A NOVEL PHARMACODYNAMIC MODEL FOR TREATMENT OF TUBERCULOSIS USING DAYS TO POSITIVITY IN AUTOMATED LIQUID MYCOBACTERIAL CULTURE

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1 A NOVEL PHARMACODYNAMIC MODEL FOR TREATMENT OF TUBERCULOSIS USING DAYS TO POSITIVITY IN AUTOMATED LIQUID MYCOBACTERIAL CULTURE Emmanuel Chigutsa 1, Kashyap Patel 2, Marianne Visser 3, Gary Maartens 1, Carl M.J. Kirkpatrick 2, Helen McIlleron 1*, Mats O. Karlsson 4 1 University of Cape Town Department of Medicine, Division of Clinical Pharmacology 2 Monash University, Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences 3 University of the Western Cape, School of Public Health, Cape Town 4 Uppsala University, Department of Pharmaceutical Biosciences

2 Introduction CFU counts on solid media are used to quantify viable Mycobacterium tuberculosis in serial sputum samples of patients as a measure of early treatment response. However, CFU counts are expensive, labour intensive, and technically challenging.

3 Time to detection (TTD) in liquid culture has been proposed as an alternative measure of viable M. Tb MGIT systems are routinely available in many settings simple to operate standardized Actively respiring bugs consume oxygen until it falls below threshold, resulting in a positive test

4 Methods 144 patients with PTB recruited as part of a study investigating vitamin A and zinc supplementation vs. placebo, in addition to TB treatment (Visser et al. Am J Clin Nutr 2011;93:93-100) Intensive phase regimen rifampicin, isoniazid, pyrazinamide and ethambutol - 5 days/week Weekly sputum collections before starting treatment (week 0), and weekly thereafter to 8 weeks Sputum cultured in liquid media (BACTEC MGIT 960; Becton Dickinson, Sparks, MD)

5 TTD was recorded in days no growth after 42 days was recorded as a negative result and treated as a censored observation in the analysis A time to event model (survival analysis) was developed using a non-linear mixed effects modeling approach implemented in NONMEM 7

6 TTD for each week on treatment in 144 patients week % negative % missing data

7 Log (Mtb in sputum) Model A bi-exponential decay model described the decline of bugs in the sputum of each patient, during the first 8 weeks of treatment Mtb in sputum = Ae t βt + Be 0,06 0,05 0,04 0,03 0,02 0, t (weeks) A - intercept for rapidly dividing Mtb α - rate constant (slope) for rapid kill B - intercept slowly dividing Mtb β - rate constant (slope) for slow kill t - time on treatment, in weeks

8 kt inc Mtb in MGIT = Mtb e -k is the overall growth rate of bacteria in MGIT tube -t inc is incubation time in days The relative concentration of Mtb from the weekly sputum sample, when cultured in the MGIT tube, grew exponentially until a positive result and the hazard of a positive sputum was estimated If the culture remained negative after 42 days the probability of survival (likelihood of not having a positive sputum result by day 42) was estimated

9 The amounts of the rapidly and slowly dividing Mtb subpopulations differ each week Growth rates in MGIT culture of the subpopulations may differ & The overall growth was the sum of the growth of the two subpopulations Hazard of positive culture result was directly related to the cumulative mass of bacteria in MGIT tube (because of increased oxygen consumption)

10 Time dependent growth in MGIT culture The growth rate of the bacteria in the tube was included in the model, with the rate decreasing after 28 days of incubation

11 Cavitation covariate Cavitation data from chest X-rays were available in 117 of the 144 patients. The presence of lung cavitation was found to significantly lower the rate of kill of fast-replicating bacteria. For those patients (n=27) who did not have cavitation data, NONMEM estimated the probability of each individual belonging to the cavitation or the no cavitation group, using a mixture model.

12 Apparent MTb concentration in MGIT inoculum RESULTS Typical MTb concentration-time profile in patient from final parameter estimates α t 1 1/2 =2.2 days for no cavitation B= A= α t 1/2 =5.5days for cavitation Time on treatment (weeks) Rapidly eliminated orgs in patients with cavitation rapidly eliminated orgs in patient with no cavitation slowly eliminated orgs overall profile for patient with cavitation overall profile for patient with no cavitation β t 1/2 =49 days

13 Final model parameter estimates Parameter Estimate A (intercept for rapid decline) α (rate constant for rapid decline) no cavitation α (rate constant for rapid decline) cavitation 2.16 (t 1/2 =2.2 days) 0.88 (t 1/2 =5.5 days) B (intercept for slow decline) β (rate constant for slow decline) 0.10 (t 1/2 =49 days) BSV_A 74% BSV_α 67% BSV_B 22% BSV_β 22% Lag time at baseline Lag time during treatment MGIT growth rate constant up to 28 days 3.4 days 6.3 days 0.6 day -1 (doubling time of 1.1 days) Rate of reduction in growth rate after 28 days 0.1 day -1 Between occasion variability in Mtb MGIT inoculum 109%

14 At baseline patients had 169 times more rapidly dividing Mtb organisms than slowly dividing organisms. The rapidly dividing organisms grew 7 times faster in the MGIT tube compared to the slowly dividing organisms. Overall growth rate each week depended upon proportions of type of bacteria in the MGIT inoculum. it would be higher if a patient had more rapid-phase bacteria

15 VPC of final model obtained from 100 simulations

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17 Discussion This is the first model describing the pharmacodynamics of the treatment of tuberculosis using a weekly time to event model based on days to positivity in liquid culture. Main purpose was method development, rather than investigation of covariates. The model can be used for investigation of covariates such as drug exposure or different Mtb chemotherapy combinations.

18 Acknowledgements Clinical Infectious Diseases Research Initiative (CIDRI) Wellcome Trust Fund National Research Foundation (NRF) South Africa ( and RCN /S50), the Norwegian Programme for Development, Research and Higher Education (NUFUPRO- 2007/10183), Research Council of Norway. South African MRC. 16-Sep-11