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PROCESS VALIDATION: A CRITICAL TOOL IN QUALITY ASSURANCE

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About Authors:
Abhilash Kumar, Navneet Upadhay
School of Pharmaceutical Sciences,
Shoolini University,
Solan
, H.P., India
*abhilashkumarchawla421@gmail.com

Abstract
The objective of this work is to overview the process validation in various pharmaceutical processes. Quality is the most important requirement in the manufacturing process. All the drugs must be manufactured to the highest quality level. Quality cannot be guaranteed just by end product testing but we have to control carefully each critical step during the manufacturing process. Thus process validation plays an important role to control each critical step in order to maintain quality of the final product. Validation involves a series of activities that are taking place during the life cycle of products and processes. It also involves careful planning of various steps in the process and all the work should be carried out in a structured way according to standardized working procedures.

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Reference Id: PHARMATUTOR-ART-1296

Introduction [1, 2, 13]
The concept of validation was first proposed by two Food and Drug Administration (FDA) officials, Ted Byers and Bud Loftus, in the mid 1970’s in order to improve the quality of pharmaceuticals. The first validation activities were focused on the processes involved in making these products, but quickly spread to associated processes including environmental control, media fill, equipment sanitization and purified water production.

In a guideline,Process validation can be defined as documented evidence that the process, operated within established parameters, can perform effectively and reproducibly to produce a medicinal product meeting its predetermined specifications and quality attributes.“(FDA 1987)

A properly designed system will provide a high degree of assurance that every step, process, and change has been properly evaluated before its implementation. Testing a sample of a final product is not considered sufficient evidence that every product within a batch meets the required specification.

Validation in itself does not improve processes but confirms that the processes have been properly developed and are under control. Adequate validation is beneficial to the manufacturer in many ways – It deepens the understanding of processes; decreases the risk of preventing problems, defect costs, regulatory non compliances and thus assures the smooth running of the process.  In general, an entire process is validated and a particular object within that process is verified. The regulations also set out an expectation that the different parts of the production process are well defined and controlled, such that the results of that production will not substantially change over time.

Basic Principles of Quality Assurance [3]
Effective process validation contributes significantly to assuring drug quality. The basic principle of quality assurance is that a drug should be produced that is fit for its intended use; this principle incorporates the understanding that the following conditions exist:
1. Quality, safety, and efficacy are designed or built into product.
2. Quality cannot be adequately assured merely by in-process and finished product inspection or testing.
3. Each step of manufacturing process is controlled to assure that the finished product meets all design characteristics and quality attributes including specifications.

Goal of Validation [1, 4]
The goal for the regulators is to ensure that quality is built into the system at every step, and not just tested for at the end, as such validation activities will commonly include training on production material and operating procedures, training of people involved and monitoring of the system whilst in production. In general, an entire process is validated; a particular object within that process is verified. The regulations also set out an expectation that the different parts of the production process are well defined and controlled, such that the results of that production will not substantially change over time. This also extends to include the development and implementation as well as the use and maintenance of computer systems. The software validation guideline states: “The software development process should be sufficiently well planned, controlled, and documented to detect and correct unexpected results from software changes.”

Why Validate? [1, 2]
Where process results cannot be fully verified during routine production by inspection and test, the process must be validated according to established procedures. When any of the conditions listed below exist, process validation is the only practical means for assuring that processes will consistently produce devices that meet their predetermined specifications:
1. Routine end-product tests have insufficient sensitivity to verify the desired safety and efficacy of the finished devices;
2. Clinical or destructive testing would be required to show that the manufacturing process has produced the desired result or product.
3. Routine end-product tests do not reveal all variations in safety and efficacy that may occur in the finished devices.
4. The process capability is unknown, or it is suspected that the process is barely capable of meeting the device specifications

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Elements of Validation [4, 11]

Design Qualification (DQ) -Defines the functional and operational specification of the instrument, program, or equipment and details the rationale for choosing the supplier.

Installation Qualification (IQ) –Demonstrates that the process or equipment meets all specifications, is installed correctly, and all required components and documentation needed for continued operation are installed and in place.

Operational Qualification (OQ) –Demonstrates that all facets of the process or equipment are operating correctly.

Performance Qualification (PQ) –Demonstrates that the process or equipment performs as intended in a consistent manner over time.

Component Qualification (CQ) –is a relatively new term developed in 2005. This term refers to the manufacturing of auxiliary components to ensure that they are manufactured to the correct design criteria. This could include packaging components such as folding cartons, shipping cases, labels or even phase change material. All of these components must have some type of random inspection to ensure that the third party manufacturer's process is consistently producing components that are used in the world of GMP at drug or biologic manufacturer.

Types of Process Validation [4, 11]
The guidelines on general principles of process validation mentions four types of validation:

1. Prospective Validation
Prospective validation is conducted before a new product is released for distribution or, where the revisions may affect the product's characteristics, before a product made under a revised manufacturing process is released for distribution.

2. Concurrent validation
Concurrent validation is a subset of prospective validation and is conducted with the intention of ultimately distributing product manufactured during the validation study.Concurrent validation may be conducted on a previously validated process to confirm that the process is validated. If there have been no changes to the process and no indications that the process is not operating in a state of control, product could be released for distribution before revalidation of the process is completed. There is some risk to early release of product in that subsequent analysis of data may show that the process is not validated.

3. Retrospective Validation
Retrospective validation is the validation of a process based on accumulated historical production, testing, control, and other information for a product already in production and distribution. This type of validation makes use of historical data and information which may be found in batch records, production log books, lot records, control charts, test and inspection results, customer complaints or lack of complaints, field failure reports, service reports, and audit reports. Historical data must contain enough information to provide an in-depth picture of how the process has been operating and whether the product has consistently met its specifications. Retrospective validation may not be feasible if all the appropriate data was not collected, or appropriate data was not collected in a manner which allows adequate analysis. If historical data is determined to be adequate and representative, an analysis can be conducted to determine whether the process has been operating in a state of control and has consistently produced product which meets its predetermined specifications and quality attributes. The analysis must be documented.

4. Revalidation
As long as the process operates in a state of control and no changes have been made to the process or output product, the process does not have to be revalidated. Whether the process is operating in a state of control is determined by analyzing day-to-day process control data and any finished device testing data for conformance with specifications and for variability.

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Planning the Process Validation Study [4, 5, 9]
Careful planning of a validation study is essential to ensure that the process is adequately validated. The plan should include design reviews. The plan for the validation study is documented in the validation protocol. A copy of the protocol and validation results is placed in the Design History File (DHF) or quality system record file. And other production-related procedures are part of the device master record (DMR). Planning for the validation should include the following elements as well as any other relevant issues that must be addressed to conduct the validation study:

1. Identification of the process to be validated;
2. Identification of device to be manufactured using this process;
3. Criteria for a successful study;
4. Length and duration of the study;
5. Assumptions (shifts, operators, equipment, components);
6. Identification of equipment to be used in the process;
7. Identification of utilities for the process equipment and quality of the utilities;
8. Identification of operators and required operator qualifications
9. Complete description of the process;
10. Relevant specifications including those for the product, components, manufacturing materials, the environment, etc.
11. Any special controls or conditions to be placed on preceding processes during the validation;
12. Process parameters to be controlled and monitored, and methods for controlling and monitoring;
13. Product characteristics to be monitored and method for monitoring;
14. Any subjective criteria used to evaluate the product;
15. Definition of what constitutes non-conformance for both measurable and subjective criteria;
16. Statistical methods for data collection and analysis;
17. Consideration of maintenance and repairs;
18. Conditions that may indicate that the process should be revalidated;
19. Stages of the study where design review is required; and
20. Approval of the protocol.

The validation plan should also cover the installation and operation qualification of any equipment used in the process, process performance qualification, and product performance qualification.

Stages of process validation [9, 11, 12]
Process validation involves a series of activities taking place over the lifecycle of the product and process.

Stage 1 – Process Design:
The commercial process is defined during this stage based on knowledge gained through development and scale-up activities.

Stage 2 – Process Qualification:
During this stage, the process design is confirmed as being capable of reproducible commercial manufacturing.

Stage 3 – Continued Process Verification:
Ongoing assurance is gained during routine production that the process remains in a state of control.

Methods and Tools for Process Validation [3, 14, 15, 16]
Validation requires documented evidence that a process consistently conforms to requirements. It requires that you first obtain a process that can consistently conform to requirements and then that you run studies demonstrating that this is the case. Statistical tools can aid in both tasks

Strategies and Tools for Reducing Variation and Optimization
Each unit of product differs to some small degree from all other units of product. These differences, no matter how small, are referred to as variation. Variation can be characterized by measuring a sample of the product and drawing a histogram.

For measurable characteristics like strip length, fill volume, and seal strength, the goal is to optimize the average and reduce the variation. Optimization of the average may mean to centre the process as in the case of fill volumes, to maximize the average as is the case with seal strengths, or to minimize the average as is the case with harmful emissions. In all cases, variation reduction is also required to ensure all units are within specifications. Reducing variation requires the achievement of stable and capable processes. The figure below shows an unstable process. The process is constantly changing. The average shifts up and down. The variation increases and decreases. The total variation increases due to the shifting.

However, stability is not the only thing required. Once a consistent performance has been achieved, the remaining variation must be made to safely fit within the specification limits. Such a process is said to be stable and capable. Such a process can be relied on to consistently produce good product.

A capability study is used to determine whether a process is stable and capable. It involves collecting samples over a period of time. The average and standard deviation of each time period is estimated and these estimates plotted in the form of a control chart. These control charts are used to determine if the process is stable. If it is, the data can be combined into a single histogram to determine its capability. To help determine if the process is capable, several capability indices are used to measure how well the histogram fits within the specification limits. One index, called Cp, is used to evaluate the variation. Another index, Cpk, is used to also evaluate the centring of the process. Together these two indices are used to decide whether the process passes. The values required to pass depend on the severity of the defect (major, minor, and critical).

While capability studies evaluate the ability of a process to consistently produce good product, it does little to help achieve such processes. Reducing variation and the achievement of stable processes requires the use of numerous variation reduction tools. Variation of the output is caused by variation of the inputs. Consider a pump. An output is flow rate. Suppose the pump uses a piston to draw solution into a chamber through one opening and then pushes it back out another opening. Valves are used to keep the solution moving in the right direction. Flow rate will be affected by piston radius, stroke length, motor speed and valve backflow to name a few. Flow rate varies because piston radius, stroke length, etc. varies. Variation of the inputs is transmitted to the output as shown below.

Descriptions of the Tools
A brief description of each of the cited tools follows:

Acceptance Sampling Plan– An acceptance sampling plan takes a sample of product and uses this sample to make accept or reject decision. Acceptance sampling plans are commonly used in manufacturing to decide whether to accept (release) or to reject (hold) lots of product. However, they can also be used during validation to accept (pass) or to reject (fail) the process. Following the acceptance by a sampling plan, one can make a confidence statement such as: "With 95% confidence, the defect rate is below 1% defective."

Analysis of Means (ANOM)– Statistical study for determining if significant differences exist between cavities, instruments, etc. It has many uses including determining if a measurement device is reproducible with respect to operators and determining if differences exists between fill heads, Simple and more graphical alternative to Analysis of Variance (ANOVA).

Analysis of Variance (ANOVA)– Statistical study for determining if significant differences exist between cavities, instruments, etc. Alternative to Analysis of Means (ANOM).

Capability Study– Capability studies are performed to evaluate the ability of a process to consistently meet a specification. A capability study is performed by selecting a small number of units periodically over time. Each period of time is called a subgroup. For each subgroup, the average and range is calculated. The averages and ranges are plotted over time using a control chart to determine if the process is stable or consistent over time. If so, the samples are then combined to determine whether the process is adequately centred and the variation is sufficiently small. This is accomplished by calculating capability indexes. The most commonly used capability indices are Cp and Cpk. If acceptable values are obtained, the process consistently produces product that meets the specification limits. Capability studies are frequently towards the end of the validation to demonstrate that the outputs consistently meet the specifications. However, they can also be used to study the behaviour of the inputs in order to perform a tolerance analysis.

Challenge Test– A challenge test is a test or check performed to demonstrate that a feature or function is working. For example, to demonstrate that the power backup is functioning, power could be cut to the process. To demonstrate that a sensor designed to detect bubbles in a line works, bubbles could be purposely introduced.

Component Swapping Study– Study to isolate the cause of a difference between two units of product or two pieces of equipment. Requires the ability to disassemble units and swap components in order to determine if the difference remains with original units or goes with the swapped components.

Control Chart– Control charts are used to detect changes in the process. A sample, typically consisting of 5 units, is selected periodically. The average and range of each sample is calculated and plot. The plot of the averages is used to determine if the process average changes. The plot of the ranges is used to determine if the process variation changes. To aid in determining if a change has occurred, control limits are calculated and added to the plots. The control limits represent the maximum amount that the average or range should vary if the process does not change. A point outside the control limits indicates that the process has changed. When a change is identified by the control chart, an investigation should be made as to the cause of the change. Control charts help to identify key input variables causing the process to shift and aid in the reduction of the variation. Control charts are also used as part of a capability study to demonstrate that the process is stable or consistent.

Designed Experiment– The term designed experiment is a general term that encompasses screening experiments, response surface studies, and analysis of variance. In general, a designed experiment involves purposely changing one or more inputs and measuring the resulting effect on one or more outputs.

Dual Response Approach to Robust Design– One of three approaches to robust design involves running response surface studies to model the average and variation of the outputs separately. The results are then used to select targets for the inputs that minimize the variation while entering the average on the target, requires that the variation during the study be representative of long term manufacturing. Alternatives are Taguchi methods and robust tolerance analysis.

References
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2. Agalloco J. Validation: an unconventional review and reinvention. PDA J. Pharm. Sci. Tech. 49:175–179 (1995).
3. Aleem H, Zhao Y, Lord S, McCarthy T and Sharratt P. Pharmaceutical process validation: an overview. J. Proc. Mech. Eng. 217: 141-151 (2003).
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11. Nash R. A. and Wachter A. H. Pharmaceutical Process Validation An International Third Edition. Revised and Expanded, Marcel Dekkar, Inc., New York, 2003; 129:760-792.
12. Virmani T and Pathak K. Validation: An Essentiality in the Pharmacy. Vol. 5:22-24 (2007).
13. Agalloco, J. (1995), 'Validation: an unconventional review and reinvention', PDA J Pharm Sci Technol., vol. 49, no. 4, pp. 175–179.
14. Akers, J. (1993), 'Simplifiying and improving Process Validation', Journal of Parenteral Science and Technology, vol. 47, no. 6, pp. 281–284.
15. Parker G, (2005) ‘Developing Appropriate Validation and Testing Strategies’ Presented for Scimcon Ltd at the Thermo Informatics World Conference. North America.
16. European Commission Enterprise Directorate-General (2001), Final Version of Annex 15 to the EU Guide to Good Manufacturing Practice, Qualification and Validation, Brussels. European Commission Enterprise Directorate-General.

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