Process Analytical Technology

By

Dr. Vikram Venkateswaran
Senior Business Consultant
Covansys-A CSC Company
Unit 13 Block 2, SDF Buildings, MEPZ Chennai-600 045
 

Introduction

The pharmaceutical industry is poised at the threshold of a change that promises to carry it into an era of monumental efficiency in timeliness, quality and cost effectiveness of drug delivery process; and Process Analytical Technology is offering to be the medium of this tectonic shift. Conventionally, the pharmaceutical manufacturing process is conducted in batches with testing conducted on samples in laboratory conditions to ensure quality. Though this process has served well in the successful delivery of quality drugs to the market, it has been at the expense of low yields (in terms of quality or quantity) and loss of batches due to unsuitability of release; ultimately translating to increased costs. Despite of recognizing these restraints, the industry had however shied away from any innovative improvements mainly due to regulatory uncertainty involving conformance to quality standards which had been mainly defined in the form of time point based quality inspection. Process Analytical Training or more commonly PAT, aims at rectifying this situation. (PAT – a framework for innovative pharmaceutical manufacturing and quality assurance US FDA Guidance report, August 2003)

What is PAT?

PAT is however not just a regulatory requirement. It is a more fundamental philosophy that attempts to view the pharmaceutical manufacturing process as a continuously evolving process over the current viewing in terms of intermediate and finished drugs and drug components. The US Food and Drug Administration in its recent regulatory approval succinctly defines the basis of PAT by stating that "Quality cannot be tested in products; it should be built-in or should be by design". . (PAT – a framework for innovative pharmaceutical manufacturing and quality assurance US FDA Guidance report, August 2003)

This premise of 'building quality into products' allows a focus on relevant multi-factorial relationships among material, manufacturing process, and environmental variables and their effects on quality. These relationships, in turn, provide a basis for identifying and understanding associations among various critical formulation and process factors and for developing effective risk mitigation strategies (e.g., product specifications, process controls and training). The data and information to help understand these relationships can be obtained through pre-formulation programs, development and scale-up studies, and from manufacturing data collected over the life cycle of a product. In short, PAT encourages process optimization based on process understanding. PAT is however not a totally new concept. It has been previously implemented in industries like petrochemicals and hence there exist advanced analytical frameworks for quality improvements that can be intelligently mapped into pharmaceutical practices.

PAT Implementation: the methodology

Conceptually, PAT can be summarized as the real-time generation and analysis of product quality information. The system generates and synchronizes variables based on product characteristics, manufacturing parameters, and process monitoring and chemometric techniques to achieve desirable quality in the end products. PAT also induces another significant improvement of on/in-line testing over off-line testing (testing of samples taking off the line). Available sophisticated techniques of in-line testing like Near Infrared (NIR), Raman and other physiochemical techniques allow detailed process monitoring.( 2005, O'Toole, Pepper, Hardy and Davis -A practical guide on how to implement a Pat program) When coupled with sophisticated analytical techniques, uniformity, drying, and mixing endpoints, and other targeted stages can be pinpointed to a high degree of certainty. Sampling error can also be minimized with in-line probes placed strategically through out the production process. Using PAT, processes can be kept under such high control that the dissolution results could be accurately predicted well before the product is analyzed. Research on the correlation between dissolution results and measured process parameters could be performed so that the impact of process, raw materials, and finished product variables would be understood. The manufacturing process could be continuously monitored and adjustments made to ensure that the finished product would meet the desired specifications. In light of the above process improvements, multivariate analysis has become an indispensable instrument towards achieving production efficiency. Defined in technical terms as 'Chemometrics', it allows inclusion of subtle variable interactions in determining process and product behavior ( Marasco and Washington, Chemical and Engineering news ISSN 0009-2347, 2005). Complex chemical and mathematical computation, through sophisticated software technology, result in large amounts of data being reduced into a few recognizable components without any loss. Two chemometric techniques that have been found to be useful are Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS). These techniques are recognized for their ability to eliminate noise, identify latent variables, and extrapolate missing data.

Conclusion

The depth of improvements the PAT promises to introduce in pharmaceutical manufacturing will also serve to move the industry to greater degrees of technological automation, thereby reducing manpower requirements in debated areas of employment. Many companies like Astra Zeneca and Pfizer have already achieved success in the realm and the rest of the industry is poised to follow. The IT industry stands with the pharmaceutical industry to achieve this monumental change in outlook and perspective.
 

Dr. Vikram Venkateswaran
Senior Business Consultant
Covansys-A CSC Company
Unit 13 Block 2, SDF Buildings, MEPZ Chennai-600 045
 

Source: E-mail January 3, 2008

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