Cleaning Validation in Continuous Manufacturing

Now Design/Shutterstock.comIt is now possible, as demonstrated by Janssen (1), for drug manufacturers to convert their processes to adopt continuous production of oral solid-dosage forms produced in compact, closed units, with a higher level of automation and minimal manual interventions. In continuous manufacturing, the sequential production steps that are part of a classic batch process are integrated into a continuous process. To ensure GMP compliance, cleaning is required on some frequency between batches of the same product and when switching between different products.

What is continuous manufacturing?

It is important to define continuous manufacturing and to understand the characteristics that differentiate it from a typical batch process. The classical definition involves the following:

• 
  All crucial steps involved to produce something from its starting materials to the end-product must be integrated into a steady uninterrupted process.


• 
  The systems should operate continuously (non-stop) targeting at least 50 weeks per year.


• 
  There should be no significant downtime in product or process changeover.

Some variations of this processing mode may be foreseen over the years. Processes that do not fit the above definition, such as infrequent manufacture (e.g., operates only once or twice per year) or a multi-product process train (i.e., same equipment used for many different products), are generally not considered continuous manufacturing (2).

Cleaning concerns

Although most companies do not have immediate plans for implementation of continuous manufacturing, many GMP professionals have questions concerning the impact of this type of process on cleaning and cleaning validation. People involved in quality, engineering, operations, and validations are asking about applying cleaning validation to pharmaceuticals produced in a continuous manufacturing process. The purpose of this article is to provide a review of the regulatory expectations on cleaning and cleaning validation and to help drive efficiency by rethinking the design of the cleaning process in continuous manufacturing.

Professionals interested in understanding cleaning and continuous manufacturing must become familiar with the following aspects:

• 
  The regulatory perspective on cleaning validation and applicability to continuous manufacturing


• 
  The importance of the lifecycle model and risk assessment tools to successfully design and implement a cleaning procedure for continuous manufacturing


• 
  Testing, sampling, process analytical technology (PAT), and equipment design concepts that should be reviewed and considered to optimize cleaning in continuous manufacturing


• 
  How to establish cleaning residue limits explicitly for continuous manufacturing.

Regulatory perspective

In the United States, the regulatory expectation is that equipment be cleaned prior to manufacturing to prevent contamination or adulteration of products (3). Similarly, in Europe, Canada, Asia, Latin America, and other territories, equipment cleaning is a regulatory requirement cited in their applicable GMPs. According to 21 Code of Federal Regulations (CFR) Part 211.67 Equipment cleaning and maintenance, “Equipment and utensils shall be cleaned, maintained, and sanitized at appropriate intervals to prevent malfunctions or contamination that would alter the safety, identity, strength, quality, or purity of the drug product beyond the official or other established requirements” (3). Also, in 1993, FDA issued the Guide to Inspections of Validation of Cleaning Processes to assist the industry in compliance with GMP requirements (4). The European Medicine Agency (EMA), Health Canada, and others subsequently issued similar guides to assist the industry in complying with the relevant regulations (5–6).

Based on these guidance documents, cleaning validation is clearly a necessity when the equipment is used to manufacture more than one drug product due to cross-contamination concerns. A question remains as to whether cleaning validation is required for dedicated equipment, which may be the case for most continuous manufacturing processes. The need to validate the cleaning procedure when the equipment is dedicated to one product is generally left to the company’s discretion, and it must be supported with proper justification. Nevertheless, cleaning validation of dedicated equipment has been discussed in other publications, and the rationale behind this can also be applied to continuous manufacturing (7). In the case of continuous manufacturing, cleaning validation of dedicated equipment should be done to demonstrate that the cleaning procedure can effectively remove residue build-up and undesirable residues (including microbial) produced during a specific length of manufacturing (i.e., campaign) that may compromise product quality and patient safety.

As of the time of publication, no specific regulation or guidance document for continuous manufacturing has been released. FDA authorities have presented a general perspective on continuous manufacturing, which addresses the concerns around “lot” and “batch” definitions (8). According to the CFR, “batch” and “lot” refer to a quantity of material with uniform characteristics and do not specify mode of manufacture; no regulations or guidance documents forbid the adoption of continuous manufacturing. In fact, continuous manufacturing seems to be consistent with philosophies, such as quality by design (QbD) and the lifecycle approach to process validation, found in current guidance documents (9–12).

Lifecycle approach

In 2011, FDA issued a process validation guide (12) focusing on three main elements:

• 
  Quality needs to be built into the process


• 
  Quality is not guaranteed by in-process or final process testing


• 
  A manufacturing process needs to be defined and continuously monitored to ensure consistent quality.

The elements of the lifecycle model are the building blocks to a harmonized approach to process validation and subsequently, cleaning validation. The lifecycle elements of design, validation, and monitoring are also the building blocks to make continuous processing an efficacious mode of manufacturing. The process lifecycle approach as discussed in the guidance document focuses on understanding the processes and ensuring that they are meeting the requirements set forth in the design stage. The elements of the lifecycle approach should not be limited to manufacturing since they may also be applied to other processes including cleaning. With this in mind, the lifecycle model can be used to improve or optimize cleaning procedures by having a better understanding of the input variables and the output attributes.

Designing the cleaning process

Process efficiency is important in continuous manufacturing and cleaning is not an exception. For example, at some pre-established schedule, the continuous manufacturing facility must be shutdown to perform equipment cleaning and maintenance. Cleaning procedures are expected to be done quickly and correctly the first time in order to meet the optimum changeover time as established in the production schedule. Also cleaning procedures should be systematically designed to reduce waste within the cleaning process.

Figure 1: CLICK TO ENLARGE. Typical equipment of oral solid-dose (OSD) batch process and a continuous manufacturing (CM) process flow with soil type considerations for cleaning evaluations. SOP is standard operating procedure; CIP is clean in place. All images are courtesy of the authors.The design stage of the lifecycle model (Stage 1) is of particular importance because the cleaning process is defined based on knowledge gained through development and scale-up activities. This stage ensures that the variables are identified and their criticality to the cleaning process is assessed. For example, the design inputs or critical cleaning process parameters for the wash step include the cleaning agent, concentration, temperature, time, cleaning method, water quality, and environmental factors (13–14). Laboratory, pilot, or field studies should be used to help define the cleaning process and identify conditions that would lead to the desired fast, effective, and lean cleaning process (see Table I). These studies may involve cleaning evaluations with wet, baked-on residue found in the drier as well as dry, compacted residue for tablet pressing equipment (see Figure 1).

A formal risk assessment for the cleaning process is also recommended using a system for identifying and managing risk such as fault tree analysis (FTA), hazard analysis and critical control point (HACCP), or failure modes and effect analysis (FMEA) (15–16). Risk assessment should be based on the knowledge gained through the design stage and focus should be placed on the issues that have potential impact on product quality and patient safety. Table II includes items to consider for building a cleaning process knowledge base. A design of experiments could be used to identify those parameters that have a significant impact to cleaning within a specified range (17).

Monitoring technology

Cleaning processes are often viewed as time- and resource-consuming activities that only add to the operational costs of product manufacturing. Delays in equipment readiness due to cleaning failures, lengthy manual cleaning procedures, or off-line sampling wait times can challenge continuous manufacturing schedules and result in costly production delays.

Table I: Design space considerations for continuous manufacturing.

Manufacturers using batch processing, including cleaning processes, perform laboratory testing conducted on pulled samples to evaluate quality attributes. PAT, however, can be used in continuous manufacturing to provide real-time continuous analysis and release of the cleaned equipment. PAT is a system for designing, analyzing, and controlling manufacturing through timely measurements, process understanding, and process control (18). In cleaning applications, PAT may be applied to complement the cleaning performance qualification and later, to support continued verification. For example, concentration-versus-conductivity plots are pre-established to monitor the final water rinse for residual cleaning agent (19). Total organic carbon (TOC) analysis can also be correlated to process residues. In a published case study, a cleaning process was evaluated using an on-line TOC analyzer integrated into the return line of a clean-in-place (CIP) system (20).

Table II: Cleaning process knowledge base.

There are sampling options (e.g., rinse and swab) universally acceptable for monitoring the cleaning performance of the targeted residues; each option has advantages and disadvantages to consider. For most cleaning applications, rinse sampling is typically expected to be faster and easier compared to swab sampling, which may require access to equipment locations through disassembly or confined-space entry and consequently compromises personnel safety.

Analytical methods can be specific or non-specific to determine the amount of residue on the surface through direct and indirect sampling methods. Non-specific detection methods, such as pH, conductivity, or TOC, can be used to measure multiple residue types. These types of methods are preferred due to the quick turnaround time, minimum waste generation, and simplicity of the assay.  Ultra high-pressure liquid chromatography (UHPLC) instruments measure residue based on detection of a specific analyte and is a faster alternative over traditional HPLC (21).

Table III: Faster testing technologies commonly used for cleaning monitoring.

Table III lists examples of at-line and in-line methods that could be used for detection of residual cleaning agent. For continuous manufacturing, it is recommended to review testing technologies and select one that makes the most sense given the analytical resources, type of residue and carry-over risk, speed of analysis, and/or adaptability to PAT.

Cleaning equipment design

Batch pharmaceutical processes employ a variety of methods to clean process equipment. Manual cleaning methods using wipes and brushes are common in legacy batch-mode processes while CIP systems are popular at newer facilities. Even though both manual and automatic cleaning methods are accepted by drug regulatory agencies around the globe, companies considering continuous manufacturing processes should opt for CIP systems because they are effective, consistent, and reliable. Consistent cleaning results are achieved because there is minimal operator intervention, reduced likelihood of human error, and consistent control of critical parameters when combined with PAT technologies. The principal objective of a CIP system is to achieve the desired cleanliness level without disassembling the process equipment. Generally, CIP cleaning is done by circulating cleaning solutions through pipes, pumps, valves, and spray devices that distribute the cleaning agent over the surface areas of the equipment. PAT technology can be used to monitor cleaning steps, such as the preparation of cleaning solution to a pre-established concentration and the rinsing of the equipment down to pre-established residue limits.

With adequate sanitary design, such as coverage, diaphragm valves, pitch, and dead-leg orientation, CIP systems can deliver faster and lean cleaning processes suitable for continuous manufacturing. All sanitary design concepts must be thoroughly reviewed to ensure equipment cleanability and minimize water consumption. Multiple sources provide details on sanitary options (22–24). In summary, a laboratory evaluation to determine critical cleaning parameters combined with a review of equipment design concepts should help improve speed of the cleaning procedure, reduce waste generated during cleaning execution, minimize cleaning agent and water consumption, and reduce human interventions.

Establishing cleaning residue limits

In setting acceptable residue limits within continuous manufacturing, two situations should be considered (25). In the first situation, the manufacturing process for Product A is interrupted (a scheduled or unscheduled stoppage) due to a minor maintenance event or light cleaning such as vacuuming or dry wiping the work surfaces; when complete, the manufacturing line is back up and running. In this situation, there is no concern of residual carry-over of the active ingredient because only one product is being manufactured; therefore, cleaning validation is not applicable. If a product impurity, cleaning agent, equipment lubricant, or similar processing aid was introduced, however, a cleaning procedure should be performed to remove those residues to safe levels prior to resuming production. This type of interruption will fall into the second situation.

In the second situation, Product A production has stopped and change-out is occurring to begin continuous manufacturing of Product B. In this scenario, there is a need to perform an in-depth or heavy cleaning to make sure that active components from Product A do not affect the quality of Product B, as well as ensuring that the non-active components (any cleaning agents used, and microbial residues remaining on the surface) are within acceptable levels. Similar to traditional batch processing, possible pharmacological and toxicity effects of Product A, as well as residue impacting the stability of Product B, need to be considered.

In establishing residue limits, safe concentration of the target residue in the subsequent product (i.e.,  Product B), referred to as Limit 1 (L1), needs to be understood. The safe dose (L0) or a scientifically derived health-based limit (i.e., an acceptable daily exposure [ADE] or permitted daily exposure [PDE]) of the residue (i.e., of Product A) is calculated using Equation 1, and L1 is calculated using Equation 2 (26–27).

[Eq. 1] L0 = Toxicity Assessment x Body Weight x Minimum Daily Dose of Product A

[Eq. 2] L1 = L0 / Maximum Daily Dose of Next Product

The L1 value calculated in Equation 2, multiplied by the batch size of the subsequent product (which, as previously discussed, must be defined in continuous manufacturing by the manufacturer), provides a maximum allowable carry-over value (MAC or MACO value, also known as L2).

From the L2 or MAC value, the limit per surface area can be calculated by dividing by the shared surface area of the equipment train.

This calculation assumes a uniform distribution of the residue and is considered a conservative approach to setting limits per surface area. Most cleaning validation swab sampling plans would include sampling the hardest-to-clean locations based on a risk assessment.

Stratified and non-uniform residue limits

The concept of uniform distribution is an ideal scenario and is not always true (28). This assumption, therefore, may lead to failing results on select equipment or sampling locations.

Stratified residue distribution on equipment splits the calculated L2 or MAC residue among the different pieces of equipment within the manufacturing process based on a risk assessment. It is important to document the risk assessment used to determine the stratification because it will be reviewed by auditors. The stratification scheme can also vary based on the target residue. For example, microbial limits may be set lower for the wet granulation and coating steps because there is a greater risk for microbial proliferation during these steps.

Non-uniform residue contamination in the product means that the residue on the surface of the equipment concentrates into the first units of production and is then reduced in subsequent units. In a tablet press, for example, the residue from the previous product will be transferred to the first tablets (or round of tablets) pressed at a higher concentration than the second and third tablets pressed. In a filling line, as another example, the residue from the previous product will be transferred to the first vials (or series of vials) filled at a higher concentration than the second and third vials filled.

Equation 3 shows a calculation for setting limits in a non-uniform contamination example. The limit (L0) of Residue A in Product B has been calculated (or defaulted) to be 10 ppm or approximately 10 μg/mL. The total surface area of the filling equipment and piping is 10,000 cm², and the limit per surface area of Residue A has been predetermined to be 1 μg/cm². Product B is filled in 10 mL vials. If the contamination of 1 μg/cm² of the total shared surface area of 10,000 cm² were concentrated into the first vial filled of 10 mL, then the residue level would be 1000 μg/mL or 1000 ppm, which is well above the 10 ppm limit. The first 10 or 100 vials should be discarded because the residue in the vials would be less than 100 or 10 ppm, respectfully.

[Eq. 3] (1 μg/cm²)(10,000 cm²)/10 mL = 1000 μg/mL or 1000 ppm  

The understanding of non-uniform and stratified sampling is important for setting residue limits within a continuous manufacturing process because a small quantity of product may migrate through a manufacturing process, concentrating residue from one piece of equipment to another and defaulting to a traditional uniform residue limit, which can adversely impact the quality of the product and potentially impact the health of the patient.

Cleaning performance qualification

In the lifecycle approach to cleaning validation, Stage 2, the performance qualification stage, is a readiness check to ensure the cleaning process is able to be validated. This stage will involve checking that suppliers have been approved, analytical methods have been validated, personnel performing the cleaning have been trained, standard operating procedures and validation documents are ready to be performed or executed, and process equipment and utilities have been successfully qualified and ready for use (29).

If the critical process parameters and critical quality attributes were well characterized during the cleaning design stage, then the performance qualification (Stage 2) should be performed using normal operating parameters.

Continued process verification

The third stage of the lifecycle model is continued process verification. Implementation of process controls such as change control, preventative maintenance, and corrective and preventative action systems ensure a validated state. The cleaning process continuously operates in a state of control. A periodic review of the cleaning program is also crucial to ensure that the cleaning process remains in control and is flexible to change when required. This periodic review of the cleaning validation program should be reviewed in a similar manner as the yearly product quality review. A review of product cleaning is generally part of the yearly product quality review, but it is generally not thorough enough and often doesn’t review similarities and difference between multiple products manufactured in the same equipment.

Table IV: Examples of impact changes.

Figure 2: Roadmap to the cleaning validation lifecycle approach.

Items that should be reviewed include change control data, monitoring data, deviations, corrective and preventive actions, maintenance, quality records, and retraining events.

If the review shows control and consistency, then summarize the findings and conclude that the cleaning program is operating in a state of control (i.e., a validated state). The review can also identify high-risk or high-waste areas that need to be improved. A corrective action plan or lean manufacturing event can be used to develop a plan to correct this deficiency.

Managing change

Depending on the type of change being proposed, it may be necessary to go back to the design stage to determine the impact of the change to the cleaning process. For this reason, validated cleaning procedures must be included in the change control management system. This ensures that any proposed changes are evaluated fully for their impact on the validated state of the procedure.

Table IV is not an all-inclusive list of possible changes but helps provide an idea of the type of changes and their potential impact (29). The impact of the change to the cleaning process may have already been assessed during the design stage; otherwise additional testing within the design stage may be warranted to mitigate risk prior to implementation of the change. This action is depicted in Figure 2 as arrows from Stage 3 to Stage 1 or 2 as a result of the change (29). 

Managing change is an important process because the goal of the design stage and continuous monitoring stage of the cleaning validation lifecycle approach for continuous manufacturing production facilities is to operate within a state of control and to drive out waste to improve efficiency and maintain flexibility of the cleaning process.

Conclusion

Continuous manufacturing focuses on streamlining production while minimizing the process footprint and waste from non-value activities. To ensure cGMP compliance, cleaning is required on some frequency between batches of the same product and when switching between different products. Developing a cleaning validation program using the process lifecycle approach provides a firm understanding of the critical cleaning process parameters as well as the critical quality attributes that need to be monitored.

The equipment used for continuous manufacturing may vary based on the type of product manufactured as well as the method of cleaning. It is important to evaluate cleaning during the drafting of the user requirement specifications and functional requirement specifications of the production equipment. The development and proof of concept of a laboratory-scale cleaning model during the design phase of the cleaning process and equipment fabrication will aid in the validation of cleaning processes. This model will also help in the ability to conduct additional investigations to support process and cleaning changes to reduce waste. The use of health-based limits, such as an ADE or PDE value, for any route of administration, along with a rationalized use of non-uniform and stratified residue limits, allows for setting practical, achievable, and justified acceptance limits.

References

1. 
   S.E. Kuehn, Pharm Manuf. 14 (8) 31-33 (2015).


2. 
   G. Malhotra, Contract Pharma. 17 (5) 74- 79 (2015).


3. 
   FDA, Code of Federal Regulations, Title 21, Part 211.67 "Equipment Cleaning and Maintenance" (Washington, DC).


4. 
   FDA, Guide to Inspections Validation of Cleaning Processes. (Silver Spring, MD, 1993).


5. 
   Pharmaceutical Inspection Convention/Pharmaceutical Inspection Co-Operation Scheme (PIC/S). Validation Master Plan Installation And Operational Qualification Non-Sterile Process Validation Cleaning Validation. (Geneva, Switzerland, Sept., 2009).


6. 
   Health Canada, Cleaning Validation Guidelines (Guide-0028). (Ottawa, Ontario, January 2008).


7. 
   C. Baccarelli, et al. BioPharm Int. 28 (8) 38-40 (2015).


8. 
   S. Chatterjee, “FDA Perspective on Continuous Manufacturing,” Presentation at IFPAC Annual Meeting (Baltimore, MD, January 2012).


9. 
   FDA, Guidance for Industry, Q8 Pharmaceutical Development (Silver Spring, MD, May 2006).


10. 
    FDA, Guidance for Industry, Q9 Quality Risk Management (Silver Spring, MD, June 2006).


11. 
    FDA, Guidance for Industry, Q10 Quality Systems Approach to Pharmaceutical cGMP Regulations (Silver Spring, MD, Sept. 2006).


12. 
    FDA, Process Validation: General Principles and Practices (Rockville, MD, November 2011).


13. 
    G. Verghese and N. Kaiser, “Cleaning Agents and Cleaning Chemistry, Chapter 7” in Cleaning and Cleaning Validation Volume I, P. Pluta Ed. (Davis Healthcare International and Parenteral Drug Association, 2009) pp 103-121.


14. 
    G. Verghese, “Selection of Cleaning Agents and Parameters for cGMP Processes,” Proceedings of the INTERPHEX Conference (Philadelphia, 1998) pp. 89-99.


15. 
    IEC, 61025-1990, Fault Tree Analysis (Geneva, Switzerland, 1990).


16. 
    IEC, 812-1985, Analysis techniques for system reliability – Procedure for failure mode and effect analysis (FMEA) (Geneva, Switzerland, 1985).


17. 
    N. Rathore, et al., Biopharm Int.  22 (3) 32-44 (2009).


18. 
    FDA, Guidance for Industry: PAT – A Framework for innovative pharmaceutical development, manufacturing, and quality assurance. (Rockville, MD, September, 2004).


19. 
    G. Verghese and P. Lopolito, "Process Analytical Technology and Cleaning," Contamination Control, Fall 2007. pp 22-26.


20. 
    K. Bader, et al., Pharm. Eng. 29 (1) 8-20 (2009).


21. 
    M. Gietl, B. Meadows, and P. Lopolito, "Cleaning Agent Residue Detection with UHPLC,". Pharm. Manufacturing (April, 2013) www.pharmamanufacturing.com/articles/2013/1304_SolutionsTroubleshooting/, accessed Oct. 20, 2016.


22. 
    E. Rivera, "Basic equipment-design concepts to enable cleaning in place: Part I," Pharm. Tech. Equipment and Processing Report, (June 15, 2011), www.pharmtech.com/basic-equipment-design-concepts-enable-cleaning-place-part-i, accessed Oct. 20, 2016.


23. 
    E. Rivera, "Basic equipment-design concepts to enable cleaning in place: Part II," Pharm. Tech. Equipment and Processing Report, (July 20, 2011), www.pharmtech.com/basic-equipment-design-concepts-enable-cleaning-place-part-ii, accessed Oct. 20, 2016.


24. 
    G. Verghese and P. Lopolito, "Cleaning Engineering and Equipment Design," in Cleaning and Cleaning Validation Volume I, P. Pluta Ed. (DHI Publishing and the Parenteral Drug Association, 2009) pp 123-150.


25. 
    D. LeBlanc, "Cleaning Validation for Continuous Manufacture," Cleaning Validation Technologies Memos (April 2014), www.cleaningvalidation.com/files/98772132.pdf, accessed Oct. 20, 2016.


26. 
    EMA, Guideline on setting health based exposure limits for use in risk identification in the manufacture of different medicinal products in shared facilities, EMA/CVMP/SWP169430/2012 (London, November 2014).


27. 
    ISPE, Baseline Guide: Risk Based Manufacture of Pharmaceutical Products (Risk-MAPP) Chapter 5, pp 33-46 (Bethesda, MD, 2010).


28. 
    G. Kleina, Pharma. Outsourcing 16 (4) 40-43 (2015).


29. 
    P. Lopolito and E. Rivera, "Cleaning Validation: Process Lifecycle Approach," in Contamination Control in Healthcare Product Manufacturing, Vol 3. R. Madsen and J. Moldenhauer, Eds. (DHI Publishing, PDA Books, 2014). 


About the Authors

Paul Lopolito and Elizabeth Rivera are technical services managers for the Life Sciences Division of STERIS Corporation in Mentor, Ohio.

Article Details

Pharmaceutical Technology
Vol. 40, No. 11
Pages: 34–42, 55

Citation

When referring to this article, please cite it as P. Lopolito and E. Rivera, "Cleaning Validation in Continuous Manufacturing," Pharmaceutical Technology 40 (11) 2016.

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Cleaning Validation for APIs | Pharmaceutical Technology

FoapAB/Shutterstock.comAt home, when washing the dishes, do we ever consider what meal they will be used for next? When plates and cutlery are taken out of the cupboard to set the table, do we ever ask what they have been used for and whether they are clean enough for the gourmet course about to be served?

Are there different levels of cleanliness used for the dishes depending on the food served or the guests we entertain? Silly questions, apparently, but the industry’s current approaches to pharmacutical cleaning validation can often seem just as random. Cleaning limits for pharmaceutical manufacturing equipment are computed based on the previous and next products for every product change. As a result, it is often difficult to explain the cleaning and cleaning validation concept. 

This article is about combining the simplicity of the household cleaning concept with the science needed to protect patients’ safety. To reach this goal, it may be necessary to revisit a few traditional ideas about cleaning validation and to establish new principles. The focus will be on API manufacturing of small molecular weight substances (i.e., with industrial organic chemistry and the corresponding traditional plant equipment such as reactors, separators, crystallizers, centrifuges, filters, dryers, mills, and blenders, plus all the connecting pumps, pipes, and hoses). The size of the vessels typically ranges from 250 to 10000 L, and a manufacturing line can have up to several hundred square meters of product contact surface.

The change from product A to product B is not a frequent case

APIs are usually produced in multipurpose equipment. Several individual modules are concatenated to build a product- specific production line or train. After a campaign, the line usually gets disconnected and the individual modules, not always all of them, are reconfigured into a new line for the next product. Some products may use exactly the same production line as others, but most of the time, every product uses a somewhat different configuration of modules. Some equipment is used for many products; other parts are practically dedicated to a few or to only one product.

When a line is set up for product B, some parts will indeed have been used for product A right before; other parts, however, may have been in contact with product X, and mobile parts may be taken from the storeroom and have been used for product Y or Z months before. There is rarely a pure product changeover from A to B; in reality, the changes are from products A, X, Y ... to product B. 

Similarly, when cleaning a piece of equipment, it is not always known what the next product will be. The change could be from A to the next products B, C, or D. This is particularly the case with interchangeable mobile equipment such as flexible hoses, filters, or tanks that go back to the storeroom after cleaning. Under such circumstances, a number of questions will arise, including the following:

• 
  What was the previous product manufactured at the facility, and what will be the next one? 


• 
  How can one make sure that all parts are clean, to the right level for making the next product(s)? 


• 
  Can the cleaning validation concept be built and the cleaning limits computed based on the ideal product. change from A to B? 

The following principles aim to answer these questions. 

The cleaning procedure is independent of the following product(s)

Equipment cleaning should be thought as a reset function. The equipment is reset to a clean ground state form where it can be used for any product. Every piece of equipment labeled clean must be ready for use, no questions asked. 

Consider the analogy with the household: doing the dishes, one doesn’t ask what will be cooked the next day, and when taking clean plates out of the cupboard, it doesn’t matter what the last meal was. A clean plate is a clean plate.

Cleaning without asking what the next product will be implies that setting the cleaning limit must be a comprehensive exercise that includes all products of a manufacturing unit. Quite often, the “cleaning validation runs” and the corresponding plans and protocols only consider the actual product change A to B, which leads to cleaning à la carte, asking: How clean does it have to be today? 

Using the same cleaning procedure for different cleanings, it can happen that different limits must be met from one cleaning to the next, because the limits get recalculated each time for the actual, ideal product change. With such moving targets, it is impossible to properly validate a cleaning process. 

A standard cleaning limit (SCL) should be set, in mg/m2

Establishing a standard cleaning limit (SCL) for all the equipment is the core element of the cleaning concept (1). The SCL is the level of cleanliness (expressed in mg/m²) that a production unit must maintain with every cleaning process, every time. The SCL applies to every piece of equipment, fixed or mobile, large or small, independently of the product changes. 

What does the cleaning practice tell us?

Most production units will have collected cleanliness data, typically results of swab tests, over quite a long period. An example of such a data set is given in 
Figure 1. The example is from a relatively old manufacturing unit (Unit A), which uses traditional equipment for industrial organic chemistry. The cleaning procedures consist mainly of flushing and boil outs with solvents and manual scrubbing with water and a detergent.

Figure 1: Graph of more than 250 swab results collected over a period of 18 months in multipurpose API unit A. Swabs were taken after using different cleaning procedures, after campaigns of different lengths, making different products, and from different pieces of equipment made of different material. The lowest values were often limited by the limit of quantitation of the analytical methods. (All figures courtesy of authors.)

Figure 1 gives a good idea of the level of cleanliness this particular production unit can achieve. The values vary a great deal and the level of cleanliness that can reasonably be guaranteed is limited by:

• 
  Physicochemical properties of the process residues


• 
  Equipment design


• 
  Cleaning methods.

The conclusion from Figure 1 was that this particular production unit could be reliably cleaned down to 25 mg/ m² . Although much lower values than 25 mg/m² were often obtained (see Figure 2), 25 mg/m² is considered the unit’s standard cleaning performance and so, for this unit, the SCL was set at 25 mg/m.²

Figure 2: Histogram with cumulated frequency of swab results from Figure 1. 75% of swabs gave ≤1mg/m², 95%≤10 mg/m².

Based on reality, rather than theory

Setting the SCL in this simple and pragmatic way, solely based on experimental data, without any theoretical considerations, reflects acceptance of the variability and limitations inherent in current cleaning practice. However, this approach represents a radical change from tradition. Previously, the limit was calculated in the validation protocol that only considered the effective product change. The quality assurance (QA) department determined to what limit the equipment was to be cleaned at this one time, without considering the big picture of years of cleaning experience.

The theoretical safety requirements for a changeover from product A to B are known. Based on the permitted daily exposure (PDE) of A, the maximal safe carryover (MSC) of product A into product B can be determined by using Equations 1. Dividing the MSC by the product contact surface used to make B gives the maximum safe surface residue of A (MSSRA) on equipment used for B.

Applying Equations 1 and 2 to all the theoretically possible product changes in a defined manufacturing unit yields the MSSR matrix. The MSSR matrix is the matrix of all possible required levels of cleanliness for a defined portfolio of products. See reference 2 for an example of a computed MSSR matrix.

Figure 1 shows a graph of more than 250 swab results collected over a period of 18 months in multipurpose API unit A. Swabs were taken after using different cleaning procedures, after campaigns of different lengths, making different products, and from different pieces of equipment made of different material. The lowest values were often limited by the limit of quantitation of the analytical methods.

Risk assessment

The risk of carryover contamination can be assessed by comparing the level of cleanliness that can be reasonably expected (the SCL) with the cleanliness required by the MSSR matrix (2). During risk assessment, the following should be addressed: 

• 
  Are the cleaning processes good enough to ensure safe changeovers? If not, is it reasonable to simply conclude that the cleaning procedures should be improved? Maybe there is room for improvement; but chances are that the cleaning processes have already reached their limits. If this is the case, can risk of the worst changeovers be mitigated by other means than cleaning? 


• 
  Can a few theoretical changeovers be eliminated from the MSSR matrix because they do not share any common equipment? 


• 
  Can manufacturing campaigns be planned in a way to avoid critical changeovers?


• 
  Do certain products call for partially of fully dedicated equipment?

Such considerations should be summarized in a risk assessment document, which should be part of the cleaning validation master plan. Additional risk assessment can be done based on analysis of the swab results shown in Figure 2. The SCL of 25mg/m² is a safe limit, because the probability of a cleaning giving a swab result >25 mg/m² is <1%. Should a failure risk of 5% be deemed acceptable, the SCL could even be lowered to ≤10 mg/m². Figure 3 shows swab data, as does Figure 1, but, in this case, they were measured in a different manufacturing unit (B) of the same company. 

The data for unit B are very similar to those for unit A and the SCL was also set at 25 mg/ m² for unit B. Because units A and B both manufacture small organic molecules, use similar equipment and very similar cleaning processes, it is not really a surprise that similar cleaning results are obtained. 

Introducing an SCL opens the possibility of having a single cleaning limit for many different manufacturing units, which can help to further standardize cleaning validation. In fact, the SCL of 25 mg/m² was eventually introduced in five different manufacturing units.

With its general applicability and ease of use, the SCL concept can be compared to the classification system for cleanrooms, where the room class is independent of the room size or the nature and quantity of product that is handled in the room. The SCL is, similarly, independent of equipment size, products, or batch size.

Via the SCL, one can imagine a classification of API manufacturing equipment (e.g., in class 5, 25, and 50 mg/m2). Such equipment classes, which in fact would reflect validated cleaning commitments, could be criteria to consider when allocating new products to manufacturing sites or outsourcing API manufacturing. Comparing the SCL and the MSSR matrix is a straightforward way to start a cleaning risk assessment and to implement the new EMA guideline on shared facilities (3). In addition, adopting the SCL offers a number of benefits. For example, it:

• 
  Allows for a consistent level of cleanliness to be enforced over a whole production unit


• 
  Simplifies cleaning validation because there is no need to compute and justify the cleaning limit in every single validation plan; the limit can instead be justified once in an appropriate document, (e.g., in the cleaning validation master plan)


• 
  Allows manufacturers to standardize validation activities in the analytical laboratory: Specifications for swab tests are constant, as are the working ranges of high-performance liquid chromatography (HPLC) methods and when spiking coupons for recovery studies.

Last but not least, the SCL is a simple concept that is easy to understand and to explain.

Acknowledgements

The authors would like to thank Sébastien Schuehmacher, Michael Wölfle, and Markus Schaefer for their relentless cleaning efforts and their support in the development of the SCL concept, and to Ian Udri for compiling all the data.

References

1 M. Voaden, A Leblanc, RJ. Forsyth, Pharmaceutical Technology, 31 (1) pp. 74-83, 2007.
2 M. Crevoisier et al, Pharmaceutical Technology 40 (1) pp. 52-56, 2016.
3 EMA, Guideline on Setting Health Based Exposure lLmits for Use in Risk Identification in the Manufacture of Different Medicinal Products in Shared Facilities, (EMA, 2014). 

Article Details

Pharmaceutical Technology
Vol. 41, No. 9
September 2017
Pages: s12-s16

Citation

When referring to this article, please cite it as M. Crevoisier and T. Peglow, " Cleaning Validation for APIs," Pharmaceutical Technology 41 (9) 2017.

About the Authors

Michel Crevoisier is former senior quality expert for Novartis Pharma AG, and Thomas Peglow is senior cleaning validation expert at Novartis Pharma AG, Switzerland. The views expressed in this article are those of the authors and not necessarily of their employers.

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Process Validation: Do We Need Brainwashing?

Michel Crevoisier is retired senior quality assurance expert, ChemOps, at Novartis Pharmaceuticals

In its 2011 Guidance for Industry on Principles and Practice of Process Validation (1), FDA radically changed its interpretation of process validation. Validation now means “the collection and evaluation of data, from the process design stage through commercial production, which establishes scientific evidence that a process is capable of consistently delivering quality product.” It will no longer be a formalized activity that is limited in time and, typically, to three validation batches.

For many people, the most challenging consequence of the new approach is that process validation never ends. As long as a process is in use, it is undergoing validation, either in stage 1 (development), stage 2 (qualification) or stage 3 (continued verification), and validation will continue until the process is no longer used.

Once we have fully understood the new concept of the life cycle approach to process validation, we have to recognize that some typical phrases and words that we have been using for decades are now obsolete.

Here are some examples:

“The process was validated.”
A common statement so far, but it no longer makes sense. Reading or hearing it should make us raise an eyebrow. As validation never ends, to speak of process validation in the past tense has become as much of a fossil as the old validation concept of “three consecutive batches.”

Instead, today, we would say, “The process was qualified.” We should further consider replacing the notion of a “validated process” with the concept of “process validity.” Is the process under control? By FDA’s definition, the collected process data and their evaluation should give us the answer.

The beloved “validation runs”
These also belong to history. Under the new definition, every run is a validation run. Depending on the maturity of the process, the validation runs can be called development, qualification, or verification runs.

“Revalidation” now makes no sense.
We cannot redo something that never ends. We can imagine situations where a process needs to be requalified or even redeveloped; but not revalidated.

Retrospective, concurrent, and prospective validation
What do they mean today where validation never ends? If process validation is “the collection and evaluation of data,” isn’t process validation simultaneously concurrent (the collection of data), retrospective (the evaluation of data), and prospective (the application of inferential statistics)? Collecting data prospectively, however, is hard to imagine.

Considering that the new validation concept changes the meaning of many hitherto common terms (think of “qualification” applied to processes, where the term had typically been used for qualifying equipment), one can ask why the FDA decided to still use the term “process validation” at all. What if they had chosen to use “process control” or “process stability” instead? Wouldn’t this wording better express what the new guidance is all about?

The difficulty may have been that process validation is not only an industrial quality assurance tool (along with keeping process control charts, and evaluations like Six Sigma, for example.)

The term also enters the domain of the application for and the approval of new drugs. To obtain marketing authorization, the applicant has to demonstrate that he is able to reliably produce commercial quantities of the new drug. The proof of the ability to manufacture is typically submitted in the form of a Process Validation package or dossier (2). Regulators must have decided to keep the term “process validation” to maintain the alignment with the wording governing the application for new drugs.

Still, it would have been interesting to have a process guidance that did not use the word “validation.” It would have allowed for a clean cut away from the old “three validation runs-based” practice and an easy leap to the modern approach to quality assurance the FDA wants us to embrace.

So, do we need brainwashing? Six years after the publication of the new FDA guidance, it seems we do. Perhaps it could begin with our quality assurance vocabulary.

References

1. FDA, Guidance for Industry; Process Validation: General Principles and Practices, January 2011.

2. EMA, “Guideline on Process Validation for Finished Products - Information and Data to be Provided in Regulatory Submissions,” Chapter 4, November 2016.

 

 

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Process Validation in Biologics Development

Kunst Bilder/shutterstock.comWhen it comes to outsourcing process validation of biologics, Abel Hastings, director of process sciences at FUJIFILM Diosynth Biotechnologies, says that the relationship between a contract development and manufacturing organization (CDMO) and sponsor is key in ensuring successful process validation.  “Customers that are open about their strategy, their data (good and bad), and their own strengths and weaknesses are most successful.” 

Pharmaceutical Technology spoke with Hastings about the specific challenges that arise in the process validation of biologics. 
 

Challenges specific to biologics

PharmTech: What are the challenges of performing process validation during Phase III of biologics development? 

Hastings: A primary challenge we see is viewing validation as phase-specific. Ideally, process validation should be an extension of the development and refinement processes. To support this, develop a long-term strategy for implementing some of the validation-readiness tools in simple versions early in development so that a transition toward validation is less a change in course and more an acceleration of the project.

PharmTech: What aspects of process validation should companies focus on when preparing to submit a biologics license application to FDA?

Hastings: As a CDMO, FUJIFILM Diosynth Biotechnologies has seen quite a variety of validation strategies. Some strategies are influenced by indication, regulatory designation, or client platform. Some common themes have shown themselves to aid in success of many of these projects. These themes include building clear attribute‒parameter linkages, taking a systematic approach to validation, and leveraging data-driven risk management.

Clear attribute‒parameter linkage begins with a fundamental understanding of the molecule attributes, the measurement systems, and the potential points of variation and ambiguity therein. This knowledge of the measurement can then be applied to the process control strategy including any ambiguity and interaction with other attributes. A team’s focus on required control becomes increasingly clear by applying a logically mapped linkage from attribute to the controlling parameters.

A systematic approach to validation that is based on consensus on both definitions and readiness criteria and is aimed at long-term commercial execution is recommended. We have refined our validation-readiness process to achieve a balance between lean efficiency and sustainability. This development required careful attention to details such as definition subtleties, systematic documentation linkage, quantitative assessment including leveraging previous data, and systematic manufacturing execution standards. This focus on mechanistically applying pre-built tools has proven to be successful with numerous clients efficiently driving their projects to success.

We also recommend building an iterative and logical risk-management program that is based on data-driven decisions. As progress continues toward validation, teams can often be influenced by process history (both recent and past), which can lead to decision making with unintended consequences. We push teams to iteratively refine risk-management documents when new data come available thereby allowing them to make decisions based on data, even in the ‘heat of the moment.’ To guide teams, we have built a series of risk-management tools that range from simple and focused on one problem to large and involved FMEA [failure mode and effects analysis]. This focus on regularly reviewing and documenting risk and data evolution has expedited project execution in a reliable manner.

 

PharmTech: What protocols or tools are used in process validation of biologics compared with solid-dosage drugs?

Hastings: Biologics, when compared with solid-dosage product, can be a sea of parameters and attributes, many with a high degree of variability. We have a systematic approach to process validation that draws on experience from more than 300 molecules; this allows us to focus attention on what matters most. We first begin with high-level process mapping intended to focus attention on parameter-attribute pairings. The next step, which includes smart use of a proprietary FMEA-like software tool, can help focus characterization and equipment adjustments to help improve process reliability. Finally, our platform quantitative assessment tool for parameter-attribute pairs confirms readiness for process validation. Taking this systematic approach, we have been successful in cutting through a large number of variables to reduce variation and ensure right-first-time success for validation campaigns with tight timelines.

Utilizing process validation data

PharmTech: How can the data obtained during process validation be used elsewhere in the production cycle?

Hastings: Process validation should be designed to demonstrate the capability of the process to support commercial manufacturing. To this end, the data generated during process validation should be the forerunner to the commercial control charts. A primary question when reviewing an initial set of commercial data should be: how do these data compare with the expected variation from my validation-preparation activities? Ideally, we expect commercial manufacturing to flow seamlessly from validation, but due to the artificial-perfection that can accompany validation, some changes are expected. Recognizing these subtle changes, and mitigating them as early as possible, can substantially improve long-term manufacturing reliability.

Supply chain considerations

PharmTech: How can process validation help companies ensure their biologic’s supply chain is reliable?

Hastings: Process validation is not just about three batches in a row. It is about launching a reliable commercial process. This is why we design process validation campaigns around an expectation that sustained commercial supply must be planned for. 

The quantitative pre-validation statistical guidelines and systematic tool-sets we use for validation-readiness have been developed and built around the expectations that projects must have data to support sustainable commercial success. Approaching statistical risk management with more pre-validation runs is a common practice for many companies, but our [process validation] philosophy extends beyond that to leverage a large body of pre-existing data and rigorous process-agnostic testing to augment process-specific data. To our clients, this means improved knowledge of their supply chain expectations prior to process validation.

Article Details

Pharmaceutical Technology

Vol. 42, No. 1

January 2018

Page: 56–57

Citation

When referring to this article, please cite it as S. Haigney, “Process Validation in Biologics Development," Pharmaceutical Technology 42 (1) 2018.

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Necessity of the verification of pharmaceutical engineering

Many pharmaceutical companies increasingly feel the difficulty of the new version of GMP certification. Among them, a considerable number of small and medium-sized pharmaceutical companies have fewer production lines and lack of risk prediction on the change of laws and regulations. There is no institutional regulation on their plant design and regular renovation and upgrading, which necessitates long-term transformation tasks before the new GMP deadline. However, discontinued production faces the threat of losing market without stopping production.

GMP, the Chinese meaning is "production quality management practices" or "good practices," "good manufacturing standards." In a nutshell, GMP requires manufacturers of pharmaceuticals and foodstuffs to have good production facilities, reasonable production processes, sound quality management and strict testing systems to ensure that the quality of the final product, including food safety and hygiene, meets regulatory requirements.

Many pharmaceutical companies increasingly feel the difficulty of the new version of GMP certification. Among them, a considerable number of small and medium-sized pharmaceutical companies have fewer production lines and lack of risk prediction on the change of laws and regulations. There is no institutional regulation on their plant design and regular renovation and upgrading, which necessitates long-term transformation tasks before the new GMP deadline. However, discontinued production faces the threat of losing market without stopping production.

First, the verification activities in the role of pharmaceutical engineering

● To ensure the integrity of the pharmaceutical engineering quality derivation process

Since verification is the process of derivation of quality, then in order to ensure the integrity of the derivation process, verifying the professional participation in the whole process of the project is an inevitable choice. This is a qualitative requirement.

This is also true. Since 2010, many foreign-funded enterprises have clearly pointed out that when looking for a construction contractor, they need to verify the full participation of experts. It is interesting to note that more than 50% of these inquiry companies are not pharmaceutical projects or even life science projects. According to the pharmaceutical industry, these projects are not validated.

Systematic to ensure the smooth progress of pharmaceutical engineering

As a process of deriving quality, verification is done through a series of activities to ensure its effectiveness. This is a quantitative requirement.

As a result, many companies request engineering contractors to present verification management systems and successful project cases after they have been asked for verification. Verify execution has become an indicator to judge the project contractor's ability.

● Pharmaceutical engineering and end-user compliance an important part of convergence

Validation is a proactive activity for the project itself; for a pharmaceutical project, not only to meet the validation needs of the project itself, but also to validate the requirements described in the pharmaceutical industry regulations. In other words, at this stage, the contents of the validation of pharmaceutical engineering may be more stringent.

Now we can understand that: If the project management company and you talk about verification, it does not necessarily mean that the GMP verification; GMP consulting firm and you to verify that it must not be verified in the engineering sense; only focus on Pharmaceutical engineering project management company and you talk about the verification, it may be that we are concerned about the validation of the pharmaceutical engineering. Unfortunately, after experiencing the industry turmoil in 2011-2013, no engineering management company focused on pharmaceutical engineering validation exists in China.

Second, verify the management system and quality management system

● Verify the relationship between the management system and the quality management system

For the engineering management company, the quality management system is the company's ISO system, there must be; verification management system is a separate set of working documents, not necessarily an integral part of the quality management system.

● project verification management system and project quality management system

Project quality management system and project verification management system are developed by engineering contractors and pharmaceutical companies and act on the designated project management system. Both can be part of a project's implementation plan.

● project verification management system

For the pharmaceutical project, the project verification management system not only meets the requirements of project verification, but also meets the GMP requirements of pharmaceutical companies for all phases of pharmaceutical engineering. There are two elements here, one based on the project itself and the other based on a specific target industry.

Therefore, in the construction of pharmaceutical engineering verification management system, we should pay attention to include the following aspects:

➤ Engineering Industry Verification Requirements.

➤ Validation Requirements for Pharmaceutical Industry.

➤ Verification Requirements for Device Vendors.

It is important to point out that the construction of the project verification management system is the result of discussions among various stakeholders in the project. Here we must pay attention to identify a misunderstanding: equipment suppliers or contractors to provide a one-sided verification management system is limited and can not be directly used as a verification project management system for the use of pharmaceutical engineering.

Verification is a quality-derived activity. Equipment suppliers also have to do their own quality to derive their own activities. In other words, device vendor verification is not a true subset of pharmaceutical engineering validation.

Strictly speaking, the verification of equipment suppliers also covers the complete life cycle of their products. In this sense, the supplier verification required by our pharmaceutical engineering verification should be the intersection of complete supplier verification and verification of pharmaceutical engineering!