Moxifloxacin-containing Regimen Greatly Reduces Time to Culture Conversion in Murine Tuberculosis

In vitro and ex vivo activity of the fluoroquinolone DC-159a against mycobacteria

Antimicrobial resistance is a global health problem. In 2021, it was estimated almost half a million of multidrug-resistant tuberculosis (MDR-TB) cases. Besides, non-tuberculous mycobacteria (NTM) are highly resistant to several drugs and the emergence of fluoroquinolone (FQ) resistant M. tuberculosis (Mtb) is also a global concern making treatments difficult and with variable outcome. The aim of this study was to evaluate the activity of the FQ, DC-159a, against Mtb and NTM and to explore the cross-resistance with the currently used FQs.A total of 12 pre-extensively drug-resistant (XDR) Mtb, 2 XDR, 36 fully drug susceptible strains and 41 NTM isolates were included to estimate the in vitro activity of DC-159a, moxifloxacin (MOX) and levofloxacin (LX), using minimal inhibitory and bactericidal concentration (MIC and MBC). The activity inside the human macrophages and pulmonary epithelial cells were also determined.DC-159a was active in vitro and ex vivo against mycobacteria. Besides, it was more active than MOX/LX. Moreover, no cross-resistance was evidenced between DC-159a and LX/MOX as DC-159a could inhibit Mtb and MAC strains that were already resistant to LX/MOX.DC-159a could be a possible candidate in new therapeutic regimens for MDR/ XDR-TB and mycobacterioses cases.

A systematic efficacy analysis of tuberculosis treatment with BPaL-containing regimens using a multiscale modeling approach

Tuberculosis (TB) is a life-threatening infectious disease. The standard treatment is up to 90% effective; however, it requires the administration of four antibiotics (isoniazid, rifampicin, pyrazinamide, and ethambutol [HRZE]) over long time periods. This harsh treatment process causes adherence issues for patients because of the long treatment times and a myriad of adverse effects. Therefore, the World Health Organization has focused goals of shortening standard treatment regimens for TB in their End TB Strategy efforts, which aim to reduce TB-related deaths by 95% by 2035. For this purpose, many novel and promising combination antibiotics are being explored that have recently been discovered, such as the bedaquiline, pretomanid, and linezolid (BPaL) regimen. As a result, testing the number of possible combinations with all possible novel regimens is beyond the limit of experimental resources. In this study, we present a unique framework that uses a primate granuloma modeling approach to screen many combination regimens that are currently under clinical and experimental exploration and assesses their efficacies to inform future studies. We tested well-studied regimens such as HRZE and BPaL to evaluate the validity and accuracy of our framework. We also simulated additional promising combination regimens that have not been sufficiently studied clinically or experimentally, and we provide a pipeline for regimen ranking based on their efficacies in granulomas. Furthermore, we showed a correlation between simulation rankings and new marmoset data rankings, providing evidence for the credibility of our framework. This framework can be adapted to any TB regimen and can rank any number of single or combination regimens.

Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens

Tuberculosis (TB) continues to be one of the deadliest infectious diseases in the world, causing ~1.5 million deaths every year. The World Health Organization initiated an End TB Strategy that aims to reduce TB-related deaths in 2035 by 95%. Recent research goals have focused on discovering more effective and more patient-friendly antibiotic drug regimens to increase patient compliance and decrease emergence of resistant TB. Moxifloxacin is one promising antibiotic that may improve the current standard regimen by shortening treatment time. Clinical trials and in vivo mouse studies suggest that regimens containing moxifloxacin have better bactericidal activity. However, testing every possible combination regimen with moxifloxacin either in vivo or clinically is not feasible due to experimental and clinical limitations. To identify better regimens more systematically, we simulated pharmacokinetics/pharmacodynamics of various regimens (with and without moxifloxacin) to evaluate efficacies, and then compared our predictions to both clinical trials and nonhuman primate studies performed herein. We used GranSim, our well-established hybrid agent-based model that simulates granuloma formation and antibiotic treatment, for this task. In addition, we established a multiple-objective optimization pipeline using GranSim to discover optimized regimens based on treatment objectives of interest, i.e., minimizing total drug dosage and lowering time needed to sterilize granulomas. Our approach can efficiently test many regimens and successfully identify optimal regimens to inform pre-clinical studies or clinical trials and ultimately accelerate the TB regimen discovery process.

Updated considerations in the diagnosis and management of tuberculosis infection and disease: integrating the latest evidence-based strategies

INTRODUCTION

Tuberculosis (TB) is a leading infectious cause of global morbidity and mortality, affecting nearly a quarter of the human population and accounting for over 10 million deaths each year. Over the past several decades, TB incidence and mortality have gradually declined, but 2021 marked a threatening reversal of this trend highlighting the importance of accurate diagnosis and effective treatment of all forms of TB.

AREAS COVERED

This review summarizes advances in TB diagnostics, addresses the treatment of people with TB infection and TB disease including recent evidence for treatment regimens for drug-susceptible and drug-resistant TB, and draws attention to special considerations in children and during pregnancy.

EXPERT OPINION

Improvements in diagnosis and management of TB have expanded the available options for TB control. Molecular testing has enhanced the detection of TB disease, but better diagnostics are still needed, particularly for certain populations such as children. Novel treatment regimens have shortened treatment and improved outcomes for people with TB. However, important questions remain regarding the optimal management of TB. Work must continue to ensure the potential of the latest developments is realized for all people affected by TB.

Discovery of natural-product-derived sequanamycins as potent oral anti-tuberculosis agents

The emergence of drug-resistant tuberculosis has created an urgent need for new anti-tubercular agents. Here, we report the discovery of a series of macrolides called sequanamycins with outstanding in vitro and in vivo activity against Mycobacterium tuberculosis (Mtb). Sequanamycins are bacterial ribosome inhibitors that interact with the ribosome in a similar manner to classic macrolides like erythromycin and clarithromycin, but with binding characteristics that allow them to overcome the inherent macrolide resistance of Mtb. Structures of the ribosome with bound inhibitors were used to optimize sequanamycin to produce the advanced lead compound SEQ-9. SEQ-9 was efficacious in mouse models of acute and chronic TB as a single agent, and it demonstrated bactericidal activity in a murine TB infection model in combination with other TB drugs. These results support further investigation of this series as TB clinical candidates, with the potential for use in new regimens against drug-susceptible and drug-resistant TB.

Tools to develop antibiotic combinations that target drug tolerance in Mycobacterium tuberculosis

Combination therapy is necessary to treat tuberculosis to decrease the rate of disease relapse and prevent the acquisition of drug resistance, and shorter regimens are urgently needed. The adaptation of Mycobacterium tuberculosis to various lesion microenvironments in infection induces various states of slow replication and non-replication and subsequent antibiotic tolerance. This non-heritable tolerance to treatment necessitates lengthy combination therapy. Therefore, it is critical to develop combination therapies that specifically target the different types of drug-tolerant cells in infection. As new tools to study drug combinations earlier in the drug development pipeline are being actively developed, we must consider how to best model the drug-tolerant cells to use these tools to design the best antibiotic combinations that target those cells and shorten tuberculosis therapy. In this review, we discuss the factors underlying types of drug tolerance, how combination therapy targets these populations of bacteria, and how drug tolerance is currently modeled for the development of tuberculosis multidrug therapy. We highlight areas for future studies to develop new tools that better model drug tolerance in tuberculosis infection specifically for combination therapy testing to bring the best drug regimens forward to the clinic.

Advances in the design of combination therapies for the treatment of tuberculosis

INTRODUCTION

Tuberculosis requires lengthy multi-drug therapy. Mycobacterium tuberculosis occupies different tissue compartments during infection, making drug access and susceptibility patterns variable. Antibiotic combinations are needed to ensure each compartment of infection is reached with effective drug treatment. Despite drug combinations' role in treating tuberculosis, the design of such combinations has been tackled relatively late in the drug development process, limiting the number of drug combinations tested. In recent years, there has been significant progress using in vitro, in vivo, and computational methodologies to interrogate combination drug effects.

AREAS COVERED

This review discusses the advances in these methodologies and how they may be used in conjunction with new successful clinical trials of novel drug combinations to design optimized combination therapies for tuberculosis. Literature searches for approaches and experimental models used to evaluate drug combination effects were undertaken.

EXPERT OPINION

We are entering an era richer in combination drug effect and pharmacokinetic/pharmacodynamic data, genetic tools, and outcome measurement types. Application of computational modeling approaches that integrate these data and produce predictive models of clinical outcomes may enable the field to generate novel, effective multidrug therapies using existing and new drug combination backbones.

Fluoroquinolone heteroresistance, antimicrobial tolerance, and lethality enhancement

With tuberculosis, the emergence of fluoroquinolone resistance erodes the ability of treatment to interrupt the progression of MDR-TB to XDR-TB. One way to reduce the emergence of resistance is to identify heteroresistant infections in which subpopulations of resistant mutants are likely to expand and make the infections fully resistant: treatment modification can be instituted to suppress mutant enrichment. Rapid DNA-based detection methods exploit the finding that fluoroquinolone-resistant substitutions occur largely in a few codons of DNA gyrase. A second approach for restricting the emergence of resistance involves understanding fluoroquinolone lethality through studies of antimicrobial tolerance, a condition in which bacteria fail to be killed even though their growth is blocked by lethal agents. Studies with Escherichia coli guide work with Mycobacterium tuberculosis. Lethal action, which is mechanistically distinct from blocking growth, is associated with a surge in respiration and reactive oxygen species (ROS). Mutations in carbohydrate metabolism that attenuate ROS accumulation create pan-tolerance to antimicrobials, disinfectants, and environmental stressors. These observations indicate the existence of a general death pathway with respect to stressors. M. tuberculosis displays a variation on the death pathway idea, as stress-induced ROS is generated by NADH-mediated reductive stress rather than by respiration. A third approach, which emerges from lethality studies, uses a small molecule, N-acetyl cysteine, to artificially increase respiration and additional ROS accumulation. That enhances moxifloxacin lethality with M. tuberculosis in culture, during infection of cultured macrophages, and with infection of mice. Addition of ROS stimulators to fluoroquinolone treatment of tuberculosis constitutes a new direction for suppressing the transition of MDR-TB to XDR-TB.

Moxifloxacin-Mediated Killing of Mycobacterium tuberculosis Involves Respiratory Downshift, Reductive Stress, and Accumulation of Reactive Oxygen Species

Moxifloxacin is central to treatment of multidrug-resistant tuberculosis. Effects of moxifloxacin on the Mycobacterium tuberculosis redox state were explored to identify strategies for increasing lethality and reducing the prevalence of extensively resistant tuberculosis. A noninvasive redox biosensor and a reactive oxygen species (ROS)-sensitive dye revealed that moxifloxacin induces oxidative stress correlated with M. tuberculosis death. Moxifloxacin lethality was mitigated by supplementing bacterial cultures with an ROS scavenger (thiourea), an iron chelator (bipyridyl), and, after drug removal, an antioxidant enzyme (catalase). Lethality was also reduced by hypoxia and nutrient starvation. Moxifloxacin increased the expression of genes involved in the oxidative stress response, iron-sulfur cluster biogenesis, and DNA repair. Surprisingly, and in contrast with Escherichia coli studies, moxifloxacin decreased expression of genes involved in respiration, suppressed oxygen consumption, increased the NADH/NAD⁺ ratio, and increased the labile iron pool in M. tuberculosis. Lowering the NADH/NAD⁺ ratio in M. tuberculosis revealed that NADH-reductive stress facilitates an iron-mediated ROS surge and moxifloxacin lethality. Treatment with N-acetyl cysteine (NAC) accelerated respiration and ROS production, increased moxifloxacin lethality, and lowered the mutant prevention concentration. Moxifloxacin induced redox stress in M. tuberculosis inside macrophages, and cotreatment with NAC potentiated the antimycobacterial efficacy of moxifloxacin during nutrient starvation, inside macrophages, and in mice, where NAC restricted the emergence of resistance. Thus, NADH-reductive stress contributes to moxifloxacin-mediated killing of M. tuberculosis, and the respiration stimulator (NAC) enhances lethality and suppresses the emergence of drug resistance.

Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements

A challenge in tuberculosis treatment regimen design is the necessity to combine three or more antibiotics. We narrow the prohibitively large search space by breaking down high-order drug combinations into drug pair units. Using pairwise in vitro measurements, we train machine learning models to predict higher-order combination treatment outcomes in the relapsing BALB/c mouse model. Classifiers perform well and predict many of the >500 possible combinations among 12 antibiotics to be improved over bedaquiline + pretomanid + linezolid, a treatment-shortening regimen compared with the standard of care in mice. We reformulate classifiers as simple rulesets to reveal guiding principles of constructing combination therapies for both preclinical and clinical outcomes. One example ruleset combines a drug pair that is synergistic in a dormancy model with a pair that is potent in a cholesterol-rich growth environment. These rulesets are predictive, intuitive, and practical, thus enabling rational construction of drug combinations.

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Evaluate the large drug combination space for potential tuberculosis treatments

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In vitro 2-drug combination measurements predict 3–4 drug treatment outcomes in vivo

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Strongly synergistic, antagonistic, or potent drug pairs drive treatment outcome

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Simple rules articulate drug combination design principles for tuberculosis

Do Anti-tuberculosis Drugs Reach Their Target?─High-Resolution Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Provides Information on Drug Penetration into Necrotic Granulomas

Tuberculosis (TB) is characterized by mycobacteria-harboring centrally necrotizing granulomas. The efficacy of anti-TB drugs depends on their ability to reach the bacteria in the center of these lesions. Therefore, we developed a mass spectrometry (MS) imaging workflow to evaluate drug penetration in tissue. We employed a specific mouse model that─in contrast to regular inbred mice─strongly resembles human TB pathology. Mycobacterium tuberculosis was inactivated in lung sections of these mice by γ-irradiation using a protocol that was optimized to be compatible with high spatial resolution MS imaging. Different distributions in necrotic granulomas could be observed for the anti-TB drugs clofazimine, pyrazinamide, and rifampicin at a pixel size of 30 μm. Clofazimine, imaged here for the first time in necrotic granulomas of mice, showed higher intensities in the surrounding tissue than in necrotic granulomas, confirming data observed in TB patients. Using high spatial resolution drug and lipid imaging (5 μm pixel size) in combination with a newly developed data analysis tool, we found that clofazimine does penetrate to some extent into necrotic granulomas and accumulates in the macrophages inside the granulomas. These results demonstrate that our imaging platform improves the predictive power of preclinical animal models. Our workflow is currently being applied in preclinical studies for novel anti-TB drugs within the German Center for Infection Research (DZIF). It can also be extended to other applications in drug development and beyond. In particular, our data analysis approach can be used to investigate diffusion processes by MS imaging in general.

Model-Based Meta-Analysis of Relapsing Mouse Model Studies from the Critical Path to Tuberculosis Drug Regimens Initiative Database

Tuberculosis (TB), the disease caused by Mycobacterium tuberculosis (Mtb), remains a leading infectious disease-related cause of death worldwide, necessitating the development of new and improved treatment regimens. Non-clinical evaluation of candidate drug combinations via the relapsing mouse model (RMM) is an important step in regimen development, through which candidate regimens that provide the greatest decrease in probability of relapse following treatment in mice may be identified for further development. Although RMM studies are a critical tool to evaluate regimen efficacy, making comprehensive "apples to apples" comparisons of regimen performance in the RMM has been a challenge, in large part due to the need to evaluate and adjust for variability across studies arising from differences in design and execution. To address this knowledge gap, we performed a model-based meta-analysis on data for 17 unique regimens obtained from a total of 1592 mice across 28 RMM studies. Specifically, a mixed-effects logistic regression model was developed that described the treatment duration-dependent probability of relapse for each regimen and identified relevant covariates contributing to inter-study variability. Using the model, covariate-normalized metrics of interest, namely treatment duration required to reach 50% and 10% relapse probability, were derived and used to compare relative regimen performance. Overall, the model-based meta-analysis approach presented herein enables cross-study comparison of efficacy in the RMM, and provides a framework whereby data from emerging studies may be analyzed in the context of historical data to aid in selecting candidate drug combinations for clinical evaluation as TB drug regimens.

Systematic measurement of combination-drug landscapes to predict in vivo treatment outcomes for tuberculosis

Lengthy multidrug chemotherapy is required to achieve a durable cure in tuberculosis. However, we lack well-validated, high-throughput in vitro models that predict animal outcomes. Here, we provide an extensible approach to rationally prioritize combination therapies for testing in in vivo mouse models of tuberculosis. We systematically measured Mycobacterium tuberculosis response to all two- and three-drug combinations among ten antibiotics in eight conditions that reproduce lesion microenvironments, resulting in >500,000 measurements. Using these in vitro data, we developed classifiers predictive of multidrug treatment outcome in a mouse model of disease relapse and identified ensembles of in vitro models that best describe in vivo treatment outcomes. We identified signatures of potencies and drug interactions in specific in vitro models that distinguish whether drug combinations are better than the standard of care in two important preclinical mouse models. Our framework is generalizable to other difficult-to-treat diseases requiring combination therapies. A record of this paper's transparent peer review process is included in the supplemental information.

Characterizing the gene mutations associated with resistance to gatifloxacin in Mycobacterium tuberculosis through whole-genome sequencing

OBJECTIVES

Gatifloxacin (GAT), a fourth-generation fluoroquinolone (FQ), is used to treat drug-resistant tuberculosis. Although DNA gyrase mutations are the leading cause of FQ resistance, mutations conferring resistance to GAT remain inadequately characterized.

METHODS

GAT-resistant mutants were selected from 7H10 agar plates containing 0.5 mg/L GAT (critical concentration). Mutations involved in GAT resistance were identified through whole-genome sequencing.

RESULTS

In total, 123 isolates demonstrated resistance to GAT. Among these isolates, 55.3% (68/123) had gyrA gene mutations [G280A (D94N), A281G (D94G), G280T (D94Y) and G262T (G88C)]. The remainder (44.7%, 55/123) harboured gyrB gene mutations [A1495G (N499D), C1497A (N499K), C1497G (N499K) and A1503C (E501D)].

CONCLUSIONS

Mutations in the gyrA and gyrB genes are the main mechanisms of GAT resistance. These findings provide new insight into GAT resistance, and contribute to molecular diagnosis of GAT resistance in the clinical setting.

The within-host evolution of antimicrobial resistance in Mycobacterium tuberculosis

Tuberculosis (TB) has been responsible for the greatest number of human deaths due to an infectious disease in general, and due to antimicrobial resistance (AMR) in particular. The etiological agents of human TB are a closely-related group of human-adapted bacteria that belong to the Mycobacterium tuberculosis complex (MTBC). Understanding how MTBC populations evolve within-host may allow for improved TB treatment and control strategies. In this review, we highlight recent works that have shed light on how AMR evolves in MTBC populations within individual patients. We discuss the role of heteroresistance in AMR evolution, and review the bacterial, patient and environmental factors that likely modulate the magnitude of heteroresistance within-host. We further highlight recent works on the dynamics of MTBC genetic diversity within-host, and discuss how spatial substructures in patients' lungs, spatiotemporal heterogeneity in antimicrobial concentrations and phenotypic drug tolerance likely modulates the dynamics of MTBC genetic diversity in patients during treatment. We note the general characteristics that are shared between how the MTBC and other bacterial pathogens evolve in humans, and highlight the characteristics unique to the MTBC.

Four-Month Rifapentine Regimens with or without Moxifloxacin for Tuberculosis

BACKGROUND

Rifapentine-based regimens have potent antimycobacterial activity that may allow for a shorter course in patients with drug-susceptible pulmonary tuberculosis.

METHODS

In an open-label, phase 3, randomized, controlled trial involving persons with newly diagnosed pulmonary tuberculosis from 13 countries, we compared two 4-month rifapentine-based regimens with a standard 6-month regimen consisting of rifampin, isoniazid, pyrazinamide, and ethambutol (control) using a noninferiority margin of 6.6 percentage points. In one 4-month regimen, rifampin was replaced with rifapentine; in the other, rifampin was replaced with rifapentine and ethambutol with moxifloxacin. The primary efficacy outcome was survival free of tuberculosis at 12 months.

RESULTS

Among 2516 participants who had undergone randomization, 2343 had a culture positive for Mycobacterium tuberculosis that was not resistant to isoniazid, rifampin, or fluoroquinolones (microbiologically eligible population; 768 in the control group, 791 in the rifapentine-moxifloxacin group, and 784 in the rifapentine group), of whom 194 were coinfected with human immunodeficiency virus and 1703 had cavitation on chest radiography. A total of 2234 participants could be assessed for the primary outcome (assessable population; 726 in the control group, 756 in the rifapentine-moxifloxacin group, and 752 in the rifapentine group). Rifapentine with moxifloxacin was noninferior to the control in the microbiologically eligible population (15.5% vs. 14.6% had an unfavorable outcome; difference, 1.0 percentage point; 95% confidence interval [CI], -2.6 to 4.5) and in the assessable population (11.6% vs. 9.6%; difference, 2.0 percentage points; 95% CI, -1.1 to 5.1). Noninferiority was shown in the secondary and sensitivity analyses. Rifapentine without moxifloxacin was not shown to be noninferior to the control in either population (17.7% vs. 14.6% with an unfavorable outcome in the microbiologically eligible population; difference, 3.0 percentage points [95% CI, -0.6 to 6.6]; and 14.2% vs. 9.6% in the assessable population; difference, 4.4 percentage points [95% CI, 1.2 to 7.7]). Adverse events of grade 3 or higher occurred during the on-treatment period in 19.3% of participants in the control group, 18.8% in the rifapentine-moxifloxacin group, and 14.3% in the rifapentine group.

CONCLUSIONS

The efficacy of a 4-month rifapentine-based regimen containing moxifloxacin was noninferior to the standard 6-month regimen in the treatment of tuberculosis. (Funded by the Centers for Disease Control and Prevention and others; Study 31/A5349 ClinicalTrials.gov number, NCT02410772.).

Higher Dosing of Rifamycins Does Not Increase Activity against Mycobacterium tuberculosis in the Hollow-Fiber Infection Model

Improvements in the translational value of preclinical models can allow more-successful and more-focused research on shortening the duration of tuberculosis treatment. Although the hollow-fiber infection model (HFIM) is considered a valuable addition to the drug development pipeline, its exact role has not been fully determined yet. Since the strategy of increasing the dose of rifamycins is being evaluated for its treatment-shortening potential, additional in vitro modeling is important. Therefore, we assessed increased dosing of rifampin and rifapentine in our HFIM in order to gain more insight into the place of the HFIM in the drug development pipeline. Total and free-fraction concentrations corresponding to daily dosing of 2.7, 10, and 50 mg of rifampin/kg of body weight, as well as 600 mg and 1,500 mg rifapentine, were assessed in our HFIM using the Mycobacterium tuberculosis H37Rv strain. Drug activity and the emergence of drug resistance were assessed by CFU counting and subsequent mathematical modeling over 14 days, and pharmacokinetic exposures were checked. We found that increasing rifampin exposure above what is expected with the standard dose did not result in higher antimycobacterial activity. For rifapentine, only the highest concentration showed increased activity, but the clinical relevance of this observation is questionable. Moreover, for both drugs, the emergence of resistance was unrelated to exposure. In conclusion, in the simplest experimental setup, the results of the HFIM did not fully correspond to preexisting clinical data. The inclusion of additional parameters and readouts in this preclinical model could be of interest for proper assessment of the translational value of the HFIM.

Predictive modeling to study the treatment-shortening potential of novel tuberculosis drug regimens, towards bundling of preclinical data

BACKGROUND

Given the persistently high global burden of tuberculosis (TB), effective and shorter treatment options are needed. Here, we explore the relationship between relapse and treatment length as well as inter-regimen differences for two novel anti-TB drug regimens using a mouse model of TB infection and mathematical modeling.

METHODS

Mycobacterium tuberculosis-infected mice were treated for up to 13 weeks with bedaquiline and pretomanid combined with moxifloxacin and pyrazinamide (BPaMZ) or linezolid (BPaL). Cure rates were evaluated 12 weeks after treatment completion. The standard regimen of isoniazid, rifampicin, pyrazinamide, and ethambutol (HRZE) was evaluated as a comparator.

RESULTS

Six weeks of BPaMZ was sufficient to cure all mice. In contrast, 13 weeks of BPaL and 24 weeks of HRZE did not achieve 100% cure rates. Based on mathematical model predictions, 95% probability of cure was predicted for BPaMZ, BPaL and HRZE to occur at 1.6, 4.3, and 7.9 months, respectively.

CONCLUSION

This study provides additional evidence for the treatment-shortening capacity of BPaMZ over BPaL and HRZE. To optimally utilize preclinical data for predicting clinical outcomes, and to overcome the limitations that hamper such extrapolation, we advocate bundling of available published preclinical data into mathematical models.

Fourteen-day PET/CT imaging to monitor drug combination activity in treated individuals with tuberculosis

Early bactericidal activity studies monitor daily sputum bacterial counts in individuals with tuberculosis (TB) for 14 days during experimental drug treatment. The rate of change in sputum bacterial load over time provides an informative, but imperfect, estimate of drug activity and is considered a critical step in development of new TB drugs. In this clinical study, 160 participants with TB received isoniazid, pyrazinamide, or rifampicin, components of first-line chemotherapy, and moxifloxacin individually and in combination. In addition to standard bacterial enumeration in sputum, participants underwent 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography and computerized tomography ([18F]FDG-PET/CT) at the beginning and end of the 14-day drug treatment. Quantitating radiological responses to drug treatment provided comparative single and combination drug activity measures across lung lesion types that correlated more closely with established clinical outcomes when combined with sputum enumeration compared to sputum enumeration alone. Rifampicin and rifampicin-containing drug combinations were most effective in reducing both lung lesion volume measured by CT imaging and lesion-associated inflammation measured by PET imaging. Moxifloxacin was not superior to rifampicin in any measure by PET/CT imaging, consistent with its performance in recent phase 3 clinical trials. PET/CT imaging revealed synergy between isoniazid and pyrazinamide and demonstrated that the activity of pyrazinamide was limited to lung lesion, showing the highest FDG uptake during the first 2 weeks of drug treatment. [18F]FDG-PET/CT imaging may be useful for measuring the activity of single drugs and drug combinations during evaluation of potential new TB drug regimens before phase 3 trials.

Two Decades of TB Drug Discovery Efforts—What Have We Learned?

After several years of limited success, an effective regimen for the treatment of both drug-sensitive and multiple-drug-resistant tuberculosis is in place. However, this success is still incomplete, as we need several more novel combinations to treat extensively drug-resistant tuberculosis, as well newer emerging resistance. Additionally, the goal of a shortened therapy continues to evade us. A systematic analysis of the tuberculosis drug discovery approaches employed over the last two decades shows that the lead identification path has been largely influenced by the improved understanding of the biology of the pathogen Mycobacterium tuberculosis. Interestingly, the drug discovery efforts can be grouped into a few defined approaches that predominated over a period of time. This review delineates the key drivers during each of these periods. While doing so, the author’s experiences at AstraZeneca R&D, Bangalore, India, on the discovery of new antimycobacterial candidate drugs are used to exemplify the concept. Finally, the review also discusses the value of validated targets, promiscuous targets, the current anti-TB pipeline, the gaps in it, and the possible way forward.

Evaluation of pharmacokinetic-pharmacodynamic relationships and selection of drug combinations for tuberculosis

AIMS

Despite evidence of the efficacy of anti-tubercular drug regimens in clinical practice, the rationale underpinning the selection of doses and companion drugs for combination therapy remains empirical. Novel methods are needed to optimise the antibacterial activity in combination therapies. A drug-disease modelling framework for rational selection of dose and drug combinations in tuberculosis is presented here.

METHODS

A model-based meta-analysis was performed to assess the antibacterial activity of different combinations in infected mice. Data retrieved from the published literature were analysed using a two-state bacterial growth dynamics model, including fast- and slow-growing bacterial populations. The contribution of each drug to the overall antibacterial activity of the combination was parameterised as relative change to the potency of the backbone drug (EC₅₀ -F and/or EC₅₀ -S). Rifampicin and bedaquiline were selected as paradigm drugs to evaluate the predictive performance of the modelling approach.

RESULTS

Pyrazinamide increased the potency (EC₅₀ -F and EC₅₀ -S) of rifampicin (RZ) and bedaquiline (BZ) by almost two-fold. By contrast, pretomanid and isoniazid were found to worsen the antibacterial activity of BZ and RZ, respectively. Following extrapolation of in vivo pharmacokinetic-pharmacodynamic relationships, the dose of rifampicin showing maximum bactericidal effect in tuberculosis patients was predicted to be 70 mg·kg^-1 when given in combination with pyrazinamide.

CONCLUSIONS

The use of a drug-disease modelling framework may provide a more robust rationale for extrapolation and selection of dose and companion drugs in humans. Our analysis demonstrates that RZ and BZ should be considered as a backbone therapy in prospective novel combination regimens against tuberculosis.

Preclinical models to optimize treatment of tuberculous meningitis - A systematic review

Tuberculous meningitis (TBM) is the most devastating form of TB, resulting in death or neurological disability in up to 50% of patients affected. Treatment is similar to that of pulmonary TB, despite poor cerebrospinal fluid (CSF) penetration of the cornerstone anti-TB drug rifampicin. Considering TBM pathology, it is critical that optimal drug concentrations are reached in the meninges, brain and/or the surrounding CSF. These type of data are difficult to collect in TBM patients. This review aims to identify and describe a preclinical model representative for human TBM which can provide the indispensable data needed for future pharmacological characterization and prioritization of new TBM regimens in the clinical setting. We reviewed existing literature on treatment of TBM in preclinical models: only eight articles, all animal studies, could be identified. None of the animal models completely recapitulated human disease and in most of the animal studies key pharmacokinetic data were missing, making the comparison with human exposure and CNS distribution, and the study of pharmacokinetic-pharmacodynamic relationships impossible. Another 18 articles were identified using other bacteria to induce meningitis with treatment including anti-TB drugs (predominantly rifampicin, moxifloxacin and levofloxacin). Of these articles the pharmacokinetics, i.e. plasma exposure and CSF:plasma ratios, of TB drugs in meningitis could be evaluated. Exposures (except for levofloxacin) agreed with human exposures and also most CSF:plasma ratios agreed with ratios in humans. Considering the lack of an ideal preclinical pharmacological TBM model, we suggest a combination of 1. basic physicochemical drug data combined with 2. in vitro pharmacokinetic and efficacy data, 3. an animal model with adequate pharmacokinetic sampling, microdialysis or imaging of drug distribution, all as a base for 4. physiologically based pharmacokinetic (PBPK) modelling to predict response to TB drugs in treatment of TBM.

Targeting Genome Integrity in Mycobacterium Tuberculosis: From Nucleotide Synthesis to DNA Replication and Repair

Mycobacterium tuberculosis (MTB) is the causative agent of tuberculosis (TB), an ancient disease which still today causes 1.4 million deaths worldwide per year. Long-term, multi-agent anti-tubercular regimens can lead to the anticipated non-compliance of the patient and increased drug toxicity, which in turn can contribute to the emergence of drug-resistant MTB strains that are not susceptible to first- and second-line available drugs. Hence, there is an urgent need for innovative antitubercular drugs and vaccines. A number of biochemical processes are required to maintain the correct homeostasis of DNA metabolism in all organisms. Here we focused on reviewing our current knowledge and understanding of biochemical and structural aspects of relevance for drug discovery, for some such processes in MTB, and particularly DNA synthesis, synthesis of its nucleotide precursors, and processes that guarantee DNA integrity and genome stability. Overall, the area of drug discovery in DNA metabolism appears very much alive, rich of investigations and promising with respect to new antitubercular drug candidates. However, the complexity of molecular events that occur in DNA metabolic processes requires an accurate characterization of mechanistic details in order to avoid major flaws, and therefore the failure, of drug discovery approaches targeting genome integrity.

A Moxifloxacin-based Regimen for the Treatment of Recurrent, Drug-sensitive Pulmonary Tuberculosis: An Open-label, Randomized, Controlled Trial

BACKGROUND

The substitution of moxifloxacin for ethambutol produced promising results for improved tuberculosis treatment outcomes.

METHODS

We conducted an open-label, randomized trial to test whether a moxifloxacin-containing treatment regimen was superior to the standard regimen for the treatment of recurrent tuberculosis. The primary and secondary outcomes were the sputum culture conversion rate at the end of 8 weeks and the proportion of participants with a favorable outcome, respectively.

RESULTS

We enrolled 196 participants; 69.9% were male and 70.4% were co-infected with human immunodeficiency virus (HIV). There was no significant difference between the study groups in the proportion of patients achieving culture conversion at the end of 8 weeks (83.0% [moxifloxacin] vs 78.5% [control]; P = .463); however, the median time to culture conversion was significantly shorter (6.0 weeks, interquartile range [IQR] 4.0-8.3) in the moxifloxacin group than the control group (7.9 weeks, IQR 4.0- 11.4; P = .018). A favorable end-of-treatment outcome was reported in 86 participants (87.8%) in the moxifloxacin group and 93 participants (94.9%) in the control group, for an adjusted absolute risk difference of -5.5 (95% confidence interval -13.8 to 2.8; P = .193) percentage points. There were significantly higher proportions of participants with Grade 3 or 4 adverse events (43.9% [43/98] vs 25.5% [25/98]; P = .01) and serious adverse events (27.6% [27/98] vs 12.2% [12/98]; P = .012) in the moxifloxacin group.

CONCLUSIONS

The replacement of ethambutol with moxifloxacin did not significantly improve either culture conversion rates at the end of 8 weeks or treatment success, and was associated with a higher incidence of adverse events.

CLINICAL TRIALS REGISTRATION

NCT02114684.

Intracellular Pharmacodynamic Modeling Is Predictive of the Clinical Activity of Fluoroquinolones against Tuberculosis

Clinical studies of new antitubercular drugs are costly and time-consuming. Owing to the extensive tuberculosis (TB) treatment periods, the ability to identify drug candidates based on their predicted clinical efficacy is vital to accelerate the pipeline of new therapies. Recent failures of preclinical models in predicting the activity of fluoroquinolones underline the importance of developing new and more robust predictive tools that will optimize the design of future trials.

Clinical studies of new antitubercular drugs are costly and time-consuming. Owing to the extensive tuberculosis (TB) treatment periods, the ability to identify drug candidates based on their predicted clinical efficacy is vital to accelerate the pipeline of new therapies. Recent failures of preclinical models in predicting the activity of fluoroquinolones underline the importance of developing new and more robust predictive tools that will optimize the design of future trials. Here, we used high-content imaging screening and pharmacodynamic intracellular (PDi) modeling to identify and prioritize fluoroquinolones for TB treatment. In a set of studies designed to validate this approach, we show moxifloxacin to be the most effective fluoroquinolone, and PDi modeling-based Monte Carlo simulations accurately predict negative culture conversion (sputum sterilization) rates compared to eight independent clinical trials. In addition, PDi-based simulations were used to predict the risk of relapse. Our analyses show that the duration of treatment following culture conversion can be used to predict the relapse rate. These data further support that PDi-based modeling offers a much-needed decision-making tool for the TB drug development pipeline.

Efficacy and Safety of High-Dose Rifampin in Pulmonary Tuberculosis. A Randomized Controlled Trial

RATIONALE

We examined whether increased rifampin doses could shorten standard therapy for tuberculosis without increased toxicity.

OBJECTIVES

To assess the differences across three daily oral doses of rifampin in change in elimination rate of Mycobacterium tuberculosis in sputum and frequency of rifampin-related adverse events.

METHODS

We conducted a blinded, randomized, controlled phase 2 clinical trial of 180 adults with new smear-positive pulmonary tuberculosis, susceptible to isoniazid and rifampin. We randomized 1:1:1 to rifampin at 10, 15, and 20 mg/kg/d during the intensive phase. We report the primary efficacy and safety endpoints: change in elimination rate of M. tuberculosis log₁₀ colony-forming units and frequency of grade 2 or higher rifampin-related adverse events. We report efficacy by treatment arm and by primary (area under the plasma concentration-time curve [AUC]/minimum inhibitory concentration [MIC]) and secondary (AUC) pharmacokinetic exposure.

MEASUREMENTS AND MAIN RESULTS

Each 5-mg/kg/d increase in rifampin dose resulted in differences of -0.011 (95% confidence interval, -0.025 to +0.002; P = 0.230) and -0.022 (95% confidence interval, -0.046 to -0.002; P = 0.022) log₁₀ cfu/ml/d in the modified intention-to-treat and per-protocol analyses, respectively. The elimination rate in the per-protocol population increased significantly with rifampin AUC_0-6 (P = 0.011) but not with AUC_0-6/MIC_99.9 (P = 0.053). Grade 2 or higher rifampin-related adverse events occurred with similar frequency across the three treatment arms: 26, 31, and 23 participants (43.3%, 51.7%, and 38.3%, respectively) had at least one event (P = 0.7092) up to 4 weeks after the intensive phase. Treatment failed or disease recurred in 11 participants (6.1%).

CONCLUSIONS

Our findings of more rapid sputum sterilization and similar toxicity with higher rifampin doses support investigation of increased rifampin doses to shorten tuberculosis treatment. Clinical trial registered with www.clinicaltrials.gov (NCT 01408914) .

Pharmacokinetic/Pharmacodynamic Background and Methods and Scientific Evidence Base for Dosing of Second-line Tuberculosis Drugs

A World Health Organization workshop systematically examined the evidence base for dosing second-line tuberculosis drugs, identifying knowledge gaps. To fill these in, pharmacokinetics/pharmacodynamics, Monte Carlo experiments, and artificial intelligence algorithms were used in hollow-fiber model studies and clinical data analyses.

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.

Moxifloxacin Replacement in Contemporary Tuberculosis Drug Regimens Is Ineffective against Persistent Mycobacterium tuberculosis in the Cornell Mouse Model

Tuberculosis (TB), which is caused by Mycobacterium tuberculosis, remains a leading killer worldwide, and disease control is hampered by the ineffective control of persistent infections. Substitution of moxifloxacin for isoniazid or ethambutol in standard anti-TB regimens reduces the treatment duration and relapse rates in animal studies, and 4-month regimens were not noninferior in clinical trials. Resuscitation-promoting factor (RPF)-dependent bacilli have recently been implicated in M. tuberculosis persistence. We aimed to investigate the therapeutic effects of the substitution of moxifloxacin for a drug used in the standard drug regimen in eradicating CFU count-positive and RPF-dependent persistent M. tuberculosis using the Cornell murine model. M. tuberculosis-infected mice were treated with regimens in which either isoniazid or ethambutol was replaced by moxifloxacin in the standard regimen. The efficacy of the regimens for bacterial CFU count elimination and removal of persistent tubercle bacilli, evaluated using culture filtrate (CF) derived from M. tuberculosis strain H37Rv, was compared to that of the standard regimen. We also measured disease relapse rates. The regimen in which moxifloxacin replaced isoniazid achieved total organ CFU count clearance at 11 weeks posttreatment, which was faster than that by the standard regimen (14 weeks), and showed a 34% lower relapse rate. The regimen in which moxifloxacin replaced ethambutol was similar to standard regimens in these regards. Importantly, neither the regimen in which moxifloxacin replaced isoniazid or ethambutol nor the standard regimen could remove CF-dependent persistent bacilli. The finding of CF-dependent persistent M. tuberculosis in TB treatment requires confirmation in human studies and has implications for future drug design, testing, and clinical applications.

Double mutation in DNA gyrase confers moxifloxacin resistance and decreased fitness of Mycobacterium smegmatis

Objectives

Ofloxacin and moxifloxacin are the most commonly used fluoroquinolones (FQs) for the treatment of tuberculosis. As a new generation FQ, moxifloxacin has been recommended for the treatment of ofloxacin-resistant TB. However, the mechanism by which ofloxacin-resistant Mycobacterium tuberculosis further gains resistance to moxifloxacin remains unclear.

Methods

We used Mycobacterium smegmatis as a model for studying FQ resistance in M. tuberculosis . Moxifloxacin-resistant M. smegmatis was selected in vitro based on strains with primary ofloxacin resistance. The gyrA and gyrB genes of the resistant strains were sequenced to identify resistance-associated mutations. An in vitro competition assay was applied to explore the influence of gyrA / gyrB mutations on bacterial fitness. Finally, we evaluated the clinical relevance of our findings by analysing the WGS data of 1984 globally collected M. tuberculosis strains.

Results

A total of 57 moxifloxacin-resistant M. smegmatis strains based on five ofloxacin-resistant strains were obtained. Sequencing results revealed that all moxifloxacin-resistant strains harboured second-step mutations in gyrA or gyrB . The relative fitnesses of the double-mutation strains varied from 0.65 to 0.93 and were mostly lower than those of their mono-mutation parents. From the genomic data, we identified 37 clinical M. tuberculosis strains harbouring double mutations in gyrA and/or gyrB and 36 of them carried at least one low-level FQ-resistance mutation.

Conclusions

Double mutation in DNA gyrase leads to moxifloxacin resistance and decreased fitness in M. smegmatis . Under current dosing of moxifloxacin, double mutations mainly happened in M. tuberculosis strains with primary low-level resistance mutations.

Inhibiting Mycobacterium tuberculosis within and without

Tuberculosis remains a scourge of global health with shrinking treatment options due to the spread of drug-resistant strains of Mycobacterium tuberculosis Intensive efforts have been made in the past 15 years to find leads for drug development so that better, more potent drugs inhibiting new targets could be produced and thus shorten treatment duration. Initial attempts focused on repurposing drugs that had been developed for other therapeutic areas but these agents did not meet their goals in clinical trials. Attempts to find new lead compounds employing target-based screens were unsuccessful as the leads were inactive against M. tuberculosis Greater success was achieved using phenotypic screening against live tubercle bacilli and this gave rise to the drugs bedaquiline, pretomanid and delamanid, currently in phase III trials. Subsequent phenotypic screens also uncovered new leads and targets but several of these targets proved to be promiscuous and inhibited by a variety of seemingly unrelated pharmacophores. This setback sparked an interest in alternative screening approaches that mimic the disease state more accurately. Foremost among these were cell-based screens, often involving macrophages, as these should reflect the bacterium's niche in the host more faithfully. A major advantage of this approach is its ability to uncover functions that are central to infection but not necessarily required for growth in vitro For instance, inhibition of virulence functions mediated by the ESX-1 secretion system severely attenuates intracellular M. tuberculosis, preventing intercellular spread and ultimately limiting tissue damage. Cell-based screens have highlighted the druggability of energy production via the electron transport chain and cholesterol metabolism. Here, I review the scientific progress and the pipeline, but warn against over-optimism due to the lack of industrial commitment for tuberculosis drug development and other socio-economic factors.This article is part of the themed issue 'The new bacteriology'.

Preclinical Efficacy Testing of New Drug Candidates

This is a review of the preclinical efficacy testing of new antituberculosis drug candidates. It describes existing dynamic in vitro and in vivo models of antituberculosis chemotherapy and their utility in preclinical evaluations of promising new drugs and combination regimens, with an effort to highlight recent developments. Emphasis is given to the integration of quantitative pharmacokinetic/pharmacodynamic analyses and the impact of lesion pathology on drug efficacy. Discussion also includes in vivo models of chemotherapy of latent tuberculosis infection.

Assessment of Bactericidal Drug Activity and Treatment Outcome in a Mouse Tuberculosis Model Using a Clinical Beijing Strain

Mycobacterium tuberculosis Beijing strains are associated with lower treatment success rates in tuberculosis (TB) patients. In contrast, laboratory strains such as H37Rv are often used in preclinical tuberculosis models. Therefore, we explored the impact of using a clinical Beijing strain on treatment outcome in our mouse tuberculosis model. Additionally, the predictive value of bactericidal activity on treatment outcome was assessed. BALB/c mice were infected with a Beijing strain and treated with one of 10 different combinations of conventional anti-TB drugs. Bactericidal activity was assessed by determining reductions in mycobacterial load after 7, 14, and 28 days and after 2, 3, and 6 months of treatment. Treatment outcome was evaluated after a 6-month treatment course and was based on lung culture status 3 months posttreatment. None of the anti-TB drug regimens tested could achieve 100% treatment success. Treatment outcome depended critically on rifampin. Four non-rifampin-containing regimens showed 0% treatment success compared to success rates between 81 and 95% for six rifampin-containing regimens. Bactericidal activity was predictive only for treatment outcome after 3 months of treatment. Our data advocate the use of multiple mycobacterial strains, including a Beijing strain, to increase the translational value of mouse TB models evaluating treatment outcome. Additionally, our findings support the notion that bactericidal activity in the first 2 months of treatment, as measured in clinical phase IIa/b trials, has limited predictive value for tuberculosis treatment outcome, thus emphasizing the need for better parameters to guide future phase IIII trials.

Applying optimization algorithms to tuberculosis antibiotic treatment regimens

Introduction

Tuberculosis (TB), one of the most common infectious diseases, requires treatment with multiple antibiotics taken over at least 6 months. This long treatment often results in poor patient-adherence, which can lead to the emergence of multi-drug resistant TB. New antibiotic treatment strategies are sorely needed. New antibiotics are being developed or repurposed to treat TB, but as there are numerous potential antibiotics, dosing sizes and potential schedules, the regimen design space for new treatments is too large to search exhaustively. Here we propose a method that combines an agent-based multi-scale model capturing TB granuloma formation with algorithms for mathematical optimization to identify optimal TB treatment regimens.

Methods

We define two different single-antibiotic treatments to compare the efficiency and accuracy in predicting optimal treatment regimens of two optimization algorithms: genetic algorithms (GA) and surrogate-assisted optimization through radial basis function (RBF) networks. We also illustrate the use of RBF networks to optimize double-antibiotic treatments.

Results

We found that while GAs can locate optimal treatment regimens more accurately, RBF networks provide a more practical strategy to TB treatment optimization with fewer simulations, and successfully estimated optimal double-antibiotic treatment regimens.

Conclusions

Our results indicate surrogate-assisted optimization can locate optimal TB treatment regimens from a larger set of antibiotics, doses and schedules, and could be applied to solve optimization problems in other areas of research using systems biology approaches. Our findings have important implications for the treatment of diseases like TB that have lengthy protocols or for any disease that requires multiple drugs.

NOS2-deficient mice with hypoxic necrotizing lung lesions predict outcomes of tuberculosis chemotherapy in humans

During active TB in humans a spectrum of pulmonary granulomas with central necrosis and hypoxia exists. BALB/c mice, predominantly used in TB drug development, do not reproduce this complex pathology thereby inaccurately predicting clinical outcome. We found that Nos2 ^-/- mice incapable of NO-production in immune cells as microbial defence uniformly develop hypoxic necrotizing lung lesions, widely observed in human TB. To study the impact of hypoxic necrosis on the efficacy of antimycobacterials and drug candidates, we subjected Nos2 ^-/- mice with TB to monotherapy before or after establishment of human-like pathology. Isoniazid induced a drug-tolerant persister population only when necrotic lesions were present. Rifapentine was more potent than rifampin prior to development of human-like pathology and equally potent thereafter, in agreement with recent clinical trials. Pretomanid, delamanid and the pre-clinical candidate BTZ043 were bactericidal independent of pulmonary pathology. Linezolid was bacteriostatic in TB-infected Nos2 ^-/- mice but significantly improved lung pathology. Hypoxic necrotizing lesions rendered moxifloxacin less active. In conclusion, Nos2 ^-/- mice are a predictive TB drug development tool owing to their consistent development of human-like pathology.

Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach

Granulomas are complex lung lesions that are the hallmark of tuberculosis (TB). Understanding antibiotic dynamics within lung granulomas will be vital to improving and shortening the long course of TB treatment. Three fluoroquinolones (FQs) are commonly prescribed as part of multi-drug resistant TB therapy: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, insufficient data are available to support selection of one FQ over another, or to show that these drugs are clinically equivalent. To predict the efficacy of MXF, LVX and GFX at a single granuloma level, we integrate computational modeling with experimental datasets into a single mechanistic framework, GranSim. GranSim is a hybrid agent-based computational model that simulates granuloma formation and function, FQ plasma and tissue pharmacokinetics and pharmacodynamics and is based on extensive in vitro and in vivo data. We treat in silico granulomas with recommended daily doses of each FQ and compare efficacy by multiple metrics: bacterial load, sterilization rates, early bactericidal activity and efficacy under non-compliance and treatment interruption. GranSim reproduces in vivo plasma pharmacokinetics, spatial and temporal tissue pharmacokinetics and in vitro pharmacodynamics of these FQs. We predict that MXF kills intracellular bacteria more quickly than LVX and GFX due in part to a higher cellular accumulation ratio. We also show that all three FQs struggle to sterilize non-replicating bacteria residing in caseum. This is due to modest drug concentrations inside caseum and high inhibitory concentrations for this bacterial subpopulation. MXF and LVX have higher granuloma sterilization rates compared to GFX; and MXF performs better in a simulated non-compliance or treatment interruption scenario. We conclude that MXF has a small but potentially clinically significant advantage over LVX, as well as LVX over GFX. We illustrate how a systems pharmacology approach combining experimental and computational methods can guide antibiotic selection for TB.

Tuberculosis (TB) is caused by infection with the bacterium Mycobacterium tuberculosis (Mtb) and kills 1.5 million people each year. TB requires at least 6 months of treatment with up to four drugs, and is characterized by formation of granulomas in patient lungs. Granulomas are spherical collections of host cells and bacteria. Fluoroquinolones (FQs) are a class of drug that could help shorten TB treatment. Three FQs that are used to treat TB are: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, it is unclear if one FQ is better than the others at treating TB, in part because little is known about how these drugs distribute and work inside the lung granulomas. We use computer simulations of Mtb infection and FQ treatment within granulomas to predict which FQ is better and why. Our computer model is calibrated to multiple experimental data sets. We compare the three FQs by multiple metrics, and predict that MXF is better than LVX and GFX because it kills bacteria more quickly, and it works better when patients miss doses. However, all three FQs are unable to kill a part of the bacterial population living in the center of granulomas. Our results can now inform future experimental studies.

A Review of Moxifloxacin for the Treatment of Drug-Susceptible Tuberculosis

Moxifloxacin, an 8-methoxy quinolone, is an important drug in the treatment of multidrug-resistant tuberculosis and is being investigated in novel drug regimens with pretomanid, bedaquiline, and pyrazinamide, or rifapentine, for the treatment of drug-susceptible tuberculosis. Early results of these studies are promising. Although current evidence does not support the use of moxifloxacin in treatment-shortening regimens for drug-susceptible tuberculosis, it may be recommended in patients unable to tolerate standard first-line drug regimens or for isoniazid monoresistance. Evidence suggests that the standard 400-mg dose of moxifloxacin used in the treatment of tuberculosis may be suboptimal in some patients, leading to worse tuberculosis treatment outcomes and emergence of drug resistance. Furthermore, a drug interaction with the rifamycins results in up to 31% reduced plasma concentrations of moxifloxacin when these are combined for treatment of drug-susceptible tuberculosis, although the clinical relevance of this interaction is unclear. Moxifloxacin exhibits extensive interindividual pharmacokinetic variability. Higher doses of moxifloxacin may be needed to achieve drug exposures required for improved clinical outcomes. Further study is, however, needed to determine the safety of proposed higher doses and clinically validated targets for drug exposure to moxifloxacin associated with improved tuberculosis treatment outcomes. We discuss in this review the evidence for the use of moxifloxacin in drug-susceptible tuberculosis and explore the role of moxifloxacin pharmacokinetics, pharmacodynamics, and drug interactions with rifamycins, on tuberculosis treatment outcomes when used in first-line tuberculosis drug regimens.

Chemotherapy of Tuberculosis

The management of tuberculosis (TB) can be a challenging process that has implications both for the affected patient and public health. Effective anti-TB chemotherapy both cures and renders the patient noncontagious. Biological factors specific to M. tuberculosis necessitate the use of multiple drugs for prolonged durations to adequately eradicate infection. Recommended regimens address the complexities of eliminating organisms from diverse reservoirs while preventing the emergence of drug resistance. First-line anti-TB therapy for drug susceptible disease effectively cures almost all patients within 6-9 months. The loss of first-line agents, due to resistance or intolerance, necessitates lengthy treatment courses, frequently 12-18 months or longer. Due to the long treatment times and the implications of missed doses, directly-observed therapy (DOT) is considered the standard of care. Drugs used for the treatment of TB have serious potential toxicities that require close monitoring and prompt response. A strong public health infrastructure and robust social supports are important elements to assure successful treatment. These numerous factors compel public health entities to take a lead role in the management of TB, either through the direct management of TB treatment or by assuring the activities of partner organizations.

Current Status of Fluoroquinolone Use for Treatment of Tuberculosis in a Tertiary Care Hospital in Korea

Background

Fluoroquinolones are considered important substitutes for the treatment of tuberculosis. This study investigates the current status of fluoroquinolone for the treatment of tuberculosis.

Methods

In 2009, a retrospective analysis was performed at one tertiary referral center for 953 patients diagnosed with tuberculosis.

Results

A total of 226 patients (23.6%), who received fluoroquinolone at any time during treatment for tuberculosis, were enrolled in this study. The most common reasons for fluoroquinolone use were adverse events due to other anti-tuberculosis drugs (52.7%), drug resistance (23.5%), and underlying diseases (16.8%). Moxifloxacin (54.0%, 122/226) was the most commonly administered fluoroquinolone, followed by levofloxacin (36.3%, 82/226) and ofloxacin (9.7%, 22/226). The frequency of total adverse events from fluoroquinolone-containing anti-tuberculosis medication was 22.6%, whereas fluoroquinolone-related adverse events were estimated to be 2.2% (5/226). The most common fluoroquinolone-related adverse events were gastrointestinal problems (3.5%, 8/226). There were no significant differences in the treatment success rate between the fluoroquinolone and fluoroquinolone-naïve groups (78.3% vs. 78.4%, respectively).

Conclusion

At our institution, fluoroquinolones are commonly used for the treatment of both multidrug-resistant tuberculosis and susceptible tuberculosis, especially as a substitute for adverse event-related drugs. Considering the low adverse event rates and the comparable treatment success rates, fluoroquinolones seem to be an invaluable drug for the treatment of tuberculosis.

Pharmacokinetic-Pharmacodynamic modelling of intracellular Mycobacterium tuberculosis growth and kill rates is predictive of clinical treatment duration

Tuberculosis (TB) treatment is long and complex, typically involving a combination of drugs taken for 6 months. Improved drug regimens to shorten and simplify treatment are urgently required, however a major challenge to TB drug development is the lack of predictive pre-clinical tools. To address this deficiency, we have adopted a new high-content imaging-based approach capable of defining the killing kinetics of first line anti-TB drugs against intracellular Mycobacterium tuberculosis (Mtb) residing inside macrophages. Through use of this pharmacokinetic-pharmacodynamic (PK-PD) approach we demonstrate that the killing dynamics of the intracellular Mtb sub-population is critical to predicting clinical TB treatment duration. Integrated modelling of intracellular Mtb killing alongside conventional extracellular Mtb killing data, generates the biphasic responses typical of those described clinically. Our model supports the hypothesis that the use of higher doses of rifampicin (35 mg/kg) will significantly reduce treatment duration. Our described PK-PD approach offers a much needed decision making tool for the identification and prioritisation of new therapies which have the potential to reduce TB treatment duration.

Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time

The current drug regimens for treating tuberculosis are lengthy and onerous, and hence complicated by poor adherence leading to drug resistance and disease relapse. Previously, using an output-driven optimization platform and an in vitro macrophage model of Mycobacterium tuberculosis infection, we identified several experimental drug regimens among billions of possible drug-dose combinations that outperform the current standard regimen. Here we use this platform to optimize the in vivo drug doses of two of these regimens in a mouse model of pulmonary tuberculosis. The experimental regimens kill M. tuberculosis much more rapidly than the standard regimen and reduce treatment time to relapse-free cure by 75%. Thus, these regimens have the potential to provide a markedly shorter course of treatment for tuberculosis in humans. As these regimens omit isoniazid, rifampicin, fluoroquinolones and injectable aminoglycosides, they would be suitable for treating many cases of multidrug and extensively drug-resistant tuberculosis.

Current antibiotic therapies for tuberculosis are lengthy and onerous. Here, the authors use an output-driven approach to optimize drug doses for two experimental drug regimens in a mouse model of tuberculosis, leading to improved regimens that reduce treatment time by 75%.

The role of moxifloxacin in tuberculosis therapy

Tuberculosis (TB) remains a global threat with more than 9 million new infections. Treatment remains difficult and there has been no change in the duration of the standard regimen since the early 1980s. Moreover, many patients are unable to tolerate this treatment and discontinue therapy, increasing the risk of resistance. There is a growing tide of multidrug resistance and few effective antibiotics to tackle the problem. Since the turn of the millennium there has been a surge in interest in developing new therapies for TB and a number of new drugs have been developed. In this review the repurposing of moxifloxacin, an 8-methoxy-fluoroquinolone, for TB treatment is discussed. The evidence that underpins the development of this agent is reviewed. The results of the recently completed phase III trials are summarised and the reasons for the unexpected outcome are explored. Finally, the design of new trials that incorporate moxifloxacin, and that address both susceptible disease and multidrug resistance, is described.