FDA's Draft Guidance for Process Validation: Can It Be Applied Universally?2

The draft guidance has a broad scope from API production through to finished dosage forms and therefore must be applicable in various situations. The validation methodology outlined provides substantial flexibility to the industry, avoiding details that might fit well in one instance but be wholly inappropriate in another. Objectively speaking, the guidance's lack of definitions can be viewed both positively and negatively. Firms with extensive knowledge of their processes will appreciate that FDA believes that they should be abe to validate their own processes without having to accommodate arbitrary precepts. On the other hand, firms lacking the requisite process knowledge would likely have preferred substantially more detail with regard to regulatory expectations for process validation. The draft describes FDA's process-validation expectations from a "what to" rather than a "how to" perspective. That approach is wholly consistent with the 21 CFR Part 211 current good manufacturing practices (CGMPs), where firms are given great latitude with respect to the methods used to realize the compliance requirements (5).
The document properly places process equipment in a secondary role. Equipment qualification is certainly expected, but only as a means to support the process and not as the focus of the effort or an end in itself. Providing clarity in this regard is an important step forward and is consistent with FDA's risk-based compliance objective, the recent American Society for Testing and Materials effort at refocusing equipment qualification activities, and industry publications that cite the need for focus on critical end-product quality concerns (6–8).
In summary, the usefulness of the draft guidance for validating pharmaceutical production processes and products is unquestionable. The life-cycle model will result in development and validation exercises that provide relevant and meaningful information. The link between the process parameters that influence the critical quality attributes will serve the industry well. The use of statistical methods will add a rigor to the validation efforts that has been sorely lacking.
Applying the guidance outside the process or product focusPerhaps the only question that need be asked is how easily the draft guidance can be applied to processes and systems that are less clearly related to end-product quality attributes, or where the development model described might be substantially different. The most important of these systems and processes commonly used in the pharmaceutical industry include:

• Critical process utilities (e.g., water systems and compressed gases)
• Classified and controlled environments (e.g., ISO 5–8 rooms and cold boxes)
• Computerized systems (e.g., process control and manufacturing resource planning)
• Inspection of product attributes (e.g., visible particle and labeling)
• Cleaning and preparation procedures (e.g., product-contact parts and surfaces)
• Sterilization processes (e.g., steam, dry heat, and radiation)
• Aseptic processing (e.g., filling and compounding)
• Manual procedures (e.g., gowning and sanitization).

The ease with which the guidance can be applied varies substantially with the particular process, and each general category is best considered individually.

FDA's Draft Guidance for Process Validation: Can It Be Applied Universally?1

By: James P. Agalloco
In November 2008, the US Food and Drug Administration issued Draft Guidance for Industry—Process Validation: General Principles and Practices (1). The guidance outlines regulatory expectations for process validation following a "life-cycle concept" that describes a "cradle-to-grave" approach for validating pharmaceutical processes (2). The life-cycle approach builds upon the results of experimental activities during development to define operating parameters and product specifications that are used in initial and ongoing process qualification. The life-cycle concept provides a robust means for the development, manufacture, and control of pharmaceutical products.
The guidance document covers validation largely at a conceptual level and avoids narrow precepts and specific examples. This approach is appropriate because the document addresses the subject from active pharmaceutical ingredient (API) production (by either chemical synthesis or biological processes) through drug-product production for all pharmaceutical dosage forms. The intended breadth of coverage embraces a myriad of unit operations in the preparation and manufacture of these products. Unstated is whether the draft guidance is intended to be applied to supportive processes that are not an inherent part of the formulation process. Among the support processes are cleaning, inspection, sterilization, and aseptic processing. Each of these processes can be an essential part of pharmaceutical manufacture that requires validation.
Basics of the draft guidance
The draft guidance recommends a defined and structured approach for process-validation activities within an organization. During the design and development stage, experiments should define the relationship between the independent process parameters and the dependent product attributes. These studies should be conducted in a predefined manner, and the results should be documented for later reference. The goal of process development is the attainment of knowledge regarding the process–product relationships to support later commercial production. The greater the knowledge accumulated at this early period, the more assurance the firm will have that it can successfully launch and maintain the process at a commercial scale. From a compliance perspective, this approach makes excellent sense; the knowledge gained during preclinical and clinical-stage experimentation provides a way to link clinical data obtained at the smaller scales with data from the later production process. A well-developed process is one for which the critical process parameters have been identified and control ranges for each have been established (3). The development experiments should evaluate the interaction between the independent and dependent variables until the results of the process are predictable and routinely acceptable. Multifactorial experiments can assess the relationships between the variables and build knowledge about the process's limitations. The experiments performed at a smaller scale establish the acceptable ranges for the various independent process parameters. Thus, when the process is operated under the appropriate conditions, operators have substantially greater confidence that the desired quality attributes of the product will be realized. The acceptability of the end result is ascertained using samples of the completed materials. Initial and ongoing qualification of production processes are the means for establishing and confirming the experimental experience at significantly larger scales of operation. Knowledge gleaned from the development simplifies later activities. Production processes are always operated within the defined operating ranges because there is no reason to experiment with conditions at the extreme ends of the ranges on this larger scale. Challenges during these stages are primarily in the number of tests performed on the produced materials. Qualification lots are customarily sampled at a substantially higher rate than are routine production lots, and testing of these expanded samples is the challenge of the commercial process. The guidance recommends using appropriate statistical tools in the full-scale qualification efforts to provide the desired confidence in process and product acceptability and thus attain the desired validated state. The expectations for statistical evidence in process validation are well founded; sampling batches at the modest levels associated with pharmacopeial tests provide little, if any, proof of end-product quality. Although those tests may be legally binding, they have only limited value. Industry has largely ignored the levels of "real quality" needed to support its claims for patient welfare (4). 

What is Disinfectant Validation? 3

Here, we define sanitation process validation as "establishing documented evidence that a sanitation process will consistently reduce microorganism populations on inanimate surfaces to preestablished levels that are considered safe." The preestablished microbial bioburden levels must be based on public health codes, regulations, industry guidelines, or a scientifically sound rationale.
In the pharmaceutical and food industries, the sanitation process typically follows a cleaning stage. Cleaning with a detergent usually removes or kills more than 90% of vegetative bacteria present on surfaces. The surviving microorganisms are mostly surface-attached, and the sanitation process will inactivate them in-situ.
Disinfectant effectiveness tests do not mirror in-service conditions. The test microorganism inoculum used in DETs does not mimic the behavior of environmental growth (e.g., biofilms and surface-adhered microorganisms). In addition, DETs fail to consider the effects of the preceding cleaning stage. Therefore, the testing conditions used by agents manufacturers for disinfection and sanitation have little relationship to the sanitation processes actually used in the pharmaceutical industry. Manufacturers rely on use-dilution and carrier tests to provide data for registration in the United States and for recommended in-use concentrations.
Validation of a sanitation process should instead be based on empirical measurements of sanitizer effectiveness in working conditions, suggesting the logical equation: successful field tests under in-use conditions = validation.
For chemical disinfectant and sanitizing agents already registered under FIFRA in the United States or the CEN TC 216 work program in the EU, there would be no need to perform in-house effectiveness tests. The DETs already performed by the manufacturer for registration should be sufficient. Abundant experience and USP draft chapter ‹1072› show that sanitizers and disinfectants are more effective in actual use than the DETs indicate. Furthermore, the high inoculum counts used in the laboratory studies represent a "worst-case" scenario. We should, therefore, be able to rely on a second logical equation:
registration = qualification.
This approach would make in-house DETs unnecessary, and would greatly simplify the sanitation validation process by removing the most difficult step; performing DETs. Only sanitizing or disinfecting agents that have not been registered for the intended purpose would then require additional qualification with DETs.
Surface tests would still be required to develop procedures for sanitation processes. These tests would assess the effectiveness of the selected sanitizer against surface-adhered microorganisms. In these assays, microorganisms are dried onto surfaces, sanitized, and then removed for counting by conventional techniques or rapid microbiological methods (RMMs). Established surface tests are straightforward and inexpensive, and can thus be carried out by most microbiology laboratories. Surface tests can reflect in-use conditions like contact times, temperatures, use-dilutions, and surface properties. These tests can be modified to follow a representative cleaning stage, and so will better mimic real in-use conditions. The proposed sanitation process would be developed from the surface tests. Finally, the proposed sanitation process would be validated via challenge in field tests.
The sanitation process validation would then be performed following a simple methodology:

• clean the equipment;
• sanitize the equipment;
• take microbial bioburden samples.

Summary
The validation of agents for sanitizing and disinfecting seems like a major undertaking, but does not need to be. These agents are not validated; they are qualified for the intended purpose, and then the sanitation process itself is validated. The most difficult part in sanitation process validation would be the execution of disinfectant effectiveness tests by the user. In-house DETs are superfluous, however, when the selected sanitizer or disinfectant has been registered in accordance with FIFRA or the CEN TC 216 work program. The sanitation process validation methodology proposed here includes cleaning as part of the sanitation process, and sanitation process validation becomes the successful execution of field tests under actual in-use conditions. The resulting methodology would be cost-effective, simple, and time-saving.
Acknowledgment
My gratitude to Daniel Y. C. Fung, professor of food science at Kansas State University, for his kindness in reviewing a draft of this article. And special thanks to Douglas McCormick, Advanstar Communications, for his assistance in the final editing of the manuscript.
José E. Martinez is a consultant for JEM Consulting Services Inc., PMB 652 Box 4956; Caguas, PR 00726, tel. 787.349.3857, jemartin@coqui.net
References
1. US Food and Drug Administration, "Guideline on General Principles of Process Validation," (FDA, Rockville, MD, 1987), http:// http://www.fda.gov/cder/guidance/pv.htm, accessed Feb. 15, 2006.
2. Draft General Informational Chapter ‹1072›, "Disinfectants and Antiseptics," Pharmacopeial Forum (2002).
3. E.C. Cole, W.A. Rutala, and G.P. Samsa, "Disinfectant Testing Using A Modified Use-Dilution Method: Collaborative Study," J. Assoc. Off. Anal. Chem. 71 (6), 1187–1194 (1988). 4. E.C. Cole and W.A. Rutala, "Bacterial Numbers on Penicylinders Used in Disinfectant Testing: Use of 24 Hour Adjusted Broth Cultures," J. Assoc. Off. Anal. Chem. 71 (1), 9–11 (1988).
5. E.C. Cole, W.A. Rutala, and J.L. Carson, "Evaluation of Penicylinders Used in Disinfectant Testing: Bacterial Attachment and Surface Texture," J. Assoc. Off. Anal. Chem. 70 (5), 903–906 (1987).
6. S.F. Bloomfield et al., "Development of Reproducible Test Inocula for Disinfectant Testing," Int. Biodeterioration & Biodegradation 36 (3–4), 311–331 (1995).
7. M.D. Johnston, E.A. Simons, and R.J.W. Lambert, "One Explanation for the Variability of the Bacterial Suspension Test," J. Appl. Microbiology 88 (2), 237–250 (2000).
8. S.B.I. Luppens, F.M. Rombouts, and T. Abee, "Disinfectants in a Suspension Test," J. Food Prot. 65 (1), 124–129 (2002).
9. V.S. Springthorpe and S.A. Sattar, "Carrier Tests to Assess Microbicidal Activities of Chemical Disinfectants for Use on Medical Devices and Environmental Surfaces," J. Assoc. Off. Anal. Chem. Int. 88 (1), 182–201 (2005).
10. Center for Food Safety and Applied Nutrition, "Comprehensive List of Terms," (FDA, Rockville, MD, 2001), http:// http://www.cfsan.fda.gov/~dms/a2z-s.html, accessed Feb. 15, 2006.
11. US Environmental Protection Agency (EPA), "Antimicrobial Pesticide Products," (EPA, Washington, DC, 2004), http:// http://www.epa.gov/pesticides/factsheets/antimic.htm, accessed Feb. 15, 2006.
12. "Disinfectants," (Essential Industries, Inc., Merton, WI), http:// http://www.essind.com/Disinfectants/DN-Glossary.htm#Disinfection, accessed Feb. 15, 2006.

What is Disinfectant Validation? 2

The CEN TC 216 methodology has three phases to its Disinfectant Effectiveness Test—European Tiered Test Approach (see Table II).
As a result of the CEN TC 216 work program, and with the concurrence of the EU Biocides Directive, several BS EN (European Standards as adopted by the British Standards Institute, test methods have been issued. These pass–fail test methods detail how disinfectants should be tested against selected cultures under controlled laboratory conditions. They also require an evaluation of the laboratory test results against field test results (see Table III).
Disinfectant effectiveness tests are not yet routine, so most drug-industry microbiologists are unfamiliar with them. The protocols take a long time to learn and a long time to perform, which makes them expensive. In addition, they are difficult to do and are done infrequently, which makes it hard to maintain laboratory personnel proficiency and hard to produce reproducible results.

Table III: British Standards (BS) EN test methods.

Reproducibility is critical to proper interpretation of results. Variability in AOAC`s tests is mostly caused by errors in preparing test solutions and media. Some studies have shown, however, that the AOAC testing technique (3, 4) and inoculum preparation (5) may also be at fault. Variations in conditions during cold storage (i.e., refrigeration, freezing, freeze-drying) can affect the predominant genotype in the stock culture or source culture (6). Thus, when pharmaceutical companies try to validate disinfection processes using AOAC protocols, they may be unable to reproduce the disinfectant manufacturers' bactericidal label claims. CEN TC 216 tests are also liable for reproducibility problems caused by variation in the inoculum size and preparation (7, 8). In the United States, the EPA shares regulatory authority with FDA, which has jurisdiction under 21 CFR 880.6890, General purpose disinfectants. This Subpart (concerned with medical devices), defines a general purpose disinfectant as a "germicide intended to process noncritical medical devices and equipment surfaces." (Noncritical medical devices make only topical contact with intact skin.) Liquid chemical sterilizers intended for use on critical or semicritical medical devices are defined and regulated by FDA under 21 CFR 880.6885, Liquid chemical sterilants/high-level disinfectants.
Sanitation process validation

Definitions and distinctions

A review of regulatory definitions (see sidebar, "Definitions and Distinctions") quickly leads to the realization that "disinfectant validation" is actually equivalent to sanitation process validation. This analysis leads to the following proposal for improving the validity of the validation process while making it easier and less time-consuming to perform. This approach is based on the CEN TC 216 work program methodology for registering disinfectants in the European Union and on the quantitative carrier tests (QCT) developed by the University of Ottawa for environmental surfaces and medical devices (9). Like the CEN TC 216 method, the Ottawa QCTs first assess microbicidal performance under ideal conditions, and then move on to assess field performance with more stringent tests.

A Risk-Management Approach to Cleaning-Assay Validation 4

Analytical methods. The model and worst-case compound evaluation did not replace any analytical-method validation activities. Analytical recovery must be established for each compound in the portfolio, but not on all surfaces. If multiple limits are to be considered, or if a range of reporting is required, the lowest limit may be evaluated, and that recovery can be applied to all acceptance limits as a conservative estimate. In the analytical method, three recovery factors were presented: Group 1, Group 2, and Group 3. Methods could be validated for any surface within a group and could be considered representative. The authors chose stainless steel 316L because it is the most prevalent, and cast iron because it is a common material on a tablet press. Type III hard anodized aluminum is the only surface in Group 3. This strategy did not ignore any uncommon surfaces. It grouped them appropriately, swabbed them, and applied a representative recovery factor.
Variability. Method variability was evaluated by performing a control sample (Compound A, 6 replicates, 0.5-μg swab, stainless steel 316L, Ra = 3.5) each day. The mean recovery of the entire experiment was 52%. These data suggested that the swabbing ability of the analyst did not change over time. The standard deviation within a day typically was less than 6. The pooled-within-run standard deviation was 3.99 over the course of the experiments. This value was used as a criterion for grouping new surfaces. The day-to-day standard deviation was 15.34.

Figure 5

Incorporating new materials of construction into the grouping strategy. Periodically, new equipment will be introduced into the CTM area that incorporates a product-contact surface made of a material of construction that is not listed in Table II. This problem is often caused by alloys of metals that have already been evaluated and by polymers of proprietary composition. Because surface recovery must be evaluated, personnel need a way to incorporate new surfaces into the groupings outlined in Table II. When a new piece of equipment is purchased, CTM-engineering employees prepare the needed documentation to evaluate the equipment with regard to the cleaning program before use. If an identified sampling location is made of a new material of construction, the engineer asks the person responsible for the cleaning program and analytical development to perform the next steps, which are shown in Figure 5. Suppose that new equipment incorporated three new materials: a crystalline thermoplastic polyester marketed under the trade name of Ertalyte (Quadrant Engineering, Reading, PA), stainless steel 420, and stainless steel 630. Without the grouping strategy in place, method revalidation would have to occur for all compounds handled by this piece of equipment. With the strategy in place, these surfaces are placed into groups based on model-compound recovery. No method revisions are required. The analytical recovery of Compound A was evaluated for Ertalyte, stainless steel 420, and stainless steel 630 at the 5.0-μg/in.² level. The authors used the validated method to evaluate the recovery of the three new surfaces compared with a representative surface from Group 1 (i.e., stainless steel 316L), Group 2 (i.e., cast iron), and Group 3 (Type III hard anodized aluminum). Recovery was evaluated for both the group representative and the new surfaces on the same two days with three replicates on each day. As an alternative, six replicates may be performed on the same day as the controls because the comparison of recovery is relative.
The new surfaces are placed in one of the three groups or define a new lower group, based on how close their average is to that of the group control. The authors obtained a cutoff value of 3.0% in the following manner. Based upon the data for the control, the within-day standard deviation was calculated to be 3.99 and was used in Equation 1. A series of one-sided hypothesis tests with an error rate of α = 0.10 were performed to assess whether the new surface mean was less than a specified control-surface mean. The confidence limit half-width for the difference between the means of two surfaces was computed using the within-day standard deviation because the replicates for each surface had to be run on the same two days, with each day treated as a block. For these calculations, it was assumed that this standard deviation was known. The lower one-sided confidence limit for the difference in means was derived using the following steps: This calculation did not indicate a difference between a control surface and the new surface under evaluation if the recovery differed by less than 3.0%. This approach was conservative because Eq. 1 categorized a new surface into Group 2 if it differed from stainless steel by more than 3.0%, which could be viewed as a strict criterion. Because the results were obtained on the same days and runs, the authors believed that this approach was reasonable. When evaluating the recovery of a new surface, this strategy helps personnel to place each material into the appropriate group. The grouping starts with a comparison of the new surface average to that of Group 1 (stainless steel 316L) and continues sequentially. If the new surface recovery (NSR) is more than 3.0% less than that of the group reference, it is compared with the reference surface in the next lower group until a group is found with which it does not differ by more than 3.0%. If no such group is found, then the new surface forms a new, lower group. The procedure is as follows:

• If the NSR > mean stainless-steel recovery – 3.0%, the new surface belongs in Group 1.
• If the NSR > mean cast-iron recovery – 3.0%, the new surface belongs in Group 2.
• If the NSR > mean Type III anodized aluminum recovery – 3.0%, the new surface belongs in Group 3.
• If the NSR < mean Type III anodized aluminum recovery – 3.0%, the new surface becomes a new group or becomes the worst-case surface for Group 3 and is used in all future method validations.

Table III: Swab recovery of three new materials of construction compared with controls from each representative group.

The data for the three new surfaces are outlined in Table III. Based on this approach, Ertalyte was placed into Group 1 because the difference between its recovery and that of stainless steel 316L (i.e., Group 1) was less than 3.0% (i.e., 2.48%). The recovery from stainless steel 420 and stainless steel 630 was more than 3.0% less than that from stainless steel 316L, but greater than that from cast iron. The authors placed stainless steel 420 and stainless steel 630 into Group 2. The placement of these two grades of stainless steel into Group 2 highlighted the conservative nature of this approach because their recoveries were only 4% and 8% less than that from stainless steel 316L. The recoveries were 12% and 9% greater than that from cast iron for stainless steel 630 and 420, respectively. Table II was updated to reflect this placement. Because the grouping strategy was conservative, it prevented the underestimation of recovery factors when assay values were reported and prevented the formation of additional groups for method validation unless the recovery value for a new surface is sufficiently low to warrant such an addition. 
Conclusion
The authors' data-driven risk-management approach to cleaning verification methods uses analytical-recovery values for a model compound to place product-contact surfaces into groupings for analytical-method validation. The data generated during the studies supported the formation of three recovery groups to validate analytical swab methods. Groups 1–3 were represented by stainless steel 316L, cast iron, and Type III hard anodized aluminum, respectively. This approach allowed all surfaces to be considered during analytical-method validation and provided an objective mechanism to incorporate new surfaces into the strategy.
The benefits of this strategy are numerous. First, only three surfaces must be validated on each compound, which drastically minimizes the number of recovery values established to support the entire portfolio. Second, the strategy includes a way to add new materials of construction to the cleaning program if new equipment is purchased. Traditionally, all swab methods must be revalidated to incorporate the new surface. With this strategy in place, a model compound is evaluated, the new surface is grouped, and no changes to existing methods are required. Third, the strategy allows for a constant state of compliance. A relative recovery value is known for any material of construction for all equipment.
Because the grouping strategy is applied to a small fraction of the total surface area, no surface material of construction is ignored, each molecule undergoes a typical method validation, and the strategy places surfaces into groups conservatively. The authors believe that the strategy controls risks appropriately and that the data set given in this study scientifically supports the strategy of grouping materials of construction to support analytical methods within the cleaning program. Acknowledgments
The authors would like to acknowledge the following colleagues at Eli Lilly: Gifford Fitzgerald, intern, for generating the swab-recovery data; Ron Iacocca, research advisor, for the SEM data; Sarah Davison, consultant chemist; Mike Ritchie, senior specialist; Mark Strege, senior research scientist; Matt Embry, associate consultant chemist; Kelly Hill, associate consultant for quality assurance; Bill Cleary, analytical chemist; and Laura Montgomery, senior technician, for their contributions and insightful suggestions throughout the project. In addition, Leo Manley, associate consultant engineer, provided the roughness measurements in support of this project.
Brian W. Pack* is a research advisor for analytical sciences research and development, and Jeffrey D. Hofer is a research advisor for statistics, discovery and development, both at Eli Lilly and Company, Indianapolis, IN, tel. 317.422.9043, packbw@lilly.com
.
*To whom all correspondence should be addressed.
Submitted: Oct. 12, 2009. Accepted: Dec. 22, 2009.
References
1. ICH, Q9 Quality Risk Management, Step 5 version (2005).
2. Code of Federal Regulations, Title 21, Food and Drugs (Government Printing Office, Washington, DC), Part 211.67.
3. ICH, Q7 Good Manufacturing Practice Guide for Active Pharmaceutical Ingredients, Step 5 version (2000).
4. FDA, Guideline to Inspection of Validation of Cleaning Processes (Rockville, MD, July 1993).
5. L. Ray et al., Pharm. Eng. 26 (2), 54–64 (2006).
6. PDA, Technical Report 29, "Points to Consider for Cleaning Validation" (PDA, Bethesda, MD, Aug. 1998).
7. G.L. Fourman and M.V. Mullen, Pharm. Technol. 17 (4), 54–60 (1993).
8. ICH, Q2 Validation of Analytical Procedures: Text and Methodology, Step 5 version (1994).
9. R.J. Forsyth, J.C. O'Neill, and J.L. Hartman, Pharm. Technol. 31 (10), 102–116 (2007).