Environmental Monitoring describes the microbiological testing undertaken in order to detect changing trends of microbial counts and micro-flora growth within cleanroom or controlled environments. The results obtained provide information about the physical construction of the room, the performance of the Heating, Ventilation, and Air-Conditioning (HVAC) system, personnel cleanliness, gowning practices, the equipment, and cleaning operations.
Over the past decade, environmental monitoring has become more sophisticated in moving from random sampling, using an imaginary grid over the room and testing in each grid, to the current focus on risk assessment and the use of risk assessment tools to determine the most appropriate methods for environmental monitoring.
This paper explores current trends in the application of risk assessment to the practice of environmental monitoring by examining the following key areas:
Determining the Frequency of Monitoring: Using the concept of risk assessment to decide how often to monitor different types of cleanrooms
Risk Assessment Tools: Applying risk assessment tools to establish methods for environmental monitoring
Numerical Approaches: Considering a numerical approach to assess risk data using a case study of an aseptic filling operation
The examples used are from a sterile drug manufacturing facility and focus mostly on aseptic filling; however, the concepts and tools are applicable to the environmental monitoring of other types of manufacturing and packaging operations.
DETERMINING THE FREQUENCY OF MONITORING
In developing an adequate environmental monitoring programme, there should be a balance between using resources efficiently and monitoring at sufficiently frequent intervals so that a meaningful picture can be obtained. Sources of guidance with respect to monitoring frequencies are very limited within Europe, and the monitoring frequencies specified within the United States Pharmacopoeia (USP) <1116> may not be suitable for all facilities. Some guidance can be obtained from the International Organization for Standardization’s (ISO) standards: principally ISO 14644 and ISO 14698. However, these do not always fit with regulatory guidance documents because they apply to controlled environments across a range of industries other than pharmaceuticals, where standards can be higher (Jahnke, 2001).
When establishing an environmental control programme, the frequency of monitoring different controlled areas can be determined based on ‘criticality factors’ relevant to each specific area.
The establishment of a criticality scheme on which to base monitoring frequencies is designed to target monitoring of critical process steps. Therefore, the final formulation process would receive more monitoring than an early manufacturing stage with a relatively closed process.
Using a criticality factor is a means of assigning a monitoring frequency based on the risk assessment of each critical area. The risk assessment relates to the potential product impact from any risk. For example, an area of open processing at an ambient temperature, a long exposure time, and the presence of water, would constitute a high risk and would attract a higher risk rating. In contrast, an area of closed processing, in a cold area, would carry a substantially lower risk and associated risk rating.
Using a range of 1 to 6, with ‘1’ being the most critical and ‘6’ the least critical, a score of 1 would be assigned to an aseptic filling operation; a score of 2 to final formulation, a score of 3 to open processing, and so on. Each user must adapt such a scheme to his or her particular area and defend it by way of supportable rationale. An example of monitoring frequencies under such a scheme can be seen in Figure 1, and an example of its application is seen in Figure 2.
Each controlled area would be evaluated against set criteria and, with the use of a series of guiding questions, the monitoring frequency would be determined. Decision criteria include considerations in two category areas: areas of higher weighting and areas of higher monitoring frequency. Examples of these categories follow:
Giving Higher Weighting to –
‘Dirtier’ activity performed in a room adjacent to a clean activity, even if the clean activity represents later processing
Areas that have a higher level of personnel transit (given that people are the main microbiological contamination source). This may include corridors and changing rooms.
Routes of transfer
Areas that receive in-coming goods
Component preparation activities and sites Duration of activity (such as a lower criticality for a 30-minute process compared to a six-hour operation)
Having Higher Monitoring Frequencies for –
Warm or ambient areas as opposed to cold rooms
Areas with water or sinks as opposed to dry, ambient areas
Open processing or open plant assembly compared to processing that is open momentarily or to closed processing (where product risk exposure time is examined)
Final formulation, purification, secondary packaging, product filling, etc.
Once the monitoring frequency for each controlled area is determined, it should be reviewed at regular intervals. This review may invoke changes to a room’s status, and hence, its monitoring frequency, or to changes for different sample types within the room. For example, it may be that after reviewing data for one year, surface samples produce higher results than air samples for a series of rooms. In this event, the microbiologist may opt to vary the frequency of monitoring and take surface samples more often than air samples. There would also be an increased focus on cleaning and disinfection practices, and their frequencies, based on such data (Sandle, 2004b).
When both types of monitoring are producing low level counts, the balance of risk would be towards air samples. This is because air samples are direct indicators of the quality of the process and assign a level of control to the process, whereas surface samples are indicators of cleaning and disinfection. If the results of surface samples are generally satisfactory, as indicated by trend analysis, then either the number of samples or the frequency at which they are taken can be reduced. If subsequent data showed an increase in counts, the monitoring frequency could easily be restored. Indeed, all types of monitoring frequencies may increase as part of an investigation, as appropriate. Therefore, the criticality factor approach not only sets the requirement for a room, it can also be used to vary the sample types within a room (Ljungqvist and Reinmuller, 1996).
RISK ASSESSMENT TOOLS
Once the status for each room has been selected, a risk assessment procedure is required to determine locations for environmental monitoring. Such risk-based approaches are recommended in ISO 14698 and regulatory authorities are increasingly asking drug manufacturers about this subject.
Risk-based approaches include Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), and Hazard Analysis and Critical Control Points (HACCP), all of which employ a scoring approach. (Other approaches include: Failure Mode, Effects, and Criticality Analysis (FMECA); Hazard Operability Analysis (HAZOP); Quantitative Microbiological Risk Assessment (QMRA); Modular Process Risk Model (MPRM); System Risk Analysis (SRA); Method for Limitation of Risks; and Risk Profiling.)
At present, no definitive method exists, and the various approaches differ in their process and in the degree of complexity involved. However, the two most commonly used methods appear to be HACCP, which originated in the food industry, and FMEA, which was developed for the engineering industry (Whyte and Eaton, 2004a).
These various analytical tools are similar in that they involve:
Constructing diagrams of work flows
Pin-pointing areas of greatest risk
Examining potential sources of contamination
Deciding on the most appropriate sample methods
Helping to establish alert and action levels
Taking into account changes to the work process and seasonal activities
These risk assessment approaches are not only concerned with selecting environmental monitoring locations. They integrate the environmental monitoring system with a complete review of operations within the cleanroom to ensure those facilities, operations, and practices are also satisfactory. The approaches recognise a risk, rate the level of the risk, and then set out a plan to minimise, control, and monitor the risk. The monitoring of the risk will help to determine the frequency, locations for, and level of environmental monitoring (for example, refer to an article by Sandle [2003a], for a more detailed example).
This paper explores an example from three different techniques:
A simple conceptualisation of risk using a table
An example using a simple table for analyzing risk in environmental monitoring situations appears in Figure 3.
The seven principles behind constructing an HACCP analysis consist of:
Identifying hazards or contamination risks and assessing their severity
Determining Critical Control Points (CCPs)
Establishing critical limits
Establishing a system to monitor and control CCPs
Establishing corrective action when a CCP is not under control
Establishing procedures for verification to confirm that the HACCP system is working effectively
Establishing documentation and reporting systems for all procedures
Each of these seven key points is a vital step in developing the risk assessment.
The seven points include:
Construct a risk diagram, or diagrams, to identify sources of contamination. Diagrams should show sources and routes of contamination.
Areas adjacent to Cleanroom or Isolator (e.g.: airlocks, changing rooms)
Air supply and Room air
People Machines and Equipment
Assess the importance of these sources and determine whether or not they are hazards that should be controlled.
Amounts of contamination on, or in, the source that is available for transfer
Ease by which the contamination is dispersed or transferred
Proximity of the source to the critical point where the product is exposed
Ease with which the contamination can pass through the control method
The use of a scoring method can greatly help in assessing the relative importance of these contamination sources.
Identify the methods that can be used to control these hazards.
Air Supply: High Efficiency Particulate Air (HEPA) filters
Dirty Areas adjacent to Cleanroom or Isolator: differential pressures, airflow movement
Room Air: air change rates, use of barriers
Surfaces: sterilisation, effectiveness of cleaning and disinfection procedures
People: cleanroom clothing and gloves, room ventilation, training
Machines and Equipment: sterilisation, effectiveness of cleaning, exhaust systems
Determine valid sampling methods to monitor either the hazards or their control methods or both.
HEPA filter integrity tests
Air supply velocity, air change rates
Room pressure differentials
Air samplers, settle plates, contact plates, etc
Establish a monitoring schedule with ‘alert’ and ‘action’ levels and the corrective measures to be taken when these levels are exceeded.
The greater the hazard, the greater the amount of monitoring required
Trend analysis for alert and action levels, in or out of control
Verify that the contamination control system is working effectively by reviewing key targets like product rejection rate, sampling results, control methods, and so on. These may require modification over time.
System for data review
Examine filling trials
Reassess - hazards, effectiveness of control systems, frequency of monitoring, appropriateness of alert and action levels
Establish and maintain documentation.
Describe the steps being taken
Describe the monitoring procedures
Describe the reporting and review procedures
Before implementing HACCP, it is important to train all staff involved in the process and to use a multi-disciplinary team. For example, the team may be comprised of personnel from Production, Engineering, Quality Control (QC), Quality Assurance (QA), Validation, and so on.
FMEA schemes vary in their approach, scoring, and categorisation. All methods share a numerical approach. The example presented here, based on a sterility testing isolator, assigns a score (from 1 to 5) to each of the following categories:
Severity is the consequence of a failure
Occurrence is the likelihood of the failure happening based on past experience
Detection is based on the monitoring systems in place and on how likely a failure can be detected
By asking a series of questions, each main part of the cleanroom or isolator system can be grouped or classified into key parts.
Such questions include:
What is the function of the equipment? What are its performance requirements?
How can it fail to fulfil these functions?
What can cause each failure?
What happens when each failure occurs?
How much does each failure matter? What are its consequences?
What can be done to predict or prevent each failure?
What should be done if a suitable proactive task cannot be found?
The scoring is 1 (very good) to 5 (very bad). Therefore, a likelihood of high severity would be rated 5; high occurrence rated 5; but a good detection system would be rated 1.
Using these criteria, a final FMEA score is produced from:
Severity score x Occurrence score x Detection score
Decisions on further action will depend upon the score produced. There is no published guidance on what the score that dictates some form of action should be. However, 27 is the suggested score for the cut-off value at which action is required. This is based on 27 being the score derived when the mid-score is applied to all three categories (i.e., the numerical value '3' for severity 3 x occurrence 3 x detection 3) and the supposition that if the mid-rating (or a higher number) is scored for all three categories, then at a minimum, the system should be examined in greater detail.
A third component of the risk assessment approach is to evaluate a risk once an activity has taken place. Then, by using a largely numerically-driven set of tools, repeatability and reproducibility can be ensured. Examples of individual out-of-limits results and data-sets relating to an operation are examined below using examples from an aseptic filling process. Following this, an example of an overall assessment of different processes over time is explored. Numerical approaches are useful in applying a level of consistency between one decision and another.
The section below details some methods that can be used to quantify the risk of contamination in pharmaceutical cleanrooms. The models outlined are based on the work performed by Whyte and Eaton (2003a and b).
Estimating the Risk to Product Using Settle Plate Counts
The method applies to the assessment of settle plates at the point-of-fill, under the Grade A zone. It allows an estimate of the probable contamination rate to the product as derived from the following equation:
The fixed value is the area of the petri dish, which for a 90mm plate, is 64 cm2.
Settle Plate Count Worked Example:
Area of petri dish = 64 cm2
Settle plate count = 1 cfu
Neck area of product = 1 cm2
Exposure time of product = 1 minute
Exposure time of settle plate = 240 minutes
By inserting these example values into the equation:
The formula can also be applied to the monitoring of product filtration activities when ‘1’ is entered as a constant for neck area of product.
There is no available guide as to what percentage constitutes which level of risk. The 0.03% figure has been used by some practitioners. This is based on the Parenteral Drug Association Survey of Aseptic Filling Practices (2002), where it is common in the pharmaceutical industry to allow 0.03% of broth bottles in a media simulation trial to exhibit growth at a ‘warning level’ (where 0.03% = 1/3,000, with 3,000 being the average size of a media fill). An ‘action level’ is often set as 3/3,000 bottles or 0.1%. This would constitute a high risk. Logically, the range between 0.03 and 0.1 would be a medium risk (Whyte and Eaton, 2004c).
Therefore, where the ‘risk’ is that of micro-organisms detected on a settle plate, with a probability of <0.1% depositing in the neck of a bottle when bottles are exposed in a unidirectional air flow, risk categories would be as shown in Figure 5.
Finger Plate Assessment
The formula can readily be applied to operations that relate to Grade A operations, for example: filtration connection, vessel to filling machine connection, the filling activity, and loading a freeze-dryer. Where the operator is only present in the Grade B room and has no impact on the Grade A operation, this is automatically considered to be low risk if there are no other special factors. (Low risk does not imply lack of action or assessment. However, it aims conceptualisation of the result in terms of probable risk to the batch.)
The following formula can be used:
In this example of a finger plate assessment, the location, activities, and duration require weighting. Examples of logic that apply to the rating of the location, activities, and duration categories can be seen in Figures 6, 7, and 8, respectively
Finger Plate Assessment Worked Example
A finger plate with a count of 1 cfu for an activity at point-of-fill, using forceps, that lasts for one minute.
Microbial count x Location x Method of intervention x Duration of operation
1 x 2.5 x 0.5 x 1 = 1.25
The score produced would be rated according to standard risk assessment categories:
These risk ratings are based, in part, on the worked example. Based on historical data over the past six-months, the highest record example of a Grade A intervention finger plate is a count of 2 cfu: using forceps to retrieve a fallen vial and lasting for more than 120 seconds. This would have given a score of 7.5, which falls within the medium risk category. The user should develop a scheme that fits his or her facility (Whyte and Eaton, 2004b).
Surface Sample Assessment
The following formula can be applied to filling and filtration activities:
Microbial count x Risk Factor A x Risk Factor B x Risk Factor C
Risk Factor A = Proximity to critical area
Risk Factor B = Ease of dispersion of micro-organisms
Risk Factor C = Effectiveness of control measure
Samples are taken using contact plates and swabs and are all post-operation.
The following approach can be used in setting the risk factors:
The first step is to assign the risk (A) factor based on proximity of location to the critical area (filled product). The logic demonstrated in Figure 9 may be used to determine risk factor A.
Surface Sample Worked Example:
Where a count of 2 is detected from a conveyor belt (a filling machine non-product contact location)
Using the formula:
Microbial count x Risk Factor A x Risk Factor B x Risk Factor C
2 x 1 x 1.5 x 1 = 3
Risks can be scored against standard risk assessment categories:
This scoring scheme is based on contamination of a product contact site being high risk by virtue of its direct proximity to the critical area or the product.
A count of 1 cfu on one of these product contact site locations would give a score of 9.4. In most filling zones and clean zones, sample results from product contact sites would be expected to record zero counts for 999 samples out of every 1000. Whereas, a count of 3 from a non-product contact site would result in a medium risk category.
Air Sample Assessment
Approaches are available for the risk assessment of active air samples that use a numerical system. However, the formulae associated with these are difficult to calculate in practice because often all information is not available and assessment of variables, such as impaction speed, are not readily calculable. Therefore, a qualitative assessment, such as the one included in the example of the numerical approach, may be more suitable.
An example of the numerical approach:
Airborne microbial count (cfu /m3) x deposition velocity of micro-organisms from air (cm/s) x area of product exposed (cm2) x time of exposure (s)
Alternatively, non-numerical risk assessment can be used based on the proximity and the operation. See Figure 12 for this example.
Assigning a Risk Factor to Areas of the Filling Room
The location where a high bio-burden is isolated within the filling area is arguably of greater consequence than the actual count. The location can be given a risk rating in relation to its proximity to the critical zone, ease of dispersion or transfer, and effectiveness of control methods.
The table shown in Figure 13 is proposed as a tool for risk assessment and to aid investigations. It supplements the risk assessment tools that have been previously examined.
An Overall Assessment
The approach taken for an overall assessment involves the historical examination of a number of operations and assigning a value above which the operation is considered to be atypical. A 95% cut-off is considered to be the most suitable cut-off point.
Criticality scoring is a way of assessing the totality of results from an environmental monitoring session. This may be, for example, a batch fill. The data from the session is examined and points are awarded for each result above a pre-set warning or action level. The total score is then summed and the results obtained are compared to a set level at which atypical sessions are indicated.
The pre-set level would be assessed from historical data over a reasonable time period (such as one year). An example of such a scheme follows:
For Grade A
The results from a filling operation are examined (for individual viable counts and for the mean particle counts taken during the fill). Each result, which equals or exceeds a warning or action level, is scored according to the criteria in Figure 15 and Figure 16. Using the criteria presented in Figures 15 and 16 produces the Grade A score.
For Grade B
The results from a filling operation are examined (for the individual viable counts and the mean particle counts taken during the fill). Each result that equals or exceeds a warning level is scored according to the criteria in Figure 17 and Figure 18.
If a warning level or action level is of the same count (cfu) as a value in the count (cfu) column, the warning or action level score should be selected. This produces the Grade B score. To produce the total criticality score, the two scores are added together (Grade A + Grade B).
Once the data has been generated, the score at which approximately 95% of the fills would be below (and 5% would be above) can be calculated. That figure would then be used as the cut-off value with which to assess the ‘atypical’ filling operations.
Figure 19 displays a simple representation of this assessment. For the data set, the criticality score was calculated at 25, which corresponded with the 95th percentile for a set of data from the filling of an example drug.
The graph in Figure 19 indicates that some fills exceeded the cut-off criticality value during a particular time period (see fill numbers 12 through 15). After some corrective action, the scores for the fills were reduced (see fill numbers 16 through 29) and the situation returned to a state of control.
The use of risk assessment approaches is an important current Good Manufacturing Practice (cGMP) topic in microbiological environmental monitoring. This paper has outlined some possible tools for such a risk assessment approach; however, each suite of cleanrooms or isolator will be subtly different. The microbiologist must consider each aspect of the environment and decide what level of monitoring best suits his or her system, and then must justify the techniques used and the locations selected.
The approach adopted should be detailed in a written rationale and approved by senior management. After this, a rigorous and defensible system will be in place to satisfy regulatory expectations, and to aid the user in assessing the risk of problematic environmental monitoring situations or results.
Article Acronym Listing
CAPA Corrective and Preventive Action
CCP Critical Control Point
CFU Colony Forming Unit
cGMP Current Good ManufacturingPractice
FMEA Failure Mode and Effects Analysis
FMECA Failure Mode, Effects, and Criticality Analysis
FTA Fault Tree Analysis
HACCP Hazard Analysis and CriticalControl Point
HAZOP Hazard Operability Analysis
HEPA High Efficiency Particulate Air
HVAC Heating, Ventilation, and Air-Conditioning
ISO International Organization for Standardization
MPRM Modular Process Risk Model
QA Quality Assurance
QC Quality Control
QMRA Quantitative Microbiological RiskAssessment
SRA System Risk Analysis
UDAF Uni-Directional Air Flow
USP United States Pharmacopoeia
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