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cleaning Acceptance Limits for ApIs This article
Cleaning Validation for the 21st
discusses how
to establish true
science-based

Century: Acceptance Limits for Active
limits using data
from clinical and

Pharmaceutical Ingredients (APIs):
toxicological
studies, a risk-

based approach
to evaluating
cleaning

validation data,
and guidance
on setting
statistical
process control
limits from that
data.

"…hazard describes the inherent property art I of this article1 discussed the his- of a compound to produce adverse effects, tory of Cleaning Validation Acceptance e.g., in patients that may be exposed to the Limits for Active Pharmaceutical compound as a trace contaminant in an- Ingredients and where the currently other pharmaceutical product." and "Each used industry limits came from, analyzed the compound has its own inherent ability to current approaches to setting acceptance limits, cause adverse effects (i.e., toxicity) – effects and discussed some of the problems and weak- that may be well documented in the case nesses of these approaches. Part II will discuss of the API…." how to establish true science-based limits using data from clinical and toxicological studies, a Once the hazard is identified, the hazard should risk-based approach to evaluating cleaning vali- be characterized by examining its dose-response dation data, and guidance on setting statistical relationship and the consequences of exposure. process control limits from that data.
The consequence is then considered in the es-tablishment of an Acceptable Daily Exposure Establishing Science-Based Limits
(ADE). In Risk-MaPP, the ADE is defined as: As discussed at the end of Part I, setting cleaning validation limits based on all available safety "The daily dose of a substance below data is much preferred over an approach that which no adverse events are anticipated, considers only one factor (therapeutic dose). by any route, even if exposure occurs for a ISPE's recently published Risk-MaPP Baseline lifetime." Guide2 goes into great detail in describing how to set health-based limits using all the toxico- Although it should be obvious, I will point out logical and clinical data available. Although here that, from Risk-MaPP's definition, the ADE Risk-MaPP is new and is structured to align is a very conservative value.
with the principles described in the recent During the identification of the hazard, a ICH Q9 document, much of its contents are formal review of all available data for the com- based on long-existing principles and long-used pound is performed. For an API, the data used procedures in toxicology. The following discus- in this analysis would be the data submitted in sion will summarize some of the guidance on the company's regulatory filing. By definition, determining health-based limits provided in this includes all of the preclinical and clinical the Risk-MaPP Guide.
data required for approval of the drug. Through Before attempting to set limits of any kind, review of these data the "critical effect" can be it is important to understand what hazard an identified. The critical effect is the first sig- API may actually present to a patient. Risk- nificant adverse effect that is observed as the MaPP states that a… dose increases. For every hazard there is a dose Continued on page 46. PHARMACEUTICAL ENGINEERING September/OctOber 2011
cleaning Acceptance Limits for ApIs below which no effects are expected and this can be the basis ADE as the starting point for calculating cleaning validation for determining an ADE. Exposures below this ADE will not limits ensures that all subsequent values are truly safe. So lead to any other adverse effects. how does using the ADE fit into current cleaning validation The next step is to define the no-observed-adverse-effect level (NOAEL) for the critical effect to be used for derivation Using the ADE is simply a matter of replacing the value of the ADE. The dose at which a significant adverse effect "Lowest Dose (A)/Safety Factor" with the ADE value. All other is first observed is the lowest-observed-adverse-effect level currently used calculations discussed in Part I would remain (LOAEL). The application of uncertainty factors and other the same; for example: adjustment factors results in ADEs that are unlikely to pro-duce any undesirable compound-related effects. "New" Swab Calculations using the ADE
The ADE is derived by dividing the NOAEL for the critical 1. ADE (mg/day) × Batch Size effect adjusted for body weight (e.g., 60 kg) by various uncer- tainty or adjustment factors to extrapolate to the "true" no- Max Daily Dose (B) adverse effect level. Uncertainty factors have been defined for *Maximum Safe Carryover (Note: MSC is equivalent to the each of the main sources of uncertainty as described below.
term Safe Threshold Value (STV) found in Risk-MaPP) Calculation of the Acceptable Daily Exposure (ADE) 2. MSC/Total Surface Area = Surface Residue μg/cm2
3. Surface Residue/cm2 × Area Swabbed = Residue on Swab 4. Residue on Swab (μg)/Dilution Volume (mL) = Residue ADE (mg/day) = _
level in swab sample (ppm) (Note: Although the calculations are the same, another dis- ADE = Acceptable Daily Exposure (mg/day) tinction that this author believes should take place is the NOAEL = No-Observed-Adverse-Effect Level (mg/kg/day) change of terminology from "Maximum Allowable Carryover"
BW = Body Weight (kg) to "Maximum Safe Carryover." Just because we in industry
UFC = Composite Uncertainty Factor can calculate a limit that is high does not mean that it is an MF = Modifying Factor allowable carryover to a regulator. Basically, no cross con-
MDD = Maximum Daily Dose (mg/day) tamination should be allowable if you can easily prevent it; PK = Pharmacokinetic Adjustment(s) the goal should be to minimize cross contamination.) Setting the acceptance criteria to a health-based limit such The calculation of the ADE takes into consideration all of the as the ADE offers many advantages. The ADE is toxicologically available data and applies corrections (UFC, MF, and PK) to and pharmacologically derived based on data generated by the data for Intraspecies Differences, Interspecies Differences, commissioned or published studies and not simply based on Subchronic-to-Chronic Extrapolations, LOAEL-to-NOAEL a dosage calculation. All the appropriate safety factors have Extrapolations, Database Completeness, Modifying Factors, already been applied in deriving the ADE. Using a health- Pharmacokinetic Adjustments, and any additional factors based limit such as the ADE also has the benefit of being that may need to be considered. The procedures used in cal- presented in the drug filing and reviewed by regulators. culating the ADE have been well established for decades and The ADE is now an appropriate starting point to set a "safe Risk-MaPP cites a number of existing guidance documents level" for cleaning residues. However, while swab sample limits and peer-reviewed articles on setting health-based exposure calculated from an ADE will definitely be safe, they will still limits in this manner.
suffer from the wide ranges shown in Table C shown in Part Suffice it to say, that well established tools already exist to I. Some ADEs will result in lower swab sample limits, but develop a truly science-based limit for exposure to pharma- many will result in higher swab sample limits. Take, for an ceutical APIs and this limit (ADE) is not only appropriate for, example, the low dose (81 mg) Aspirin used for prevention of but can easily be used in, cleaning validation. Choosing the heart attack. The ADE will in all likelihood be much higher Figure 1. Relationship of cleaning data to "safe" levels (MSC).
PHARMACEUTICAL ENGINEERING September/OctOber 2011
cleaning Acceptance Limits for ApIs Pharmaceutical equipment should be equally clean regard- Drug Type/
1/1,000th of
Compound
Adverse Effects
Therapeutic
less of what drug product was manufactured on it, the type of equipment it is, or which company is using the equipment; all pharmaceutical equipment product contact surfaces should Low dose Aspirin NSAID/low side effects 81 mg be cleaned as well as possible. It is not logical or reasonable Anti-viral/teratogen or even compliant to clean one piece of equipment less than another simply because "the calculated limits say we can." numerous side effects As discussed in Part I, the calculations for swab samples Table A. Comparison of 1/1,000th limits for low and high risk results in limits that are either grossly too high or too low. Can we really use these ADE-derived "safe" levels as "limits"? My answer is no; limits based on safety data alone may result in than 0.081 mg. So this brings us back to square one – how can acceptance criteria that are well above the actual ability to we use the ADE if it suffers from the same failings discussed clean the equipment. However, I would then add that these in Part I as the 1/1,000th approach? What it brings this author calculated "safe" levels can still be very, very useful. We should not use these calculated "safe" levels as "limits," but rather use them for assessing "risk." The "risk" to a patient can be Why Are We Calling These "Limits"?
assessed if these calculated "safe" levels are used for: A definition of a "limit" that would be commonly understood in the pharmaceutical industry is "a point or line beyond which Statistical Evaluation of
data may not exceed." For example, the upper monograph Cleaning Validation Residue Data
"limit" for Content Uniformity may be 110 and a tablet data One of the primary principles of ICH Q9 is that: point at 109.9 would be considered to pass this limit and be acceptable as visualized on the left in Figure 1. For cleaning "The evaluation of the risk to quality should be based on validation we consider the calculated "limits" as being a "safe" scientific knowledge and ultimately link to the protection level. Higher levels than these would potentially present a risk of the patient." to a patient. Therefore, theoretically, residue data for cleaning validation should really be as far away from the "safe" level This principle can be employed in the evaluation of cleaning as possible as shown on the right in Figure 1. validations. As discussed in Part I (see Figure 2), the distance Cleaning procedures should strive to reduce residues to the between a "Safe" Level and the actual drug residues after lowest levels that are possible to consistently achieve (without cleaning can be viewed as a "Margin of Safety." It should heroic efforts) regardless of what levels the calculated limits be quite obvious that the larger this distance, the safer the may seem to allow. As seen in Table A, the limits for the che- patient is from developing an adverse health effect from any motherapy product suggest that residues 100X higher than residues that may get into the next product. It is equally for the NSAID product would be acceptable. This should not obvious that from a regulator's perspective the larger the be, even from a cleaning standpoint. "Margin of Safety" the greater the confidence in the degree In our daily lives, I believe none of us have higher stan- of control in the cleaning process. Thus, the application of a dards for peanut butter residues remaining on our dishes science-based limit and a significant Margin of Safety is a than for jelly because jelly is easier to remove, or require our powerful demonstration of process control and patient safety forks to be freer of egg residue than a plate. Regardless of the compared to the application of arbitrary safety factors and a residue type our dishes and utensils should be equally clean. small Margin of Safety (due to arbitrarily low limits).
Figure 2. Effect of cleaning better on Margin of Safety.
Continued on page 48. September/OctOber 2011 PHARMACEUTICAL ENGINEERING
cleaning Acceptance Limits for ApIs Figure 3. Graphical representation and actual process capability graph of "Margin of Safety." The residue data collected for cleaning validations should be procedures have been statistically shown to pose no or little statistically analyzed to determine how effective the cleaning risk, it is then possible to move on to: has been and if greater efforts are required. The residue data for each product can be evaluated against its ADE to measure Setting Statistical Process Control Limits
the relative risk to the patient posed by the residues remain- The FDA recently posted their new Guide to Process Valida- ing on the equipment. This is shown graphically in Figure 3 tion.3 Its rationale and nearly all of its elements are directly on the left. The residues for products "A" and "B" have both applicable to cleaning validation. In the Guide, they point been reduced as much as possible and are then compared to their respective ADE values.
While this graphically shows the relative safety of the clean- "Valid in-process specifications ….shall be derived
ing process, the question remains. How safe is it? Well, the from previous acceptable process average and
residue data can actually be evaluated statistically in terms of process variability estimates where possible and
Process Capability using readily available statistical software determined by the application of suitable statistical
packages. The graph on the right shows the results of statistical procedures where appropriate. This requirement, in
analysis of residue data using Minitab Statistical Software and part, establishes the need for manufacturers to analyze how the "Margin of Safety" can be quantified as the Process process performance and control batch-to-batch vari- Performance Capability Index. These software packages are ability." also capable of calculating the number of potential failures based on the residue data (see Exp. Overall Performance on This is not a new requirement and the Guide referred to 21CFR right chart in Figure 3). This approach is simple to perform, 211.110(b). This concept can be directly applied to cleaning can quantify the level of risk, and also predict the possibility also and as we will see allow us to set valid specifications for of failures. ICH Q9 points out that: cleaning residues. After the residue data have been collected and evaluated against the ADE and the level of risk found to "Effective quality risk management can facilitate better be acceptable, the residue data can then be used to calculate a and more informed decisions, can provide regulators Statistical Process Control (SPC) Limit. The calculation of an with greater assurance of a company's ability to deal SPC Limit is simple; the mean +3 or +4 standard deviations with potential risks and can beneficially affect the extent of the residue data. A CpK of 1.33 is obtained when using 4 and level of direct regulatory oversight." standard deviations. Figure 4 shows an SPC Limit (green line) that has been set at the mean +4 standard deviations When a company can show an inspector that the residue data based on the underlying residue data. demonstrates that the cleaning process is highly capable of Setting SPC Limits based on process data is a long estab- providing a wide "Margin of Safety" and patient safety is lished practice dating back to Walter Shewart in the 1930s.4 clearly not an issue, the inspector can move on to consider While SPC has been used extensively in many industries for more risky operations. Therefore, the acceptance criteria for years, the practice is relatively new to the pharmaceutical API residues should consider the cleaning process capability industry. However, this simple and powerful tool has started of the manufacturing equipment or equipment train. This to make inroads. An article in BioPharm International was cleaning process capability should include an evaluation of published in 2006 showing how specifications for impurities the difficult to clean areas and the history of the "cleanability" could be derived in this manner which could easily be applied of the equipment or equipment surface. Once the cleaning to cleaning validation data.5 More recently, in 2008 a presenta- PHARMACEUTICAL ENGINEERING September/OctOber 2011
cleaning Acceptance Limits for ApIs ADEs, risk analysis based on residue data and limits based on Statistical Process Control. The older paradigms, while clearly providing a platform to work from for cleaning valida-tion in the past, should now yield to a newer science-based, risk-based, and statistical paradigm.
1. Walsh, A. "Cleaning Validation for the 21st Century: Ac- ceptance Limits for Active Pharmaceutical Ingredients (APIs): Part I," Pharmaceutical Engineering, July/August 2011, Volume 31, Number 4, pp. 74-83, www.ispe.org.
2. ISPE Baseline® Pharmaceutical Engineering Guide, Vol. 7 – Risk-Based Manufacture of Pharmaceutical Products, International Society for Pharmaceutical Engineering (ISPE), First Edition, September 2010, www.ispe.org.
Figure 4. Process control limits and comparison to ADE-derived 3. Guidance for Industry Process Validation: General Prin- "safe" levels.
ciples and Practices, January 2011.
tion was given at an ISPE conference showing how a Process 4. Shewhart, W. A. "Economic Control of Quality of Manu- Control Limit could be derived from cleaning validation data factured Product," New York, D. Van Nostrand Company, for a fluid bed dryer.6 This technique should see much more use in Process Validation in the near future and its use in 5. Orchard, Terry "Specification Setting: Setting Acceptance Cleaning Validation should follow as well.
Criteria from Statistics of the Data," BioPharm Interna- One of the benefits of setting a Process Control Limit is tional, 1 November 2006.
that the cleaning of subsequent products simply needs to meet 6. Kowal, Robert "Process Capability – A Case Study" pre- these statistically derived limits. New product introductions sented at the ISPE Conference Cleaning Session, Wash- typically trip over cleaning validation and can slow the launch ington D.C., June 2008.
of the product. The ADEs of new products can be quickly evaluated against such a Process Control Limit to determine whether the current cleaning procedure is capable of safely The author wishes to thank Dr. Richard Berkof, Nick Haycocks, cleaning the new product before it enters the facility. Robert Kowal, Dr. Bruce Naumann, Mohammed Ovais, and Joel Young for reviewing the manuscript and for providing their insightful comments and helpful suggestions.
The ADE is a value based on ALL of the available safety
data, not simply the lowest dose, and provides a clearly safe
About the Author
starting point for subsequent cleaning validation calculations. Andrew Walsh is an Industry Professor
Using the ADE eliminates much of the guess-work involved in at Stevens Institute of Technology in their using the dose-based criterion and employs all of the science Pharmaceutical Manufacturing Program at hand in the company.
where he teaches courses on validation and The use of the ADE also will provide a scientific basis for the lean six Sigma. In 2009, Walsh founded the "Margin of Safety" when evaluating cleaning residue data, and Stevens Pharmaceutical Research Center from an operational standpoint, this will allow much greater (SPRC), a research lab focusing on Phar- flexibility than with the dose-based criterion. As stated earlier, maceutical manufacturing topics, such as the ADE is a very conservative value and using it in cleaning cleaning process development, total organic carbon analysis validation will result in very conservative "safe" levels. and method development, visual inspection method develop- In a large number of cases, we have been overly restrictive ment and automation of GMP systems. A current Chair of an using the dose-based criterion and this has resulted in the international task team to write a cleaning Guide for ISPE and unnecessary dedication of parts, equipment, and even whole ASTM, he was one of the contributors to the ISPE Risk-Based manufacturing trains and packaging lines. In some cases Manufacture of Pharmaceutical Products (Risk-MaPP) Base- the flexibility to manufacture products is severely restricted line® Guide. He has more than 20 years of diverse validation based on the order of products manufactured as it appears experience in pharmaceutical and biotech companies, includ- they cannot be cleaned well enough. These overly restrictive ing Johnson & Johnson, Schering-Plough, and Hoffmann-La dose-based limits also have led to the unnecessary develop- Roche. Walsh has given numerous presentations over the ment of "disposable" equipment. Use of the ADE should help past 15 years with IIR, Barnett, WorldPharm, IPA, IVT, and alleviate some of these issues.
ISPE. He can be contacted by telephone: +1-201-216-5533 or We have seen the progression of the development of clean- email: andrew.walsh@stevens.edu. ing validation limits based on pesticide levels in food, to frac- Stevens Institute of Technology, Castle Point on Hudson, tions of the therapeutic dose, and now on to the calculation of Hoboken, New Jersey 07030, USA.
September/OctOber 2011 PHARMACEUTICAL ENGINEERING

Source: https://web.stevens.edu/ses/documents/fileadmin/documents/pdf/Cleaning_Validation_for_the_21st_Century_-Acceptance_Limits_for_APIs_-_Part_II.pdf

acrrm.org.au2

Structured Assessment using Multiple Patient Scenarios (StAMPS) Preparation including Practice Scenarios 1. Preparing for the StAMPS assessment process The information provided in this first section is a ‘generic' discussion about the StAMPS assessment. The following sections provide some sample ‘practice' exams and discuss in finer detail what is relevant to the specific StAMPS examination. (NB at this stage there are only examples from the Primary Curriculum and Emergency Medicine examinations available). StAMPS is an assessment modality that is designed to test higher order functions in a highly contextualised framework where candidates have the opportunity to explain what they do and demonstrate their clinical reasoning for a given scenario, as opposed to simply providing evidence of knowledge, listing facts or recalling protocols. The methodology for this assessment has been extensively researched and found to be a high level tool; StAMPS has been accepted as such by the Australian Medical Council (AMC). StAMPS is essentially a ‘hybrid' between a viva voce and OSCE examination with the emphasis on the viva voce. Candidates are strongly advised to consider the relevant curriculum statements and prescribed reading list when preparing for the assessment, as well as familiarising themselves with the format of this assessment modality. The Primary Curriculum Assessment Blueprint provides a good guide as to which domain areas the StAMPS examination will focus on and is also of benefit for the AST StAMPs assessments.

Preface-2-06.dvi

A roadmap towards healthcare information systems interoperability in Greece Alexander Berler, Anastassios Tagaris, Pantelis Angelidis, and Dimitris Koutsouris Abstract— The advantages of the introduction of information ment in patient care, have been satisfactorily embraced by and communication technologies (ICT) in the complex health-