Pharmacodynamic Modeling of Bacillary Elimination Rates and Detection of Bacterial Lipid Bodies in Sputum to Predict and Understand Outcomes in Treatment of Pulmonary Tuberculosis.

Derek J Sloan; Henry C Mwandumba; Natalie J Garton; Saye H Khoo; Anthony E Butterworth; Theresa J Allain; Robert S Heyderman; Elizabeth L Corbett ORCID logo; Mike R Barer; Geraint R Davies; (2015) Pharmacodynamic Modeling of Bacillary Elimination Rates and Detection of Bacterial Lipid Bodies in Sputum to Predict and Understand Outcomes in Treatment of Pulmonary Tuberculosis. Clinical infectious diseases, 61 (1). pp. 1-8. ISSN 1058-4838 DOI: 10.1093/cid/civ195
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BACKGROUND: Antibiotic-tolerant bacterial persistence prevents treatment shortening in drug-susceptible tuberculosis, and accumulation of intracellular lipid bodies has been proposed to identify a persister phenotype of Mycobacterium tuberculosis cells. In Malawi, we modeled bacillary elimination rates (BERs) from sputum cultures and calculated the percentage of lipid body-positive acid-fast bacilli (%LB + AFB) on sputum smears. We assessed whether these putative measurements of persistence predict unfavorable outcomes (treatment failure/relapse). METHODS: Adults with pulmonary tuberculosis received standard 6-month therapy. Sputum samples were collected during the first 8 weeks for serial sputum colony counting (SSCC) on agar and time-to positivity (TTP) measurement in mycobacterial growth indicator tubes. BERs were extracted from nonlinear and linear mixed-effects models, respectively, fitted to these datasets. The %LB + AFB counts were assessed by fluorescence microscopy. Patients were followed until 1 year posttreatment. Individual BERs and %LB + AFB counts were related to final outcomes. RESULTS: One hundred and thirty-three patients (56% HIV coinfected) participated, and 15 unfavorable outcomes were reported. These were inversely associated with faster sterilization phase bacillary elimination from the SSCC model (odds ratio [OR], 0.39; 95% confidence interval [CI], .22-.70) and a faster BER from the TTP model (OR, 0.71; 95% CI, .55-.94). Higher %LB + AFB counts on day 21-28 were recorded in patients who suffered unfavorable final outcomes compared with those who achieved stable cure (P = .008). CONCLUSIONS: Modeling BERs predicts final outcome, and high %LB + AFB counts 3-4 weeks into therapy may identify a persister bacterial phenotype. These methods deserve further evaluation as surrogate endpoints for clinical trials.


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