Cleaning Validation for APIs

At 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 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

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.