Environmental Microbiology Papers & Publications

Monitoring and controlling of clean area environment is of paramount importance to ensure product safety and quality. This comprehensive analysis evaluates environmental monitoring (EM) data from Class C and Class D controlled environments in pharmaceutical manufacturing, utilizing Active Air (AA), Passive Air (PA), and Contact Plate (CP) or Replicate Organism Detection And Counting (RODAC) surface samples. The study aims to identify contamination trends, anomalies, and compliance with ISO 14644-1 and EU GMP Annex 1 standards. Results reveal unexpected findings: Class C Active Air (43 CFU/m³) and RODAC (3 CFU/plate) overall averages are higher than Class D Active Air (34 CFU/m³) and RODAC (2 CFU/plate), respectively, deviating from expected cleanroom classification. Class D Passive Air (22 CFU/plate) is higher than Class C (17 CFU/plate), aligning with expectations. Persistent hotspots were identified in Class C (e.g., location labelled “AA C 12 NG0”AA averages± Standard Deviation (SD): 67.33±17 CFU/m³), indicating localized control failures, while Class D showed extreme individual spikes (e.g., AA D 99 Ac: Max 171 CFU/m³). Sporadic contamination events in Class C suggest transient breaches, necessitating root-cause investigations. The study also highlights limitations of Class D monitoring, which obscures temporal trends and risks missing critical excursions due to long intervals between samples. Recommendations include targeted engineering assessments for high-load zones, enhanced Standard Operating Procedures (SOPs) for cleaning and gowning, adoption of real-time biofluorescent particle counters to replace manual sampling, and increased monitoring frequency in Class D hotspots.

Mostafa Eissa

Background: Water distribution systems within healthcare facilities are complex ecosystems that can harbor opportunistic pathogens, posing a significant risk to patient safety. Ensuring the microbiological quality of water requires rigorous monitoring and a deep understanding of the entire system, from source to point-of-use. This study undertakes a comprehensive statistical analysis of microbiological data from a healthcare facility’s water treatment and distribution network to identify contamination hotspots, evaluate the efficacy of critical treatment barriers, and map potential contamination pathways. Methods: This retrospective case study analyzed heterotrophic plate count (HPC) data collected from 29 distinct sampling points throughout a healthcare facility’s water system. The points represented various stages, including municipal source water, intermediate treatment steps (softening, ultrafiltration, reverse osmosis), storage tanks, and multiple points-of-use. Non-parametric statistical methods were employed due to the non-normal distribution of microbial data. A Kruskal-Wallis test with Dunn's post-hoc analysis was used to compare microbial loads across all sampling points. A focused Mann-Whitney U test was performed to assess the performance of the ultrafiltration (UF) unit. Results: The analysis revealed significant variability in microbiological quality throughout the system (Kruskal-Wallis, p<0.0001). One point-of-use and a pre-UF exhibited high median microbial counts and extreme variability, indicating chronic contamination and potential biofilm proliferation. Critically, the ultrafiltration unit failed to demonstrate a statistically significant reduction in microbial load between the pre-filter. Conclusion: The data reveals variability in microbiological levels across the water system, suggesting that the ultrafiltration barrier's performance is a significant factor influencing downstream water quality. The data points to systemic issues, likely involving widespread biofilm, that compromise water quality at the points-of-use.

Mostafa Eissa