Continued Process Verification for Cleaning Validation– Challenges and Pitfalls

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Pharmaceutical Technology, Pharmaceutical Technology, January 2022 Issue, Volume 46, Issue 1
Pages: 34-37

Continued process verification for a cleaning validation program begins once the validation study is complete.

Peer-Reviewed

Submitted: June 23, 2021
Accepted: September 13, 2021

Abstract

Continued process verification (CPV) for a cleaning validation (CV) program begins once the validation study is complete. Planning for the CPV needs to be considered, however, as the cleaning validation is planned. Otherwise, the necessary parameters for the CPV might not be captured in a way to allow the smooth transition from the CV study to the CPV program to maintain the validated state of cleaning.

Validating cleaning in a pharmaceutical manufacturing facility is a regulatory requirement (1–4). In regulatory guidance documents, program basics, regulatory expectations including prerequisites, and acceptance criteria are reviewed along with the strategy for selection of the product(s) and equipment to validate. Although cleaning validation (CV) execution is described in general terms, guidance documents are limited to describing what to do, but not how to do it. The more recent the guidance update (2), the greater level of specific expectations are included.

The guidance documents include general instructions on how to proceed once cleaning validation is completed. The validated state of cleaning is to be monitored using ongoing testing of the cleaning process to demonstrate continued control of cleaning. This linear approach to cleaning validation resulted in the cleaning maintenance part of the program being slowly neglected, resulting in programs that fell out of compliance. The concept of lifecycle control of manufacturing process validation (5) addressed the shortcomings of the linear approach to process validation using Stage 3–Continued Process Verification (CPV). Using a similar approach, an ongoing program to collect and analyze cleaning parameter data can be applied to cleaning validation. Although the lifecycle approach better addresses the post-validation cleaning program, it still leaves the details up to the individual facility. And the concept of CPV is not mentioned until after the initial validation is complete.

Waiting for the completion of cleaning validation to address CPV will prove problematic as to how to document, gather, and trend the appropriate cleaning process data, critical process parameters (CPPs), hold times, and campaign lengths to clearly demonstrate continued control of the validated cleaning process. CPV is a factor to be considered as the cleaning validation strategy is defined. Addressing cleaning parameters for the long term needs to be addressed as part of the cleaning strategy. Otherwise, there might not be a viable mechanism to capture and trend ongoing cleaning parameters. The CPV strategy should address what cleaning parameters to check only for compliance and what parameters to check for compliance as well as to track and check trends.

Background

To understand the issues of addressing CPV after completion of cleaning validation, start by considering the requirements for cleaning validation:

  • The hardest-to-clean product(s)
  • The lowest cleaning limit
  • Equipment grouping
  • Product/equipment matrix
  • CPPs
  • Demonstration of acceptable and consistent cleaning
  • Dirty equipment hold time (DHT)
  • Clean equipment hold time (CHT)
  • Campaign length.

There are alternatives for establishing the products and equipment to execute cleaning validation, but a grouping strategy is accepted by the regulatory agencies (1–4) and is the most practical approach. A hardest-to-clean product for cleaning can be established using solubility of the formulation API, but this approach ignores excipients and practical experience. A more rugged approach is to look at the cleanability of the formulations using a three-prong methodology (6): formulation composition, personnel experience, and formulation cleanability.

The cleaning limit should be established for every product (6), and the lowest cleaning limit should be the target for the cleaning validation study.Equipment needs to be grouped for validation so that one piece of equipment is representative for the entire group. The product/equipment matrix, which contains the equipment train for each product, is used to decide the most efficient cleaning validation strategy to cover all products and equipment groups.

The product and equipment selection must be made before cleaning validation can commence. The cleaning data to demonstrate acceptable and consistent cleaning as well as the critical cleaning parameters (CCPs) (e.g., time) are captured during cleaning validation execution. The agreed upon DHT, CHT, and campaign length are targeted prior to execution and then confirmed during execution. The CCPs, DHT, CHT, and campaign length can be conveniently captured in the cleaning validation study protocol documentation along with the product and equipment being validated.

Once cleaning validation is successfully completed and a final report documented, the controlled, validated state needs to be maintained.

Continued process verification

Product and equipment. To ensure a smooth transition, a flexible plan or protocol to address CPV should be in place as the cleaning validation study draws to completion. The CPV plan should be drafted by the same team that implemented the CV program and should address all the captured parameters and their ongoing status. The worst-case product, cleaning limit, and equipment grouping will not be affected unless a change is made to the validated state. Changes could include: a new product or a new piece of equipment is introduced; a change is made in a product manufacturing process or equipment configuration; or a product or equipment is retired from service. Any change should go through the change control system and an assessment made as to whether an additional CV study is required. The assessment should include an update of the original product or equipment assessment documentation. The assessment and document update should fall on the CV representative to the Change Control Committee.

Critical cleaning parameters. CCPs were established during cleaning development and are those parameters that have a direct impact on the level on cleanliness from the cleaning process. A clean-in-place (CIP) cleaning cycle records all cleaning parameters and issues a report at the conclusion. A manual cleaning procedure is a more problematic situation as far as identifying and recording CCPs. During development, it can be shown that detergent concentration and water temperature are not critical. Personnel can be trained and qualified for consistent cleaning. Minimum cleaning times can be established, and specific cleaning tools can be identified. During CV of a manual procedure, the CCPs can be recorded by an observer to corroborate the cleaning outcome. However, post-CV recording of CCPs falls on the personnel cleaning the equipment, which is problematic for someone wearing wet, soapy gloves and could lead to CCPs being captured immediately after cleaning.

Trending of CCP data is even more challenging. The CCP data must be extracted from the cleaning checklists, entered into a logbook or database, and trended over time.

Acceptable and consistent cleaning data. The CPV plan should include periodic testing of equipment after cleaning to demonstrate continued control of the validated cleaning. The frequency of the testing should be risk-based with the rationale clearly defined in the CPV plan. Factors that might increase testing frequency include manual cleaning methods vs. CIP methods; low cleaning limits; and CV data that passed but are close to the acceptable residue limit (ARL); all of which increase the risk of subsequent cleaning failures. Capturing and trending CV and CPV data is probably the clearest demonstration of continued control of equipment cleaning in the facility. All data should be trended to demonstrate consistency of cleaning. The challenge here is to update and maintain the CV database. Data processing and trending could fall on the personnel assigned to perform sampling or testing. An alternative would be to mirror the effort used to trend in-process manufacturing data.

Dirty hold time. DHT is established in the CV protocol execution. The end of manufacturing activity is recorded in the product batch record. After a designated DHT, the beginning of cleaning is recorded in the cleaning record or checklist. The executed CV protocol captures both times and determines the effective dirty hold time. DHT is captured for the three validation runs, and the longest of the three is designated the validated DHT based on the equivalency of all CV data. A single DHT, which is applied to all equipment for all products, is applied going into the CPV stage of the cleaning lifecycle.

Post-validation, capturing and relating the two times with the established DHT becomes more problematic. The two times and DHT are in separate documents, and personnel filling out the manufacturing batch record do not see the cleaning record, while personnel cleaning the equipment do not have easy access to the manufacturing batch record. The established DHT is reported in the CV final report, and because the DHT is a cleaning parameter, the end of manufacturing time and established DHT need to be readily available to personnel performing the subsequent cleaning.

There are several options how to capture, maintain, and trend DHT data. A logbook kept with the equipment is the most straight-forward solution. The validated DHT is known and is recorded in the logbook header. The date and time of completion of manufacturing activity are entered each time the piece of equipment is used. At the beginning of cleaning, the date and time are recorded, the DHT is calculated and confirmed to be less than the established DHT.

A second alternative can be capturing DHT electronically in a production record system. This is preferable because system checks can be included to prevent moving forward without appropriate action if the DHT is exceeded.

A third alternative is to capture the DHT at the beginning of the equipment cleaning checklist or record. This would ensure that the DHT is captured but makes trending more difficult in that there is no centralized documentation of the DHT data in a logbook.

Attempting to trend the DHT data is more problematic, because collating DHT data from logbooks for all equipment or individual cleaning records is resource intensive. The best option is to periodically download the DHT information into a database for ongoing trending.

If an electronic manufacturing batch record is used and the equipment cleaning information is captured electronically, then DHT data can be captured. Trending of electronically captured DHT would have to be programmed to download and trend, but once implemented, would not require continued personnel involvement.

The value of the logbook option is limited because the records are paper-based and all entries and data analysis are handwritten. However, the advantage is that the logbook stays with the equipment and compliance is more readily confirmed.

Trending of DHT data provides another level of control to the CPV program. If DHT times increase over time, trending could indicate a problem with scheduling equipment cleaning or manpower resource issues, which could be addressed before a DHT excursion by extending the DHT.

Alternatively, depending on how the equipment is handled at the end of manufacturing, it could be argued that DHT is not critical (7). If the equipment is scraped, vacuumed, and wiped down with solvent (e.g., 70% isopropyl alcohol) to minimize the amount of product residue left on the equipment, then the DHT might be a non-issue because the equipment would be rid of most residue and would already be dry. This strategy would minimize the API released into the wastewater stream during cleaning and limit exposure of personnel during cleaning.

Clean hold time. The issues for tracking and trending CHT are comparable to the DHT issues. CHT is established in a separate CV protocol execution. The end of cleaning activity is recorded in the cleaning record or checklist. After a designated CHT, the beginning of manufacturing is recorded in the product batch record. The executed CV protocol captures both times and determines the effective CHT. The CHT is captured for the three validation runs, and the longest of the three is designated the validated CHT based on the equivalency of all CV data. A single CHT, which is applied to all equipment after cleaning, is applied going into the CPV stage of the cleaning lifecycle.

The options to capture, maintain, and trend CHT data parallel those for DHT. For the logbook option, it might be able to capture both the CHT and DHT in the same logbook and trend quarterly. Downloading and trending the DHT and CHT from a logbook for every piece of equipment is labor intensive.

CHT criticality could also be minimized during validation (7). If CHT is established for an extended period (e.g., > 45–60 days), then equipment held in the same controlled conditions can be used up to the validated CHT.

For clean equipment held outside the manufacturing area, a standard policy of recleaning any equipment being brought into the manufacturing area is customary.

A second option to minimize concern for CHT post validation is to routinely rinse or wipe the equipment with 70% isopropyl alcohol immediately before use.This action further mitigates risk of bioburden proliferation during the CHT.

Trending of CHT data also provides a level of control to the CPV program. If CHT times increase over time, trending could indicate a problem with scheduling of manufacturing batches or manpower resource issues, which could be addressed before a CHT excursion by extending the CHT through a protocol execution.

Campaign length. The maximum campaign length is established in the CV protocol execution. The number of batches manufactured and the length of time to manufacture are captured both on the equipment logs and the protocol. The campaigns are executed three times for validation, and the longest campaign length is designated as the maximum campaign length based on comparable cleaning data.

Post validation, the batch campaign length is known based on scheduling and equipment use. However, the length of time to manufacture a campaign is not typically noted and would normally only be unduly extended due to mechanical equipment issues. And while bulk hold times are considered for manufacturing process validation, the potential effect of increased campaign time on the subsequent cleaning is not considered. One option is to have the equipment card designed to include a check of the maximum campaign parameters, both number of batches and number of days.The campaign data can be periodically downloaded into a database for ongoing trending.

Non-compliance. The risk of not complying with validated parameters is low as long as the validated parameters are readily available, limited in complexity (e.g., one DHT value of 10 days for all equipment), and the parameters are recorded contemporaneously. If there is a non-compliance with any parameter, it should be immediately recognized and addressed before a non-compliant situation develops.For example, if the DHT is exceeded, testing after cleaning can be arranged after cleaning to verify a successful level of cleaning before the cleaned equipment is used again. Additionally, if this type of occurrence could be addressed proactively and a protocol documented and approved prior to equipment cleaning, the cleaning data might be used to lengthen the DHT for the cleaning process. Any delay in recording data increases the risk of non-compliance.

Non-compliance investigation. If a non-compliance for a cleaning parameter is only recognized after the equipment is reused, an investigation is necessary. If a critical cleaning parameter (time, temperature, detergent concentration), DHT, or campaign length is non-compliant, then the equipment might not have been sufficiently cleaned, and carry-over into the next batch is possible. A risk assessment should determine the level of risk of the non-compliance. The higher the cleaning limit of the cleaned API, the lower the risk of unacceptable carry-over. Conversely, an API with a low cleaning limit increases the risk of an unacceptable carry-over.

If not already established, a visible residue limit (VRL) of the cleaned API should be determined (8, 9). If the VRL is lower than the cleaned API cleaning limit, it provides a clear indication that the visually clean equipment was sufficiently cleaned and further batch investigation might be avoided.

The cleaning records and equipment log need to be checked to verify the extent of the non-compliance and what subsequent batches are potentially impacted. As necessary, the subsequent batches should be tested for the presence of the carry-over API, which might require some analytical method development and validation. Personnel should be interviewed to determine the root cause and corrections implemented.

Compliance verification vs. trending

Verification of cleaning validation parameters on an ongoing basis is critical for maintaining the validated state of the cleaning program. If a parameter is exceeded, then action must be taken to correct the non-compliant condition. The value of trending some cleaning validation parameters might not be as obvious. Certainly, trending cleaning data in the form of swab sample or comparable data results adds value in that it provides an ongoing picture of the consistency of cleaning and the level of risk for a potential cleaning failure in the future. But once cleaning validation is complete, cleaning data are not generated after every equipment cleaning. Based on the risk of a cleaning failure, the frequency of swab testing can decrease, and the lower frequency of cleaning data points presents a periodic snapshot of cleaning rather than a continuous record of equipment cleanliness. In addition, relying solely on cleaning data for CPV is akin to solely relying on release testing for manufacturing process control, which is not acceptable to the regulatory agencies.

Therefore, other cleaning parameters that continue to be captured after every post-validation cleaning become more indicative of continued cleaning verification. They include CCPs (times, temperature, detergent concentration as necessary), DHT, CHT, and campaign length. Along with a visual inspection performed by qualified personnel, trending of these parameters individually might not provide assurance of CPV, but taken together, provide a picture of a cleaning system that continues to be in a state of control.

Conclusion

Tracking and trending CPV parameters are necessary to demonstrate that a cleaning validation system is maintained in a state of control. Options are available, but the easiest path forward is often the more labor-intensive approach. Consideration for long-term use of CCPs, cleaning swab test data, DHT, CHT, and campaign length should be taken during the CV planning phase rather than waiting until CV is complete, to ensure a robust ongoing CV program.

References

1. FDA, Guide to Inspection of Validation of Cleaning Processes
(Division of Field Investigations, Office of Regional Operations, Office of Regulatory Affairs, Washington, D.C., July 1993).

2. EC, EudraLex Volume 4, EU Guidelines for Good Manufacturing Practice for Medicinal Products for Human and Veterinary Use, Annex 15: Qualification and Validation, Section 10: Cleaning Validation (2015) Section 39, Page 243.

3. Health Canada, Canada Health Products and Food Branch
Inspectorate Guidance Document, Cleaning Validation Guidelines GUIDE-0028 (2008).

4. PIC/S, PIC/S Validation-Master Plan, IQ, OQ, Non-sterile Process Validation, Cleaning Validation (PI 006-3) (September 2007).

5. FDA, Guidance for Industry Process Validation: General Principles
and Practices
, (Division of Field Investigations, Office of Regional Operations, Office of Regulatory Affairs, Washington, D.C., January 2011).

6. R. J. Forsyth, Pharm. Technol. 45 (8) (2021).

7. R. J. Forsyth, Pharm. Technol. 32 (4) 64–73 (2008).

8. R. J. Forsyth, Pharm. Technol. 33 (3) 102–111 (2009).

9. R. J. Forsyth, Pharm. Technol. 40 (4) 50–57 (2016). PT

About the authors

Richard Forsyth* is a Principal Consultant with Forsyth Pharmaceutical Consulting. Dr. Sabine Imamoglu is Product Supply, Pharmaceuticals, Pharmaceutical Affairs, Bayer AG.

*To whom all correspondence should be addressed.

Article Details

Pharmaceutical Technology
Vol. 46, No. 1
January 2022
Pages: 34–37

Citation

When referring to this article, please cite it as R. Forsyth and S. Imamoglu, “Continued Process Verification for Cleaning Validation–Challenges and Pitfalls,” Pharmaceutical Technology 46 (1) (2022).