased validation, forwardlooking companies can cut through the uncertainty, move past outdated methods of validation and begin to realize the potential of recent ICH guidelines:

• reduced compliance risk
• greater regulatory flexibility
• more robust processes
• significant financial benefits.

The goal: managing variability

A more realistic and rewarding approach to validation recognizes the inescapable fact of variability. Instead of seeking to stamp out variability, such an approach seeks to manage it by developing a process that can accommodate the range of variables while still maintaining product quality. Such a process would operate within the DS, defined by ICH Q8 as "the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality" (ICH, 2005). With an understanding of the DS, the manufacturing processes within that DS could be continuously improved without further regulatory review. The manufacturer would gain more regulatory room to operate, and regulators could be more flexible, using, for example, risk-based approaches to reviews and inspections set forth in ICH Q9 'Quality Risk Management'.

An effective tool: predictive modelling

An effective and practical way to achieve and demonstrate the requisite level of process understanding lies in developing predictive models of the form Y=f(X). Y is the process output that measures the performance of the process and the Xs are process inputs, controlled process variables and uncontrolled process variables.

In pharmaceutical manufacturing, the process output (Y) will be a function of raw material properties and process parameters (Xs). These models should identify critical raw material and process parameters, and reliably predict the behaviour of the process with the wide range of complex multivariate relations among those critical parameters and the outputs they generate.

Although we understand the first principles of kinetics, thermodynamics, heat and mass transfer, we don't have data about the possible behaviours of all the compounds we deal with. Our predictive models for the behaviour of any novel formulation must, therefore, be developed empirically. While validation has always entailed at least some basic empirical techniques, such as simply testing whether a given set of process parameters produces an in-specification result, the application of sophisticated statistical modelling has often lagged. Used with other techniques and bodies of knowledge — raw material science, formulation science and engineering — statistical modelling can help realize the potential of ICH Q8 and Q9.