PROCESS VALIDATION
XII. WHAT ARE THE PROCESS VALIDATION OPTIONS?
The guidelines on general principles of process validation [1] mention three options: (1) prospective process validation (also called premarket validation), (2) retrospective process validation, and (3) revalidation. In actuality there are four possible options.
A. Prospective Process Validation
In prospective process validation, an experimental plan called the validation protocol is executed (following completion of the qualification trials) before the process is put into commercial use. Most validation efforts require some degree of prospective experimentation to generate validation support data. This particu- lar type of process validation is normally carried out in connection with the introduction of new drug products and their manufacturing processes. The for- malized process validation program should never be undertaken unless and until the following operations and procedures have been completed satisfactorily:
1. The facilities and equipment in which the process validation is to be conducted meet CGMP requirements (completion of installation qualification)
2. The operators and supervising personnel who will be “running” the validation batch(es) have an understanding of the process and its re- quirements
3. The design, selection, and optimization of the formula have been completed
4. The qualification trials using (10× size) pilot-laboratory batches have been completed, in which the critical processing steps and process variables have been identified, and the provisional operational control limits for each critical test parameter have been provided
5. Detailed technical information on the product and the manufacturing process have been provided, including documented evidence of prod- uct stability
6. Finally, at least one qualification trial of a pilot-production (100× size) batch has been made and shows, upon scale-up, that there were no significant deviations from the expected performance of the process The steps and sequence of events required to carry out a process validation assignment are outlined inTable 8. The objective of prospective validation is to prove or demonstrate that the process will work in accordance with a validation master plan or protocol prepared for pilot-product (100× size) trials.
In practice, usually two or three pilot-production (100× ) batches are pre- pared for validation purposes. The first batch to be included in the sequence
Table 8 Master Plan or Outline of a Process Validation Program
Objective Proving or demonstrating that the process works Type of validation Prospective, concurrent, retrospective, revalidation Type of process Chemical, pharmaceutical, automation, cleaning Definition of process Flow diagram, equipment/components, in-process, fin-
ished product
Definition of process output Potency, yield, physical parameters
Definition of test methods Method, instrumentation, calibration, traceability, preci- sion, accuracy
Analysis of process Critical modules and variables defined by process capa- bility design and testing program
Control limits of critical vari- Defined by process capability design and testing pro-
ables gram
Preparation of validation pro- Facilities, equipment, process, number of validation tri- tocol als, sampling frequency, size, type, tests to perform,
methods used, criteria for success Organizing for validation Responsibility and authority
Planning validation trials Timetable and PERT charting, material availability, and disposal
Validation trials Supervision, administration, documentation Validation finding Data summary, analysis, and conclusions
Final report and recommenda- Process validated, further trials, more process design,
tions and testing
may be the already successfully concluded first pilot batch at 100× size, which is usually prepared under the direction of the organizational function directly responsible for pilot scale-up activities. Later, replicate batch manufacture may be performed by the pharmaceutical production function.
The strategy selected for process validation should be simple and straight- forward. The following factors are presented for the reader’s consideration:
1. The use of different lots of components should be included, i.e., APIs and major excipients.
2. Batches should be run in succession and on different days and shifts (the latter condition, if appropriate).
3. Batches should be manufactured in equipment and facilities desig- nated for eventual commercial production.
4. Critical process variables should be set within their operating ranges and should not exceed their upper and lower control limits during process operation. Output responses should be well within finished product specifications.
5. Failure to meet the requirements of the validation protocol with re- spect to process inputs and output control should be subjected to re- qualification following a thorough analysis of process data and formal review by the CMC Coordination Committee.
B. Retrospective Validation
The retrospective validation option is chosen for established products whose manufacturing processes are considered stable and when on the basis of eco- nomic considerations alone and resource limitations, prospective validation pro- grams cannot be justified. Prior to undertaking retrospective validation, wherein the numerical in-process and/or end-product test data of historic production batches are subjected to statistical analysis, the equipment, facilities and subsys- tems used in connection with the manufacturing process must be qualified in conformance with CGMP requirements. The basis for retrospective validation is stated in 21CFR 211.110(b): “Valid in-process specifications for such charac- teristics shall be consistent with drug product final specifications and shall be derived from previous acceptable process average and process variability esti- mates where possible and determined by the application of suitable statistical procedures where appropriate.”
The concept of using accumulated final product as well as in-process nu- merical test data and batch records to provide documented evidence of product/ process validation was originally advanced by Meyers [26] and Simms [27] of Eli Lilly and Company in 1980. The concept is also recognized in the FDA’s Guidelines on General Principles of Process Validation [1].
Using either data-based computer systems [28,29] or manual methods, retrospective validation may be conducted in the following manner:
1. Gather the numerical data from the completed batch record and in- clude assay values, end-product test results, and in-process data. 2. Organize these data in a chronological sequence according to batch
manufacturing data, using a spreadsheet format.
3. Include data from at least the last 20–30 manufactured batches for analysis. If the number of batches is less than 20, then include all manufactured batches and commit to obtain the required number for analysis.
4. Trim the data by eliminating test results from noncritical processing steps and delete all gratuitous numerical information.
5. Subject the resultant data to statistical analysis and evaluation. 6. Draw conclusions as to the state of control of the manufacturing pro-
cess based on the analysis of retrospective validation data. 7. Issue a report of your findings (documented evidence).
One or more of the following output values (measured responses), which have been shown to be critical in terms of the specific manufacturing process being evaluated, are usually selected for statistical analysis.
1. Solid Dosage Forms
1. Individual assay results from content uniformity testing 2. Individual tablet hardness values
3. Individual tablet thickness values 4. Tablet or capsule weight variation
5. Individual tablet or capsule dissolution time (usually at t50%) or disinte-
gration time
6. Individual tablet or capsule moisture content 2. Semisolid and Liquid Dosage Forms
1. pH value (aqueous system) 2. Viscosity
3. Density
4. Color or clarity values
5. Average particle size or distribution 6. Unit weight variation and/or potency values
The statistical methods that may be employed to analyze numerical output data from the manufacturing process are listed as follows:
1 Basic statistics (mean, standard deviation, and tolerance limits) [21] 2. Analysis of variance (ANOVA and related techniques) [21] 3. Regression analysis [22]
4. Cumulative sum analysis (CUSUM) [23] 5. Cumulative difference analysis [23]
6. Control charting (averages and range) [24,25]
Control charting, with the exception of basic statistical analysis, is proba- bly the most useful statistical technique to analyze retrospective and concurrent process data. Control charting forms the basis of modern statistical process con- trol.
C. Concurrent Validation
In-process monitoring of critical processing steps and end-product testing of current production can provide documented evidence to show that the manufac- turing process is in a state of control. Such validation documentation can be provided from the test parameter and data sources disclosed in the section on retrospective validation.
Test parameter Data source Average unit potency End-product testing Content uniformity End-product testing
Dissolution time End-product testing
Weight variation End-product testing
Powder-blend uniformity In-process testing
Moisture content In-process testing
Particle or granule size distribution In-process testing
Weight variation In-process testing
Tablet hardness In-process testing
pH value In-process testing
Color or clarity In-process testing
Viscosity or density In-process testing
Not all of the in-process tests enumerated above are required to demon- strate that the process is in a state of control. Selections of test parameters should be made on the basis of the critical processing variables to be evaluated.
D. Revalidation
Conditions requiring revalidation study and documentation are listed as follows: 1. Change in a critical component (usually refers to raw materials) 2. Change or replacement in a critical piece of modular (capital) equip-
ment
3. Change in a facility and/or plant (usually location or site)
4. Significant (usually order of magnitude) increase or decrease in batch size
5. Sequential batches that fail to meet product and process specifications In some situations performance requalification studies may be required prior to undertaking specific revalidation assignments.
The FDA process validation guidelines [1] refer to a quality assurance system in place that requires revalidation whenever there are changes in packag- ing (assumed to be the primary container-closure system), formulation, equip- ment or processes (meaning not clear) which could impact on product effective- ness or product characteristics and whenever there are changes in product characteristics.
Approved packaging is normally selected after completing package perfor- mance qualification testing as well as product compatibility and stability studies. Since in most cases (exceptions: transdermal delivery systems, diagnostic tests, and medical devices) packaging is not intimately involved in the manufacturing process of the product itself, it differs from other factors, such as raw materials.
The reader should realize that there is no one way to establish proof or evidence of process validation (i.e., a product and process in control). If the manufacturer is certain that its products and processes are under statistical con- trol and in compliance with CGMP regulations, it should be a relatively simple matter to establish documented evidence of process validation through the use of prospective, concurrent, or retrospective pilot and/or product quality informa- tion and data. The choice of procedures and methods to be used to establish validation documentation is left with the manufacturer.
This introduction was written to aid scientists and technicians in the phar- maceutical and allied industries in the selection of procedures and approaches that may be employed to achieve a successful outcome with respect to product performance and process validation. The authors of the following chapters ex- plore the same topics from their own perspectives and experience. It is hoped that the reader will gain much from the diversity and richness of these varied approaches.
REFERENCES
1. Guidelines on General Principles of Process Validation, Division of Manufacturing and Product Quality, CDER, FDA, Rockville, Maryland (May 1987).
2. Current Good Manufacturing Practices in Manufacture, Processing, Packing and Holding of Human and Veterinary Drugs, Federal Register 43(190), 45085 and 45086, September 1978.
3. Good Manufacturing Practices for Pharmaceuticals, Willig, S. H. and Stoker, J. R., Marcel Dekker, New York (1997).
4. Commentary, Pre-approval Inspections/Investigations, FDA, J. Parent. Sci. & Tech. 45:56–63 (1991).
5. Mead, W. J., Process validation in cosmetic manufacture, Drug Cosmet. Ind., (Sep- tember 1981).
6. Chapman, K. G., A history of validation in the United States, Part I, Pharm. Tech., (November 1991).
7. Nash, R. A., The essentials of pharmaceutical validation in Pharmaceutical Dosage Forms: Tablets, Vol. 3, 2nd ed., Lieberman, H. A., Lachman, L. and Schwartz, J. B., eds., Marcel Dekker, New York (1990).
8. Nash, R. A., Product formulation, CHEMTECH, (April 1976).
9. Pharmaceutical Process Validation, Berry, I. R. and Nash, R. A., eds., Marcel Dekker, New York (1993).
10. Nash, R. A., Making the Paper Match the Work, Pharmaceutical Formulation & Quality (Oct/Nov 2000).
11. Guidance for Industry, Scale-Up & Postapproval Changes, CDER, FDA (Nov 1995).
12. Bala, G., An integrated approach to process validation, Pharm. Eng. 14(3) (1994). 13. Farkas, D. F., Unit operations optimization operations, CHEMTECH, July 1977.
14. Nash, R. A., Streamlining Process Validation, Amer. Pharm. Outsourcing May 2001.
15. Ishikawa, K., What is Total Quality Control? The Japanese Way, Prentice-Hall, Englewood Cliffs, NJ (1985).
16. Nash, R. A., Practicality of Achieving Six Sigma or Zero-Defects in Pharmaceutical Systems, Pharmaceutical Formulation & Quality, Oct./Nov. 2001.
17. CGMP: Amendment of Certain Requirements, FDA Federal Register, May 3, 1996.
18. Box, G. E. and Hunter, J. S., Statistics for Experimenters, John Wiley, N.Y. (1978). 19. Hendrix, C. D., What every technologist should know about experimental design,
CHEMTECH (March 1979).
20. Chapman, K. G., The PAR approach to process validation, Pharm. Tech., Dec. 1984.
21. Bolton, S., Pharmaceutical Statistics: Practical and Clinical Applications, 3rd ed., Marcel Dekker, New York (1997).
22. Schwartz, J. B., Optimization techniques in product formulation. J. Soc. Cosmet. Chem. 32:287–301 (1981).
23. Butler, J. J., Statistical quality control, Chem. Eng. (Aug. 1983). 24. Deming, S. N., Quality by Design, CHEMTECH, (Sept. 1988).
25. Contino, AV., Improved plant performance with statistical process control, Chem. Eng. (July 1987).
26. Meyer, R. J., Validation of Products and Processes, PMA Seminar on Validation of Solid Dosage Form Processes, Atlanta, GA, May 1980.
27. Simms, L., Validation of Existing Products by Statistical Evaluation, Atlanta, GA, May 1980.
28. Agalloco, J. P., Practical considerations in retrospective validation, Pharm. Tech. (June 1983).