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Peer Review Article | Open Access | Published 26th March 2026
Comparative analysis of pharmaceutical facility changing room microbiota
Tim Sandle, Ph.D., CBiol, FIScT | EJPPS | 3101 (2026) | https://doi.org/10.37521/ejpps31111
Abstract
Changing rooms act as critical control points within pharmaceutical manufacturing facilities, functioning as microbial airlocks that limit personnel‑borne contamination entering classified processing areas. This study presents a five‑year comparative analysis (2022–2025) of microorganisms recovered from changing rooms and adjacent corridors, spanning cleanroom Grades D, C, and B.
Environmental monitoring data—comprising surface contact plates, settle plates, and active air samples—were analysed and microbial profiles were evaluated. Across all areas, Gram‑positive cocci dominated, especially Micrococcus, Kocuria, and coagulase‑negative Staphylococcus, consistent with human skin flora. Grade D changing rooms showed the highest overall bioburden and organism diversity. Progressive reductions in microbial burden were observed as personnel transitioned through Grade C into Grade B areas. However, opportunities for improvement in gowning, cleaning, and moisture control were identified.
This study provides a comprehensive dataset relating to pharmaceutical changing‑room microbiota and offers a reproducible framework for microbial profiling, trending, and benchmarking across cleanroom facilities.
Introduction
Pharmaceutical changing rooms serve as airlocks [1], designed to keep microbial contamination within the changing environment and to prevent excessive levels of contamination from being deposited into process areas. There are several determinants that aid this concept, including room design (air changes and airflows) together with the method by which personnel gown (and how they adhere to this) and the quality of the gown [2].
In addition, there are different measures of how well the changing room functions. These include physical measurements relating to the cleanroom and environmental monitoring to determine the cleanliness levels within the changing room (especially during maximum occupancy) and the transfer into adjacent areas [3].
As well as actual counts, a useful measure is microbial profiling. Despite the ubiquity of pharmaceutical microbiology, there remains a scarcity of research papers presenting microbial data in the pharmaceutical context [4]. This paper seeks to contribute to closing this gap by presenting data analysis from a pharmaceutical facility based in the UK, comparing the microbiota of the changing room to access corridors.
Advantages and limitations of microbiota studies
Examinations of microbiota can be of great advantage for contamination profiling and for measuring contamination control. This is with species types, diversity and typicality (the latter considering ‘are we seeing what we expect to see?’). Typically, in a changing room, we would expect to see an abundance of organisms associated with human skin; other organisms may signal control breakdown in terms of personnel hygiene, HVAC malfunctions, or the transfer of items. Such examinations also allow for comparison – what do we see within the changing room in comparison with what we are seeing on the other side (the processing area) [5].
As personnel change, the release of skin particles rises. Through the work of Whyte and different collaborators, we understand that skin squama size averages between 35 µm and 40.0µm. and that around 10% of this released skin matter contains microorganisms, and each particle of skin matter that contains microorganisms carries a mean level of 4 microorganisms [6-8]. Control variables include the quality of gowning [9] (such as the weave and its relative cleanliness) [10]; double gowning [11]; the physical design of the changing room [12], including airflow, air changes, and the size of the physical space [13]. Many inferences have been drawn from the use of bioaerosol chambers (or ‘body boxes’) [14].
There are limitations with microbiota studies (including this case study) which need to be acknowledged. One reason is the accuracy and applicability of the data, as this is dependent upon the culture media used, the incubation conditions and the incubation time, plus the identification method used (not least the content of the database). With the study described in this paper, this is overcome to an extent through the use of general-purpose media (tryptone soya agar) and dual incubation; moreover, the identification method used was a MALDI-TOF instrument, a proteomic technology that is seemingly becoming the identification instrument of choice in pharmaceutical microbiology laboratories. It also stands that while there may be different outcomes in terms of specific species, from different identification technologies, grouping the organisms typically recovered from human skin follows a reproducible pattern in terms of the resident organisms, particularly with Staphylococci, Micrococci and Coryneforms [15-19]. Relative proportions as shown in Figure 1.
To understand what we may or may not expect to find from the human, the two generations of the Human Microbiome Project (HMP), utilising non-culture-based methods like metagenomics [20], have yielded very valuable information (2007-2014 [21] and 2014 – 2016 [22]). The HMP has demonstrated that the skin harbours vastly greater species richness than cultural techniques can reveal [23] and a variety of diversity across three primary regions: dry, moist and oily. These studies have revealed the following patterns [24-28]:
· Belly button and between toes (moist microenvironment) were mainly comprised of Actinobacteriota and Firmicutes. These areas are dominated by species of Staphylococcus and Corynebacterium.
· Behind the ears (oily microenvironment) was dominated by Firmicutes. This region is dominated by Staphylococcus and Anaerococcus.
· Calves and forearms (dry microenvironment) included a combination of those three phyla. Here, there is a high proportion of Staphylococcus, Micrococcus, and Corynebacterium, but some Streptococcus.
· Proteobacteria (predominantly Pseudomonas and Janthinobacterium) constitute the majority of the inner elbow microbiota.
· Forehead and hair follicles (oily regions) see Cutibacterium species predominate in sebaceous sites. Sebaceous sites are less even and species-rich than moist and dry sites.
· Various fungi have been notably found on different body regions, including Malassezia spp., Aspergillus, Cladosporium, Epicoccum, Phoma, Cryptococcus, Rhodotorula, Microsporum, Trichophyton, Saccharomyces, Candida, and Epidermophyton. There is a strong concentration of yeast-like fungi with hair follicles and filamentous fungi with the foot.
Therefore, microenvironments with comparable physiological characteristics tend to harbour similar bacterial communities [29].
As well as ‘natural’ (or universally shared skin residents), it is with transient organisms where geographical differences will occur, and the applicability of the data becomes more limited [30]. The transient organisms common to south-east England will not be the same as those carried by personnel working in Mexico City. Nonetheless, the methodology described in this study can be applied to Mexico even if the resultant microbial profiles differ [31]. Other extrinsic factors include lifestyle, hygiene routine, cosmetics, antibiotics, skin care product use, individual stress, climate and seasonality [32].
Cleanroom changing room study
Methodology
This paper looks at the types of microbial contamination generated in changing rooms and the likely transfer of organisms from the changing rooms to process areas. The data examined is based on a long-term, comparative analysis of microbial identification data collected between 2022 and 2025.
The research considers different grades of areas and different changing room combinations within a pharmaceutical facility. The areas examined are:
· Grade D changing rooms interfacing with a general access Grade D corridor.
· Grade D corridor leading to a Grade C changing room to Grade C corridor.
· Grade D changing rooms to Grade C areas to Grade B changing rooms to enter an Aseptic Filling Suite.
The data collected consisted of air samples (settle plates and active air samples, the active air samples were taken with an impaction sampler) and surface samples (contact plates). The culture medium used was tryptone soya agar. For surface samples, the medium was supplemented with a disinfectant neutraliser (Dey-Engley formulation) [33]. Samples were incubated at:
· 20-25 °C for five days, followed by
· 30-35 °C for three days.
The incubation approach has been qualified, and it is regarded as a standard incubation regime [34].
Data interpretation
With the cleanrooms examined, EU GMP grade notations have been used. When considering the data, the following microbial morphology abbreviations are used:
· GPC: Gram-positive coccus
· GNR: Gram-negative rod
· GPR: Gram-positive rod (non-sporing)
· GNC: Gram-negative coccus
· Fungi: Filamentous fungus
· Yeast: Yeast-like fungus
· GPSR: Gram-positive sporing rod
In addition, spp. represents ‘species’.
In each case, two or three sets of data were compared in order to present changing room data and access corridor data in separate sets for analysis. Data was divided:
§ By morphology (e.g., GPC, GNR, GPR, Fungi, Yeast),
§ The genus line,
§ The species/descriptor line (when present) and combined the latter two into a single organism name (e.g., “Micrococcus luteus E”).
The analysis was based on tallied plate counts per set.
Assessment 1: Grade D changing rooms interfacing with a general access Grade D corridor
This assessment compares data gathered from four Grade D changing rooms and the interfacing corridor, which is coded P146. This is shown on the following diagram, with the changing rooms highlighted in yellow and the corridor circled in red ink, as per Figure 2[A1] [A2] :
Microorganisms identified across the period 2022 to 2025 were divided thus (for analysis):
o Set 1: Grade D changing rooms n=167
o Set 2: Access Grade D corridor (P146) n=47
These sets compare, as illustrated in Figure 3:
Data sets:
Set 1 — Grade D changing rooms
· GPC: 104
· GNR: 22
· GPR: 22
· GPSR: 9
· GNC: 8
· Fungi: 1
· Non-viable: 1
Generally, this is a typical profile for a Grade D changing area: heavy skin flora dominance (GPC → Micrococcus and Staphylococci) [35], moderate GNR presence, and a very small fungal load.
Set 2 — Grade D Access Corridor (P341)
· GPC: 31
· GNR: 4
· GPR: 6
· GPSR: 3
· Yeast: 2
· Fungi: 1
There is noticeably a lower contamination burden than the changing room, as expected for a corridor, with minimal GNR and a more modest fungal/yeast presence. The recovery of Gram-positive cocci remains dominant.
Dominant organisms (Top taxa)
Set 1 — Changing room (Top 10)
1. Micrococcus luteus – 38
2. Staphylococcus haemolyticus – 8
3. Paracoccus yeeii – 8
4. Kocuria rhizophila – 8
5. Micrococcus species – 8
6. Micrococcus luteus E – 6
7. Acinetobacter lwoffii – 4 (Gram-negative)
8. Pseudomonas stutzeri – 4 (Gram-negative environmental isolate)
9. Staphylococcus aureus ss aureus – 4
10. Staphylococcus arlettae – 3
The changing room data is strongly dominated by skin commensals (Micrococcus, Kocuria, and coagulase negative Staphylococci). That these species of bacteria are found in high abundance is unsurprising and consistent with studies into pharmaceutical environment microbial recovery [36, 37].
The recovery of Staphylococcus aureus is always an interesting observation. This is because this organism is generally restricted to the nares [38] and groin areas [39]. The recovery from cleanrooms tends to be associated with face mask control. In this case, S. aureus detections were low (n=4). Over the five-year period, the numbers are not significant for a Grade D entry environment and did not warrant, on this occasion, a review of compliance.
There were multiple GNR (Acinetobacter and Pseudomonas) recoveries. Acinetobacter can be associated with the human skin microbiome (being found between toe webs, for example) [40]. These recoveries are sufficiently low to suggest there are no consistent risks from moisture niches during changing. However, oversight remains an important area for ongoing trending.
Set 2 — Access Corridor P146 (Top 10)
1. Micrococcus luteus – 10
2. Staphylococcus epidermidis – 3
3. Micrococcus luteus E – 3
4. Staphylococcus warneri – 2
5. Staphylococcus hominis – 2
6. Kocuria rhizophila – 2
7. Brachybacterium conglomeratum/paraconglomeratum – 2
8. Clavibacter michiganensis ss insidiosus – 1
9. Cryptococcus albidosimilis/diffluens/liquefaciens – 1
10. Corynebacterium spp. – 1
The access corridor primarily recovers skin and environmental flora. There are notably fewer GNRs than in the changing room, indicating these organisms are more likely to be confined to the changing environment, or they are not prone to surviving. There are no trends of concern. For a comparison of the two data sets, refer to Table 1.
Feature | Grade D changing rooms | Access corridor |
Total isolates | 167 | 47 |
GNR burden | Higher (n=22) | Lower (n=4) |
Fungal burden | Low | Low |
S. aureus | Present, in low numbers | Very rare |
Typical flora | Human skin dominated | Human skin is dominated by some environmental contaminants |
The data in Table 1 indicates that the changing room generates a given level of contamination borne from personnel, which reduces as gowned personnel access the corridor. Where organisms are transferred, these are primarily skin organisms which could also be shed through operator activities within the processing area.
Assessment 2: Grade D corridor leading to a Grade C changing room to Grade C corridor.
To enter the Grade C area, personnel move through the Grade D changing room (above) and into the Grade D corridor; next, personnel enter a changing room coded P134/134A and then exit into a Grade C corridor. This is illustrated in the following diagram (Figure 4):
Microorganisms identified for the period 2022 to 2025 were divided into three sets:
§ Zone 1 — Access Grade D corridor (coded P341)
§ Zone 2 — Grade C changing room (coded P134)
§ Zone 3 — Grade C access corridor
The pattern is presented in Figure 5.
Based on the chart displayed as Figure 5, the access Grade D corridor (P341) has a mid-range level of bioburden. This bioburden shows a strong GPC dominance (Micrococcus, Kocuria, and Staphylococcus), and there is a low-level pattern of GNRs. Again, the pattern is consistent with human skin commensurable organisms [41]. On entering the Grade C changing room (P134), the bioburden increases, as a consequence of gowning and the time personnel spend in a relatively small space. Some GPRs (Bacillus and Brevibacterium) remain present, suggesting some scope for an improvement initiative. The level of GNRs is reduced, but a few are still present. Given the specific nature of these organisms, this suggests a transfer risk from Grade D.
On leaving the Grade C changing room and entering the Grade C access corridor, this should, in theory, be the cleanest zone. It is noted that GPC levels are still high (expected for personnel traffic area); however, also indicative that the Grade C changing room does, perhaps, not work as effectively as the Grade D room, especially as personnel entering the Grade C area will have donned cleanroom suits within the Grade D area. The fact that some GNR are still present shows how transfer occurs from the Grade D corridor.
Overall microorganism profile
After parsing, the dataset shows heavy dominance of GPC (skin flora), followed by moderate GPR and lower levels of GNR, fungi, and yeasts. The dominant groups identified are:
· GPC (Gram-positive cocci) → highest proportion
· GPR (Gram‑positive rods) → Bacillus, Brevibacterium, Microbacterium
· GNR (Gram-negative rods) → Roseomonas, Acinetobacter, Ochrobactrum, Moraxella, Stenotrophomonas
· Yeast → Cryptococcus, Naganishia, Candida
· Fungi → Cladosporium spp.
This profile is consistent with mixed personnel and environmental flora, progressively cleaner from Zone 1 → Zone 3.
The main microorganisms are:
Personnel‑associated flora (very common)
o Micrococcus luteus (extremely high frequency)
o Kocuria spp. (Rhizophila, palustris)
o Staphylococcus spp.: epidermidis, haemolyticus, hominis, capitis, saprophyticus, warneri, cohnii
o Streptococcus downei (occasional)
These types of organisms are expected in Grade D and Grade C support rooms. They remain important to trend, since a high frequency may indicate:
· Gowning practices
· Traffic levels
· Hand hygiene compliance
Moisture-associated Gram-negative rods
Detected organisms:
· Acinetobacter guillouiae / schindleri / lwoffii
· Roseomonas mucosa / cervicalis / aerofrigidensis
· Ochrobactrum anthropi
· Moraxella osloensis
· Stenotrophomonas maltophilia
· Halomonas johnsonii
· Insolitispirillum peregrinum ss integrum
These are found in relatively low numbers; however, their presence is generally unexpected (whilst Acinetobacter can have a human skin association, it would not be expected that these regions of the body are exposed at this stage, given that facility undergarments have already been donned). Hence, other origins need to be considered, and these organisms may indicate:
· Water residue
· Condensation
· Poor drying of cleaning tools
Should trends develop, in this case study, this should lead to a focus on cleaning and disinfection as a key place to start an investigation.
Spore-forming / environmental GPR
The most frequent genera are:
· Bacillus spp. (amyloliquefaciens / pumilus / velezensis / cereus group)
· Paenibacillus spp.
· Lysinibacillus spp.
· Peribacillus simplex
· Cytobacillus firmus/oceanisediminis
A shift in trend with these organisms will signal cleaning and disinfection concerns, such as:
· Floor‑level contamination
· Dust ingress
· Storage cleanliness issues
· Airflow changes
With the Grade D to C interface, Zone 1 — Access Grade D corridor (P146) appears to have the highest bioburden. This locale is:
· Dominated by Micrococcus, Staphylococcus, and Kocuria
· Presence of multiple GNR (Acinetobacter, Roseomonas, Moraxella)
· Fungal spores (Cladosporium) detected
The profile is generally as expected for Grade D, but should there be a shift upwards, this should trigger monitoring for moisture and airborne ingress.
With Zone 2 — Grade C changing room (P134/134A), this acts as a bridge zone between Grades D and C. The profile is mainly of Staphylococcus epidermidis / hominis, indicating personnel influence as expected. A changing room should show a decreasing level of GNRs and fungi. However, the dataset shows some GNR presence, indicating transfer from Grade D. In addition, the presence of Bacillus species suggests contamination at the floor level or from materials.
With Zone 3 — Grade C access corridor, while this should be the cleanest zone, it still contains GNR such as Roseomonas mucosa, Moraxella osloensis, Acinetobacter lwoffii, and low-level numbers of fungi are present (Cladosporium sphaerospermum). This shows that some transfer occurs from the changing room, and therefore, a level of work needs to be undertaken in improving contamination control.
Assessment 3: Grade D changing rooms to Grade C areas to Grade B changing rooms to enter an Aseptic Filling Suite
Access to the Aseptic Filling Suite (AFS) in the facility is through two separate Grade D changing rooms (G9 and G11), which feed into a Grade C area (G5/8) via access corridors. These changing rooms are divided into female and male (respectively) because of the requirement for personnel to remove their clothing down to undergarments (the changing rooms previously examined were gender neutral). These lead into changing room P216, which is a Grade B area enabling access into the AFS. The changing room is highlighted in yellow, and the route into P216 is shown in Figure 6.
Table 2: Event counts (identification frequency)
Room | Grade | ID Events |
P216 | B | 65 |
G11 | C | 37 |
G5/8 | C | 48 |
G06 | C | 0 |
G09 | C | 0 |
Table 3: Microbial numbers as colony forming unit (CFU) totals
Room | Total CFU |
P216 | 117 |
G11 | 168 |
G5/8 | 76 |
The data shows:
G11 (Grade C) – male changing room[A1]
· 168 CFU, highest among the Grade C rooms.
· 37 ID events, indicating frequent recoveries.
G11 shows broad organism diversity, including:
· Gram-positive rods (GPR)
Corynebacterium auriscanis, Corynebacterium freneyi, Brevibacterium casei, etc.
· Gram-negative rods (GNR)
Acinetobacter lwoffii (at low levels)
· Fungi
Cladosporium cladosporoides/herbarum/phaenocomae (at low levels)
G11 appears to have:
· High human flora shedding (Corynebacterium, Brevibacterium).
· Occasional environmental Gram-negatives and moulds.
· The highest overall CFU among the Grade C areas.
This pattern suggests a relatively high-traffic changing area and increased environmental shedding. Given that this is the busiest changing room, where men remove their outdoor clothing, the pattern is not surprising. The primary issue is the degree to which contamination is contained. That a male-only changing room carries a high level of recovery compared with a female-only changing room also reflects the fact that gender plays a significant role in bacterial dispersion, with male microbial release rates invariably being in excess of female counterparts (typically 1.5 times more, including releasing greater levels of S. aureus) [42].
G5/8 (Grade C) – general access area to collect second gowns
· 76 CFU, about half of G11’s burden.
· 48 ID events, meaning frequent low-level events.
Although G5/8 has many identification events, the organism types are narrower:
· Gram-positive cocci (GPC) dominate
Mainly Staphylococcus spp. (e.g., epidermidis, hominis, capitis). These are typical skin flora.
· Occasional Gram-positive rods
Corynebacterium spp. in low CFU amounts.
· Minimal fungi or Gram-negative
G5/8 shows:
· High personnel-related flora (consistent with changing activity).
· Lower environmental contamination vs G11.
· Lower CFU burden per event.
There are a high number of events compared with G11MC; however, the lower CFU total suggests many smaller recoveries.
P216 (Grade B) – final stage changing room
· 117 CFU across 65 ID events.
· Even though Grade B areas are expected to be cleaner, their CFU total is between G11 and G5/8.
The characterised microbiota includes:
o Staphylococcus spp. (GPC) – dominant skin flora. For example, Staphylococcus hominis, Staphylococcus epidermidis, Kocuria rhizophila
· Corynebacterium spp. (GPR) – common in changing rooms. For example, Corynebacterium auriscanis
o Occasional Gram-negative rods (Acinetobacter, Pseudomonas group).
o Few fungi.
Hence, for a Grade B area, P216 shows:
· Many low-level recoveries.
· Flora consistent with cleanroom gowning (skin flora).
· Bioburden lower than G11 but higher than G5/8.
While there is a higher event count, there is a far more moderate total CFU, indicating many low‑CFU recoveries, as Figures 7 and 8 reflect. The higher recoveries than G5/8 are not surprising since G5/8 is a transitory area, whereas personnel are actively gowning in P216. The data, in Figures 7 and 8, indicate generally good control, reflective of the high usage and sampling intensity.
Figures 7 and 8 show the diversity of microorganisms from the different cleanrooms, based on morphology and species. There are some clear distinctions:
· G11 → Mixed flora (GPR, GNR, fungi) → environmental exposure
· G5/8 → Mostly GPC → personnel-driven
· P216 → Balanced profile → indicates controlled but active gowning area
In terms of proportions from Figures 7 and 8:
G11 shows:
· Very high GPR % contribution → dominated by Corynebacterium, Brevibacterium, etc.
· Moderate GPC → human skin flora.
· Small GNR + fungi contributions.
· Overall: Most diverse and highest environmental burden.
G5/8 shows:
· Almost entirely GPC (Staphylococci dominate).
· Minimal GPR or GNR.
· Very personnel-driven microbial signature.
P216 shows:
· Balanced mix of GPC and GPR, consistent with controlled Grade B gowning.
· Small but present GNR component.
· Few or no fungi.
· More events than G5/8, but each event has a lower CFU.
The top genera contributing to overall bioburden are:
· G11 → Corynebacterium, Brevibacterium, Acinetobacter, Cladosporium
· G5/8 → Staphylococcus spp.
· P216 → Mixture of Staphylococcus, Corynebacterium, Kocuria
Based on CFU burden, event frequency, flora severity, and grade expectations:
· G11 → Highest risk
· P216 (Grade B) → Moderate–High risk
· G5/8 → Moderate
· G06 & G09 → Very low risk
Overall, the passage into the Aseptic Filling Suite sees a bioburden reduction and an effective series of controls.
Discussion
This paper has presented data gathered across five years for different changing rooms and different cleanroom classifications. The outcomes indicate different levels of control, with the aseptic area being the most controlled, and some concerns being expressed about the Grade C area. The general findings are:
a) Changing rooms contain more frequent isolation and greater diversity of contamination compared with the adjacent process areas.
b) The general patterns of contamination are consistent with what is expected from a changing room environment, based on our understanding of the human skin microbiome.
c) The microbial profile is dominated by coagulase-negative Staphylococcus spp., Micrococcus/Kocuria, and occasional Corynebacterium/Bacillus at low levels. These are widely associated with people and dry surfaces.
d) Overall, the changing rooms function as intended, by minimising contamination spread into process areas.
With assessment 1 (Grade D changing rooms interfacing with a general access Grade D corridor), while the findings are generally satisfactory, the presence of some recoveries of Staphylococcus aureus was noted. If such trends were to alter, such as repeated S. aureus detections, this should trigger an investigation of gowning, exposed skin, mask fit and related areas. Moreover, there was also a low level of Gram-negative rods recovered. If the trends for GNRs increase, it would be prudent for the facility to audit:
· floors after cleaning
· mop storage
· sinks/drains (if any)
· PPE storage and trolleys
However, it is not the core purpose of the paper to suggest or to track improvements (this is something too specific to the facility), but rather to present data on microbial trends (something that is still of a scarcity in pharmaceutical microbiology) and to provide a suggested format for data presentation. Such a case study aims to offer a model for pharmaceutical facilities to benchmark against.
The data gathered also shows the value of microbial identification, and with sorting, trending and tracking such information. Rapid microbial identification techniques like MALDI-TOF are providing increasingly large data sets [43] (here, artificial intelligence can, as it evolves, aid our interpretation of these data). With good quality data sets, comparisons can be made against the ever-expanding profiles of microorganisms and their potential origins [44]. For this, as noted in the introduction, the findings from the Human Microbiome Project have been invaluable in shaping our understanding. At the same time, we also need to be cognizant of the weaknesses in environmental monitoring. There are meteorological measurements weaknesses with sampling instruments [45, 46] there are limitations with growth media and incubation temperatures and time [47] there are microorganisms that cannot be cultured; and there are microorganisms that could be cultured under one set of circumstances (healthy laboratory grown cultures) but not under another set of circumstances (after being subjected to stress factors from within the cleanroom – lack of nutrients, few water sources, ultraviolet light etc) which propels them into a state of dormancy often referred to as ‘viable but non-culturable’ or ‘active but non-culturable’ [48, 49].
The likelihood of detection will relate to the location on the body (and arguably from the cleanroom suit and into the environment) where contamination is more concentrated. This is represented in Figure 9.
To make sense of the data, contamination indices can be constructed. There are different ways to achieve this, although an optimal approach should consider the key factors to assess and the relative weighting of these. Table 4 provides a suggestion weighted for cleanroom use.
Table 4: Suggested contamination assessment metrics
Category | Weight | Description |
Bioburden level (CFU) | 35% | Relative microbial load |
Event frequency | 25% | Number of recoveries requiring ID |
Microbiota severity | 25% | Gram-negative, fungi, spore‑formers |
Grade expectation gap | 15% | How far the area deviates from expected performance for its grade |
To seek improvements (based on the types of data collated to construct Table 4), training, review of cleanroom parameters and audit are key. With training, this would include reinforcing hand hygiene, face‑mask fit, and gowning sequence. Assessing cleanroom parameters, this would include verifying HEPA integrity and air change rates, plus pressure cascades at peaks (shift changes). For audit, observing gowning, confirming routine sporicidal rotation (frequency and contact times), inspecting for dust reservoirs (ceilings, tops of lockers, door frames), reviewing material entry practices and changing room occupation numbers. A suitable contamination control strategy is always one centred on continual improvement [50].
References
1. Prabu, S., Suriyaprakash, T., Ruckmani, K., Thirumurugan, R. (2017) GMP in Pharma Manufacturing—Description of GMP as Related to Air-Handling Units and Prevention of Contamination and Implementation of GMP Regulatory Requirements. In Kohli, R. and Mittal, K. Developments in Surface Contamination and Cleaning: Types of Contamination and Contamination Resources, William Andrew Publishing, pp. 85-123
2. Sandle, T. (2018) The Human Microbiome and the implications for cleanroom control, European Journal of Parenteral and Pharmaceutical Science, 23 (3): 89-98
3. Whyte, W. and Eaton, T. (2004) Microbial risk assessment in pharmaceutical cleanrooms. European Journal of Parenteral and Pharmaceutical Sciences, 9(1), pp. 16-23
4. Sandle T. (2011) A review of cleanroom microflora: types, trends, and patterns. PDA J Pharm Sci Technol.;65(4):392-403
5. Tršan, M., Seme, K., Srčič, S. (2019). The environmental monitoring in hospital pharmacy cleanroom and microbiota catalogue preparation, Saudi Pharmaceutical Journal, 27 (4): 455-462
6. Whyte, W. (1981). Setting and impaction of particles into containers in manufacturing pharmacies. PDA Journal of Pharmaceutical Science and Technology;35(5):255-61
7. Whyte W, Vesley D and Hodgson R. (1976) Bacterial dispersion in relation to operating room clothing. The Journal of Hygiene ;76(3):367-77
8. Whyte W and Bailey PV. (1985) Reduction of microbial dispersion by clothing. Journal of Parenteral Science and Technology;39(1):51-61
9. Ljungqvist B. and Reinmüller B. (2004) Cleanroom clothing systems; people as a contamination source. PDA/DHI Publishing, LLC, River Grove, Il, USA
10. Eaton, T. and Whyte, W. (2020). Effective re-usable cleanroom garments and evaluation of garment life. European Journal of Parenteral and Pharmaceutical Sciences, 25(4): https://www.ejpps.online/effectivereusablecleanroomgarments
11. Kasina P., Tammelin A., Blomfeldt A.M., Ljungqvist B., Reinmüller B. and Ottosson C (2016) Comparison of three distinct clean air suits to decrease the bacterial load in the operating room: an observational study. Patient Safety in Surgery, 10(1), 2-6
12. Mahnert A, Vaishampayan P, Probst AJ, et al. (2015). Cleanroom Maintenance Significantly Reduces Abundance but Not Diversity of Indoor Microbiomes. PLOS One.;10(8):e0134848. DOI: 10.1371/journal.pone.0134848
13. Whyte W and Bailey PV. (1989) Particle dispersion in relation to clothing. Journal of Environmental Sciences;32(2):43-49
14. Carsten M. (2015) Measuring airborne germs produced by humans inside body box. Cleanroom Technology, October 2015, 33-36
15. Sanford JA, Gallo RL. (2013) Functions of the skin microbiota in health and disease. Semin Immunol. 30;25(5):370-7. doi: 10.1016/j.smim.2013.09.005
16. Chen YE, Tsao H. (2013) The skin microbiome: current perspectives and future challenges. J Am Acad Dermatol.;69(1):143-55. doi: 10.1016/j.jaad.2013.01.016
17. Grice EA, Segre JA. (2011) The skin microbiome. Nat Rev Microbiol.; 9(4):244-53. doi: 10.1038/nrmicro2537.
18. Roth RR, James WD. (1989) Microbiology of the skin: resident flora, ecology, infection. J Am Acad Dermatol.; 20(3):367-90. doi: 10.1016/s0190-9622(89)70048-7
19. Nakamizo S, Egawa G, Honda T, et al. (2015). Commensal bacteria and cutaneous immunity. Semin Immunopathol.;37(1):73-80
20. Ulyantsev, V., Kazakov, S., Dubinkina, V. et al. (2016) MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data. Bioinformatics, btw312 DOI: 10.1093/bioinformatics/btw312
21. Turnbaugh PJ, Ley RE, Hamady M, et al. (2007) The human microbiome project. Nature. 449 (7164): 804–10
22. Ferré C, Callaghan W, Olson C, et al. (2016). Effects of Maternal Age and Age-Specific Preterm Birth Rates on Overall Preterm Birth Rates - United States, 2007 and 2014. MMWR. Morbidity and Mortality Weekly Report. 65 (43): 1181–1184
23. Byrd, A. L., Belkaid, Y., and Segre, J. A. (2018). The human skin microbiome. Nat. Rev. Microbiol. 16, 143–155. doi: 10.1038/nrmicro.2017.157
24. Skowron, K., Bauza-Kaszewska, J., Kraszewska, Z. et al. (2021). Human skin microbiome: impact of intrinsic and extrinsic factors on skin microbiota. Microorganisms 9:543. doi: 10.3390/microorganisms9030543
25. Dimitriu, P. A., Iker, B., Malik, K. (2019). New insights into the intrinsic and extrinsic factors that shape the human skin microbiome. MBio 10:839. doi: 10.1128/mBio.00839-19
26. Grice, E.A., Kong, H.H., Renaud, G., Young, A.C. (2008) A diversity profile of the human skin microbiota. Genome Res. 18(7):1043-50. doi:10.1101/gr.075549.107
27. Li, H., Goh, B.N., Teh, W.K. (2018) Skin commensal Malassezia globosa secreted protease attenuates Staphylococcus aureus biofilm formation. J. Invest. Dermatol. ;138:1137-1145
28. Findley K, Oh J, Yang J, et al. (2013). Topographic diversity of fungal and bacterial communities in human skin. Nature. ;498:367–70. doi: 10.1038/nature12171
29. Smythe P, Wilkinson HN. (2023) The Skin Microbiome: Current Landscape and Future Opportunities. Int J Mol Sci. ;24(4):3950. doi: 10.3390/ijms24043950
30. Tao R, Li T, Wang Y, et al. (2024). The facial microbiome and metabolome across different geographic regions. Microbiol Spectr 12:e03248-23 https://doi.org/10.1128/spectrum.03248-23
31. Cao L, Yang L, Swanson CS, Li S, He Q (2020) Comparative analysis of impact of human occupancy on indoor microbiomes. Front Environ Sci Eng 15(5):10
32. Madison A, Kiecolt-Glaser JK. (2019) Stress, depression, diet, and the gut microbiota: Human-bacteria interactions at the core of psychoneuroimmunology and nutrition.
33. Sutton, S.V., Wrzosek, T. and Proud, D. (1991) Neutralization efficacy of Dey-Engley medium in testing of contact lens disinfecting solutions, Journal of Applied Bacteriology, 70 (4): 351-354
34. Sandle T. (2014) Examination of the order of incubation for the recovery of bacteria and fungi from pharmaceutical-grade cleanrooms. Int J Pharm Compd.; 18(3):242-7
35. Harris-Tryon TA, Grice EA. (2022) Microbiota and maintenance of skin barrier function. Science 376:940–945
36. Moheb, N., Mohamed, A.F., Elbaghdady, K.Z. et al. (2025) Monitoring and controlling bacteria in cleanrooms of pharmaceutical plant model: an in vitro study. Environ Monit Assess 197 (3): https://doi.org/10.1007/s10661-024-13445-w
37. Wu, Gf., Liu, Xh. (2007) Characterization of predominant bacteria isolates from clean rooms in a pharmaceutical production unit. J. Zhejiang Univ. - Sci. B 8, 666–672 https://doi.org/10.1631/jzus.2007.B0666
38. Chand S, Rrapi R, Gabel CK et al. (2022) Groin and buttocks involvement and methicillin-resistant Staphylococcus aureus nasal swab independently predict cutaneous abscess and methicillin-resistant Staphylococcus aureus infection in inpatient pediatric cellulitis: A retrospective cohort study. J Am Acad Dermatol.;87(5):1205-1207. doi: 10.1016/j.jaad.2022.02.065
39. Peters PJ, Brooks JT, McAllister SK et al. (2013) Methicillin-resistant Staphylococcus aureus colonization of the groin and risk for clinical infection among HIV-infected adults. Emerg Infect Dis.; 19(4):623-9. doi: 10.3201/eid1904.121353
40. Berlau J, Aucken H, Malnick H, Pitt T. (1999) Distribution of Acinetobacter species on skin of healthy humans. Eur J Clin Microbiol Infect Dis.; 18(3):179-83
41. Ashour, M., Mansy, M.; Eissa, M. (2011) Microbiological Environmental Monitoring in Pharmaceutical Facility, Egyptian Academic Journal of Biological Sciences, 3 (1): 63-74. DOI: 10.21608/eajbsg.2011.16696
42. Whyte W and Hejab M. (2007) Particle and microbial airborne dispersion from people. European Journal of Pharmaceutical Sciences;12(2):39-46
43. Sengupta P, Sivabalan SKM, Mahesh A, et al. (2023) Big Data for a Small World: A Review on Databases and Resources for Studying Microbiomes. Journal of the Indian Institute of Science. :1-17
44. Tršan M, Seme K, Srčič S. (2019) The environmental monitoring in hospital pharmacy cleanroom and microbiota catalogue preparation. Saudi Pharmaceutical Journal ;27(4):455-462
45. Romano F., Gustén J., Joppolo C.M., Ljungqvist B., and Reinmüller B. (2015) Some aspects of the sampling efficiency of microbial impaction air samplers. Particuology, 20, 110-113
46. Lee, K. S., Bartlett, K. H., Brauer, M., et al. (2004) A field comparison of four samplers for enumerating fungal aerosol I. Sampling characteristics. Indoor Air, 14, 360–366
47. Sandle, T. (2012). Environmental Monitoring: A Practical Approach. In Moldenhauer, J. Environmental Monitoring: a comprehensive handbook, Volume 6, PDA/DHI: River Grove, USA, pp. 29-54
48. Xie J, Acosta EM, Gitai Z. (2023) Bacterial viability in the built environment of the home. PLOS One.; 18(11):e0288092. DOI: 10.1371/journal.pone.0288092
49. Sandle, T. (2022). Walk on the wild side: The application of environmental isolates in microbiological testing, European Journal of Parenteral and Pharmaceutical Sciences, 27 (1). DOI: https://doi.org/10.37521/ejpps.27104
50. Sandle, T. (2013). Contamination Control Risk Assessment in Masden, R.E. and Moldenhauer, J. (Eds.) Contamination Control in Healthcare Product Manufacturing, Volume 1, DHI Publishing, River Grove: USA, pp. 423-474
Author
Corresponding Author: Tim Sandle, Head of Microbiology
Bio Products Laboratory,
UK Operations, England
Email: timsandle@btinternet.com
The focus on gowning and moisture control improvements is crucial; these small changes could significantly reduce contamination risks in cleanrooms. ai music detector free