ABOUT AUTHORS:
Ghodke D.V*, Bhusnure O.G, Kulkarni A.A
Department Of Quality Assurance in Maharashtra College of M .Pharmacy Nilanga.* Dist –Latur
Department Of Medicinal chemistry in Maharashtra College of Pharmacy Nilanga, Dist –Latur
*ghodke.deepa@gmail.com
ABSTRACT
Pharma Industry is facing growing demands for increased productivity and reduced manufacturing costs and also has to meet the evolving need for higher quality standards and higher drug expectations. In traditional approach quality of the raw material attributes both physically and chemically testing was done by off-line process. The application of Process Analytical Technology in pharmaceutical production checks the quality at-line, in-line or on-line thereby decreasing the chances of contamination and cross contamination. Implementation of Process Analytical Technology to pharma industry increased process understanding and continuous improvement also improve regulatory compliance. PAT involves the use of different technologies and tools to build quality into the products. Effective PAT implementation is based on detailed, science-based understanding of the physical, chemical and mechanical properties of all elements of the proposed drug product. In this article, Process Analytical Technology has been introduced its application different tools have been discussed to ensures quality of the pharmaceutical products.
REFERENCE ID: PHARMATUTOR-ART-1777
INTRODUCTION[1-6]
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. Process Analytical Technology (PAT) has been the subject of recent interest in the pharmaceutical industry due to the attention that FDA’s Center for Drug Evaluation and Research (CDER) has placed on the use of PAT as a means to improve manufacturing pharmaceutical quality control, reduce reliance on finished product testing and boost manufacturing efficiencies. Process Analytical Technology, or PAT for short, is arevolution in the pharmaceutical industry initiated by theUnited States Food and Drug Administration to reduce therisk of making a poor quality product. Process Analytical Technology (PAT) is a system for designing, analyzing, and controlling manufacturing processes based on an understanding of the scientific and engineering principals involved, and identification of the variables which affect product quality. The PAT initiative is based on the FDA (The US Food and Drug Administration) belief that: “quality cannot be tested into products; it should be built-in or should be by design. The primary goal of PAT is to provide processes which consistently generate products of a predetermined quality. Effective PAT implementation is founded on detailed, science-based understanding of the chemical and mechanical properties of all elements of the proposed drug product. In order to design a process that provides a consistent product, the chemical, physical, and biopharmaceutical characteristics of the drug and other components of the drug product must be determined
BACKGROUND
The Food & Drug Administration (FDA) recognized that significant regulatory barriers inhibited pharmaceutical manufacturers from adopting state-of-the art manufacturing practices within their industry. As a result, the FDA issued its guidance in its final form in September 2004.Process analytical technology (PAT) is a system for designing, analyzing and controlling manufacturing processes. It is based on timely measurements (recorded during processing) of critical quality and performance attributes of raw materials, in-process materials, and processes. The goal is to ensure final product quality. PAT further allows real time follow-up on product quality while enhancing increased process understanding.PAT enables Right-First-Time manufacturing. In this way, post-process testing will be reduced or eliminated because products result from a tightly-controlled process designed to yield good output. Moreover, online quality monitoring will reduce off-spec production, which reduces production costs. The FDA believes that PAT will encourage innovation in pharmaceutical manufacturing and quality assurance. FDA regulators expect that PAT will allow companies to more easily improve their manufacturing processes and thereby reduce product development times. This QbD approach is not yet mandatory, but likely will become the regulatory standard-reference for the pharmaceutical industry in the 21st century.
THE OBJECTIVE FOR PAT IMPLEMENTATION[6-9]
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Improved efficiency from conversion of the batch process into a continuous process
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Cost reduction because of reduced waste and less energy consumption
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Reduction in the production cycle time by using online/at-line or in-line measurements and control
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Real-time release of the batches
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Improved yield because of prevention of the scrap, rejects, and reprocessing
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Better process understanding
ADVANTAGES OF PAT[10]
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Reduction in batch failure
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Increased operational efficiency
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Increased process understanding and continuous improvement
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Ability to transfer the learning to other unit operations and products
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More data-driven decisions
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Improve Regulatory Compliance
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Increase Productivity
PAT TOOLS
A. Multivariate tools for design, data acquisition and analysis
B. Process analyzers
C. Process control tools
D. Continuous improvement and knowledge management tools
A. Multivariate Tools for Design, Data Acquisition and Analysis
A physical, chemical, or biological perspective, pharmaceutical products and processes are complex multi-factorial systems. There are many development strategies that can be used to identify optimal formulations and processes. The knowledge acquired in these development programs is the foundation for product and process design. This knowledge base can help to support and justify flexible regulatory paths for innovation in manufacturing and post approval changes. A knowledge base can be of most benefit when it consists of scientific understanding of the relevant multi-factorial relationships (e.g., between formulation, process, and quality attributes), as well as a means to evaluate the applicability of this knowledge in different scenarios (i.e., generalization). This benefit can be achieved through the use of multivariate mathematical approaches, such as statistical design of experiments, response surface methodologies, process simulation, and pattern recognition tools, in conjunctionwith 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 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 address interactions among product and process variables. Experiments conducted during product and process development can serve as building throughout the life of a product. Information from such structured experiments supports 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. When used appropriately, these tools enable the identification and evaluation of product and process variables that may be critical to product quality and performance. The tools may also identify potential failure modes and mechanisms and quantify their effects on product quality.
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B. Process Analyzers
Process analysis has advanced significantly during the past several decades, due to an increasing appreciation for the value of collecting process data. Industrial drivers of productivity, quality, and environmental impact have supported major advancements in this area. Available tools have evolved from those that predominantly take univariate process measurements, such as pH, temperature, and pressure, to those that measure biological, chemical, and physical attributes. Indeed some process analyzers provide nondestructive measurements that contain information related to biological, physical, and chemical attributes of the materials being processed. These measurements can be:
At-line: Measurement where the sample is removed, isolated from, and analyzed in close proximity to the process stream.
On-line: Measurement where the sample is diverted from the manufacturing process, and may be returned to the process stream.
In-line: Measurement where the sample is not removed from the process stream and can be invasive or noninvasive
Process analyzers typically generate large volumes of data. Certain data are likely to be relevant for routine quality assurance and regulatory decisions. In a PAT environment, batch records should include scientific and procedural information indicative of high process quality and product conformance. For example, batch records could include a series of charts depicting acceptance ranges, confidence intervals, and distribution plots (inter- and intrabatch) showing measurement results. Ease of secure access to these data is important for real time manufacturing control and quality assurance. Installed information technology systems should accommodate such functions. Measurements collected from these process analyzers need not be absolute values of the attribute of interest. The ability to measure relative differences in materials before (e.g., within a lot, lot-to-lot, different suppliers) and during processing will provide useful information for process control. A flexible process may be designed to manage variability of the materials being processed. Such an approach can be established and justified when differences in quality attributes and other process information are used to control (e.g., feed-forward and/or feed-back) the process. Advances in process analyzers make real time control and quality assurance during manufacturing feasible. However, multivariate methodologies are often necessary to extract critical process knowledge for real time control and quality assurance. Design and construction of the process equipment, the analyzer, and their interfaces are critical to ensure that collected data are relevant and representative of process and product attributes. Robust design, reliability, and ease of operation are important considerations. Installation of process analyzers on existing process equipment in production should be done after risk analysis to ensure this installation does not adversely affect process or product quality.
C. Process Control Tools
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 process end points to ensure consistent quality of the output materials and the final product.
Design and optimization of drug formulations and manufacturing processes within the PAT framework can include the following steps (the sequence of steps can vary):
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
Within the PAT framework, a process end point is not a fixed time; rather it is 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, and considerations for addressing significant deviations from acceptable process times should be developed. Where PAT spans the entire manufacturing process, the fraction of in-process materials and final product evaluated during production could be substantially greater than what is currently achieved using laboratory testing. Thus, an opportunity to use more rigorous statistical principles for a quality decision is provided. Rigorous statistical principles should be used for defining acceptance criteria for end point attributes that consider measurementand sampling strategies. Multivariate Statistical Process Control can be feasible and valuable to realizing the full benefit of real time measurements. Quality decisions should be based on process understanding and the prediction and control of relevant process/product attributes. This is one way to be consistent with relevant CGMP requirements, as such control procedures that validate the performance of the manufacturing process. Systems that promote greater product and process understanding can provide a high assurance of quality on every batch and provide alternative, effective mechanisms to demonstrate validation .In a PAT framework, validation can be demonstrated through continuous quality assurance where a process is continually monitored, evaluated, and adjusted using validated in-process measurements, tests, controls, and process end points.
D. Continuous Improvement and Knowledge Management
Continuous learning through data collection and analysis over the life cycle of a product is important. These data can contribute to justifying proposals for postapproval changes. 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 Agency.
Opportunities need to be identified to improve the usefulness of available relevant product and process knowledge during regulatory decision making. A knowledge base can be of most benefit when it consists of scientific understanding of the relevant multi-factorial relationships (e.g., between formulation, process, and quality attributes) as well as a means to evaluate the applicability of this knowledge in different scenarios (i.e., generalization). Today's information technology infrastructure makes the development and maintenance of this knowledge base practical.
CONCLUSION
The utilization of process analytical technologies (PAT) facilitates the integration of chemistry, engineering and analytics during API process development and scale-up activities. The benefit to this development 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. Process Analytical Technology in pharmaceutical production checks the quality at-line, in-line or on-line thereby reduce production cycle time.
REFERENCE
1. A.S. Hussain, The Subcommittee on Process Analytical Technologies (PAT): Closing Remarks Document for the FDA’s Advisory Committee for Pharmaceutical Science, February 26, 2002 .
2. Everything You Need to Know about Process Analytical Technology (PAT) Implementations, Thermo scientific paper, Thermo Fischer Scientific Inc. UK, September 14, 2006.
3. Pharmaceutical cGMPs for the 21st Century: A Risk-Based Ap¬proach, FDA, Fall 2004.
4. Guidance for Industry: PAT –A Framework for Innovative Pharma¬ceutical Development, Manufac¬turing and Quality Assurance, FDA, September 2004.
5. A New Pharmaceutical Quality Assessment System for the 21st Century, FDA, December 2009.
6. U.S. Department of Health and Human Services, Food and Drug Administration (2004) Guidance for industry: PAT—a framework for innovative pharmaceutical development, manufacturing and quality assurance fda.gov/downloads/Drugs/Guidance Compliance Regulatory Information/Guidances/ucm070305.pdf Accessed 28 Dec 2009
7. Rathore AS, Gerhardt AS, Montgomery SH, Tyler SM (2009) Biopharm Int 22(1):36–44
8. Read EK, Park JT, Shah RB, Riley BS, Brorson KA, Rathore AS(2009) Biotechnol Bioeng 105(2):276–284
9. Technology Tools for Developing Improved Pharmaceutical Manufacturing Processes. Presented at the Advancing Manufacturing Summit, West Lafayette, IN, May 20, 2003.
10. fda.gov/cder/OPS/PAT.htm
ABBREVATIONS
PAT Process Analytical Technology
FDA The Food & Drug Administration
CDER Center for Drug Evaluation and Research
QbD Quality By Design
CGMP Current Good Manufacturing Practices
API Active Pharmaceutical Ingradient
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