Evaluation and Failure Risk of Microbiological Air Quality in ...

Research Article

Evaluation and Failure Risk of Microbiological Air Quality in Production Area of Pharmaceutical Plant Mostafa Essam Eissa1*, Ahmed Mohamed Mahmoud2, Ahmed Saber Nouby3 Department of Quality Control, Microbiology Quality Control Section Head in HIKMA Pharma Pharmaceutical Company, P.O. Box 1913, EGYPT. 2 Department of Quality Control, Microbiology Laboratory Supervisor in HIKMA Pharma Pharmaceutical Company, P.O. Box 1913, EGYPT. 3 Department of Quality Control, Microbiology Senior Analyst in HIKMA Pharma Pharmaceutical Company, P.O. Box 1913, EGYPT. 1

ABSTRACT Purpose: Microbiological environmental monitoring of air quality is a key tool to determine the appropriateness of manufacturing area in clean rooms for microbiologically safe medicinal products production. Since aerial microbial distributions are highly dynamic and show significant variability from time to time depending on several influential variables, a monitoring system was adopted in order to establish control on trends, major sources of risks and defects. Methodology: The present work covered both active and passive air samples during course of two years of study in liquid oral and semisolid products manufacturing class C area. Trend of results showed progressively rising microbial count in air using commonly available statistical software package. Findings: Airlocks and corridor regions were significant contributors to failure risk of active air samples by a factor of 2.65. Performance capability of the process (Ppk) of the manufacturing rooms with and without passage ways was 1.23 and 1.11, respectively. Significant, positive but moderate correlation existed between both active and passive air samples. The average relative microbial recovery ratio of active/ passive samples was 1.50. The probability of microbiological failure from passive air samples was approximately 84.54 times that of active air samples. Application value: Both air monitoring techniques provided indispensable different prospective view of clean area quality. Key words: Environmental monitoring, Active, Passive air samples, Airlocks, corridor, Performance capability, correlation.

INTRODUCTION Major ecological sources of microbial pollution–that originate from aqueous, surface and air-can act as reservoirs for viable particles and assumed to have crucial role as vehicles for transmission of infection.1 Viable micro organisms are measured by methods including active and passive air sampling.2 In the active air sampling technique, the device sucks a predetermined size of air through or over a microbiological media. After incubation at specific temperature, the microbial presence can be assessed by enumerating the number of viable cells as colonies in plate per cubic meter of air. The process is effective with low number of microorganisms.3-6 Passive monitoring uses conventional culture plates containing RGUHS J Pharm Sci | Vol 5 | Issue 4 | Oct–Dec, 2015

growth media, onto which viable microbial particles settle by gravity for a given time in order to gather the viable particles that sediment onto plates. The dishes are then incubated and results are expressed in CFU/plate/time or in CFU/m2/hour.7 This technique measures those parts of airborne microbes that sediment with gravity on a critical location.8 Several studies have compared the two sampling methods with conflicting results. In some studies the gathered data showed significant correlation with each other,9-13 while other studies did not show any correlation.14,15 On the other hand, Friberg et al. was able to deduce an equation that permits the conversion of the number of

Received Date : 07/11/2015 Revised Date : 30/12/2015 Accepted Date : 31/12/2015 DOI: 10.5530/rjps.2015.4.5 Address for correspondence Mostafa Essam Eissa, Microbiological Quality Control Department, Hikma Pharma Pharmaceutical Company, P.O. Box 1913, EGYPT. Ph no: 0020100 615 4853 E-mail: mostafaessameissa @yahoo.com

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Mostafa Essam Eissa et al.: Microbial Air Quality Assessment in Clean Area

viable particles that settle onto the plates of passive air sampling into air contamination units (CFU/m3).16,17 The current study was aimed to provide simple yet effective method for monitoring and controlling of the microbiological environmental air samples in the clean area in a newly established and installed pharmaceutical production plant. The study included trending of data, measurement of the process efficacy, assessment of the risk of failure, examining the relation between both active and passive air samples and to establish tool to uncover sources of the process defects or weakness in the manufacturing area. The study was done in colla boration between our microbiology laboratory and new drug manufacturing facility established, installed by other company and monitored by our team to guide the new firm in establishing strict control on its manufacturing environment.

Agar plates used in microbiological sampling of air (surface area of approximately 64 cm2) were filled with an appropriate recovery medium (supplemented with Tween 80 and lecithin).21 All statistical analysis and six sigma tools along with their criteria were used as described in details by Eissa et al., 2015.22 Culture media used in surface monitoring were subjected to growth promotion tests according to the methods and specifications by USP 2013 and passed the acceptance criteria.23 Microbial enumeration was performed using digital colony counter (Digital Colony Counter Model: 361, Laxman Mahtre Rd. Navagaon, Dahisar West, Mumbai). Illustrations of generated data and calculations were performed using Microsoft Office Excel 2007.24 Control charts, cumulative distribution function (CDF) and fitted line plots were constructed using Minitab® v17.1.0. GraphPad Prism® v6.01 for windows was used for statistical analysis.

MATERIAL AND METHODS

Relationship between active and passive air samples count

Active air samples (AAS) and passive air samples (PAS) were taken from a newly established and installed class C medicinal products pharmaceutical manufacturing firm (liquid oral products and semisolid medicinal drugs: creams, ointments, suppositories area and their associated corridor) using methods and limits described by Ashour et al.18 In addition to airlocks which are clean room areas to be monitored. The grade of the airlock should correspond to that of the adjoining area with the highest grade. WHO recommends limits for microorganisms during operation which was followed during the course of the study.2 The total number of samples was adjusted to cover the working weeks-but excluding shutdown and maintenance times - from class C on weekly basis covering two years period from January 2013 to December 2014. All the nutrient media and chemicals were purchased from OXOID (Basingstoke, Hampshire) and Sigma-Alrich (St. Louis, MO 63103), respectively. All media were sterilized by autoclaving in steam sterilizer (FEDEGARI FOB3, Fedegari Autoclavi SpA, SS 235 km 8, 27010 Albuzzano (PV), Italy). 9 cm diameter, plastic, sterile Petri plates were purchased from Sterilin Limited (Solaar House, 19 Mercers Row, Cambridge, UK). Results of samples were obtained from the microbiology laboratory in the quality control department after incubation in Series BD 115 Incubators (BINDER GmbH, Im Mittleren, Ösch 5, 78532 Tuttlingen, Germany) and Hotpack incubator 175 series, model 417532 (Hotpack, Dutton Rd. Philadelphia, USA).19,20 Calibrated RCS Plus and Hiflow (Biotest AG, Landsteiner Str. 5, Germany) were used for active air sampling and its growth promotion-tested Agar Strips TCI media (Biotest AG, Germany). 156

Ideally, active air sampling (AAS) process is able to capture significantly and quantitatively higher number of microorganisms from air than passive air sampling (PAS) culture plates10,25 can do which depends on settlements of particles of diameter greater than 10 μm by gravity.26,27 However, the lengthy sampling time of PAS which may reach 24 times greater than AAS (about 10 min. for 1 m3 of air)2 - increases the chance of capturing sporadic unusual event or non-good manufacturing practice (non-GMP) activity during manufacturing. Unless the AAS was conducted during sporadic action along with PAS, the microbial bioburden of settle plates may be equal or even higher in count than those taken by active air samplers depending on the degree of noncompliance to GMP and the closeness to the sampling area. Provided that environmental qualification was performed and passed required tests at rest stage for the clean area physically and microbiologically and postqualification monitoring was maintained and the results met the acceptance criteria for all utilities including heating, ventilation and air-conditioning system (HVAC),2 then any excursions in microbiological air results could be easily traced to human factor with the performance of associated activity in the affected rooms. RESULTS The present work covered both active and passive air samples during course of two years of study in liquid oral and semisolid products manufacturing class C area. These non-sterile pharmaceutical products possess high water activity which makes them vulnerable to microbial contamination and proliferation. Trending of results RGUHS J Pharm Sci | Vol 5 | Issue 4 | Oct–Dec, 2015


Table 1: Averaged overall microbial count for two years study of both active air samples (AAS) and passive air samples (PAS) Descriptive Statistics

AAS

PAS

Minimum

2.4

0.40

25% Percentile

14

14

Median

24

20

75% Percentile

39

26

Maximum

71

39

10% Percentile

6.4

3.4

90% Percentile

53

32

Mean

28

19

Std. Deviation

17

9.9

Std. Error of Mean

2.1

1.2

Lower 95% CI of mean

24

16

Upper 95% CI of mean

32

21

Lower 95% CI of median

20

16

Upper 95% CI of median

31

22

D’Agostino & Pearson omnibus normality test K2

4.1

3.0

P value

0.1257

0.2212

Passed normality test (alpha=0.05)?

Yes

Yes

P value summary

ns

ns

KS normality test KS distance

0.095

0.099

P value

0.1266

0.0929

Passed normality test (alpha=0.05)?

Yes

Yes

P value summary

ns

ns

0.0

0.0

One sample t test Theoretical mean Actual mean

28

19

Discrepancy

−28

−19

95% CI of discrepancy

23 to 32

16 to 21

t, df

t=13 df=67

t=15 df=67

P value (two tailed)

< 0.0001

< 0.0001

Significant (alpha=0.05)?

Yes

Yes

Coefficient of variation

61.28%

53.23%

Geometric mean

22

14

Lower 95% CI of geo. mean

18

11

Upper 95% CI of geo. mean

26

18

Skewness

0.57

−0.18

Kurtosis

−0.39

−0.70

Sum

1876

1267

(Results generated using GraphPad Prism®v6.01 for windows).

showed progressively rising microbial count in air and this is evident in (Figure 1A and Figure 1B) for the active air samples during years 2013 and 2014, respectively and similarly (Figure 2A and Figure 2B) for the settle plates counts through the same periods. Passageways were the regions in the clean area that showed most progressive RGUHS J Pharm Sci | Vol 5 | Issue 4 | Oct–Dec, 2015

deterioration in the microbiological air quality. On the other hand, (Figure 3A) shows the overall performance in class C area with notably higher magnitude of declining efficacy of AAS in comparison with PAS. On the other hand, (Figure 3B) demonstrates the pattern of the bioburden distribution of both types of air sampling 157


Figure 1: Active air samples from class C production area during the course of two years study (2013: A, 2014: B). Regions investigated were denoted as the following: SSOP=Ointment and cream preparation, SSOF=Ointment and cream filling, GPAL=Personnel airlock, SSSR=Suppositories room, SSMC=Semisolid corridor, LFGQ=Liquid filling, LPPL=Personnel airlock of liquid preparation, LPGQ=Liquid preparation, LPML=Liquid preparation material airlock, SSML=Semisolid material airlock and LFML=Liquid filling material airlock. (The graph was generated using Microsoft Office Excel 2007)

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Figure 2: Passive air samples from class C production area during the course of two years study (2013: A, 2014: B). Regions investigated were denoted as the following: SSOP=Ointment and cream preparation, SSOF=Ointment and cream filling, SSSR=Suppositories room, LFGQ=Liquid filling and LPGQ=Liquid preparation. (The graph was generated using Microsoft Office Excel 2007)


159


Figure 3: Time series plot graph (A) for both averaged total active air samples (AAS)-in blue line-and passive air samples (PAS)– n red line-from January 2013 to December 2014 excluding airlocks and corridor regions. The general increasing trend lines shown for both. (The graph was generated using Microsoft Office Excel 2007)

Box and whisker diagram (B) displaying the distribution of data based on the five distinct descriptive characters: minimum, first quartile, median, third quartile, and maximum without any outlier observed. (The plot was generated using Minitab® v17.1.0)

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Figure 4: Linear (B) and quadratic (A) regression model types showing moderated positive correlation between active air samples (AAS) and passive air samples (PAS) with the later being more appropriate. (The plot was generated using Minitab® v17.1.0)


161


Figure 5: Capability Sixpack Quality Tools chronological comparison between averaged microbial count of all-regions active air samples (AAS) (A) and AAS without passages within production area (B). (The plot was generated using Minitab® v17.1.0)

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Figure 6: Capability Sixpack Quality Tools chronological comparison between averaged microbial count of active air samples (AAS) (B) and passive air samples (PAS) (A) within semisolid and liquid class C production area. (The plot was generated using Minitab® v17.1.0)


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Figure 7: Empirical Cumulative distribution function (CDF) showing the fitness of the selected distribution for both active air samples (AAS) and passive air samples (PAS). (The plot was generated using Minitab® v17.1.0)

techniques. The average relative microbial recovery ratio of active/passive samples was 1.50 and hence, the AAS could measure the overall microbiological quality of air rather than settling particles only, although there was an interesting sporadic part of the curve that showed the reverse attitude and so it required further analysis and investigation to elucidate possible improper activity that was captured by settle but not the active air sampling technique. Box plot diagram showed the distribution of data for both sampling methods. On the other hand, Table 1 demonstrated the statistical analysis performed for the trends illustrated in (Figure 3A). A significantly positive but moderate correlation existed between both active and passive air samples with the quadratic regression model in Figure 4A showing greater fit than linear one as shown in Figure 4B. Airlocks and corridor regions were significant contributors to failure risk of active air samples by more than 2.65 times as could be seen from (Figure 5A) in which the rate of microbiological defected samples were significantly higher than that of (Figure 5B). From the capability plot of Sixpack report of (Figure 5A and Figure 5B), the upper control limits (UCL) could be deduced and used as an alerting threshold with notably repeated outof-control states during the last part section of the study period which may attribute to the overall deterioration of the general trends. This part requires further extensive research. Performance capability of the PAS and AAS 164

process (Ppk) of the manufacturing rooms from (Figure 6A and Figure 6B) was 1.05 and 1.43, respectively showing that greater contribution of defects may be attributed to the operators and their accompanying activities rather than the quality of the area itself with the associated utilities and machines. The out-of-control state in the microbiological quality of air was detected earlier in AAS control chart in (Figure 6B) than that of PAS in (Figure 6A). The control charts of PAS and AAS showed decrease in the microbiological quality performance in 2014 more than 2013, which was evident by higher rates of excursions in the last part of the control charts. Interestingly, there was a higher rate of repeated out-of-control state in the later parts of each of 2013 and 2014 years than in the first part of each year. The probability of failure from PAS was approximately 84.54 times that of AAS which might indicate that non-GMP activities were recorded by PAS rather than AAS because the last sampling method time remained for much shorter period to capture any unusual event. CDF shown in Figure 7 represented the fitness of the distribution for both averaged overall PAS and AAS during the course of two years of the study. The average CFU/Sample ± SD was 28 ± 17 and 19 ± 10 for AAS and PAS, respectively. The cumulative 80 % of the data were below 46 CFU and below 28 CFU for AAS and PAS respectively. RGUHS J Pharm Sci | Vol 5 | Issue 4 | Oct–Dec, 2015


DISCUSSION Microbial cells and spores that are suspended in air may be present as tiny separated particles that are dispersed for significant times or as bigger lumps and aggregates that settle rapidly onto surfaces and hence can be captured by both active and passive air samples. They constitute a health hazard in healthcare facilities and may affect critical production area, nevertheless routine observations of airborne microbes is underestimated frequently. Under normal circumstances, the main source of airborne viable contamination is the shedding of skin fragments that carry bacteria from the operators in area. Another potential source of airborne micro organisms is air supplies that are not properly filtered.28 Several factors may contribute to fluctuation in microbial count in controlled area. The airborne microbial concen tration is correlated with suspended particulate matter sized 5-7 μm,29 human activity, number of people in a space, and apparel worn by personnel in working area.30 The frequency with which people enter and exit specific area may also increase the number of microorganisms in indoor environments.31 Airborne droplets usually harbor microorganisms as Gram-positive cocci and gram-negative rods, whose presence is considered objectionable in pharmaceutical products.32 Indoor air contamination is linked with inappropriate environmental control measures of the buildings, including materials-of-construction, heating, ventilation and air conditioning (HVAC), and the other sources are related to human-being , such as inappropriate behavior and numbers of people in constrained spaces.33-35 This was evident in the trending where corridor and airlocks showed higher rate of excursions than observed in manufacturing rooms. So, by continuous monitoring of the buildings and its utilities and ensuring that all tests fall into its criteria, the remaining probable cause of the environmental microbiological excursions will

be related to the operators factor with the quality and frequency of the associated activities and out-of-control states will increase as long as non-consistent GMP actions and attitudes are extended and growing. Thus, it is not surprising that progressive increase in microbial count trends could be attributed to the workers and adherence to GMP rules. CONCLUSION The currently applied technique for assessment of airquality in pharmaceutical plant is simple and rapid yet it is convenient in identifying sources of microbial risk of excursions and trend analysis. The overall assessment of the classified area was more effective in data interpretation and deriving conclusions rather than dealing with individual results with specific cut-off values as only pass or fail. The study outlined that passageways e.g. corridors and airlocks are area of high probability of failure in microbiological air quality samples and the personnel activities in the clean rooms have significant impact on aerial distribution of microbes in the production environment which may aggravate out-of-control states in microbial counts. ACKNOWLEDGEMENTS This work was supported and partially financially by HIKMA Pharma Pharmaceutical Company–2nd Industrial Zone-6th of October city, Egypt. Reference and writing style review was performed by Dr. Engy Refaat Rashed. Data gathering and collection was performed by the microbiology laboratory team of the quality control department. CONFLICT OF INTEREST The authors declared no conflict of interest.

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