Justication & Qualication Of Visual Inspection For
Cleaning Validation In A Low-Risk, Multiproduct Facility
By Andrew Walsh, Dongni (Nina) Liu, and Mohammad Ovais
Part of the Cleaning Validation For The 21 Century series
Proposals for the use of visual inspection (VI) as an analytical method for cleaning validation have
been rising for several years now. This article discusses regulatory views on the use of VI as a sole
criterion in cleaning validation, presents a case study on how inspectors can be qualified for VI,
recommends the use of statistical techniques, and suggests how VI could be implemented as part of
a control strategy in a cleaning validation program based on the level of risk.
Current Regulatory Views
In its 2015 update to Annex 15, the European Medicines Agency (EMA) indicated the possibility of
using visual inspection alone in cleaning validation where it stated:
"A visual check for cleanliness is an important part of the acceptance criteria for cleaning
validation. It is not generally acceptable for this criterion alone to be used." (emphasis
Implied in the wording of this guidance is that VI may be acceptable under certain conditions, as the
original wording in the 2014 draft stated, “It is not acceptable for this criterion alone to be used.”
Based on industry interest, and considering this wording change, the possibility of using VI as a sole
acceptance criterion was included in the newly released American Society for Testing and Materials
(ASTM) E3106 "Standard Guide for Cleaning Process Development and Validation" under the
"Using visual inspection alone for validation may be acceptable only when the risk is low and
100 percent of the equipment surface can be inspected under appropriate viewing conditions.”
Guest Column | August 3, 2018
This wording was selected to acknowledge that VI may be acceptable within these two criteria.
However, E3106 does not provide guidance for determining when a risk is low enough or whether VI
might still be used when somewhat less than 100 percent of equipment surfaces can be inspected by
VI (e.g., 95 percent). This standard was meant to provide the first defined criteria that could be
acceptable to regulators. (Note: Two of this article’s authors as well as several peer reviewers were
co-authors of the E3106 standard.)
On April 16, 2018, the EMA posted an update to its Q&A on the Guideline for Setting Health Based
Exposure Limits (HBELs). In this Q&A, there are two new Q&As (#7 and #8) that are directly
applicable to the use of VI. These Q&As state:
Q7. Is analytical testing required at product changeover, on equipment in shared facilities,
following completion of cleaning validation?
A: Analytical testing is expected at each changeover unless justified otherwise via a robust,
documented quality risk management (QRM) process. The QRM process should consider, at a
minimum, each of the following:
• the repeatability of the cleaning process (manual cleaning is generally less repeatable
than automated cleaning);
• the hazard posed by the product;
• whether visual inspection can be relied upon to determine the cleanliness of the
equipment at the residue limit justified by the HBEL.
Q8. What are the requirements for conducting visual inspection as per Q&A 7?
A. When applying visual inspection to determine cleanliness of equipment, manufacturers
should establish the threshold at which the product is readily visible as a residue. This should
also take into account the ability to visually inspect the equipment, for example, under the
lighting conditions and distances observed in the field.
Visual inspection should include all product contact surfaces where contamination may be held,
including those that require dismantling of equipment to gain access for inspection and/or by
use of tools (for example mirror, light source, boroscope) to access areas not otherwise visible.
Non-product contact surfaces that may retain product that could be dislodged or transferred
into future batches should be included in the visual inspection.
Written instructions specifying all areas requiring visual inspection should be in place and
records should clearly confirm that all inspections are completed.
Operators performing visual inspection require specific training in the process including
periodic eye sight testing. Their competency should be proven through a practical assessment.
From these Q&As, there is now sufficient regulatory guidance for industry to begin determining
acceptable approaches to implementing VI as a control strategy in cleaning validation programs.
This article proposes a systematic and comprehensive approach to address these points that are:
2. Risk-based and
We believe these are minimum requirements for successfully implementing such programs. The
following case study will illustrate how these three aspects were combined using the concepts in
ASTM E3106 to implement successful usage of VI at the facility as a sole acceptance criterion for
A pharmaceutical facility was instituting a new cleaning validation program in response to a 483
observation. A risk assessment and cleaning validation studies were performed following the
concepts in ASTM E3106. In accordance with ASTM E3106 and ICH Q9, this approach included the
following four quality risk management (QRM) steps:
1. Hazard (risk) identification
2. Risk analysis
3. Risk evaluation
4. Risk control
Hazard (Risk) Identification
All raw materials used for manufacturing the 107 products at the facility were reviewed to determine
the level of risk posed by any of these substances to patient safety. The cleaning agent components
were also reviewed, as residues of the cleaning agent may have safety implications for patients as
well. Over 350 excipients, 10 active pharmaceutical ingredients, and three cleaning agent
components were included in this review. Only four of the active pharmaceutical ingredients were
identified as posing a potential hazard to patient safety, so acceptable daily exposure (ADE) values
were determined for them by a qualified toxicologist/pharmacologist (Table 1).
Table 1: List of APIs and their ADEs
The other six APIs already had existing safety assessments documenting satisfactory safety profiles
at product use levels. All of the cleaning agent components were on the FDA’s Select Committee on
GRAS [generally recognized as safe] Substances (SCOGS) Database. Therefore, these compounds
were considered safe and cleaning validation studies for them were deemed unnecessary based on
the level of risk.
The risk analysis included selection of analytical methods, calculation of maximum safe carryovers
(MSRs), calculation of maximum safe surface residues (MSSRs), and calculation of analytical swab
limits from the MSSRs. As these APIs contained organic carbon, total organic carbon (TOC) was
chosen as the analytical method. Although TOC is becoming the method of choice for cleaning
validation, it is not applicable to APIs with no organic carbon content.
Table 2 shows the lowest possible TOC swab limits calculated for all the tanks in the manufacturing
area. The manufacturing tanks have the largest surface areas in the facility and, since limits are
inversely proportional to the equipment surface area, only the limits in the manufacturing area were
calculated. All TOC swab limits for the other areas (e.g., packaging) would be substantially higher.
Table 2: MSSRs and TOC Swab Limits for All Tanks for All ADEs
The lowest possible TOC swab limit was for Tank-1 (largest surface area) for API-1 (lowest ADE). So
the TOC swab limit for API-1 in Tank-1 was chosen for setting the acceptance limits for this study.
TOC data was then collected for the cleaning procedures for five kettles, two packaging lines, and the
raw material preparation area. The statistical analysis of these data showed these cleaning
procedures are controlled well below the TOC swab limit for API-1 in Tank-1 of 17,000 ppb (17
ppm). The control chart in Figure 1 shows the data for these cleanings compared to the API-1 in
Tank-1 (17 ppm). The upper control limit (UCL) for all of the TOC swab data was only 487 ppb,
meaning that 99.87 percent of all the TOC swab data fell below this value.
Figure 1: Control chart for all TOC results
Note: The data for runs #6-8 (packaging lines and raw material preparation area samples) use the
Tank-1 limits in this chart, but it should be realized that their actual TOC swab limits would have
been much higher than 17 ppm. These data should clearly satisfy the first criterion listed by EMA in
its Q&A #7.
Cleaning Risk Dashboard
Figure 2 presents a “cleaning risk dashboard” showing the level of relative risk for the cleaning
processes at this facility based on three main risk factors associated with cleaning. These risk factors
1. The Toxicity Score of API 1,
2. The Process Capability Score for API 1,
3. The Detectability Score of API 1 for VI.
It should be noted that all of these scores were derived directly from actual objective data.
Figure 2: Cleaning risk dashboard for facility
The results of the risk assessment and the cleaning validation studies demonstrated that the
products and cleaning procedures presented a very low risk to patient safety from cross
contamination due to product carryover after cleaning. This analysis should clearly satisfy the
second criterion listed by EMA in Q&A #7. These points above, combined with the ICH Q9
principle that the amount of validation effort, the formality, and the level of documentation should
be commensurate with the level of risk, made this facility a strong candidate for converting to VI
Visual Inspection Qualification Case Study
Based on this risk assessment and supported by cleaning validation studies, the manufacturing site
decided to institute a VI program. A qualification study that included 33 personnel from four
different departments was developed and performed.
Materials and Methods
A total of 30 316L/#4 finish stainless-steel coupons were prepared for the qualification study by
"spiking" three different levels of API 1 on to them as shown in Table 3. These coupons were
individually numbered from 53 to 82 and randomly assigned to one of the three groups to help
prevent inspectors from remembering coupons.
Table 3: Coupon Data
A list of all manufacturing personnel who were to participate in the qualification study was provided
to the Center for Pharmaceutical Cleaning Innovation (CPCI). From the list provided, CPCI created a
Measurement Systems Analysis for Attribute Data using Minitab 18 and generated data sheets for
each inspector. This attribute analysis study was set up for three inspections for each of the
Prior to the qualification study, a survey of light levels (in lux) in the manufacturing areas was taken
using an Omega HHLM1337 Digital Illuminance Meter, and these results are shown in Table 4.
Table 4: Survey of Light Levels in Facility (lux)
The coupons were randomly arranged along the edge of a large table in a manufacturing area and
the light levels around the edge of the table were recorded (Figure 3) and were equivalent to those
found in the other manufacturing areas (Table 4).
Figure 3: Light levels at coupon evaluation area (lux)
All personnel were provided no additional instructions other than to inspect the coupons for product
residue as they normally inspect equipment after cleaning and to designate the coupons as either
"Dirty" or "Clean" on data sheets provided. Theoretically, the inspectors should identify the blank
coupons as clean and the low residue level and high residue level coupons as dirty. All personnel
performed these inspections three times over the course of three days, with the exception of two
individuals who had been absent and performed them over two days, with two of the inspections on
one day (one in the morning and one in the afternoon). Before each inspection, the coupons were
rearranged to prevent the inspectors from remembering the coupons.
In addition to the inspection data, metadata was also collected about the inspectors to examine
whether these factors played any role in the results of the study. The metadata collected included the
department of the inspector, inspector age, inspector years of service, inspector gender, and whether
the inspector wore glasses or not.
Risk Evaluation (Evaluation of Results Against Acceptance Criteria)
The completed data sheets, including the metadata, were sent to the CPCI laboratory in
Hillsborough, NJ for analysis. The acceptance criterion for the VRL was the level at which all
inspectors could identify the dirty coupons correctly 100 percent of the time. All inspectors were
able to correctly identify all of the dirty coupons at the 0.2 mcg/cm level 100 percent of the time but
could not do so at the 0.02 mcg/cm level (approximately 90 percent did so).
A data analyst at CPCI analyzed and graphed the data using Minitab 18, R and SAS statistical
software. The collected attribute data (Dirty/Clean) was analyzed using Minitab™ 18. Minitab 18 can
analyze up to 10 inspectors for attribute analysis at a time. However, each department contained 10
personnel or fewer, so the inspection data was evaluated by department. Figures 4 through 6 show
three graphs generated by Minitab that give extensive information on the analyzed data.
1. Summary Report - This graph is an overall summary of the results including the overall
misclassification rates and the overall percentage accuracy of each inspector. The first graph in
the report shows the overall percentage accuracy on a scale that rates the acceptability of the
results . The misclassification rates show the rates that the clean coupons were rated dirty
and the dirty coupons were rated clean. In this case, none of the dirty coupons were rated
clean, while 19.5 percent of the clean coupons were rated dirty. This can be considered
acceptable, since clean equipment that is suspected of being dirty will simply be cleaned again.
The percentage accuracy by appraiser is also shown, and the differences among the appraisers
can be seen in this graph. One appraiser was accurate 100 percent of the time, while another
appraiser was accurate only 76.7 percent of the time, indicating a possible need for training .
Figure 4: Attribute agreement summary report
2. Accuracy Report - This graph shows the percentage accuracy for each inspector by
appraiser, by standard, by trial, and by appraiser and standard collectively. Percentage
accuracy by appraiser shows that three appraisers are significantly lower in accuracy than the
others . The percentage accuracy by standard type (dirty or clean) indicates how well the two
types of standards were identified. Here we see that the dirty coupons were all identified
correctly, while the clean coupons were correctly identified >80 percent of the time . The
percentage accuracy by trial shows whether there was any decrease over time. It can be seen
that the accuracy seemed to decrease slightly as the qualification process went on . However,
the results for all three trials are within the 95 percent confidence intervals, so the trials can be
seen as equivalent at this confidence level. The final graph on the right shows the percentage
accuracy of each inspector for both standard types. This is important for this study as it
indicates whether all inspectors can correctly identify a dirty coupon at that level. For this
group, all appraisers correctly identified the dirty coupons 100 percent of the time and two
appraisers correctly identified the clean coupons 100 percent of the time . One appraiser
incorrectly identified the clean coupons as dirty about half of the time, indicating that this
person may require additional training.
Figure 5: Attribute agreement accuracy report
3. Misclassification Report - This graph provides details on the misclassification rates for
both the coupons and the inspectors. For the coupons, the percentage of dirty rated clean was
0 . For the percentage clean rated dirty, the graph reveals that two of the coupons (#066 and
#055) were misclassified at a very high rate . These coupons were examined to determine
why they had such high misclassification rates. Coupon #066 was found to have a slight
discoloration on its surface that was not noticed while preparing the coupons, which many
inspectors mistakenly identified as residue. Coupon #055 was found to have a spot on its
surface that was not present at the time of preparation and must have occurred during the
qualification. This report not only helps explain the rate of misclassifying the clean coupons as
dirty but also points to the need for increased scrutiny of the clean coupons prior to the
qualification process and careful monitoring of the coupons during the qualification to prevent
errors from occurring. The graphs in the appraiser misclassification rates provide insight into
the inspectors. For example, for this group we see that one inspector correctly identified all
clean coupons, while another inspector misclassified the clean coupons as dirty over 40
percent of the time .
Figure 6: Attribute agreement misclassification report
Risk Evaluation (Detectability Of Cleaning Process Failure)
Table 1 showed the MSSRs calculated for all the tanks in the manufacturing area. The tanks have the
largest surface areas in the facility and, since limits are inversely proportional to the equipment
surface area, only the limits in the manufacturing area were calculated. As with the TOC swab limits,
all the MSSRs for the other areas would be much higher. The lowest possible MSSR was for Tank-1
(largest surface area) for API-1 (lowest ADE). Therefore, API-1 was chosen for this evaluation.
At the 0.2 μg/cm level for API-1, all inspectors correctly identified all of the dirty coupons, while at
the 0.02 μg/cm level for API-1, inspectors correctly identified all of the dirty coupons only 90
percent of the time. Therefore, the 0.2 μg/cm level for API-1 level was selected to be the VRL for
API-1. A scale for evaluating detectability based on the VRL has been described in a previous
article and was used to calculate a detectability score, or visual detection index (VDI), based on a
VRL of 0.2 μg/cm for API-1 that was determined in this study.
Figure 7: Detectability score for API-1
The VDI can be used in conjunction with the ADE-derived toxicity scale and the Cpu-derived
probability scale as tools to evaluate the level of risk in cleaning validation. Going further, the
toxicity scale could also help define the circumstances for VI (low hazard) and the detectability scale
can provide the justification (easy to see at levels well below the MSSR for that hazard).
For analysis of the metadata collected during the study (department, age, years of service, gender,
and use of glasses), click here.
Discussion of Results
The qualification study for performing VI for the manufacturing equipment demonstrated that all
personnel can identify the presence of API 1 residues on manufacturing equipment surfaces at a
level of 0.2 µg/cm correctly 100 percent of the time.
This 0.2 µg/cm level is more than 100 times lower than the lowest ADE-based residue limit
of 28 µg/cm for API 1 in Tank-1. Considering that all other API limits are more than 10 to
more than 1,000 higher than API 1, these compounds would also be clearly visible at these
levels. Since all the visible residue limits are much higher for all other equipment, this
qualification is considered to apply to these products as well.
The evaluation of detectability also demonstrated that detection limits for residues by TOC and
VI were 2.75 and 2.15 orders of magnitude, respectively, below the acceptance criteria for these
methods, indicating both of these methods were more than capable of detecting residues at
No noticeable differences were found with VI regarding the department, age, years of service,
or the gender of the personnel or whether they wore glasses or not and these are not factors
that affect VI.
The results of this qualification study, including the analysis of the metadata, should satisfy the third
criterion listed by EMA in its Q&A #7.
Therefore, it was recommended that this facility could move from TOC swab testing to VI for future
studies and to qualify all operators and inspectors on VI. Unless a new product is introduced with a
lower ADE than API-1, VI will be considered acceptable as the sole criterion for the
cleaning validation acceptance limit for all products, current and future, manufactured at
Implementing Visual Inspection As Part Of A Cleaning Control Strategy
Based on the data collected, their analysis, and the experiences in this case study, the following
observations and recommendations on implementing VI programs can be made.
During this study, a number of observations were made about the coupons used. As also noted in the
previous article, coupons can be easily damaged or contaminated and this could affect the results of
the study, so storage, handling, and maintenance of coupons are important. Coupons for this study
were kept in a storage box and each coupon had "feet" added to each corner of the underside of the
coupon to facilitate handling. All the coupons were labeled as to the material of construction (316L
SS/#4 Finish), with the date (month/year) of manufacture, and individually numbered. (Figure 8).
Figure 8: Coupons and storage boxes
For VI qualification studies to be valid, the coupons must be prepared in a manner that leaves a
residue on the coupon that is the same in appearance as will be encountered in the manufacturing
area. Evaporative drying has been studied for many solvents, including water, and there are
significant differences in the deposition patterns of residues depending on the solvent.
Consequently, the improper preparation of coupons may lead to erroneous conclusions. Some
workers have been using solvents (e.g., methanol) to deposit the compounds and drying them under
conditions not encountered in operations (e.g., under a nitrogen stream). Such techniques are not
recommended. The coupons for this study were spiked and dried in a manner that simulated the
actual conditions in the facility's manufacturing area. API-1 was dissolved in purified water, spiked
onto the coupons and then dried in an oven at 90°C. This procedure simulated the actual conditions
in operations, that is, a hot purified water rinse with hot equipment surfaces for the API-1 residue to
dry quickly on. After preparation, all coupons should be examined to ensure they have been
prepared correctly, including verifying that the blank coupons do not have stains, scratches, or
fingerprints that may mislead the inspectors and confound the qualification study as has been
pointed out above. Also, for one product to represent other products in a VI study, the residues of
the other products must be similar in appearance (e.g., a white residue may not be similar to a blue
Most inspectors throughout the industry have been inspecting and releasing manufacturing
equipment for many years and most know through experience what product residues look like and
can accurately identify them. However, some may have not seen, or have not been formally shown,
product residues so they may not truly be sure what product residue looks like. This study revealed
that a few inspectors can miss product residues that should be seen, and some misidentified a
discoloration on a clean coupon as product residue. In this study, no training was provided
deliberately to see how well the inspectors would do without training. While the majority did not
require specific training, it was clear that training of inspectors prior to the study on what to look for
is both beneficial and necessary. However, this training should be provided as a means to identify
product residues on manufacturing surfaces and not as a means to identify product residues on
coupons. Therefore, inspectors are best trained using residues on actual manufacturing equipment
and not with coupons. The same training for identifying product residues on manufacturing surfaces
should be sufficient for inspectors to accurately identify product residues on coupons. This would
further help legitimize the results of a VI qualification study. While SOP training is necessary and
required, it must go beyond training the inspectors to look at equipment and fill out inspection
Viewing (Lighting) Conditions
Light levels are typically suspected by most industry workers to be critical parameters, but
experiments and experience has not held this to be true. Some studies have been performed showing
no differences in inspection when light levels are between 200 and 1,400 lux. This should not be
considered unusual. The human eye is capable of rapid adaptation to changing light levels over a
very wide range of intensities and the eye adapts to minor differences in light levels almost
instantaneously and unnoticeably. Therefore, minor changes in light levels, or minor changes in
distance or the angle of viewing during inspection, may have little impact on the ability to inspect
successfully. As mentioned, the human eye is very compensating; regardless, workers performing
VIs should still be trained to correctly identify product residues and ensure that an appropriate
inspection is performed.
A means of documenting VI on a continuous basis for routine monitoring should also be
implemented. The documentation level for VI should be commensurate with the level of risk and the
complexity of the equipment being inspected. In addition, documentation should be reviewed for
trends and anomalies as a part of knowledge management program.
The use of attribute analysis for statistically analyzing the data collected in this study greatly
increased the amount of information about the inspection, while being relatively easy and simple to
perform. Since there were only two levels used in the study (0.2 and 0.02 µg/cm ), with the 0.2 level
achieving 100 percent accuracy and the 0.02 only achieving 90 percent accuracy, the designation of
the VRL was set to 0.2 µg/cm . However, it may well be the case that the accuracy could be 100
percent at the 0.15 µg/cm or even at the 0.1 µg/cm level. Logistic regression is a powerful
statistical analysis that could be used to determine the VRL exactly by using several levels. A
subsequent study is planned to explore the use of logistic regression while still being designed to be
simple and easy to implement.
VI Qualification Programs
Most cleaning validation workers are well aware of the time and resources involved in developing
and validating swab methods, the sampling of equipment, analyzing the samples, and releasing the
equipment for use. While the idea of using VI only may seem simple and very attractive, the process
of qualifying a large group of inspectors for VI should not consume an equal or greater amount of
time and resources. For VI to become desirable, valuable, and accepted, the qualification of VI must
also be an easy program to implement, document, and maintain (requalification), accepted by
regulators, and ultimately valid.
The approach described in this article was relatively simple to set up, execute, and analyze. The
coupons were prepared in less than one day. The set up and collection of data took only three days
and only a total of about 15 minutes of each inspector's time (three inspections x five minutes each).
The statistical analysis of the data took approximately three days by one analyst. This is a reasonable
amount of effort and time that yielded a great deal of process knowledge and understanding.
We believe that, going forward, the products selected for VI must be low hazard products based on
their HBELs (science-based), should have demonstrated reliable cleaning processes that do not
present any significant concerns for patient safety (risk-based), the VI data collected must be
analyzed appropriately to demonstrate that the VI is valid (statistics-based), and the VRL should be
shown to be well enough below the MSSR to be legitimate to use. As shown above, statistical analysis
of VI data is very revealing and can identify issues with inspectors and problems with coupons and
provides significant insight into the inspection process, so it should not be considered optional.
As stated in the introduction, we believe that this study met all the criteria provided by the EMA's
Annex 15 Guideline and the new Q&As #7 and #8. The authors hope that the study described in this
article will satisfy regulatory concerns, increase the science, risk analysis, and use of statistics behind
qualifying VI, and help other companies to implement VI on science-based and risk-based
The authors wish to thank our peer reviewers Bharat Agrawal; Thomas Altmann; James Bergum,
Ph.D.; Alfredo Canhoto, Ph.D.; Gabriela Cruz, Ph.D.; Mallory DeGennaro; Parth Desai; Kenneth
Farrugia; Ioanna-Maria Gerostathi; Igor Gorsky; Miquel Romero Obon; Laurence O'Leary; and
Osamu Shirokizawa for reviewing this article and for their insightful comments and helpful
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