Ruth Bowness1, Martin J Boeree2, Rob Aarnoutse3, Rodney Dawson4, Andreas Diacon5, Chacha Mangu6, Norbert Heinrich7, Nyanda E Ntinginya6, Anke Kohlenberg8, Bariki Mtafya6, Patrick P J Phillips9, Andrea Rachow7, Georgette Plemper van Balen2, Stephen H Gillespie10. 1. School of Medicine, University of St Andrews, Fife KY16 9AJ, UK rec9@st-andrews.ac.uk. 2. Radboud University Medical Center, Department of Pulmonary Diseases, Nijmegen, The Netherlands. 3. Radboud University Medical Center, Department of Clinical Pharmacy, Nijmegen, The Netherlands. 4. Division of Pulmonology, Department of Medicine and University of Cape Town Lung Institute, Cape Town, South Africa. 5. Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa. 6. NIMR-Mbeya Medical Research Centre, PO Box 2410, Mbeya, Tanzania. 7. Department for Infectious Diseases and Tropical Medicine, University of Munich, Munich, Germany DZIF German Centre for Infection Research, Munich, Germany. 8. NIMR-Mbeya Medical Research Centre, PO Box 2410, Mbeya, Tanzania Department for Infectious Diseases and Tropical Medicine, University of Munich, Munich, Germany. 9. Medical Research Council (MRC) Clinical Trials Unit at UCL, London, UK. 10. School of Medicine, University of St Andrews, Fife KY16 9AJ, UK.
Abstract
OBJECTIVES: The relationship between cfu and Mycobacterial Growth Indicator Tube (MGIT) time to positivity (TTP) is uncertain. We attempted to understand this relationship and create a mathematical model to relate these two methods of determining mycobacterial load. METHODS: Sequential bacteriological load data from clinical trials determined by MGIT and cfu were collected and mathematical models derived. All model fittings were conducted in the R statistical software environment (version 3.0.2), using the lm and nls functions. RESULTS: TTP showed a negative correlation with log10 cfu on all 14 days of the study. There was an increasing gradient of the regression line and y-intercept as treatment progressed. There was also a trend towards an increasing gradient with higher doses of rifampicin. CONCLUSIONS: These data suggest that there is a population of mycobacterial cells that are more numerous when detected in liquid than on solid medium. Increasing doses of rifampicin differentially kill this group of organisms. These findings support the idea that increased doses of rifampicin are more effective.
OBJECTIVES: The relationship between cfu and Mycobacterial Growth Indicator Tube (MGIT) time to positivity (TTP) is uncertain. We attempted to understand this relationship and create a mathematical model to relate these two methods of determining mycobacterial load. METHODS: Sequential bacteriological load data from clinical trials determined by MGIT and cfu were collected and mathematical models derived. All model fittings were conducted in the R statistical software environment (version 3.0.2), using the lm and nls functions. RESULTS: TTP showed a negative correlation with log10 cfu on all 14 days of the study. There was an increasing gradient of the regression line and y-intercept as treatment progressed. There was also a trend towards an increasing gradient with higher doses of rifampicin. CONCLUSIONS: These data suggest that there is a population of mycobacterial cells that are more numerous when detected in liquid than on solid medium. Increasing doses of rifampicin differentially kill this group of organisms. These findings support the idea that increased doses of rifampicin are more effective.
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