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a proven approach to sciencebased validation, forwardlooking companies can cut through the uncertainty, move past outdated methods of validation and begin to realize the potential of recent ICH guidelines: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
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.
Two examples illustrate the power and value of statistical modelling for validation.
This picture of the DS was created using the optimum settings for each of the six significant variables. It brings together 15 3D response surface plots, each of which was originally created in the modelling software. The X and Y axes are made up of the DoE variables, and the Z axis (the contour curves) represents dissolution (the response variable). In the red regions, dissolution is out of specification. In the green regions, the dissolution rate is within specification. By finding the DS in which PV2 — by far the most influential variable — interacting with other variables can produce inspecification dissolution, it is possible to optimize the path to achieve the desired rate of dissolution. In this case, the PV2 variable needs to be kept at a high level while the RM2 variable should be maximized. The RM1 variable should be kept in the centre of the range used, thereby maximizing the 'green space'.
The optimum conditions were entered into the model, two confirmation batches were processed and the results conformed to those predicted by the model. Ultimate confirmation of the power of the technique came with the successful validation and launch of the product.
The results: scientific rigour
- Acknowledges that raw materials and processes entail some inherent variability.
- Allows for the management of that variability.
- Recognizes that not all variables are equally important, which, in turn, allows risk managementbased approaches to regulation.
Finally, although recent guidelines encourage frequent communication with regulatory authorities, some organizations, fearing they may become prematurely entangled in regulatory red tape, have been wary of such open communication. However, when armed with increased process understanding, and the powerful data derived from predictive modelling, manufacturers can undertake such communication with confidence. By demonstrating the scientificallybased understanding of processes that regulators are encouraging, manufacturers can establish the mutual trust that is necessary for a productive working relationship, and for realizing the full benefits of the revolution in validation.
Jason J. Kamm is a Managing Consultant with Tunnell Consulting (PA, USA).
Philippe Cini is a Vice President with Tunnell Consulting (PA, USA). http://www.tunnellconsulting.com
1. ICH Harmonised Tripartite Guideline: Pharmaceutical Development, Q8, 2005. http://www.ich.org
2. ICH Harmonised Tripartite Guideline: Quality Risk Management, 2005. http://www.ich.org
3. M. Helle, J. Yliruusi and J. Mannermaa, Pharm. Technol. Eur.,15(3), 52–57 (2003).
4. J. Kamm, Pharmaceutical Manufacturing, May (2007). http://www.pharmamanufacturing.com