A time-to-event pharmacodynamic model describing treatment response in patients with pulmonary tuberculosis using days to positivity in automated liquid mycobacterial culture

Analysis of time-to-positivity data in tuberculosis treatment studies: Identifying a new limit of quantification

The BACTEC Mycobacteria Growth Indicator Tube (MGIT) machine is the standard globally for detecting viable mycobacteria in patients' sputum. Samples are observed for no longer than 42 days, at which point the sample is declared "negative" for tuberculosis (TB). This time to detection of bacterial growth, referred to as time-to-positivity (TTP), is increasingly of interest not solely as a diagnostic tool, but as a continuous biomarker wherein change in TTP over time can be used for comparing the bactericidal activity of different TB treatments. However, as a continuous measure, there are oddities in the distribution of TTP values observed, particularly at higher values. We explored whether there is evidence to suggest setting an upper limit of quantification (ULOQM) lower than the diagnostic limit of detection (LOD) using data from several TB-PACTS randomized clinical trials and PanACEA MAMS-TB. Across all trials, less than 7.1% of all weekly samples returned TTP measurements between 25 and 42 days. Further, the relative absolute prediction error (%) was highest in this range. When modeling with ULOQMs of 25 and 30 days, the precision in estimation improved for 23 of 25 regimen-level slopes as compared to models using the diagnostic LOD while also improving the discrimination between regimens based on Bayesian posteriors. While TTP measurements between 25 days and the diagnostic LOD may be important for diagnostic purposes, TTP values in this range may not contribute meaningfully to its use as a quantitative measure, particularly when assessing treatment response, and may lead to under-powered clinical trials.

Analysis of Dynamic Efficacy Endpoints of the Nix-TB Trial

BACKGROUND

Safer, better, and shorter treatments for multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) are an urgent global health need. The phase 3 clinical trial Nix-TB (NCT02333799) tested a 6-month treatment of MDR and XDR-TB consisting of high-dose linezolid, bedaquiline, and pretomanid (BPaL). In this study, we investigate the relationship between the pharmacokinetic characteristics of the drugs, patient characteristics and efficacy endpoints from Nix-TB.

METHODS

Pharmacokinetic data were collected at weeks 2, 8, and 16. Efficacy endpoints including treatment outcomes, time to stable culture conversion, and longitudinal time to positivity in the mycobacterial growth indicator tube assay were each characterized using nonlinear mixed-effects modeling. Relationships between patient, treatment pharmacokinetics, and disease characteristics and efficacy endpoints were evaluated.

RESULTS

Data from 93 (85% of the total) participants were analyzed. Higher body mass index was associated with a lower incidence of unfavorable treatment outcomes. Median time to stable culture conversion was 3 months in patients with lower baseline burden compared with 4.5 months in patients with high baseline burden. Participants with minimal disease had steeper time to positivity trajectories compared with participants with high-risk phenotypes. No relationship between any drugs' pharmacokinetics (drug concentration or exposure metrics) and any efficacy outcomes was observed.

CONCLUSIONS

We have successfully described efficacy endpoints of a BPaL regimen from the Nix-TB trial. Participants with high-risk phenotypes significantly delayed time to culture conversion and bacterial clearance. The lack of a relationship between pharmacokinetic exposures and pharmacodynamic biomarkers opens the possibility to use lower, safer doses, particularly for toxicity-prone linezolid.

CLINICAL TRIALS REGISTRATION

NCT02333799.

Combined quantitative tuberculosis biomarker model for time-to-positivity and colony forming unit to support tuberculosis drug development

Biomarkers are quantifiable characteristics of biological processes. In Mycobacterium tuberculosis, common biomarkers used in clinical drug development are colony forming unit (CFU) and time-to-positivity (TTP) from sputum samples. This analysis aimed to develop a combined quantitative tuberculosis biomarker model for CFU and TTP biomarkers for assessing drug efficacy in early bactericidal activity studies. Daily CFU and TTP observations in 83 previously patients with uncomplicated pulmonary tuberculosis after 7 days of different rifampicin monotherapy treatments (10–40 mg/kg) from the HIGHRIF1 study were included in this analysis. The combined quantitative tuberculosis biomarker model employed the Multistate Tuberculosis Pharmacometric model linked to a rifampicin pharmacokinetic model in order to determine drug exposure-response relationships on three bacterial sub-states using both the CFU and TTP data simultaneously. CFU was predicted from the MTP model and TTP was predicted through a time-to-event approach from the TTP model, which was linked to the MTP model through the transfer of all bacterial sub-states in the MTP model to a one bacterial TTP model. The non-linear CFU-TTP relationship over time was well predicted by the final model. The combined quantitative tuberculosis biomarker model provides an efficient approach for assessing drug efficacy informed by both CFU and TTP data in early bactericidal activity studies and to describe the relationship between CFU and TTP over time.

Finding the right hazard function for time‐to‐event modeling: A tutorial and Shiny application

Parametric time‐to‐event analysis is an important pharmacometric method to predict the probability of an event up until a certain time as a function of covariates and/or drug exposure. Modeling is performed at the level of the hazard function describing the instantaneous rate of an event occurring at that timepoint. We give an overview of the parametric time‐to‐event analysis starting with graphical exploration by Kaplan–Meier plotting for the event data including censoring and nonparametric hazard estimators such as the kernel‐based visual hazard comparison for the underlying hazard. The most common hazard functions including the exponential, Gompertz, Weibull, log‐normal, log‐logistic, and circadian functions are described in detail. A Shiny application was developed to graphically guide the modeler which of the most common hazard functions presents a similar shape compared to the data in order to guide which hazard functions to test in the parametric time‐to‐event analysis. For the chosen hazard function(s), the Shiny application can additionally be used to explore corresponding parameter values to inform on suitable initial estimates for parametric modeling as well as on possible covariate or treatment relationships to certain parameters. Moreover, it can be used for the dissemination of results as well as communication, training, and workshops on time‐to‐event analysis. By guiding the modeler on which functions and what parameter values to test and compare as well as to assist in dissemination, the Shiny application developed here greatly supports the modeler in complicated parametric time‐to‐event modeling.

Explorative Analysis of Treatment Outcomes of Levofloxacin- and Moxifloxacin-Based Regimens and Outcome Predictors in Ethiopian MDR-TB Patients: A Prospective Observational Cohort Study

Purpose/Background

Although Ethiopia is among the thirty high multi-drug resistant tuberculosis (MDR-TB) burden countries in the world, comparative therapeutic efficacy of moxifloxacin and levofloxacin has not been explored, particularly in MDR-TB patients. We therefore aimed to prospectively compare clinical outcomes and determine potential predictors of the outcomes among patients on moxifloxacin or levofloxacin-based MDR-TB drug regimens.

Methods

We analyzed clinical parameters and laboratory data of eighty MDR-TB patients on moxifloxacin- or levofloxacin-based regimens. The clinical outcomes were compared using the Kaplan–Meier survival functions and the outcome definitions of the 2013 World Health Organization. Monthly sputum culture conversions and a molecular line probe assay results were also assessed. Observed outcomes and patient-related variables between the two groups were compared using chi-square, Wilcoxon Rank and Fisher exact tests. We also determined the potential predictors influencing treatment outcomes of moxifloxacin and levofloxacin using Cox proportional hazard model.

Results

The levofloxacin-based treatment group had a lower failure rate and adverse drug events as well as better treatment success than the moxifloxacin-based group. Overall treatment success was 65%. Disaggregating the data revealed that 53.8% were cured, 11.2% completed treatment, 10.0% died, 11.2% failed, and 13.8% were lost-to-follow-up. The line probe assay result showed that 11.3% of the clinical isolates were resistant to fluoroquinolones and 3.8% were resistant to both fluoroquinolones and injectable anti-TB agents. Treatment regimen type, culture conversion rate, alcohol use, cavity lesion, serum levels of creatinine and alanine aminotransferase were independent predictors of treatment outcome.

Conclusion

The levofloxacin-based regimen group has a better overall treatment success than the moxifloxacin-based group among MDR-TB patients. Clinical parameters and substance use history of the patients influenced treatment outcomes. We recommend further broader clinical studies to substantiate our findings as an input to review MDR-TB treatment guidelines.

Effects of increasing concentrations of rifampicin on different Mycobacterium tuberculosis lineages in a Whole blood Bactericidal Activity Assay

Objectives: High-dose rifampicin improved bactericidal activity and culture conversion in early-phase trials, done mainly in Africa. We performed a Whole Blood Bactericidal Activity (WBA) study to determine whether the effects of high-dose rifampicin differ across globally-relevant strains; and whether effects are similar in dormant bacilli that will be required for enhancing cure. Methods: Whole blood from healthy volunteers was spiked with rifampicin (range 0.63-60mg/L) and incubated with one of 4 Mycobacterium tuberculosis (Mtb) clinical strains (Haarlem, Latin American-Mediterranean [LAM], East African-Indian [EAI] and Beijing lineages) or a dormant strain (Streptomycin-Starved Mtb 18b strain [ss18b]). Change in bacterial cfu was estimated after inoculation of WBA cultures in MGIT. Results: WBA increased with higher concentrations of rifampicin in all strains. At rifampicin concentrations up to 5mg/L the rate of increase in WBA per unit increase in rifampicin concentration was similar in all 4 clinical strains (p>0.51). Above 5mg/L, EAI (p<0.001) and Beijing (p=0.007) strains showed greater increase in WBA than did LAM; Haarlem was similar to LAM. The dormant strain showed a lower rate of increase in WBA than clinical strains at rifampicin concentrations up to 5mg/L; above 5mg/L the rate of increase was similar to LAM, Beijing and Haarlem strains. Conclusions: Increasing rifampicin concentration enhanced WBA in all strains; the greatest effects were seen in strains common in Asia, suggesting that early-phase trial findings may be generalizable beyond Africa. Similar effects of high concentrations of rifampicin on the dormant strain support the concept that this intervention may enhance sterilizing activity.

A Longitudinal Model-Based Biomarker Analysis of Exposure Response in Adults with Pulmonary Tuberculosis

The identification of sensitive, specific, and reliable biomarkers that can be quantified in the early phases of tuberculosis treatment and predictive of long-term outcome is key for the development of an effective short-course treatment regimen. Time to positivity (TTP), a biomarker of treatment outcome against Mycobacterium tuberculosis, measures longitudinal bacterial growth in mycobacterial growth indicator tube broth culture and may be predictive of standard time to stable culture conversion (TSCC). In two randomized phase 2b trials investigating dose-ranging rifapentine (Studies 29 and 29X), 662 participants had sputum collected over 6 months where TTP, TSCC, and time to culture conversion were quantified. The goals of this post hoc study were to characterize longitudinal TTP profiles and to identify individual patient characteristics associated with delayed time to culture conversion. In order to do so, a nonlinear mixed-effects model describing longitudinal TTP was built. Independent variables associated with increased bacterial clearance (increased TTP), assessed by subject-specific and population-level trajectories, were higher rifapentine exposure, lower baseline grade of sputum acid-fast bacillus smear, absence of productive cough, and lower extent of lung infiltrates on radiographs. Importantly, sensitivity analysis revealed that major learning milestones in phase 2b trials, such as significant exposure-response and covariate relationships, could be detected using truncated TTP data as early as 6 weeks from start of treatment, suggesting alternative phase 2b study designs. The TTP model built depicts a novel phase 2b surrogate endpoint that can inform early assessment of experimental treatment efficacy and treatment failure or relapse in patients treated with shorter and novel TB treatment regimens, improving efficiency of phase 2 clinical trials. (The studies discussed in this paper have been registered at ClinicalTrials.gov under identifiers NCT00694629 and NCT01043575.)

Mechanisms of Drug-Induced Tolerance in Mycobacterium tuberculosis

Successful treatment of tuberculosis (TB) can be hampered by Mycobacterium tuberculosis populations that are temporarily able to survive antibiotic pressure in the absence of drug resistance-conferring mutations, a phenomenon termed drug tolerance. We summarize findings on M. tuberculosis tolerance published in the past 20 years. Key M. tuberculosis responses to drug pressure are reduced growth rates, metabolic shifting, and the promotion of efflux pump activity. Metabolic shifts upon drug pressure mainly occur in M. tuberculosis's lipid metabolism and redox homeostasis, with reduced tricarboxylic acid cycle activity in favor of lipid anabolism. Increased lipid anabolism plays a role in cell wall thickening, which reduces sensitivity to most TB drugs. In addition to these general mechanisms, drug-specific mechanisms have been described. Upon isoniazid exposure, M. tuberculosis reprograms several pathways associated with mycolic acid biosynthesis. Upon rifampicin exposure, M. tuberculosis upregulates the expression of its drug target rpoB Upon bedaquiline exposure, ATP synthesis is stimulated, and the transcription factors Rv0324 and Rv0880 are activated. A better understanding of M. tuberculosis's responses to drug pressure will be important for the development of novel agents that prevent the development of drug tolerance following treatment initiation. Such agents could then contribute to novel TB treatment-shortening strategies.

Once-a-week tigecycline for the treatment of drug-resistant TB

BACKGROUND

MDR-TB and XDR-TB have poor outcomes.

OBJECTIVES

To examine the efficacy of tigecycline monotherapy in the hollow fibre system model of TB.

METHODS

We performed pharmacokinetic/pharmacodynamic studies using tigecycline human-like concentration-time profiles in the hollow fibre system model of TB in five separate experiments using Mycobacterium tuberculosis in log-phase growth or as semi-dormant or intracellular bacilli, as monotherapy. We also compared efficacy with the isoniazid/rifampicin/pyrazinamide combination (standard therapy). We then applied extinction mathematics, morphisms and Latin hypercube sampling to identify duration of therapy with tigecycline monotherapy.

RESULTS

The median tigecycline MIC for 30 M. tuberculosis clinical and laboratory isolates (67% MDR/XDR) was 2 mg/L. Tigecycline monotherapy was highly effective in killing M. tuberculosis in log-phase-growth and semi-dormant and intracellular M. tuberculosis. Once-a-week dosing had the same efficacy as daily therapy for the same cumulative dose; thus, tigecycline efficacy was linked to the AUC0-24/MIC ratio. Tigecycline replacement by daily minocycline after 4 weeks of therapy was effective in sterilizing bacilli. The AUC0-24/MIC ratio associated with optimal kill was 42.3. Tigecycline monotherapy had a maximum sterilizing effect (day 0 minus day 28) of 3.06 ± 0.20 log10 cfu/mL (r2 = 0.92) compared with 3.92 ± 0.45 log10 cfu/mL (r2 = 0.80) with optimized standard therapy. In our modelling, at a tigecycline monotherapy duration of 12 months, the proportion of patients with XDR-TB who reached bacterial population extinction was 64.51%.

CONCLUSIONS

Tigecycline could cure patients with XDR-TB or MDR-TB who have failed recommended therapy. Once-a-week tigecycline could also replace second-line injectables in MDR-TB regimens.

Assessing whether isoniazid is essential during the first 14 days of tuberculosis therapy: a phase 2a, open-label, randomised controlled trial

Background

Clinical studies suggest that isoniazid contributes rapid bacterial killing during the initial two days of tuberculosis treatment but that isoniazid's activity declines significantly after day three. We conducted a 14-day phase IIa open label, randomized trial to assess the essentiality of isoniazid in standard tuberculosis therapy.

Methods

A total of 69 adults with newly diagnosed sputum-positive tuberculosis from the South African Western Cape region were enrolled and randomized to a four-arm parallel assignment model. Participants were followed for 14 days as inpatients at either the University of Cape Town Lung Institute or at the TASK Applied Science clinical research organization. All arms received standard daily rifampicin, ethambutol, and pyrazinamide but differed as follows: isoniazid only on days one and two (n=17), isoniazid on days one and two then moxifloxacin on days three through 14 (n=16), no isoniazid (n=18), and a control group that received isoniazid for all 14 days (standard therapy, n=18). The primary endpoint was the rate of colony forming unit (CFU) decline during the first 14 days of treatment.

Results

For 62 participants analyzed, the initial 14-day mean daily fall in log₁₀ CFU (95% CI) was 0·14 (0·11, 0·18) for participants receiving isoniazid for two days only; 0·13 (0·09, 0·17) for participants receiving isoniazid for two days followed by moxifloxacin; 0·12 (0·08, 0·15) for those not receiving isoniazid; and 0·13 (0·09, 0·16) for the standard therapy group.

Conclusions

The 14 day EBA for the combination rifampicin, ethambutol, and pyrazinamide was not significantly changed by the addition of isoniazid for the first two days or for the first 14 days of treatment. In a post hoc analysis, significantly higher day-two EBAs were observed for all groups among participants with higher baseline sputum CFUs. Our finding that INH does not contribute to EBA suggests that INH could be replaced with another drug during standard treatment to improve efficacy and decrease rates of resistance to first-line drugs. (Funded by the NIH AIDS Clinical Trial Groups and NIH; A5307 ClinicalTrials.gov number, NCT01589497).

Modeling and Simulation of Pretomanid Pharmacodynamics in Pulmonary Tuberculosis Patients

Pretomanid (PA-824) is a nitroimidazole in clinical testing for the treatment of tuberculosis. A population pharmacodynamic model for pretomanid was developed using a Bayesian analysis of efficacy data from two early bactericidal activity (EBA) studies, PA-824-CL-007 and PA-824-CL-010, conducted in Cape Town, South Africa. The two studies included 122 adult male and female participants with newly diagnosed pulmonary tuberculosis who received once daily oral pretomanid doses of either 50, 100, 150, 200, 600, 1,000, or 1,200 mg for 14 days. The structural model described capacity-limited growth and saturable drug-induced bacterial killing with separate rate equations for sputum solid culture colony forming unit (CFU) counts and liquid culture time to positivity (TTP) that were linked through a time constant. The posterior population geometric means and interindividual variability percent coefficients of variation were, respectively; 0.152±0.013 log₁₀ CFU/mL sputum/day and 54%±6% for the maximum kill rate constant, 20.4±1.0 h and 20.8%±0.1% for the time constant of proportionality between the CFU and TTP rate equations, and 770±140 ng/mL and 48%±17% for the pretomanid half-maximum effect plasma concentration. Model simulations showed once daily pretomanid at 100 mg, 200 mg, and 300 mg, attained 58%, 73%, and 80%, respectively, of an expected maximum 14-day EBA of 0.136 log₁₀CFU/mL sputum/day. These results establish a pretomanid exposure-efficacy relationship with dual outcomes for CFU counts and TTP, and with potential applications to dose optimization of pretomanid-containing regimens.

Model-Based Relationship between the Molecular Bacterial Load Assay and Time to Positivity in Liquid Culture

The molecular bacterial load (MBL) assay is a new tuberculosis biomarker which provides results in ∼4 hours. The relationship between MBL and time-to-positivity (TTP) has not been thoroughly studied, and predictive models do not exist. We aimed to develop a model for MBL and identify the MBL-TTP relationship in patients. The model was developed on data from 105 tuberculosis patients from Malawi, Mozambique, and Tanzania with joint MBL and TTP observations quantified from patient sputum collected for 12 weeks. MBL was quantified using PCR of mycobacterial RNA and TTP using the mycobacterial growth indicator tube (MGIT) 960 system. Treatment consisted of isoniazid, pyrazinamide, and ethambutol in standard doses together with rifampin 10 or 35 mg/kg of body weight. The developed MBL-TTP model included several linked submodels, a component describing decline of bacterial load in sputum, another component describing growth in liquid culture, and a hazard model translating bacterial growth into a TTP signal. Additional components for contaminated and negative TTP samples were included. Visual predictive checks performed using the developed model gave good description of the observed data. The model predicted greater total sample loss for TTP than MBL due to contamination and negative samples. The model detected an increase in bacterial killing for 35 versus 10 mg/kg rifampin (P = 0.002). In conclusion, a combined model for MBL and TTP was developed that described the MBL-TTP relationship. The full MBL-TTP model or each submodel was used separately. Second, the model can be used to predict biomarker response for MBL given TTP data or vice versa in historical or future trials.

Accelerating the transition of new tuberculosis drug combinations from Phase II to Phase III trials: New technologies and innovative designs

Geraint Davies and colleagues discuss the potential for innovative early-phase clinical trial methods and technologies to reduce risk and speed up drug development for tuberculosis.

Quantitative assessment of the activity of antituberculosis drugs and regimens

Introduction: Identification of optimal drug doses and drug combinations is crucial for optimized treatment of tuberculosis. Areas covered: An unprecedented level of research activity involving multiple approaches is seeking to improve tuberculosis treatment. This report is a review of the quantitative methods currently used on clinical data sets to identify drug exposure targets and optimal drug combinations for tuberculosis treatment. A high-level summary of the methods, including the strengths and weaknesses of each method and potential methodological improvements is presented. Methods incorporating data generated from multiple sources such as in vitro and clinical studies, and their potential to provide better estimates of pharmacokinetic/pharmacodynamic (PK/PD) targets, are discussed. PK/PD relationships identified are compared between different studies and data analysis methods. Expert opinion: The relationships between drug exposures and tuberculosis treatment outcomes are complex and require analytical methods capable of handling the multidimensional nature of the relationships. The choice of a method is guided by its complexity, interpretability of results, and type of data available.

Transformation Morphisms and Time-to-Extinction Analysis That Map Therapy Duration From Preclinical Models to Patients With Tuberculosis: Translating From Apples to Oranges

Background

A major challenge in medicine is translation of preclinical model findings to humans, especially therapy duration. One major example is recent shorter-duration therapy regimen failures in tuberculosis.

Methods

We used set theory mapping to develop a computational/modeling framework to map the time it takes to extinguish the Mycobacterium tuberculosis population on chemotherapy from multiple hollow fiber system model of tuberculosis (HFS-TB) experiments to that observed in patients. The predictive accuracy of the derived translation transformations was then tested using data from 108 HFS-TB Rapid Evaluation of Moxifloxacin in Tuberculosis (REMoxTB) units, including 756 colony-forming units (CFU)/mL. Derived transformations, and Latin hypercube sampling-guided simulations were used to predict cure and relapse after 4 and 6 months of therapy. Outcomes were compared to observations, in 1932 patients in the REMoxTB clinical trial.

Results

HFS-TB serial bacillary burden and serial sputum data in the derivation dataset formed a structure-preserving map. Bactericidal effect was mapped with a single step transformation, while the sterilizing effect was mapped with a 3-step transformation function. Using the HFS-TB REMoxTB data, we accurately predicted the proportion of patients cured in the 4-month REMoxTB clinical trial. Model-predicted vs clinical trial observations were (i) the ethambutol arm (77.0% [95% confidence interval {CI}, 74.4%-79.6%] vs 77.7% [95% CI, 74.3%-80.9%]) and (ii) the isoniazid arm (76.4% [95% CI, 73.9%-79.0%] vs 79.5% [95% CI, 76.1%-82.5%]).

Conclusions

We developed a method to translate duration of therapy outcomes from preclinical models to tuberculosis patients.

Robust Bayesian nonlinear mixed-effects modeling of time to positivity in tuberculosis trials

Early phase 2 tuberculosis (TB) trials are conducted to characterize the early bactericidal activity (EBA) of anti-TB drugs. The EBA of anti-TB drugs has conventionally been calculated as the rate of decline in colony forming unit (CFU) count during the first 14 days of treatment. The measurement of CFU count, however, is expensive and prone to contamination. Alternatively to CFU count, time to positivity (TTP), which is a potential biomarker for long-term efficacy of anti-TB drugs, can be used to characterize EBA. The current Bayesian nonlinear mixed-effects (NLME) regression model for TTP data, however, lacks robustness to gross outliers that often are present in the data. The conventional way of handling such outliers involves their identification by visual inspection and subsequent exclusion from the analysis. However, this process can be questioned because of its subjective nature. For this reason, we fitted robust versions of the Bayesian nonlinear mixed-effects regression model to a wide range of TTP datasets. The performance of the explored models was assessed through model comparison statistics and a simulation study. We conclude that fitting a robust model to TTP data obviates the need for explicit identification and subsequent "deletion" of outliers but ensures that gross outliers exert no undue influence on model fits. We recommend that the current practice of fitting conventional normal theory models be abandoned in favor of fitting robust models to TTP data.

Modelling of mycobacterial load reveals bedaquiline's exposure-response relationship in patients with drug-resistant TB

Background

Bedaquiline has been shown to reduce time to sputum culture conversion (SCC) and increase cure rates in patients with drug-resistant TB, but the influence of drug exposure remains uncharacterized.

Objectives

To investigate whether an exposure–response relationship could be characterized by making better use of the existing information on pharmacokinetics and longitudinal measurements of mycobacterial load.

Methods

Quantitative culture data in the form of time to positivity (TTP) in mycobacterial growth indicator tubes obtained from a randomized placebo-controlled Phase IIb registration trial were examined using non-linear mixed-effects methodology. The link to individual bedaquiline exposures and other patient characteristics was evaluated.

Results

The developed model included three simultaneously fitted components: a longitudinal representation of mycobacterial load in patients, a probabilistic component for bacterial presence in sputum samples, and a time-to-event model for TTP. Data were described adequately, and time to SCC was well predicted. Individual bedaquiline exposure was found to significantly affect the decline in mycobacterial load. Consequently, the proportion of patients without SCC at week 20 is expected to decrease from 25% (95% CI 20%–31%) without bedaquiline to 17% (95% CI 13%–21%), 12% (95% CI 8%–16%) and 7% (95% CI 4%–11%), respectively, with half the median, median and double the median bedaquiline exposure observed in patients with standard dosing. Baseline bacterial load and level of drug resistance were other important predictors.

Conclusions

To our knowledge, this is the first successful description of bedaquiline’s exposure–response relationship and may be used when considering dose optimization. Characterization of this relationship was possible by integrating quantitative information in existing clinical data using novel models.

Concentration-Dependent Synergy and Antagonism of Linezolid and Moxifloxacin in the Treatment of Childhood Tuberculosis: The Dynamic Duo

Background. No treatment regimens have been specifically designed for children, in whom tuberculosis is predominantly intracellular. Given their activity as monotherapy and their ability to penetrate many diseased anatomic sites that characterize disseminated tuberculosis, linezolid and moxifloxacin could be combined to form a regimen for this need.

Methods. We examined microbial kill of intracellular Mycobacterium tuberculosis (Mtb) by the combination of linezolid and moxifloxacin multiple exposures in a 7-by-7 mathematical matrix. We then used the hollow fiber system (HFS) model of intracellular tuberculosis to identify optimal dose schedules and exposures of moxifloxacin and linezolid in combination. We mimicked pediatric half-lives and concentrations achieved by each drug. We sampled the peripheral compartment on days 0, 7, 14, 21, and 28 for Mtb quantification, and compared the slope of microbial kill of Mtb by these regimens to the standard regimen of isoniazid, rifampin, and pyrazinamide, based on exponential decline regression.

Results. The full exposure-response surface identified linezolid-moxifloxacin zones of synergy, antagonism, and additivity. A regimen based on each of these zones was then used in the HFS model, with observed half-lives of 4.08 ± 0.66 for linezolid and 3.80 ± 1.34 hours for moxifloxacin. The kill rate constant was 0.060 ± 0.012 per day with the moxifloxacin-linezolid regimen in the additivity zone vs 0.083 ± 0.011 per day with standard therapy, translating to a bacterial burden half-life of 11.52 days vs 8.53 days, respectively.

Conclusions. We identified doses and dose schedules of a linezolid and moxifloxacin backbone regimen that could be highly efficacious in disseminated tuberculosis in children.

The importance of clinical pharmacokinetic-pharmacodynamic studies in unraveling the determinants of early and late tuberculosis outcomes

Tuberculosis remains a major infectious cause of morbidity and mortality worldwide. Current antibiotic regimens, constructed prior to the development of modern pharmacokinetic-pharmacodynamic (PK–PD) tools, are based on incomplete understanding of exposure–response relationships in drug susceptible and multidrug resistant tuberculosis. Preclinical and population PK data suggest that clinical PK–PD studies may enable therapeutic drug monitoring for some agents and revised dosing for others. Future clinical PK–PD challenges include: incorporation of PK methods to assay free concentrations for all active metabolites; selection of appropriate early outcome measures which reflect therapeutic response; elucidation of genetic contributors to interindividual PK variability; conduct of targeted studies on special populations (including children); and measurement of PK–PD parameters at the site of disease.

Bacterial Loads Measured by the Xpert MTB/RIF Assay as Markers of Culture Conversion and Bacteriological Cure in Pulmonary TB

INTRODUCTION

Biomarkers are needed to monitor tuberculosis (TB) treatment and predict treatment outcomes. We evaluated the Xpert MTB/RIF (Xpert) assay as a biomarker for TB treatment during and at the end of the 24 weeks therapy.

METHODS

Sputum from 108 HIV-negative, culture-positive pulmonary TB patients was analyzed using Xpert at time points before and during anti-TB therapy. Results were compared against culture. Direct Xpert cycle-threshold (Ct), a change in the Ct (delta Ct), or a novel "percent closing of baseline Ct deficit" (percent closing) were evaluated as classifiers of same-day and end-of-treatment culture and therapeutic outcomes.

RESULTS

Xpert was positive in 29/95 (30.5%) of subjects at week 24; and positive one year after treatment in 8/64 (12.5%) successfully-treated patients who remained free of tuberculosis. We identified a relationship between initial bacterial load measured by baseline Xpert Ct and time to culture conversion (hazard ratio 1.06, p = 0.0023), and to the likelihood of being among the 8 treatment failures at week 24 (AUC = 72.8%). Xpert Ct was even more strongly associated with culture conversion on the day the test was performed with AUCs 96.7%, 99.2%, 86.0% and 90.2%, at Day 7, Week 4, 8 and 24, respectively. Compared to baseline Ct measures alone, a combined measure of baseline Ct plus either Delta Ct or percent closing improved the classification of treatment failure status to a 75% sensitivity and 88.9% specificity.

CONCLUSIONS

Genome loads measured by Xpert provide a potentially-useful biomarker for classifying same day culture status and predicting response to therapy.

A multistate tuberculosis pharmacometric model: a framework for studying anti-tubercular drug effects in vitro

OBJECTIVES

Mycobacterium tuberculosis can exist in different states in vitro, which can be denoted as fast multiplying, slow multiplying and non-multiplying. Characterizing the natural growth of M. tuberculosis could provide a framework for accurate characterization of drug effects on the different bacterial states.

METHODS

The natural growth data of M. tuberculosis H37Rv used in this study consisted of viability defined as cfu versus time based on data from an in vitro hypoxia system. External validation of the natural growth model was conducted using data representing the rate of incorporation of radiolabelled methionine into proteins by the bacteria. Rifampicin time-kill curves from log-phase (0.25-16 mg/L) and stationary-phase (0.5-64 mg/L) cultures were used to assess the model's ability to describe drug effects by evaluating different linear and non-linear exposure-response relationships.

RESULTS

The final pharmacometric model consisted of a three-compartment differential equation system representing fast-, slow- and non-multiplying bacteria. Model predictions correlated well with the external data (R(2) = 0.98). The rifampicin effects on log-phase and stationary-phase cultures were separately and simultaneously described by including the drug effect on the different bacterial states. The predicted reduction in log10 cfu after 14 days and at 0.5 mg/L was 2.2 and 0.8 in the log-phase and stationary-phase systems, respectively.

CONCLUSIONS

The model provides predictions of the change in bacterial numbers for the different bacterial states with and without drug effect and could thus be used as a framework for studying anti-tubercular drug effects in vitro.

Tuberculous Pericarditis is Multibacillary and Bacterial Burden Drives High Mortality

BACKGROUND

Tuberculous pericarditis is considered to be a paucibacillary process; the large pericardial fluid accumulation is attributed to an inflammatory response to tuberculoproteins. Mortality rates are high. We investigated the role of clinical and microbial factors predictive of tuberculous pericarditis mortality using the artificial intelligence algorithm termed classification and regression tree (CART) analysis.

METHODS

Patients were prospectively enrolled and followed in the Investigation of the Management of Pericarditis (IMPI) registry. Clinical and laboratory data of 70 patients with confirmed tuberculous pericarditis, including time-to-positive (TTP) cultures from pericardial fluid, were extracted and analyzed for mortality outcomes using CART. TTP was translated to log10 colony forming units (CFUs) per mL, and compared to that obtained from sputum in some of our patients.

FINDINGS

Seventy patients with proven tuberculous pericarditis were enrolled. The median patient age was 35 (range: 20-71) years. The median, follow up was for 11.97 (range: 0·03-74.73) months. The median TTP for pericardial fluid cultures was 22 (range: 4-58) days or 3.91(range: 0·5-8·96) log10CFU/mL, which overlapped with the range of 3.24-7.42 log10CFU/mL encountered in sputum, a multi-bacillary disease. The overall mortality rate was 1.43 per 100 person-months. CART identified follow-up duration of 5·23 months on directly observed therapy, a CD4 + count of ≤ 199.5/mL, and TTP ≤ 14 days (bacillary load ≥ 5.53 log10 CFU/mL) as predictive of mortality. TTP interacted with follow-up duration in a non-linear fashion.

INTERPRETATION

Patients with culture confirmed tuberculous pericarditis have a high bacillary burden, and this bacterial burden drives mortality. Thus proven tuberculosis pericarditis is not a paucibacillary disease. Moreover, the severe immunosuppression suggests limited inflammation. There is a need for the design of a highly bactericidal regimen for this condition.

Transcriptional Adaptation of Drug-tolerant Mycobacterium tuberculosis During Treatment of Human Tuberculosis

BACKGROUND

Treatment initiation rapidly kills most drug-susceptible Mycobacterium tuberculosis, but a bacterial subpopulation tolerates prolonged drug exposure. We evaluated drug-tolerant bacilli in human sputum by comparing messenger RNA (mRNA) expression of drug-tolerant bacilli that survive the early bactericidal phase with treatment-naive bacilli.

METHODS

M. tuberculosis gene expression was quantified via reverse-transcription polymerase chain reaction in serial sputa from 17 Ugandans treated for drug-susceptible pulmonary tuberculosis.

RESULTS

Within 4 days, bacterial mRNA abundance declined >98%, indicating rapid killing. Thereafter, the rate of decline slowed >94%, indicating drug tolerance. After 14 days, 16S ribosomal RNA transcripts/genome declined 96%, indicating slow growth. Drug-tolerant bacilli displayed marked downregulation of genes associated with growth, metabolism, and lipid synthesis and upregulation in stress responses and key regulatory categories-including stress-associated sigma factors, transcription factors, and toxin-antitoxin genes. Drug efflux pumps were upregulated. The isoniazid stress signature was induced by initial drug exposure, then disappeared after 4 days.

CONCLUSIONS

Transcriptional patterns suggest that drug-tolerant bacilli in sputum are in a slow-growing, metabolically and synthetically downregulated state. Absence of the isoniazid stress signature in drug-tolerant bacilli indicates that physiological state influences drug responsiveness in vivo. These results identify novel drug targets that should aid in development of novel shorter tuberculosis treatment regimens.

Impact of nonlinear interactions of pharmacokinetics and MICs on sputum bacillary kill rates as a marker of sterilizing effect in tuberculosis

The relationships between antituberculosis drug exposure and treatment effects on humans receiving multidrug therapy are complex and nonlinear. In patients on treatment, an analysis of the rate of decline in the sputum bacillary burden reveals two slopes. The first is the α-slope, which is thought to reflect bactericidal effect, followed by a β-slope, which is thought to reflect sterilizing activity. We sought to characterize the effects of standard first-line treatment on sterilizing activity. Fifty-four patients receiving combination therapy for pulmonary tuberculosis in a clinical trial had drug concentrations measured and Mycobacterium tuberculosis isolates available for MIC identification. Sputum sample cultures were performed at baseline and weekly for 8 weeks. A time-to-event model based on the days to positivity in the liquid cultures was used to estimate the β-slope. The pharmacokinetic parameters of rifampin, isoniazid, ethambutol, and pyrazinamide were determined for each patient. Multivariate adaptive regression splines analyses, which simultaneously perform linear and nonlinear analyses, were used to identify the relationships between the predictors and the β-slope. The potential predictors examined included HIV status, lung cavitation, 24-h area under the concentration-time curve (AUC), peak drug concentration (Cmax), AUC/MIC ratio, Cmax/MIC ratio, and the time that that concentration persisted above MIC. A rifampin Cmax of >8.2 mg/liter and a pyrazinamide AUC/MIC of >11.3 were key predictors of the β-slope and interacted positively to increase the β-slope. In patients with a rifampin AUC of <35.4 mg · h/liter, an increase in the pyrazinamide AUC/MIC and/or ethambutol Cmax/MIC increased the β-slope, while increasing isoniazid Cmax decreased it, suggesting isoniazid antagonism. Antibiotic concentrations and MICs interact in a nonlinear fashion as the main drivers of a sterilizing effect. The results suggest that faster speeds of sterilizing effect might be achieved by omitting isoniazid and by increasing rifampin, pyrazinamide, and ethambutol exposures. However, isoniazid and ethambutol exposures may only be of importance when rifampin exposure is low. These findings need confirmation in larger studies. (This study has been registered at controlled-trials.com under registration no. ISRCTN80852505.).

Clinical evaluation of tuberculosis viability microscopy for assessing treatment response

Tuberculosis viability microscopy predicted, within 1 hour, quantitative culture results that became available weeks later. Viability microscopy provides promising results for informing decisions concerning drug susceptibility testing, treatment changes, and infection control measures in resource-constrained settings where most tuberculosis occurs.

Background. It is difficult to determine whether early tuberculosis treatment is effective in reducing the infectiousness of patients' sputum, because culture takes weeks and conventional acid-fast sputum microscopy and molecular tests cannot differentiate live from dead tuberculosis.

Methods. To assess treatment response, sputum samples (n = 124) from unselected patients (n = 35) with sputum microscopy–positive tuberculosis were tested pretreatment and after 3, 6, and 9 days of empiric first-line therapy. Tuberculosis quantitative viability microscopy with fluorescein diacetate, quantitative culture, and acid-fast auramine microscopy were all performed in triplicate.

Results. Tuberculosis quantitative viability microscopy predicted quantitative culture results such that 76% of results agreed within ±1 logarithm (r_S = 0.85; P < .0001). In 31 patients with non-multidrug-resistant (MDR) tuberculosis, viability and quantitative culture results approximately halved (both 0.27 log reduction, P < .001) daily. For patients with non-MDR tuberculosis and available data, by treatment day 9 there was a >10-fold reduction in viability in 100% (24/24) of cases and quantitative culture in 95% (19/20) of cases. Four other patients subsequently found to have MDR tuberculosis had no significant changes in viability (P = .4) or quantitative culture (P = .6) results during early treatment. The change in viability and quantitative culture results during early treatment differed significantly between patients with non-MDR tuberculosis and those with MDR tuberculosis (both P < .001). Acid-fast microscopy results changed little during early treatment, and this change was similar for non-MDR tuberculosis vs MDR tuberculosis (P = .6).

Conclusions. Tuberculosis quantitative viability microscopy is a simple test that within 1 hour predicted quantitative culture results that became available weeks later, rapidly indicating whether patients were responding to tuberculosis therapy.

The pyrazinamide susceptibility breakpoint above which combination therapy fails

Objectives

To identify the pyrazinamide MIC above which standard combination therapy fails.

Methods

MICs of pyrazinamide were determined for Mycobacterium tuberculosis isolates, cultured from 58 patients in a previous randomized clinical trial in Cape Town, South Africa. The MICs were determined using BACTEC MGIT 960 for isolates that were collected before standard treatment with isoniazid, rifampicin, pyrazinamide and ethambutol commenced. Weekly sputum collections were subsequently made for 8 weeks in order to culture M. tuberculosis in Middlebrook broth medium. Classification and regression tree (CART) analysis was utilized to identify the pyrazinamide MIC predictive of sputum culture results at the end of pyrazinamide therapy. The machine learning-derived susceptibility breakpoints were then confirmed using standard association statistics that took into account confounders of 2 month sputum conversion.

Results

The pyrazinamide MIC range was 12.5 to >100 mg/L for the isolates prior to therapy. The epidemiological 95% cut-off value was >100 mg/L. The 2 month sputum conversion rate in liquid cultures was 26% by stringent criteria and 48% by less stringent criteria. CART analysis identified an MIC breakpoint of 50 mg/L, above which patients had poor sputum conversion rates. The relative risk of poor sputum conversion was 1.5 (95% CI: 1.2–1.8) for an MIC >50 mg/L compared with an MIC ≤50 mg/L.

Conclusions

We propose a pyrazinamide susceptibility breakpoint of 50 mg/L for clinical decision making and for development of rapid susceptibility assays. This breakpoint is identical to that identified using computer-aided simulations of hollow fibre system output.