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Conventional limit-setting techniques are not health-based and can make risk assessment more difficult.
In a previous article, the authors discussed the lack of scientific justification for the 10-parts-per-million (PPM) limit commonly used in cleaning validation and the lack of any regulatory requirement for it (1). This article discusses how the 10ppm limit, along with the 0.001 dose limit, are not truly risk-based approaches and are also unsound from an operational standpoint as they have caused unnecessary difficulties for many companies. A statistical process-based approach to demonstrate that acceptable cleaning has been achieved compared to a health-based limit is proposed.
The use of a default limit value expressed as a concentration (ppm) has its origins in food safety concerns related to pesticide residues (2). The value of 10 ppm comes from an article written by Gary Fourman and Dr. Michael Mullen more than 20 years ago (3), which suggested a combination of the following for setting acceptance limits for cleaning:
Since that time, many pharmaceutical companies have adopted some or all of the criteria suggested in this article, including the 10-ppm value (4). Due to difficulties and inconsistencies in the application of these approaches to the wide variety of pharmaceutical dosage forms, some modifications to these limits were suggested such as using 0.01 or 0.1 (5). Some companies have had issues meeting these limits, in particular the 10 ppm, and have moved to higher values, and some have even published their justifications (6). Consequently, no single, consistent approach exists for setting limits for cleaning validation (2).
In 2010, the International Society of Pharmaceutical Engineering (ISPE) published its Risk-Based Manufacturing of Pharmaceutical Products (RiskMaPP) Baseline Guide (7) that introduced the acceptable daily exposure (ADE) as the appropriate value for assessing pharmaceutical manufacturing risks, including for cleaning validation, as it is a value based on all the available pre-clinical and clinical data for a compound. The European Medicine Agency (EMA) uses the term permitted daily exposure (PDE), which it has declared to be “effectively synonymous” with ADE and defines it as “a substance-specific dose that is unlikely to cause an adverse effect if an individual is exposed at or below this dose every day for a lifetime” (8).
Although the PDE is very conservative and has a superior scientific basis compared with previous approaches, there has been some objection and resistance to the use and implementation of PDE (9) but nothing substantive has ever been published in a peer-reviewed journal. In 2014, EMA issued a guideline that required all companies to develop PDEs by June 2015 for any medicinal product newly introduced into shared manufacturing facilities. It also required that PDEs be used to identify risks by December 2015 for medicinal products already produced in a shared manufacturing facility (8).
While the guideline was being developed, some biopharmaceutical companies lobbied against it, asserting that their products are denatured or degraded by cleaning and should therefore be exempt from the requirement to establish ADEs for such drug products. EMA subsequently provided language in the final version of the guideline permitting these companies to use “alternative approaches” if they can be “scientifically justified.”
It has been demonstrated, however, that within the risk-based approach of International Council for Harmonization Q9 (10), the risk associated with any residual biologic (i.e., based on its ADE) still must be addressed (11). Also, some companies have been trying to justify retaining the use of 10ppm and 0.001 dose limits because in many cases these old approaches result in lower limits than the ones based on ADEs. Their rationale is that the most stringent limit of the three limit-setting methods should be imposed.
The following discussion addresses why retaining either the 10ppm or the 0.001 dose limits is unwarranted. First, neither of them are “health based” and, therefore, not “risk-based.” Second, they have caused many operational issues, and third, and more importantly, they actually interfere with an appropriate risk assessment, which should now be necessary in accordance with ICH Q9.
The issue of the 0.001 dose limits not being health-based has been addressed in a previous article (2) and will be revisited briefly here.
Table I shows how three compounds with widely varying severity of hazards (low to high) will have cleaning limits set using the 0.001 lowest clinical dose approach that are inconsistent with a health-based assessment. The fundamental problem with basing a cleaning limit on the therapeutic dose is that the choice of medication and dose is tailored to an implicit risk-benefit medical decision for individual patients who are suffering from a particular disease or condition, which may be contraindicated in other patient populations. For example, a pregnant woman who takes a Cetirizine hydrochloride tablet should not be exposed to unsafe levels of teratogenic drugs such as Ribavirin or oncology drugs such as Capecitabine. So logically, how could the cleaning limit for Cetirizine hydrochloride be set so much lower than that for a teratogen or anti-cancer drug?
This simple comparison should be sufficient evidence that the use of 0.001 of the lowest therapeutic dose ignores the underlying scientific data for a drug. If the first effect of a substance (also known as lead effect) is identical with the desired therapeutic effect, then there is a direct relationship between the therapeutic dose and the no-observed-effect level (NOEL) of this substance. In many cases though it is sufficient to lower the therapeutic dose by a factor much less than 1000 to reach this NOEL; for a few drugs, however, a factor higher than 1000 may be necessary. If the lead effect of the substance is qualitatively different from the desired therapeutic effect (e.g., teratogenicity or genotoxicity), then this effect may have a different dose-response curve from the dose-response curve of the desired effect in the target patient. In cases where there is no correlation at all between the NOEL for the lead effect and that for the therapeutic dose, the ADE cannot be a simple fraction of the therapeutic dose.
The principles behind applying risk in pharmaceutical manufacturing were introduced in the ICH Q9 (formally adopted by FDA and the Ministry of Health, Labour and Welfare (MHLW) in 2006) and its applicability to cleaning (including acceptance limits) mentioned in its Annex II.4 and to validation mentioned in Annex II.6. In accordance with ICH Q9, risk can be defined as Equation 1:
Likewise, risk can be defined as a function of the probability of adverse health effects arising from exposure to a hazard. The types and probabilities of effects from exposure to a drug are API-specific and dose-dependent. Consequently, for cleaning if the identified hazard is an API then the above equation for risk can be expressed as Equation 2:
where the ADEAPI provides the measure of the severity of the hazard that the API presents in long-term exposure. It should be noted that this concept can be applied to any agent, not just APIs.
The ADE for an API (or any compound) provides a value that can be substituted for the “severity of harm” in Equation 1 (as shown in Equation 2); but what value should be substituted for “probability of exposure”?
Figure 1 shows the “probability of exposure” to API residues can be determined by comparing the data collected during cleaning to the limits calculated from the ADE of the API. The distance between the data and the ADE-based limit provides the margin of safety and indicates the probability of patient exposure to the API residues resulting from ineffective cleaning. If this margin of safety could be measured, then this value could be used for the “probability of exposure” parameter in Equation 2.
A simple statistical method has already been presented for quantifying the “margin of safety” and that is the process capability index (Cpk) or more precisely Cpu (12, 13). The Cpu measures the capability of a process only for an upper specification limit (USL). It is a simple, straightforward comparison of the spread of the data (its variability) to the distance between the mean of the data and the upper specification limit (Equation 3).
This equation can be satisfied using cleaning data such as shown in Figure 2.
Readily available software provides powerful statistical and graphical abilities that can calculate the Cpu and quickly help visualize the relationship of data to limits. Figure 3 shows a comparison of two cleaning processes against ADE-based limits using Minitab 17 (14).
On the left of Figure 3, residue data for a cleaning process are distributed well below the ADE-based limit, thereby providing a very wide “margin of safety,” and hence a very low probability of patient exposure. On the right, however, residue data for a cleaning process are distributed relatively close to the ADE-based limit, thereby not providing an adequate “margin of safety.” Consequently, this scenario presents a high probability of patient exposure. It should be noted that although no individual data point failed the limit on the right (they would pass in a traditional cleaning validation), the statistical evaluation demonstrates that failures are likely (PPM > USL = 13,532) and the cleaning process has a risk of failures (i.e., exceeding the ADE-based limit).
It can be seen that the Cpu can be easily used as a measure for the “probability of exposure” parameter in the cleaning validation risk evaluation.
Figure 4 shows an actual evaluation of a very low hazard product using total organic carbon (TOC) analysis. Using the ADE, the calculated swab limit was 3179ppm (USL value) in the analytical sample. Because the cleaning procedure was effective, the TOC swab results were far below this limit, resulting in a Cpu value of 6120 with a lower 99% confidence limit for the Cpu of 3084. To put this result into perspective, in a Six Sigma organization, the goal for a process capability is only 2. Clearly, this analysis shows a very low-risk cleaning situation.
The implementation of the ADE as the starting point in limit calculations for cleaning validation has required revisiting older calculations that use 0.001 dose and 10 ppm. This has revealed situations where the previous limits were not low enough and potentially posed a risk to patients, and situations where the previous limits were lower (often much lower) than those calculated using the ADE.
Figure 5 shows a comparison of the ADE values vs. 0.001 of the lowest clinical dose for 304 pharmaceutical products (data were provided by several pharmaceutical companies). The data were plotted as a ratio of the ADE to the 0.001 dose, which makes 1.00 on the Y axis the point at which the ADE equals the 0.001 dose (Note: the ratios had to be plotted on a logarithmic scale in order to get all the data on one graph because the values of the ratios varied widely).
These ratios show that, in more than 15% of the cases, the ADE was lower than the 0.001 dose including two instances in which the ADE was 100 times lower indicating a potential issue for patient exposure. However, these ratios also show that in 85% of the cases, the 0.001 of the lowest clinical dose was lower than the ADE. In fact, of these cases, 47% were 10 times lower, 12% were 100 times lower, and there were six instances that were 1000-8000 times lower than the ADE. This demonstrates that, for the vast majority of drugs, the 0.001 dose limit has been overly conservative.
While at first it may be startling to realize that most cleaning acceptance limits based on the use of 0.001 dose have been grossly overstated, the most important thing to realize about the increase in cleaning acceptance limits for most APIs as a result of implementation of health-based ADEs is its impact on the ability to evaluate the true risk. Figure 6 shows how the true margin of safety can now be measured using the ADE-based cleaning acceptance limit. Where cleaning data were close to failing using the 0.001 dose-based limits and had a poor Cpu, moving to the ADE-based limits shows that there is a wide margin of safety and an excellent Cpu.
Figure 7 is a comparison of the ADE/0.001 dose data of one of the products from Figure 5. A hypothetical set of TOC swab data with a mean of 75 parts per billion (ppb) and a standard deviation of 7.5 ppb was evaluated against analytical swab limits calculated using both the ADE and the 0.001 dose limits for that product. The calculation of both analytical swab limits assumed a maximum daily dose of 1000 mg, a batch size of 500kg, and a total surface area of 100,000 in2 for the next product. For sampling, a swab area of 100 in2, a dilution volume of 10 mL, and a recovery factor of 100% were assumed. This resulted in an ADE-based analytical swab limit of 250,000 ppb and a 0.001 dose-based analytical swab limit of only 100 ppb.
For the ADE-based limit on the left of Figure 7, there is a wide margin of safety with a Cpu of 9224 even for the 99% lower confidence level. Because they are calculated from the mean and standard deviation, Cpus can have confidence intervals calculated just as any mean can). Using the 0.001 dose-based limit, there is no margin of safety and a Cpu of only 0.84 for the 99% lower confidence level. The true margin of safety cannot be measured using the 0.001 low-clinical dose-based limit methodology. Applying the 0.001 low-clinical dose based limit, the process capability is artificially low across most of the compounds and could likely lead to false conclusions of cleaning failures for low risk products.
Back in 1993, Fourman and Mullen had recognized that limits calculated based on dose increased as the potency decreased and resulted in higher and higher limits for low potency (i.e., high therapeutic dosage) drugs (3). To account for this effect, they suggested that a 10-ppm limit be imposed when the 0.001 low-clinical dose-based limit went too high and the maximum allowable carryover (MAC) into the next batch would exceed 10 ppm. The only justification these authors provided was that maximum allowable ppm limits were used in the food industry. There was no explanation as to why a maximum value of 10 ppm was selected. In the graphical representation in Figure 8, the implications of imposing the arbitrary lower limit of 10 ppm criterion after applying the 0.001 dose-based limit are seen. The end result is that, after a certain point, all lower potency drugs have the same cleaning acceptance limit, regardless of how low their potency or how low their risk levels are.
As seen in Figure 6, the true margin of safety cannot be measured when imposing the 10-ppm limit as shown in Figure 9. The 10-ppm limit simply cuts off, or truncates, the ability to measure the margin of safety, making a relatively low-risk compound look like a high-risk compound, because the 10-ppm limit is only imposed when the 0.001 dose limit is higher than the 10-ppm limit. Figure 9 illustrates a hypothetical example of a low potency drug where the ADE is higher than the 0.001 dose-based limit, which in turn is higher than the 10-ppm criterion (data not to scale).
The recent implementation of a regulatory requirement mandating the use of health-based cleaning limits for shared medicinal manufacturing facilities has resulted in a significant increase in the level of cleaning limits for most drugs. It should be understood that the previous use of 0.001 low-clinical dose limit, in the case of most APIs, has been excessively conservative, which has caused significant needless complications with manufacturing operations, especially for low-hazard products. Many companies have gone from using disposable items such as scoops and other utensils, and modifying manufacturing schedules, to dedicating parts, pieces of equipment, or even entire manufacturing lines to deal with these artificially low limits, all the while knowing that their products were low risk. While there have been calls to return to the 10-ppm and 0.001 dose limits as they are more stringent, this reversion would simply have the industry using limits that do not consider all the pre-clinical and clinical data available for each API. And worse, it would perpetuate the many unjustified operational issues for low-hazard products. If the argument for stringency was truly valid, the case should be made to move to a 0.00001 low-clinical dose limit to also cover those 15% of the compounds where the ADE concept has revealed that the 0.001 low-clinical dose limit has been insufficiently protective. Thus, reverting to the 0.001 low-clinical dose limit would be against the science- and health-based evaluation process EMA has sought to institute for cleaning validation. Moreover, such a move would lead to many products being falsely judged as failing cleaning validation, and perhaps ultimately requiring their manufacture in dedicated facilities. The proposal to keep cleaning limits as low as possible is not a valid argument if the real goal is to correctly and objectively identify and control the level of risk to the patient. Maintaining the 10-ppm limit as a default to prevent excessive carryover is equally unjustified, as “visually clean,” a third criterion not discussed herein and which is almost universally implemented to satisfy the inspection criterion required by 21 Code of Federal Regulations (CFR) 211.67(b) (15), is more than satisfactory to keep any product carryover low.
The pharmaceutical industry has been moving toward a science and risk-based approach since 2001 with the roll out of FDA’s CGMPs for the 21st Century (16), the process analytical technology (PAT) initiative (17), the process validation guidance (18), and the ICH Q series guidelines, among others. While this movement has been slow, the authors believe that the time is right for cleaning validation to embrace a science and risk-based approach to acceptance limits, in conjunction with the process capability methods identified here.
According to ICH Q9, “The level of effort, formality, and documentation of the quality risk management process should be commensurate with the level of risk.” Consequently, cleaning validation efforts should be focused on where the risks are, with the science behind the ADE informing us which products have the greatest risk (see Figure 10). This was one of the stated goals of the Risk-MaPP Guide. The Cpu then informs us what the probability of patient exposure is and can be used to complete the calculation shown in Equation 2.
Continuing the use of 10-ppm and 0.001 low-clinical dose criteria obscures the true patient risk and presents a potential compliance gap as well as operational difficulties, which is why both of them should be abandoned. With the requirement to have established API-specific ADEs, the 10-ppm and 0.001 low-clinical dose should now be relegated to a chapter of history. The ADE must now be used for risk assessment for all products. In concert, statistical process control (SPC) methodologies should be used for establishing ongoing cleaning validation limits based on actual process data as has previously been described (12).
The authors wish to thank Osamu Shirokizawa, Mariann Neverovitch, and Joel Young for reviewing this article and for their insightful comments and helpful suggestions.
1. M. Crevoisier et al., Pharm. Technol., 40 (1) pp. 52-56 (January 2016).
2. A. Walsh, Pharmaceutical Engineering, 31 (4): 74-83, (July/August 2011).
3. G. Fourman and M. Mullen, Pharmaceutical Technology 17 (4): 54-60, (April 1993).
4. D. LeBlanc, “Residue Limits for Cleaning Validation in Finished Dosage Form Manufacturers,” PDA Letter, Parenteral Drug Association 2006 Survey on Cleaning Acceptance Limits, May 2007.
5. Parenteral Drug Association, “Points to Consider for Cleaning Validation,” Technical Report No. 29, Bethesda, MD, 1998.
6. ECA Academy, “Justification of Limits for Cleaning Validation in the Manufacture of Active Pharmaceutical Ingredients,” May 14 2007, www.gmp-compliance.org/eca_news_928.html
7. ISPE, ISPE Baseline Guide: Risk-Based Manufacture of Pharmaceutical Products (Risk-MaPP), First Edition (ISPE, September 2010).
8. EMA, EMA Guideline on Setting Health Based Exposure Limits for Use in Risk Identification in the Manufacture of Different Medicinal Products in Shared Facilities, EMA/CHMP/CVMP/ SWP/169430/2012, 20 November 2014, www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2014/11/WC500177735.pdf.
9. D. LeBlanc, Risk-MaPP-Gate Website, https://sites.google.com/site/riskmappgate/home
10. ICH, Q9 Quality Risk Management, Step 4 (ICH Nov. 9 2005), www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q9/Step4/Q9_Guideline.pdf.
11. A. Walsh, BioPharm International (November 2015).
12. A. Walsh, Pharmaceutical Engineering 31 (5) 44-49 (September/October 2011).
13. D. C. Montgomery, Introduction to Statistical Quality Control, 7th Edition (John Wiley & Sons, Inc., Hoboken, NJ, 2013).
14. Minitab Inc. Minitab 17. State College, PA. (2016).
15. 21 Code of Federal Regulations (CFR) 211.67(b)
16. FDA, Pharmaceutical cGMPs for the 21st Century Industry-A Risk-Based Approach: Final Report (FDA, September 2004 (FDA), www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/Manufacturing/QuestionsandAnswersonCurrentGoodManufacturingPracticescGMPforDrugs/UCM176374.pdf.
17. FDA, Guidance for Industry: PAT-A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance (FDA, September 2004), www.fda.gov/downloads/Drugs/.../Guidances/ucm070305.pdf.
18. FDA, Guidance for Industry: Process Validation: General Principles and Practices (FDA, January 2011), www.fda.gov/downloads/Drugs/.../Guidances/UCM070336.pdf.
Vol. 40, No. 8
When referring to this article, please cite it as A. Walsh et al., "Cleaning Limits-Why the 10-ppm and 0.001-Dose Criteria Should be Abandoned, Part II," Pharmaceutical Technology 40 (8) 2016.