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

Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy

BACKGROUND

New treatment options are needed to maintain and improve therapy for tuberculosis, which caused the death of 1.5 million people in 2013 despite potential for an 86 % treatment success rate. A greater understanding of Mycobacterium tuberculosis (M.tb) bacilli that persist through drug therapy will aid drug development programs. Predictive biomarkers for treatment efficacy are also a research priority.

METHODS AND RESULTS

Genome-wide transcriptional profiling was used to map the mRNA signatures of M.tb from the sputa of 15 patients before and 3, 7 and 14 days after the start of standard regimen drug treatment. The mRNA profiles of bacilli through the first 2 weeks of therapy reflected drug activity at 3 days with transcriptional signatures at days 7 and 14 consistent with reduced M.tb metabolic activity similar to the profile of pre-chemotherapy bacilli. These results suggest that a pre-existing drug-tolerant M.tb population dominates sputum before and after early drug treatment, and that the mRNA signature at day 3 marks the killing of a drug-sensitive sub-population of bacilli. Modelling patient indices of disease severity with bacterial gene expression patterns demonstrated that both microbiological and clinical parameters were reflected in the divergent M.tb responses and provided evidence that factors such as bacterial load and disease pathology influence the host-pathogen interplay and the phenotypic state of bacilli. Transcriptional signatures were also defined that predicted measures of early treatment success (rate of decline in bacterial load over 3 days, TB test positivity at 2 months, and bacterial load at 2 months).

CONCLUSIONS

This study defines the transcriptional signature of M.tb bacilli that have been expectorated in sputum after two weeks of drug therapy, characterizing the phenotypic state of bacilli that persist through treatment. We demonstrate that variability in clinical manifestations of disease are detectable in bacterial sputa signatures, and that the changing M.tb mRNA profiles 0-2 weeks into chemotherapy predict the efficacy of treatment 6 weeks later. These observations advocate assaying dynamic bacterial phenotypes through drug therapy as biomarkers for treatment success.

Assessment of treatment response in tuberculosis

Antibiotic treatment of tuberculosis has a duration of several months. There is significant variability of the host immune response and the pharmacokinetic-pharmacodynamic properties of Mycobacterium tuberculosis sub-populations at the site of disease. A limitation of sputum-based measures of treatment response may be sub-optimal detection and monitoring of Mycobacterium tuberculosis sub-populations. Potential biomarkers and surrogate endpoints should be benchmarked against hard clinical outcomes (failure/relapse/death) and may need tailoring to specific patient populations. Here, we assess the evidence supporting currently utilized and future potential host and pathogen-based models and biomarkers for monitoring treatment response in active and latent tuberculosis. Biomarkers for monitoring treatment response in extrapulmonary, pediatric and drug resistant tuberculosis are research priorities.

Phenotypically Adapted Mycobacterium tuberculosis Populations from Sputum Are Tolerant to First-Line Drugs

Tuberculous sputum contains multiple Mycobacterium tuberculosis populations with different requirements for isolation in vitro. These include cells that form colonies on solid media (plateable M. tuberculosis), cells requiring standard liquid medium for growth (nonplateable M. tuberculosis), and cells requiring supplementation of liquid medium with culture supernatant (SN) for growth (SN-dependent M. tuberculosis). Here, we describe protocols for the cryopreservation and direct assessment of antimicrobial tolerance of these M. tuberculosis populations within sputum. Our results show that first-line drugs achieved only modest bactericidal effects on all three populations over 7 days (1 to 2.5 log₁₀ reductions), and SN-dependent M. tuberculosis was more tolerant to streptomycin and isoniazid than the plateable and nonplateable M. tuberculosis strains. Susceptibility of plateable M. tuberculosis to bactericidal drugs was significantly increased after passage in vitro; thus, tolerance observed in the sputum samples from the population groups was likely associated with mycobacterial adaptation to the host environment at some time prior to expectoration. Our findings support the use of a simple ex vivo system for testing drug efficacies against mycobacteria that have phenotypically adapted during tuberculosis infection.

Serial image analysis of Mycobacterium tuberculosis colony growth reveals a persistent subpopulation in sputum during treatment of pulmonary TB

Faster elimination of drug tolerant ‘persister’ bacteria may shorten treatment of tuberculosis (TB) but no method exists to quantify persisters in clinical samples. We used automated image analysis to assess whether studying growth characteristics of individual Mycobacterium tuberculosis colonies from sputum on solid media during early TB treatment facilitates ‘persister’ phenotyping. As Time to Detection (TTD) in liquid culture inversely correlates with total bacterial load we also evaluated the relationship between individual colony growth parameters and TTD. Sputum from TB patients in Malawi was prepared for solid and liquid culture after 0, 2 and 4 weeks of treatment. Serial photography of agar plates was used to measure time to appearance (lag time) and radial growth rate for each colony. Mixed-effects modelling was used to analyse changing growth characteristics from serial samples. 20 patients had colony measurements recorded at ≥1 time-point. Overall lag time increased by 6.5 days between baseline and two weeks (p = 0.0001). Total colony count/ml showed typical biphasic elimination, but long lag time colonies (>20days) had slower, monophasic decline. TTD was associated with minimum lag time (time to appearance of first colony1). Slower elimination of long lag time colonies suggests that these may represent a persister subpopulation of bacilli.

Limited role of culture conversion for decision-making in individual patient care and for advancing novel regimens to confirmatory clinical trials

BACKGROUND

Despite recent increased clinical trials activity, no regimen has proved able to replace the standard 6-month regimen for drug-sensitive tuberculosis. Understanding the relationship between microbiological markers measured during treatment and long-term clinical outcomes is critical to evaluate their usefulness for decision-making for both individual patient care and for advancing novel regimens into time-consuming and expensive pivotal phase III trials.

METHODS

Using data from the randomized controlled phase III trial REMoxTB, we evaluated sputum-based markers of speed of clearance of bacilli: time to smear negative status; time to culture negative status on LJ or in MGIT; daily rate of change of log10(TTP) to day 56; and smear or culture results at weeks 6, 8 or 12; as individual- and trial-level surrogate endpoints for long-term clinical outcome.

RESULTS

Time to culture negative status on LJ or in MGIT, time to smear negative status and daily rate of change in log10(TTP) were each independent predictors of clinical outcome, adjusted for treatment (p <0.001). However, discrimination between low and high risk patients, as measured by the c-statistic, was modest and not much higher than the reference model adjusted for BMI, history of smoking, HIV status, cavitation, gender and MGIT TTP.

CONCLUSIONS

Culture conversion during treatment for tuberculosis, however measured, has only a limited role in decision-making for advancing regimens into phase III trials or in predicting the outcome of treatment for individual patients. REMoxTB ClinicalTrials.gov number: NCT00864383.