INTRODUCTION:

Process Analytical Technology, or PAT for short, is a revolution in the pharmaceutical industry initiated by the United States Food and Drug Administration to reduce the risk of making a poor quality product¹. PAT can be defined as a system for designing, analyzing, and controlling pharmaceutical manufacturing through timely quality measurements and performance attributes of materials and processes. PAT allows for and encourages continuous process manufacturing improvement. It includes chemical, physical, microbiological, mathematical and risk analysis conducted in an integrated manner. It uses real time information to reduce process variation and manufacturing capability² and demands a solid understanding of the various processes involved in the operation. Simply put PAT is real-time testing and adjustment based on the complete understanding of how the components and related processes affect the final product. This is in accordance with the fundamental principle that quality cannot be tested, but is instead built into the medicinal product by design.

Background:

Conventional pharmaceutical manufacturing is generally accomplished using batch processing with final laboratory testing conducted on representative samples to ensure quality of the product.

This conventional approach has been successful in providing quality pharmaceuticals to the public3. The problem with this type of approach is that if at final testing product fails to pass the quality specifications the whole batch has to be discarded incurring a huge loss to the organization. Not only this, another problem is that if that particular representative sample is not upto the quality specification but the overall batch is good, in that case also the whole batch will be discarded based on the result of the sample. It may also happen that the representative samples passes the test but the overall batch is of low quality and based on the result of the test sample, product is released, only to be recalled later from the market. However, to overcome this type of uncertainty today significant opportunities exist which improves the efficiency of pharmaceutical manufacturing and quality assurance through the innovative application of novel product and process development, process controls, and modern process analytical chemistry tools. Pharmaceutical manufacturing will need to employ innovation, cutting edge scientific and engineering knowledge, along with the best principles of quality management to respond to the challenges of new discoveries and ways of doing business. Pharmaceutical manufacturing continues to evolve with increased emphasis on science and engineering principles. Effective use of the most current pharmaceutical science and engineering principles and knowledge - throughout the life cycle of a product - can improve the efficiencies of both the manufacturing and regulatory processes³. So the PAT approach is in the vogue which is based on science and engineering principles for assessing and mitigating risks related to poor product and process quality⁴.

Goal:

A desired goal of the PAT framework is to design and develop processes that can consistently ensure a predefined quality at the end of the manufacturing process. Such procedures would be consistent with the basic tenet of quality by design and could reduce risks to quality and regulatory concerns while improving efficiency. Gains in quality, safety and/or efficiency will vary depending on the product and are likely to come fromfollowing parameters⁵ :

· Reducing production cycle times by using on, in, and/or at-line measurements and controls.

· Preventing rejects, scrap, and re-processing.

· Considering the possibility of real time release.

· Increasing automation to improve operator safety and reduce human error.

· Facilitating continuous processing to improve efficiency and manage variability

· Using small-scale equipment (to eliminate certain scale-up issues) and dedicated manufacturing facilities.

· Improving energy and material use and increasing capacity.

PAT aims to ensure that all sources of variability affecting a process are identified, explained and managed by appropriate process measurements, so that the finished product consistently meets its predefined characteristics from the start. It is done by use of multivariate analysis, in combination with modern process analytical chemistry methods and knowledge management tools that enhance the identification of critical parameters that affect the process and thus results in a more in-depth process understanding⁶.

How PAT Works – An Instant:

The first step away from off-line testing would be at-line testing. This is the movement of process dedicated testing equipment to the production line to provide rapid results. One advantage is elimination of the transfer of samples involving time delays. Apart from traditional tests such as dissolution, assay, friability, hardness, and thickness, this could also include accelerated dissolution rate analysis, and near infrared (NIR) tablet analysis. One approach of process analytical chemistry is on-line testing, which either draws samples or monitors periodically. Another mode is known as in-line testing, which places probes in constant contact with drug product. The advantage of on/in line testing is better control of the process. Near infrared (NIR) is one of the techniques that has gained recent recognition as a means to add on or in-line analysis at the production level. The near-infrared light does not destroy or react with samples and is able to penetrate into and through solid samples^7,8. While NIR has gotten most of the attention, PAT is not limited to NIR but can include many other forms of monitoring, such as Raman, Mid-IR, acoustic emission signals, and other imaging techniques.

PAT Tools:

There are many current and new tools available that enable scientific, risk-managed pharmaceutical development, manufacture, and quality assurance. These tools, when used within a system can provide effective and efficient means for acquiring information to facilitate process understanding, develop risk-mitigation strategies, achieve continuous improvement, and share information and knowledge. In the PAT framework, these tools can be categorized according to the following ^3,9,10:

1. Multivariate data acquisition and analysis tools

2. Modern process analyzers or process analytical chemistry tools

3. Process and endpoint monitoring and control tools

4. Continuous improvement and knowledge management tools

1) Multivariate data acquisition and analysis tools:

From a physical, chemical, or biological perspective, pharmaceutical products and processes are complex multi-factorial systems. There are many different development strategies that can be used to identify optimal formulation and process conditions for these systems. The knowledge acquired in these development programs are the foundation for product and process design. Some manufacturers use multivariate mathematical approaches, such as statistical design of experiments, response surface methodologies, process simulation, and pattern recognition tools, in conjunction with knowledge management systems. The applicability and reliability of knowledge in the form of mathematical relationships and models can be assessed by statistical evaluation of model predictions. Methodological experiments (e.g., factorial design experiments) based on statistical principles of orthogonality, reference distribution, and randomization provide effective means for identifying and studying the effect and interaction of product and process variables. Traditional one-factor-at-a-time experiments do not effectively address interactions between product and process variables. Interactions essentially are the inability of the one factor to produce the same effect on the response at different levels of another factor. Experiments conducted during product and process development can serve as building blocks of knowledge that grow to accommodate a higher degree of complexity throughout the life-cycle of a product. Information from such structured experiments support development of a knowledge system for a particular product and its processes. This information, along with information from other development projects, can then become part of an overall institutional knowledge base. As this institutional knowledge base grows in coverage (range of variables and scenarios) and data density, it can be mined to determine useful patterns for future development projects. These experimental databases can also support the development of process simulation models, which can contribute to continuous learning and help to reduce overall development time.

2) Modern process analyzers or process analytical chemistry tools:

Process analytical chemistry as a discipline has grown significantly during the past several decades, due to an increasing appreciation for the value of collecting process data during production. From the simple process measurements such as pH, temperature, and pressure, modern tools that measure chemical composition and physical attributes have evolved. These modern process analysis tools provide nondestructive measurements that contain information related to both physical and chemical attributes of the materials being processed. These measurements can be performed in the following manner:

§ off-line in a laboratory

§ at-line in the production area, during production close to the manufacturing process

§ on-line where measurement system is connected to the process via a diverted sample stream; the sample may be returned to the process stream after measurement

§ in-line where process stream may be disturbed (e.g., probe insertion), and measurement is done in real time

§ noninvasive, when the sensor is not in contact with the material (e.g., Raman spectroscopy through a window) in the processor, the process stream is not disturbed

3) Process and endpoint monitoring and control tools:

Following steps can be included for design and optimization of drug formulations and manufacturing processes within the PAT framework:

§ Identify and measure critical material and process attributes relating to product quality

§ Design a process measurement system to allow real time or near-real time (e.g., on-, in-, or at-line) monitoring of all critical attributes

§ Design process controls that provide adjustments to ensure control of all critical attributes

§ Develop mathematical relationships between product quality attributes and measurements of critical material and process attributes

Therefore, it is important to emphasize that a strong link between product design and process development is essential to ensure effective control of all critical quality attributes. Process monitoring and control strategies are intended to monitor the state of a process and actively manipulate it to maintain a desired state. Strategies should accommodate the attributes of input materials, the ability and reliability of process analyzers to measure critical attributes, and the achievement of pre-established process endpoints to ensure consistent quality of the output materials and the final product. Within the PAT framework, a process endpoint need not be a fixed time, but can be the achievement of the desired material attribute. This, however, does not mean that process time is not considered. A range of acceptable process times (process window) is likely to be achieved during the manufacturing phase and should be evaluated, considerations for addressing significant deviations from acceptable process times should be developed. Process end points intended for use in real time release should be considered more critical than those that are only used for in-process control.

4) Continuous improvement and knowledge management tools:

Continuous learning through data collection and analysis over the life cycle of a product is important. Data can contribute to justifying proposals for post-approval changes including introduction of new technologies. Approaches and information technology systems that support knowledge acquisition from such databases are valuable for the manufacturers and can also facilitate scientific communication with the regulatory agency.

CONCLUSION:

The use of process analytical technology can provide huge benefits to pharmaceutical industry by increasing product quality while delivering superior asset utilization and financial value. PAT provides better knowledge of raw materials, by characterizing it both physically and chemically, understanding of manufacturing parameters all of which is having the impact on the finished product quality. Combining together all of these results in a more robust process, better product, better process control and huge time saving which ultimately result in a good cost savings along with creation of a unique brand image for the organization.

REFERENCES:

1. Everything You Need to Know about Process Analytical Technology (PAT) Implementations, Thermo scientific paper, Thermo Fischer Scientific Inc. UK, September 14, 2006.

2. Processanalyticaltechnology.com

3. FDA, Guidance for industry: PAT — A framework for innovative pharmaceutical development, manufacturing and quality assurance; September 2004.

4. Peter Scott, Process analytical technology in the pharmaceutical industry: a toolkit for continuous improvement; PDA Journal of Pharmaceutical Science and Technology. 2006; 60(1):17-53.

5. Office of Pharmaceutical Science (OPS), Process Analytical Technology (PAT) Initiative, FDA, US, December 2005.

6. Inspections - Process Analytical Technology, EMEA Requirements, July 2008.

7. Peter Scott, Process Analytical Technology: Applications to the Pharmaceutical Industry, Quality Assurance Analytical Services, AstraZeneca, Westborough, MA,2006.

8. Y. Roggo, A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies Journal of Pharmaceutical and Biomedical Analysis, Volume 44, Issue 3, 27 July 2007, Pages 683-700.

9. FDA, Pharamaceutical cGMPs for the 21st century — A risk based approach; Final Report, September 2004.

10. Eriksson L, Johansson E, Kettaneh-Wold N and Wold S. Multi- and Megavariate Data Analysis, Principles and Applications. 1^st Edition, Umetrics Academy, June 2001.

Received on 17.11.2008 Modified on 17.12.2008

Research J. Pharm. and Tech. 2(2): April.-June.2009,;Page 225-227