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GPC in Pharma!

For pharmaceutical companies, QbD and PAT are current hot topics. Fundamental to these concepts are design spaces, control spaces and operating spaces. These concepts are relatively easy to understand in two or three variable problems, but in real-world situations where many more variables exist, visualising these problems is infinitely more complex.

Methods such as chemometrics, neural networks and multi-variate statistics use advanced mathematical calculations to try to visualise these problems. Alternately, Geometric Process Control offers a less complex, easy to understand alternative to not only visualise the problems but also to model design, control and operating spaces. This gives experimenters the ability to be able to predict results of certain experiments which has significant QbD impact and can greatly reduce the cost and time to market for a given drug. Additionally GPC can also help with predicting quality using current operating conditions, advancing PAT through the ability to operate within a defined envelope providing confirmation of processing conditions and product quality, thus enabling auto-release.

For formulators, the C Visual Explorer (CVE) and C Response Surface Visualiser (CRSV) packages provide significant benefits and require no statistical skills. CVE enables formulators to identify the critical relationships and interactions within a formulation. CRSV permits them to explore the effect of varying one or more components on the properties of the formulation and to identify an optimal region for further experimentation. Taken together, the two programs represent valuable tools in the formulator's armoury and offer the potential to increase the understanding of a formulation, the efficiency of its development and confidence in its performance.

If you have an interest in finding out more about the GPC applications in pharmaceutical process control then do email us at This email address is being protected from spambots. You need JavaScript enabled to view it. for more information and a free, no-obligation demonstration using some of your own data.


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