Geographical Information System for Spatial Microbiological Data Analysis in Pharmaceutical Manufacturing
| aut.embargo | Yes | en_NZ |
| aut.thirdpc.contains | No | en_NZ |
| aut.thirdpc.permission | No | en_NZ |
| aut.thirdpc.removed | No | en_NZ |
| dc.contributor.advisor | Bollard-Breen, Barbara | |
| dc.contributor.advisor | Brooks, John | |
| dc.contributor.author | Pruckner, Christian | |
| dc.date.accessioned | 2014-11-10T23:23:49Z | |
| dc.date.available | 2017-08-07T02:34:38Z | |
| dc.date.copyright | 2014 | |
| dc.date.created | 2014 | |
| dc.date.issued | 2014 | |
| dc.date.updated | 2014-11-10T10:11:37Z | |
| dc.description.abstract | Microbiological data analysis and trending in pharmaceutical cleanrooms is a legislative requirement, and part of quality assurance. It has historically been conducted using statistical tools, such as control charting, which lack the geographic component. Three years of environmental monitoring data from the cleanrooms of a pharmaceutical facility were analysed to find cleanrooms with microbiological percentage recovery rate hot spots in the air and surface of the facility. The data set was evaluated to determine if it has changed over the three year period using a χ2 analysis. The cleanroom microflora was analysed to see if it differs among the different cleanroom grades, and potential contamination routes in the cleanrooms were ascertained. Given the lack of published studies that use GIS to perform spatial analysis of microbiological environmental monitoring data, this approach is novel. The research concluded that areas which are highly frequented by personnel (connection corridor, vial washing areas, documenting rooms, and air locks) have higher percentage recovery rates, and some are microbial hot spots. The microbiological percentage recovery rates from air and surface monitoring in some of these and other areas have improved over the three year period, while in some cleanroom areas they have not. The microbial percentage recovery rates from air monitoring have improved more than those from surface monitoring over the three year period. The percentage recovery rates in most cleanrooms, however, have remained stable over the three year period. The microbial distribution within Grade D and the Aseptic Processing Area (cleanroom grades A, A2 and B) during the three year period is very similar in terms of microbial class distribution and microbial species identification. Grade D, however, has a higher percentage of moulds and a lower number of Gram-positive rods without spores compared with the Aseptic Processing Area. The material and personnel flow within the facility is responsible for the majority of microbiological contamination of the cleanrooms. This correlates with the findings of the literature review. | en_NZ |
| dc.identifier.uri | https://hdl.handle.net/10292/7869 | |
| dc.language.iso | en | en_NZ |
| dc.publisher | Auckland University of Technology | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Pharmaceutical manufacturing | en_NZ |
| dc.subject | GIS | en_NZ |
| dc.subject | Microbiological data analysis | en_NZ |
| dc.subject | Areas in the manufacturing cleanroom facility with higher air and surface microbial counts | en_NZ |
| dc.subject | Microbiological flora associated to certain cleanroom areas | en_NZ |
| dc.subject | Contamination routes in the cleanrooms | en_NZ |
| dc.title | Geographical Information System for Spatial Microbiological Data Analysis in Pharmaceutical Manufacturing | en_NZ |
| dc.type | Thesis | |
| thesis.degree.discipline | ||
| thesis.degree.grantor | Auckland University of Technology | |
| thesis.degree.level | Masters Theses | |
| thesis.degree.name | Master of Applied Science | en_NZ |