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

Levofloxacin activity at increasing doses in a murine model of fluoroquinolone-susceptible and -resistant tuberculosis

High-dose levofloxacin was explored in a clinical trial against multidrug-resistant tuberculosis and failed to show increased efficacy. In this study, we used a murine model to explore the efficacy of a dose increase in levofloxacin monotherapy beyond the maximum dose evaluated in humans. A total of 120 4-week-old female BALB/c mice were intravenously infected with 106 CFU of Mycobacterium tuberculosis H37Rv wild-type (WT) or isogenic H37Rv mutants harboring GyrA A90V or D94G substitutions; the MICs were 0.25, 4, and 6 µg/mL, respectively. Levofloxacin 250 and 500 mg/kg were given every 12 h (q12h) orally for 4 weeks. Pharmacokinetic parameters were determined after five doses. These two regimens decreased lung bacillary load in mice infected with H37Rv WT but not in mice infected with the A90V and D94G mutants. Levofloxacin 250 mg/kg q12h in mice generated pharmacokinetic parameters equivalent to 1,000 mg/d in humans, whereas 500 mg/kg q12h generated a twofold greater exposure than the highest equivalent dose tested in humans (1,500 mg/d). In our dose-response model, the effective concentration at 50% (EC50) produced an AUC/MIC (AUC0-24h/MIC) ratio of 167.9 ± 27.5, and at EC80 it was 281.2 ± 97.3. Based on this model, high-dose levofloxacin regimens above 1,000 mg/d are not expected to cause a significant increase in bactericidal activity. This study suggests no benefit of high-dose levofloxacin above 1,000 mg/d in the treatment of fluoroquinolone-susceptible or -resistant tuberculosis.

Extrapolation of lung pharmacokinetics of antitubercular drugs from preclinical species to humans using PBPK modelling

OBJECTIVES

To develop physiologically based pharmacokinetic (PBPK) models for widely used anti-TB drugs, namely rifampicin, pyrazinamide, isoniazid, ethambutol and moxifloxacin lung pharmacokinetics (PK)-regarding both healthy and TB-infected tissue (cellular lesion and caseum)-in preclinical species and to extrapolate to humans.

METHODS

Empirical models were used for the plasma PK of each species, which were connected to multicompartment permeability-limited lung models within a middle-out PBPK approach with an appropriate physiological parameterization that was scalable across species. Lung's extracellular water (EW) was assumed to be the linking component between healthy and infected tissue, while passive diffusion was assumed for the drug transferring between cellular lesion and caseum.

RESULTS

In rabbits, optimized unbound fractions in intracellular water of rifampicin, moxifloxacin and ethambutol were 0.015, 0.056 and 0.08, respectively, while the optimized unbound fractions in EW of pyrazinamide and isoniazid in mice were 0.25 and 0.17, respectively. In humans, all mean extrapolated daily AUC and Cmax values of various lung regions were within 2-fold of the observed ones. Unbound concentrations in the caseum were lower than unbound plasma concentrations for both rifampicin and moxifloxacin. For rifampicin, unbound concentrations in cellular rim are slightly lower, while for moxifloxacin they are significantly higher than unbound plasma concentrations.

CONCLUSIONS

The developed PBPK approach was able to extrapolate lung PK from preclinical species to humans and to predict unbound concentrations in the various TB-infected regions, unlike empirical lung models. We found that plasma free drug PK is not always a good surrogate for TB-infected tissue unbound PK.

Liposome-siderophore conjugates loaded with moxifloxacin serve as a model for drug delivery against Mycobacterium tuberculosis

Tuberculosis (TB) is an infectious disease that annually affects millions of people, and resistance to available antibiotics has exacerbated this situation. Another notable characteristic of Mycobacterium tuberculosis, the primary causative agent of TB, is its ability to survive inside macrophages, a key component of the immune system. In our quest for an effective and safe treatment that facilitates the targeted delivery of antibiotics to the site of infection, we have proposed a nanotechnology approach based on an iron chelator. Iron chelators are the primary mechanism by which bacteria acquire iron, a metal essential for their metabolism. Four liposomes were synthesized and characterized using the dynamic light scattering technique (DLS), nanoparticle tracking analysis (NTA), and transmission electron microscopy (TEM). All of these methods revealed the presence of spherical particles, approximately 200 nm in size. NTA indicated a concentration of around 1011 particles/mL. We also developed and validated a high-performance liquid chromatography method for quantifying Moxifloxacin to determine encapsulation efficiency (EE) and release profiles (RF). The EE was 51.31 % for LipMox and 45.76 % for LipIchMox. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) confirmed the phagocytosis of liposomal vesicles by macrophages. Functionalizing liposomes with iron chelators can offer significant benefits for TB treatment, such as targeted drug delivery to intracellular bacilli through the phagocytosis of liposomal particles by cells like macrophages.

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.

A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model comparisons

Computational models of disease progression have been constructed for a myriad of pathologies. Typically, the conceptual implementation for pathology-related in-silico intervention studies has been ad-hoc and similar in design to experimental studies. We introduce a multi-scale interventional design (MID) framework toward two key goals: tracking of disease dynamics from within-body to patient to population scale; and tracking impact(s) of interventions across these same spatial scales. Our MID framework prioritizes investigation of impact on individual patients within virtual pre-clinical trials, instead of replicating the design of experimental studies. We apply a MID framework to develop, organize, and analyze a cohort of virtual patients for the study of tuberculosis (TB) as an example disease. For this study, we use HostSim: our next-generation whole patient-scale computational model of individuals infected with Mycobacterium tuberculosis. HostSim captures infection within lungs by tracking multiple granulomas, together with dynamics occurring with blood and lymph node compartments, the compartments involved during pulmonary TB. We extend HostSim to include a simple drug intervention as an example of our approach and use our MID framework to quantify the impact of treatment at cellular and tissue (granuloma), patient (lungs, lymph nodes and blood), and population scales. Sensitivity analyses allow us to determine which features of virtual patients are the strongest predictors of intervention efficacy across scales. These insights allow us to identify patient-heterogeneous mechanisms that drive outcomes across scales.

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.

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.

Concomitant immunity to M. tuberculosis infection

Some persistent infections provide a level of immunity that protects against reinfection with the same pathogen, a process referred to as concomitant immunity. To explore the phenomenon of concomitant immunity during Mycobacterium tuberculosis infection, we utilized HostSim, a previously published virtual host model of the immune response following Mtb infection. By simulating reinfection scenarios and comparing with data from non-human primate studies, we propose a hypothesis that the durability of a concomitant immune response against Mtb is intrinsically tied to levels of tissue resident memory T cells (Trms) during primary infection, with a secondary but important role for circulating Mtb-specific T cells. Further, we compare HostSim reinfection experiments to observational TB studies from the pre-antibiotic era to predict that the upper bound of the lifespan of resident memory T cells in human lung tissue is likely 2-3 years. To the authors' knowledge, this is the first estimate of resident memory T-cell lifespan in humans. Our findings are a first step towards demonstrating the important role of Trms in preventing disease and suggest that the induction of lung Trms is likely critical for vaccine success.

Systems biology approaches to investigate the role of granulomas in TB-HIV coinfection

The risk of active tuberculosis disease is 15-21 times higher in those coinfected with human immunodeficiency virus-1 (HIV) compared to tuberculosis alone, and tuberculosis is the leading cause of death in HIV+ individuals. Mechanisms driving synergy between Mycobacterium tuberculosis (Mtb) and HIV during coinfection include: disruption of cytokine balances, impairment of innate and adaptive immune cell functionality, and Mtb-induced increase in HIV viral loads. Tuberculosis granulomas are the interface of host-pathogen interactions. Thus, granuloma-based research elucidating the role and relative impact of coinfection mechanisms within Mtb granulomas could inform cohesive treatments that target both pathogens simultaneously. We review known interactions between Mtb and HIV, and discuss how the structure, function and development of the granuloma microenvironment create a positive feedback loop favoring pathogen expansion and interaction. We also identify key outstanding questions and highlight how coupling computational modeling with in vitro and in vivo efforts could accelerate Mtb-HIV coinfection discoveries.

The treatment effect of Levofloxacin, Moxifloxacin, and Gatifloxacin contained in the conventional therapy regimen for pulmonary tuberculosis: Systematic review and network meta-analysis

BACKGROUND

Tuberculosis (TB) is one of the serious epidemics that highly threaten the global public health. To explore the treatment effect of Levofloxacin, Moxifloxacin, and Gatifloxacin contained in the conventional therapy regimen for pulmonary tuberculosis.

METHODS

Medline, PubMed, Embase, and Cochrane Library were searched with the keyword such as "Levofloxacin," "Moxifloxacin," "Gatifloxacin," and "tuberculosis", through June 1992 to 2017. According to the inclusion and exclusion criteria, 2 researchers independently screened the literature, extracted the data, and evaluated the quality of the included studies. The Cochrane system was evaluated by RevMan5.2 and the network meta-analysis was performed by Stata 15.

RESULTS

A total of 891 studies were included, with a total of 6565 patients. The results of network meta-analysis showed that Moxifloxacin + conventional therapy (CT) regimen was superior to CT regimen only on the spectrum culture negative. Both Levofloxacin + CT and Moxifloxacin + CT were superior to the CT regimen in treatment success rate. For the adverse events, the Levofloxacin + CT showed much safer results than CT group, while Moxifloxacin + CT had more adverse events than CT group.

CONCLUSION

Levofloxacin, Moxifloxacin, and Gatifloxacin have different superiority, comparing to CT regimen in spectrum culture negative, treatment success rate, and adverse events. Hence, combined utilization of these quinolone is important on the clinical treatment for tuberculosis.

Pharmacometrics in tuberculosis: progress and opportunities

Tuberculosis remains one of the leading causes of death by a communicable agent, infecting up to one-quarter of the world's population, predominantly in disadvantaged communities. Pharmacometrics employs quantitative mathematical models to describe the relationships between pharmacokinetics and pharmacodynamics, and to predict drug doses, exposures, and responses. Pharmacometric approaches have provided a scientific basis for improved dosing of antituberculosis drugs and concomitantly administered antiretrovirals at the population level. The development of modelling frameworks including physiologically-based pharmacokinetics, quantitative systems pharmacology and machine learning provides an opportunity to extend the role of pharmacometrics to in silico quantification of drug-drug interactions, prediction of doses for special populations, dose optimization and individualization, and understanding the complex exposure-response relationships of multidrug regimens in terms of both efficacy and safety, informing regimen design for future study. In this short clinically-focused review, we explore what has been done, and what opportunities exist for pharmacometrics to impact tuberculosis pharmacotherapy.

A high content screening assay for discovery of antimycobacterial compounds based on primary human macrophages infected with virulent Mycobacterium tuberculosis

Drug resistance in Mycobacterium tuberculosis is an emerging threat that makes the discovery of new candidate drugs a priority. In particular, drugs with high sterilizing activity within host cells are needed to improve efficacy and reduce treatment duration. We aimed to develope and validate a High Content Screening assay based on Mycobacterium tuberculosis-infected primary human monocyte-derived macrophages as its natural reservoir. Infected primary human monocyte-derived macrophages were exposed to control antibiotics or tested compounds on 384 well plates. Intracellular bacterial growth and macrophage numbers were evaluated using an ImageXpress High Content Screening system and Z'-factor was calculated to assess the reproducibility. The combination of isoniazid and rifampicin as a positive control rendered a Z'-factor above 0.4, demonstrating suitability of the assay for screening and compound profiling purposes. In a validation experiment, isoniazid, rifampicin, moxifloxacin and levofloxacin all effectively inhibited intracellular growth as expected. Finally, a pilot screening campaign including 5700 compounds from diverse libraries resulted in the identification of three compounds with confirmed antimycobacterial activity in the low micromolar range and low host cell toxicity. The assay represents an attractive screening platform for both academic research on host-pathogen mechanisms in tuberculosis and for the identification and characterization of novel antimycobacterial compounds.

Moxifloxacin Pharmacokinetics, Cardiac Safety, and Dosing for the Treatment of Rifampicin-Resistant Tuberculosis in Children

BACKGROUND

Moxifloxacin is a recommended drug for rifampin-resistant tuberculosis (RR-TB) treatment, but there is limited pediatric pharmacokinetic and safety data, especially in young children. We characterize moxifloxacin population pharmacokinetics and QT interval prolongation and evaluate optimal dosing in children with RR-TB.

METHODS

Pharmacokinetic data were pooled from 2 observational studies in South African children with RR-TB routinely treated with oral moxifloxacin once daily. The population pharmacokinetics and Fridericia-corrected QT (QTcF)-interval prolongation were characterized in NONMEM. Pharmacokinetic simulations were performed to predict expected exposure and optimal weight-banded dosing.

RESULTS

Eighty-five children contributed pharmacokinetic data (median [range] age of 4.6 [0.8-15] years); 16 (19%) were aged <2 years, and 8 (9%) were living with human immunodeficiency virus (HIV). The median (range) moxifloxacin dose on pharmacokinetic sampling days was 11 mg/kg (6.1 to 17). Apparent clearance was 6.95 L/h for a typical 16-kg child. Stunting and HIV increased apparent clearance. Crushed or suspended tablets had faster absorption. The median (range) maximum change in QTcF after moxifloxacin administration was 16.3 (-27.7 to 61.3) ms. No child had QTcF ≥500 ms. The concentration-QTcF relationship was nonlinear, with a maximum drug effect (Emax) of 8.80 ms (interindividual variability = 9.75 ms). Clofazimine use increased Emax by 3.3-fold. Model-based simulations of moxifloxacin pharmacokinetics predicted that current dosing recommendations are too low in children.

CONCLUSIONS

Moxifloxacin doses above 10-15 mg/kg are likely required in young children to match adult exposures but require further safety assessment, especially when coadministered with other QT-prolonging agents.

A virtual host model of Mycobacterium tuberculosis infection identifies early immune events as predictive of infection outcomes

Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the world's deadliest infectious diseases and remains a significant global health burden. TB disease and pathology can present clinically across a spectrum of outcomes, ranging from total sterilization of infection to active disease. Much remains unknown about the biology that drives an individual towards various clinical outcomes as it is challenging to experimentally address specific mechanisms driving clinical outcomes. Furthermore, it is unknown whether numbers of immune cells in the blood accurately reflect ongoing events during infection within human lungs. Herein, we utilize a systems biology approach by developing a whole-host model of the immune response to Mtb across multiple physiologic and time scales. This model, called HostSim, tracks events at the cellular, granuloma, organ, and host scale and represents the first whole-host, multi-scale model of the immune response following Mtb infection. We show that this model can capture various aspects of human and non-human primate TB disease and predict that biomarkers in the blood may only faithfully represent events in the lung at early time points after infection. We posit that HostSim, as a first step toward personalized digital twins in TB research, offers a powerful computational tool that can be used in concert with experimental approaches to understand and predict events about various aspects of TB disease and therapeutics.

Micro-Computed Tomography Analysis of the Human Tuberculous Lung Reveals Remarkable Heterogeneity in Three-dimensional Granuloma Morphology

Rationale: Our current understanding of tuberculosis (TB) pathophysiology is limited by a reliance on animal models, the paucity of human TB lung tissue, and traditional histopathological analysis, a destructive two-dimensional approach that provides limited spatial insight. Determining the three-dimensional (3D) structure of the necrotic granuloma, a characteristic feature of TB, will more accurately inform preventive TB strategies.Objectives: To ascertain the 3D shape of the human tuberculous granuloma and its spatial relationship with airways and vasculature within large lung tissues.Methods: We characterized the 3D microanatomical environment of human tuberculous lungs by using micro computed tomography, histopathology, and immunohistochemistry. By using 3D segmentation software, we accurately reconstructed TB granulomas, vasculature, and airways in three dimensions and confirmed our findings by using histopathology and immunohistochemistry.Measurements and Main Results: We observed marked heterogeneity in the morphology, volume, and number of TB granulomas in human lung sections. Unlike depictions of granulomas as simple spherical structures, human necrotic granulomas exhibit complex, cylindrical, branched morphologies that are connected to the airways and shaped by the bronchi. The use of 3D imaging of human TB lung sections provides unanticipated insight into the spatial organization of TB granulomas in relation to the airways and vasculature.Conclusions: Our findings highlight the likelihood that a single, structurally complex lesion could be mistakenly viewed as multiple independent lesions when evaluated in two dimensions. In addition, the lack of vascularization within obstructed bronchi establishes a paradigm for antimycobacterial drug tolerance. Lastly, our results suggest that bronchogenic spread of Mycobacterium tuberculosis reseeds the lung.

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.

One Size Fits All? Not in In Vivo Modeling of Tuberculosis Chemotherapeutics

Tuberculosis (TB) remains a global health problem despite almost universal efforts to provide patients with highly effective chemotherapy, in part, because many infected individuals are not diagnosed and treated, others do not complete treatment, and a small proportion harbor Mycobacterium tuberculosis (Mtb) strains that have become resistant to drugs in the standard regimen. Development and approval of new drugs for TB have accelerated in the last 10 years, but more drugs are needed due to both Mtb's development of resistance and the desire to shorten therapy to 4 months or less. The drug development process needs predictive animal models that recapitulate the complex pathology and bacterial burden distribution of human disease. The human host response to pulmonary infection with Mtb is granulomatous inflammation usually resulting in contained lesions and limited bacterial replication. In those who develop progressive or active disease, regions of necrosis and cavitation can develop leading to lasting lung damage and possible death. This review describes the major vertebrate animal models used in evaluating compound activity against Mtb and the disease presentation that develops. Each of the models, including the zebrafish, various mice, guinea pigs, rabbits, and non-human primates provides data on number of Mtb bacteria and pathology resolution. The models where individual lesions can be dissected from the tissue or sampled can also provide data on lesion-specific bacterial loads and lesion-specific drug concentrations. With the inclusion of medical imaging, a compound's effect on resolution of pathology within individual lesions and animals can also be determined over time. Incorporation of measurement of drug exposure and drug distribution within animals and their tissues is important for choosing the best compounds to push toward the clinic and to the development of better regimens. We review the practical aspects of each model and the advantages and limitations of each in order to promote choosing a rational combination of them for a compound's development.

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs

Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

Model-Informed Drug Development for Anti-Infectives: State of the Art and Future

Model-informed drug development (MIDD) has a long and rich history in infectious diseases. This review describes foundational principles of translational anti-infective pharmacology, including choice of appropriate measures of exposure and pharmacodynamic (PD) measures, patient subpopulations, and drug-drug interactions. Examples are presented for state-of-the-art, empiric, mechanistic, interdisciplinary, and real-world evidence MIDD applications in the development of antibacterials (review of minimum inhibitory concentration-based models, mechanism-based pharmacokinetic/PD (PK/PD) models, PK/PD models of resistance, and immune response), antifungals, antivirals, drugs for the treatment of global health infectious diseases, and medical countermeasures. The degree of adoption of MIDD practices across the infectious diseases field is also summarized. The future application of MIDD in infectious diseases will progress along two planes; "depth" and "breadth" of MIDD methods. "MIDD depth" refers to deeper incorporation of the specific pathogen biology and intrinsic and acquired-resistance mechanisms; host factors, such as immunologic response and infection site, to enable deeper interrogation of pharmacological impact on pathogen clearance; clinical outcome and emergence of resistance from a pathogen; and patient and population perspective. In particular, improved early assessment of the emergence of resistance potential will become a greater focus in MIDD, as this is poorly mitigated by current development approaches. "MIDD breadth" refers to greater adoption of model-centered approaches to anti-infective development. Specifically, this means how various MIDD approaches and translational tools can be integrated or connected in a systematic way that supports decision making by key stakeholders (sponsors, regulators, and payers) across the entire development pathway.

Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.

Integrating Pharmacokinetics and Pharmacodynamics in Operational Research to End Tuberculosis

Tuberculosis (TB) elimination requires innovative approaches. The new Global Tuberculosis Network (GTN) aims to conduct research on key unmet therapeutic and diagnostic needs in the field of TB elimination using multidisciplinary, multisectorial approaches. The TB Pharmacology section within the new GTN aims to detect and study the current knowledge gaps, test potential solutions using human pharmacokinetics informed through preclinical infection systems, and return those findings to the bedside. Moreover, this approach would allow prospective identification and validation of optimal shorter therapeutic durations with new regimens. Optimized treatment using available and repurposed drugs may have an increased impact when prioritizing a person-centered approach and acknowledge the importance of age, gender, comorbidities, and both social and programmatic environments. In this viewpoint article, we present an in-depth discussion on how TB pharmacology and the related strategies will contribute to TB elimination.

CaliPro: A Calibration Protocol That Utilizes Parameter Density Estimation to Explore Parameter Space and Calibrate Complex Biological Models

Introduction

Mathematical and computational modeling have a long history of uncovering mechanisms and making predictions for biological systems. However, to create a model that can provide relevant quantitative predictions, models must first be calibrated by recapitulating existing biological datasets from that system. Current calibration approaches may not be appropriate for complex biological models because: 1) many attempt to recapitulate only a single aspect of the experimental data (such as a median trend) or 2) Bayesian techniques require specification of parameter priors and likelihoods to experimental data that cannot always be confidently assigned. A new calibration protocol is needed to calibrate complex models when current approaches fall short.

Methods

Herein, we develop CaliPro, an iterative, model-agnostic calibration protocol that utilizes parameter density estimation to refine parameter space and calibrate to temporal biological datasets. An important aspect of CaliPro is the user-defined pass set definition, which specifies how the model might successfully recapitulate experimental data. We define the appropriate settings to use CaliPro.

Results

We illustrate the usefulness of CaliPro through four examples including predator-prey, infectious disease transmission, and immune response models. We show that CaliPro works well for both deterministic, continuous model structures as well as stochastic, discrete models and illustrate that CaliPro can work across diverse calibration goals.

Conclusions

We present CaliPro, a new method for calibrating complex biological models to a range of experimental outcomes. In addition to expediting calibration, CaliPro may be useful in already calibrated parameter spaces to target and isolate specific model behavior for further analysis.

Mathematical model and tool to explore shorter multi-drug therapy options for active pulmonary tuberculosis

Standard treatment for active tuberculosis (TB) requires drug treatment with at least four drugs over six months. Shorter-duration therapy would mean less need for strict adherence, and reduced risk of bacterial resistance. A system pharmacology model of TB infection, and drug therapy was developed and used to simulate the outcome of different drug therapy scenarios. The model incorporated human immune response, granuloma lesions, multi-drug antimicrobial chemotherapy, and bacterial resistance. A dynamic population pharmacokinetic/pharmacodynamic (PK/PD) simulation model including rifampin, isoniazid, pyrazinamide, and ethambutol was developed and parameters aligned with previous experimental data. Population therapy outcomes for simulations were found to be generally consistent with summary results from previous clinical trials, for a range of drug dose and duration scenarios. An online tool developed from this model is released as open source software. The TB simulation tool could support analysis of new therapy options, novel drug types, and combinations, incorporating factors such as patient adherence behavior.

A comprehensive in-silico model of pulmonary tuberculosis successfully predicted previous clinical trials and could simulate future therapeutics.

Both Pharmacokinetic Variability and Granuloma Heterogeneity Impact the Ability of the First-Line Antibiotics to Sterilize Tuberculosis Granulomas

Tuberculosis (TB) remains as one of the world's deadliest infectious diseases despite the use of standardized antibiotic therapies. Recommended therapy for drug-susceptible TB is up to 6 months of antibiotics. Factors that contribute to lengthy regimens include antibiotic underexposure in lesions due to poor pharmacokinetics (PK) and complex granuloma compositions, but it is difficult to quantify how individual antibiotics are affected by these factors and to what extent these impact treatments. We use our next-generation multi-scale computational model to simulate granuloma formation and function together with antibiotic pharmacokinetics and pharmacodynamics, allowing us to predict conditions leading to granuloma sterilization. In this work, we focus on how PK variability, determined from human PK data, and granuloma heterogeneity each quantitatively impact granuloma sterilization. We focus on treatment with the standard regimen for TB of four first-line antibiotics: isoniazid, rifampin, ethambutol, and pyrazinamide. We find that low levels of antibiotic concentration due to naturally occurring PK variability and complex granulomas leads to longer granuloma sterilization times. Additionally, the ability of antibiotics to distribute in granulomas and kill different subpopulations of bacteria contributes to their specialization in the more efficacious combination therapy. These results can inform strategies to improve antibiotic therapy for TB.

Multi-scale cellular engineering: From molecules to organ-on-a-chip

Recent technological advances in cellular and molecular engineering have provided new insights into biology and enabled the design, manufacturing, and manipulation of complex living systems. Here, we summarize the state of advances at the molecular, cellular, and multi-cellular levels using experimental and computational tools. The areas of focus include intrinsically disordered proteins, synthetic proteins, spatiotemporally dynamic extracellular matrices, organ-on-a-chip approaches, and computational modeling, which all have tremendous potential for advancing fundamental and translational science. Perspectives on the current limitations and future directions are also described, with the goal of stimulating interest to overcome these hurdles using multi-disciplinary approaches.

The Genetic Background Modulates the Evolution of Fluoroquinolone-Resistance in Mycobacterium tuberculosis

Fluoroquinolones (FQ) form the backbone in experimental treatment regimens against drug-susceptible tuberculosis. However, little is known on whether the genetic variation present in natural populations of Mycobacterium tuberculosis (Mtb) affects the evolution of FQ-resistance (FQ-R). To investigate this question, we used nine genetically distinct drug-susceptible clinical isolates of Mtb and measured their frequency of resistance to the FQ ofloxacin (OFX) in vitro. We found that the Mtb genetic background led to differences in the frequency of OFX-resistance (OFX-R) that spanned two orders of magnitude and substantially modulated the observed mutational profiles for OFX-R. Further, in vitro assays showed that the genetic background also influenced the minimum inhibitory concentration and the fitness effect conferred by a given OFX-R mutation. To test the clinical relevance of our in vitro work, we surveyed the mutational profile for FQ-R in publicly available genomic sequences from clinical Mtb isolates, and found substantial Mtb lineage-dependent variability. Comparison of the clinical and the in vitro mutational profiles for FQ-R showed that 51% and 39% of the variability in the clinical frequency of FQ-R gyrA mutation events in Lineage 2 and Lineage 4 strains, respectively, can be attributed to how Mtb evolves FQ-R in vitro. As the Mtb genetic background strongly influenced the evolution of FQ-R in vitro, we conclude that the genetic background of Mtb also impacts the evolution of FQ-R in the clinic.

An Agent-Based Systems Pharmacology Model of the Antibody-Drug Conjugate Kadcyla to Predict Efficacy of Different Dosing Regimens

The pharmaceutical industry has invested significantly in antibody-drug conjugates (ADCs) with five FDA-approved therapies and several more showing promise in late-stage clinical trials. The FDA-approved therapeutic Kadcyla (ado-trastuzumab emtansine or T-DM1) can extend the survival of patients with tumors overexpressing HER2. However, tumor histology shows that most T-DM1 localizes perivascularly, but coadministration with its unconjugated form (trastuzumab) improves penetration of the ADC into the tumor and subsequent treatment efficacy. ADC dosing schedule, e.g., dose fractionation, has also been shown to improve tolerability. However, it is still not clear how coadministration with carrier doses impacts efficacy in terms of receptor expression, dosing regimens, and payload potency. Here, we develop a hybrid agent-based model (ABM) to capture ADC and/or antibody delivery and to predict tumor killing and growth kinetics. The results indicate that a carrier dose improves efficacy when the increased number of cells targeted by the ADC outweighs the reduced fractional killing of the targeted cells. The threshold number of payloads per cell required for killing plays a pivotal role in defining this cutoff. Likewise, fractionated dosing lowers ADC efficacy due to lower tissue penetration from a reduced maximum plasma concentration. It is only beneficial when an increase in tolerability from fractionation allows a higher ADC/payload dose that more than compensates for the loss in efficacy from fractionation. Overall, the multiscale model enables detailed depictions of heterogeneous ADC delivery, cancer cell death, and tumor growth to show how carrier dosing impacts efficacy to design the most efficacious regimen.

Gatifloxacin Pharmacokinetics/Pharmacodynamics-based Optimal Dosing for Pulmonary and Meningeal Multidrug-resistant Tuberculosis

Background

Gatifloxacin is used for the treatment of multidrug-resistant tuberculosis (MDR-TB). The optimal dose is unknown.

Methods

We performed a 28-day gatifloxacin hollow-fiber system model of tuberculosis (HFS-TB) study in order to identify the target exposures associated with optimal kill rates and resistance suppression. Monte Carlo experiments (MCE) were used to identify the dose that would achieve the target exposure in 10000 adult patients with meningeal or pulmonary MDR-TB. The optimal doses identified were validated using probit analyses of clinical data from 2 prospective clinical trials of patients with pulmonary and meningeal tuberculosis. Classification and regression-tree (CART) analyses were used to identify the gatifloxacin minimum inhibitory concentration (MIC) below which patients failed or relapsed on combination therapy.

Results

The target exposure associated with optimal microbial kill rates and resistance suppression in the HFS-TB was a 0-24 hour area under the concentration-time curve-to-MIC of 184. MCE identified an optimal gatifloxacin dose of 800 mg/day for pulmonary and 1200 mg/day for meningeal MDR-TB, and a clinical susceptibility breakpoint of MIC ≤ 0.5 mg/L. In clinical trials, CART identified that 79% patients failed therapy if MIC was >2 mg/L, but 98% were cured if MIC was ≤0.5 mg/L. Probit analysis of clinical data demonstrated a >90% probability of a cure in patients if treated with 800 mg/day for pulmonary tuberculosis and 1200 mg/day for meningeal tuberculosis. Doses ≤400 mg/day were suboptimal.

Conclusions

Gatifloxacin doses of 800 mg/day and 1200 mg/day are recommended for pulmonary and meningeal MDR-TB treatment, respectively. Gatifloxacin has a susceptible dose-dependent zone at MICs 0.5-2 mg/L.

Agent-based models of inflammation in translational systems biology: A decade later

Agent-based modeling is a rule-based, discrete-event, and spatially explicit computational modeling method that employs computational objects that instantiate the rules and interactions among the individual components ("agents") of system. Agent-based modeling is well suited to translating into a computational model the knowledge generated from basic science research, particularly with respect to translating across scales the mechanisms of cellular behavior into aggregated cell population dynamics manifesting at the tissue and organ level. This capacity has made agent-based modeling an integral method in translational systems biology (TSB), an approach that uses multiscale dynamic computational modeling to explicitly represent disease processes in a clinically relevant fashion. The initial work in the early 2000s using agent-based models (ABMs) in TSB focused on examining acute inflammation and its intersection with wound healing; the decade since has seen vast growth in both the application of agent-based modeling to a wide array of disease processes as well as methodological advancements in the use and analysis of ABM. This report presents an update on an earlier review of ABMs in TSB and presents examples of exciting progress in the modeling of various organs and diseases that involve inflammation. This review also describes developments that integrate the use of ABMs with cutting-edge technologies such as high-performance computing, machine learning, and artificial intelligence, with a view toward the future integration of these methodologies. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Models of Systems Properties and Processes > Organismal Models.

Phenotypic and genotypic characterization of levofloxacin- and moxifloxacin-resistant Mycobacterium tuberculosis clinical isolates in southern China

Background

Levofloxacin (LVX) and Moxifloxacin (MXF) are the cornerstones for treatment of multidrug-resistant tuberculosis (MDR-TB). China is one of the highest MDR- and fluoroquinolones (FQ)-resistant TB burdens countries. DNA gyrase encoded by gyr genes is the main target of FQ in Mycobacterium tuberculosis (MTB). The prevalence and molecular characterization of LVX- and MXF-resistant MTB strains from southern China were examined in this study.

Methods

Drug susceptibility testing (DST) of 400 MTB clinical isolates was evaluated by proportion method on Löwenstein-Jensen (LJ) medium against ten drugs. The sequencing of entire gyrA and gyrB genes and multiplex PCR were performed to distinguish the prevalence of mutant types in Beijing and non-Beijing genotypes.

Results

Three hundred and twenty-one out of four hundred (80.25%) drug-resistant isolates (resistant > one drug) were categorized as 83/321 (25.80%) MDR, 174/321 (54.20%) pre-XDR and 64/321 (19.93%) XDR-MTB. Overall, 303/400 (75.75%) LVX- and 292/400 (73.00%) MXF-resistant (R) MTB strains were identified. Two hundred seventy-one out of three hundred and three (89.43%) resistant strains carried mutations in gyrA and 91/303 (30.03%) in gyrB. Interestingly, 18 novel mutations were detected in gyrA and gyrB genes. Mutations at (A90, D94) and (T500, G510, G512) frequently existed in QRDR(s) of gyrA and gyrB respectively in 286/400 (71.50%) LVX^RMXF^R strains. The novel mutations in- and out-side the QRDR of gyrA (L105R, A126E, M127K, D151T, V165A) and gyrB (D461H, N499S, G520A) increased the sensitivity and consistency of genotypic tests. Notably, 25 LVX^RMXF^R strains were found with unknown resistance mechanisms.

Conclusions

Mutations in QRDR(s) were concomitantly associated with Beijing and non-Beijing genotypes. The prevalence of resistance and cross-resistance between LVX and MXF in MTB isolates from southern China was immensely higher than other countries. Our valuable findings provide the substantial implications to improve the reliability of genotypic diagnostic tests relying on potential resistance conferring mutations in entire gyr genes.

In Vitro Efficacies, ADME, and Pharmacokinetic Properties of Phenoxazine Derivatives Active against Mycobacterium tuberculosis

Mycobacterium tuberculosis, the causative agent of tuberculosis, remains a leading infectious killer globally, demanding the urgent development of faster-acting drugs with novel mechanisms of action. Riminophenazines such as clofazimine are clinically efficacious against both drug-susceptible and drug-resistant strains of M. tuberculosis We determined the in vitro anti-M. tuberculosis activities, absorption, distribution, metabolism, and excretion properties, and in vivo mouse pharmacokinetics of a series of structurally related phenoxazines. One of these, PhX1, displayed promising drug-like properties and potent in vitro efficacy, supporting its further investigation in an M. tuberculosis-infected animal model.

Mycobacterium tuberculosis Metabolism

Mycobacterium tuberculosis is the cause of tuberculosis (TB), a disease which continues to overwhelm health systems in endemic regions despite the existence of effective combination chemotherapy and the widespread use of a neonatal anti-TB vaccine. For a professional pathogen, M. tuberculosis retains a surprisingly large proportion of the metabolic repertoire found in nonpathogenic mycobacteria with very different lifestyles. Moreover, evidence that additional functions were acquired during the early evolution of the M. tuberculosis complex suggests the organism has adapted (and augmented) the metabolic pathways of its environmental ancestor to persistence and propagation within its obligate human host. A better understanding of M. tuberculosis pathogenicity, however, requires the elucidation of metabolic functions under disease-relevant conditions, a challenge complicated by limited knowledge of the microenvironments occupied and nutrients accessed by bacilli during host infection, as well as the reliance in experimental mycobacteriology on a restricted number of experimental models with variable relevance to clinical disease. Here, we consider M. tuberculosis metabolism within the framework of an intimate host-pathogen coevolution. Focusing on recent advances in our understanding of mycobacterial metabolic function, we highlight unusual adaptations or departures from the better-characterized model intracellular pathogens. We also discuss the impact of these mycobacterial "innovations" on the susceptibility of M. tuberculosis to existing and experimental anti-TB drugs, as well as strategies for targeting metabolic pathways. Finally, we offer some perspectives on the key gaps in the current knowledge of fundamental mycobacterial metabolism and the lessons which might be learned from other systems.

Fluoroquinolones in Drug-Resistant Tuberculosis: Culture Conversion and Pharmacokinetic/Pharmacodynamic Target Attainment To Guide Dose Selection

Fluoroquinolones are group A drugs in tuberculosis guidelines. We aim to compare the culture conversion between new-generation (levofloxacin and moxifloxacin) and old-generation (ciprofloxacin and ofloxacin) fluoroquinolones, develop pharmacokinetic models, and calculate target attainment for levofloxacin and moxifloxacin. We included three U.S. tuberculosis centers.

Fluoroquinolones are group A drugs in tuberculosis guidelines. We aim to compare the culture conversion between new-generation (levofloxacin and moxifloxacin) and old-generation (ciprofloxacin and ofloxacin) fluoroquinolones, develop pharmacokinetic models, and calculate target attainment for levofloxacin and moxifloxacin. We included three U.S. tuberculosis centers. Patients admitted between 1984 and 2015, infected with drug-resistant tuberculosis, and who had received fluoroquinolones for ≥28 days were included. Demographics, sputum cultures and susceptibility, treatment regimens, and serum concentrations were collected. A time-to-event analysis was conducted, and Cox proportional hazards model was used to compare the time to culture conversion. Using additional data from ongoing studies, pharmacokinetic modelling and Monte Carlo simulations were performed to assess target attainment for different doses. Overall, 124 patients received fluoroquinolones. The median age was 40 years, and the median weight was 60 kg. Fifty-six patients (45%) received old-generation fluoroquinolones. New-generation fluoroquinolones showed a faster time to culture conversion (median 16 versus 40 weeks, P = 0.012). After adjusting for isoniazid and clofazimine treatment, patients treated with new-generation fluoroquinolones were more likely to have culture conversion (adjusted hazards ratio, 2.16 [95% confidence interval, 1.28 to 3.64]). We included 178 patients in the pharmacokinetic models. Levofloxacin and moxifloxacin were best described by a one-compartment model with first-order absorption and elimination. At least 1,500 to 1,750 mg levofloxacin and 800 mg moxifloxacin may be needed for maximum kill at the current epidemiologic cutoff values. In summary, new-generation fluoroquinolones showed faster time to culture conversion compared to the old generation. For optimal target attainment at the current MIC values, higher doses of levofloxacin and moxifloxacin may be needed.

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.

Fluoroquinolone Efficacy against Tuberculosis Is Driven by Penetration into Lesions and Activity against Resident Bacterial Populations

Fluoroquinolones represent the pillar of multidrug-resistant tuberculosis (MDR-TB) treatment, with moxifloxacin, levofloxacin, or gatifloxacin being prescribed to MDR-TB patients. Recently, several clinical trials of “universal” drug regimens, aiming to treat drug-susceptible and drug-resistant TB, have included a fluoroquinolone.

Fluoroquinolones represent the pillar of multidrug-resistant tuberculosis (MDR-TB) treatment, with moxifloxacin, levofloxacin, or gatifloxacin being prescribed to MDR-TB patients. Recently, several clinical trials of “universal” drug regimens, aiming to treat drug-susceptible and drug-resistant TB, have included a fluoroquinolone. In the absence of clinical data comparing their side-by-side efficacies in controlled MDR-TB trials, a pharmacological rationale is needed to guide the selection of the most efficacious fluoroquinolone. The present studies were designed to test the hypothesis that fluoroquinolone concentrations (pharmacokinetics) and activity (pharmacodynamics) at the site of infection are better predictors of efficacy than the plasma concentrations and potency measured in standard growth inhibition assays and are better suited to determinations of whether one of the fluoroquinolones outperforms the others in rabbits with active TB. We first measured the penetration of these fluoroquinolones in lung lesion compartments, and their potency against bacterial populations that reside in each compartment, to compute lesion-centric pharmacokinetic-pharmacodynamic (PK/PD) parameters. PK modeling methods were used to quantify drug penetration from plasma to tissues at human-equivalent doses. On the basis of these metrics, moxifloxacin emerged with a clear advantage, whereas plasma-based PK/PD favored levofloxacin (the ranges of the plasma AUC/MIC ratio [i.e., the area under the concentration-time curve over 24 h in the steady state divided by the MIC] are 46 to 86 for moxifloxacin and 74 to 258 for levofloxacin). A comparative efficacy trial in the rabbit model of active TB demonstrated the superiority of moxifloxacin in reducing bacterial burden at the lesion level and in sterilizing cellular and necrotic lesions. Collectively, these results show that PK/PD data obtained at the site of infection represent an adequate predictor of drug efficacy against TB and constitute the baseline required to explore synergies, antagonism, and drug-drug interactions in fluoroquinolone-containing regimens.

High-resolution mapping of fluoroquinolones in TB rabbit lesions reveals specific distribution in immune cell types

Understanding the distribution patterns of antibiotics at the site of infection is paramount to selecting adequate drug regimens and developing new antibiotics. Tuberculosis (TB) lung lesions are made of various immune cell types, some of which harbor persistent forms of the pathogen, Mycobacterium tuberculosis. By combining high resolution MALDI MSI with histology staining and quantitative image analysis in rabbits with active TB, we have mapped the distribution of a fluoroquinolone at high resolution, and identified the immune-pathological factors driving its heterogeneous penetration within TB lesions, in relation to where bacteria reside. We find that macrophage content, distance from lesion border and extent of necrosis drive the uneven fluoroquinolone penetration. Preferential uptake in macrophages and foamy macrophages, where persistent bacilli reside, compared to other immune cells present in TB granulomas, was recapitulated in vitro using primary human cells. A nonlinear modeling approach was developed to help predict the observed drug behavior in TB lesions. This work constitutes a methodological advance for the co-localization of drugs and infectious agents at high spatial resolution in diseased tissues, which can be applied to other diseases with complex immunopathology.

Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology

Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long-term control of infection while also preventing an over-zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro- and anti-inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback. Identifying the signals, cells, cytokines, and other immune response factors that mediate this balance over time has been difficult using traditional research strategies. Computational modeling studies based on data from traditional systems can identify how this balance contributes to immunity. Here we provide evidence from both experimental and mathematical/computational studies to support the concept of a dynamic balance operating during persistent and other infection scenarios. We focus mainly on tuberculosis, currently the leading cause of death due to infectious disease in the world, and also provide evidence for other infections. A better understanding of the dynamically balanced immune response can help shape treatment strategies that utilize both drugs and host-directed therapies.

Drug permeation and metabolism in Mycobacterium tuberculosis: Prioritising local exposure as essential criterion in new TB drug development

Anti-tuberculosis (TB) drugs possess diverse abilities to penetrate the different host tissues and cell types in which infecting Mycobacterium tuberculosis bacilli are located during active disease. This is important since there is increasing evidence that the respective "lesion-penetrating" properties of the front-line TB drugs appear to correlate well with their specific activity in standard combination therapy. In turn, these observations suggest that rational efforts to discover novel treatment-shortening drugs and drug combinations should incorporate knowledge about the comparative abilities of both existing and experimental anti-TB agents to access bacilli in defined physiological states at different sites of infection, as well as avoid elimination by efflux or inactivation by host or bacterial metabolism. However, while there is a fundamental requirement to understand the mode of action and pharmacological properties of any current or experimental anti-TB agent within the context of the obligate human host, this is complex and, until recently, has been severely limited by the available methodologies and models. Here, we discuss advances in analytical models and technologies which have enabled investigations of drug metabolism and pharmacokinetics (DMPK) for new TB drug development. In particular, we consider the potential to shift the focus of traditional pharmacokinetic-pharmacodynamic analyses away from plasma to a more specific "site of action" drug exposure as an essential criterion for drug development and the design of dosing strategies. Moreover, in summarising approaches to determine DMPK data for the "unit of infection" comprising host macrophage and intracellular bacillus, we evaluate the potential benefits of including these analyses at an early stage in the preclinical drug development algorithm. © 2018 IUBMB Life, 70(9):926-937, 2018.

The present state of the tuberculosis drug development pipeline

Tuberculosis now ranks as the leading cause of death in the world due to a single infectious agent. Current standard of care treatment can achieve very high cure rates for drug-sensitive disease but requires a 6-month duration of chemotherapy. Drug-resistant disease requires significantly longer treatment durations with drugs associated with a higher risk of adverse events. Thus, there is a pressing need for a drug regimen that is safer, shorter in duration and superior to current front-line chemotherapy in terms of efficacy. The TB drug pipeline contains several candidates that address one or more of the required attributes of chemotherapeutic regimens that may redefine the standard of care of this disease. Several new drugs have been reported and novel targets have been identified allowing regimens containing new compounds to trickle into clinical studies. Furthermore, a recent paradigm-shift in understanding the pharmacokinetics of anti-tubercular drugs is revolutionizing the way we select compounds for clinical progression.

Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems

Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that uses medium-fidelity analysis (Monte Carlo simulations) and/or low-fidelity analysis (Markov chain models) to analyze high-fidelity stochastic model results. Medium-fidelity analysis can produce large numbers of possible rare event trajectories for a single high-fidelity model simulation. This allows prediction of both rare event dynamics and probability distributions at much lower frequencies than high-fidelity models. Low-fidelity analysis can calculate probability distributions for rare events over time for any frequency by updating the probabilities of the rare event state space after each discrete event of the high-fidelity model. To validate the approach, we apply multifidelity analysis to a high-fidelity model of tuberculosis disease. We validate the method against high-fidelity model results and illustrate the application of multifidelity analysis in predicting rare event trajectories, performing sensitivity analyses and extrapolating predictions to very low frequencies in complex systems. We believe that our approach will complement ongoing efforts to enable accurate prediction of rare event dynamics in high-fidelity computational models.

Hit Generation in TB Drug Discovery: From Genome to Granuloma

Current tuberculosis (TB) drug development efforts are not sufficient to end the global TB epidemic. Recent efforts have focused on the development of whole-cell screening assays because biochemical, target-based inhibitor screens during the last two decades have not delivered new TB drugs. Mycobacterium tuberculosis (Mtb), the causative agent of TB, encounters diverse microenvironments and can be found in a variety of metabolic states in the human host. Due to the complexity and heterogeneity of Mtb infection, no single model can fully recapitulate the in vivo conditions in which Mtb is found in TB patients, and there is no single “standard” screening condition to generate hit compounds for TB drug development. However, current screening assays have become more sophisticated as researchers attempt to mirror the complexity of TB disease in the laboratory. In this review, we describe efforts using surrogates and engineered strains of Mtb to focus screens on specific targets. We explain model culture systems ranging from carbon starvation to hypoxia, and combinations thereof, designed to represent the microenvironment which Mtb encounters in the human body. We outline ongoing efforts to model Mtb infection in the lung granuloma. We assess these different models, their ability to generate hit compounds, and needs for further TB drug development, to provide direction for future TB drug discovery.