Mechanistic Modeling of Mycobacterium tuberculosis Infection in Murine Models for Drug and Vaccine Efficacy Studies

Emergence of antibiotic-specific Mycobacterium tuberculosis phenotypes during prolonged treatment of mice

A major challenge in tuberculosis (TB) therapeutics is that antibiotic exposure leads to changes in the physiology of M. tuberculosis (Mtb), which may enable the pathogen to withstand treatment. While antibiotic-treated Mtb has been evaluated in in vitro experiments, it is unclear if and how long-term in vivo treatment with diverse antibiotics with varying treatment-shortening activity (sterilizing activity) affects Mtb physiologic processes differently. Here, we used SEARCH-TB, a pathogen-targeted RNA-sequencing platform, to characterize the Mtb transcriptome in the BALB/c high-dose aerosol infection mouse model following 4 weeks of treatment with three sterilizing and three non-sterilizing antibiotics. Certain transcriptional changes were shared among most antibiotics, including decreased expression of genes associated with protein synthesis and metabolism and the induction of certain genes associated with stress responses. However, the magnitude of this shared response differed between antibiotics. Sterilizing antibiotics rifampin, pyrazinamide, and bedaquiline generated a more quiescent Mtb state than did non-sterilizing antibiotics isoniazid, ethambutol, and streptomycin, as indicated by the decreased expression of genes associated with translation, transcription, secretion of immunogenic proteins, metabolism, and cell wall synthesis. Additionally, we identified distinguishing transcriptional effects specific to each antibiotic, indicating that different mechanisms of action induce distinct patterns in response to cellular injury. In addition to elucidating the Mtb physiologic changes associated with antibiotic stress, this study demonstrates the value of SEARCH-TB as a highly granular pharmacodynamic assay that reveals antibiotic effects that are not apparent based on culture alone.

stormTB: a web-based simulator of a murine minimal-PBPK model for anti-tuberculosis treatments

Introduction

Tuberculosis (TB) poses a significant threat to global health, with millions of new infections and approximately one million deaths annually. Various modeling efforts have emerged, offering tailored data-driven and physiologically-based solutions for novel and historical compounds. However, this diverse modeling panorama may lack consistency, limiting result comparability. Drug-specific models are often tied to commercial software and developed on various platforms and languages, potentially hindering access and complicating the comparison of different compounds.

Methods

This work introduces stormTB: SimulaTOr of a muRine Minimal-pbpk model for anti-TB drugs. It is a web-based interface for our minimal physiologically based pharmacokinetic (mPBPK) platform, designed to simulate custom treatment scenarios for tuberculosis in murine models. The app facilitates visual comparisons of pharmacokinetic profiles, aiding in assessing drug-dose combinations.

Results

The mPBPK model, supporting 11 anti-TB drugs, offers a unified perspective, overcoming the potential inconsistencies arising from diverse modeling efforts. The app, publicly accessible, provides a user-friendly environment for researchers to conduct what-if analyses and contribute to collective TB eradication efforts. The tool generates comprehensive visualizations of drug concentration profiles and pharmacokinetic/pharmacodynamic indices for TB-relevant tissues, empowering researchers in the quest for more effective TB treatments. stormTB is freely available at the link: https://apps.cosbi.eu/stormTB.

Prospectively predicting BPaMZ phase IIb/III trial outcomes using a translational mouse-to-human platform

Despite known treatments, tuberculosis (TB) remains the world's top infectious killer, highlighting the pressing need for new drug regimens. To prioritize the most efficacious drugs for clinical testing, we previously developed a PK-PD translational platform with bacterial dynamics that reliably predicted short-term monotherapy outcomes in Phase IIa trials from preclinical mouse studies. In this study, we extended our platform to include PK-PD models that account for drug-drug interactions in combination regimens and bacterial regrowth in our bacterial dynamics model to predict cure at the end of treatment and relapse 6 months post-treatment. The Phase III STAND trial testing a new regimen comprised of pretomanid (Pa), moxifloxacin (M), and pyrazinamide (Z) (PaMZ) was suspended after a separate ongoing trial (NC-005) suggested that adding bedaquiline (B) to the PaMZ regimen would improve efficacy. To forecast if the addition of B would, indeed, benefit the PaMZ regimen, we applied an extended translational platform to both regimens. We predicted currently available short- and long-term clinical data well for drug combinations related to BPaMZ. We predicted the addition of B to PaMZ to shorten treatment duration by 2 months and to have similar bacteriological success to standard HRZE treatment (considering only treatment success but not withdrawal from side effects and other adverse events), both at the end of treatment for treatment efficacy and 6 months after treatment has ended in relapse prevention. Using BPaMZ as a case study, we have demonstrated our translational platform can predict Phase II and III outcomes prior to actual trials, allowing us to better prioritize the regimens most likely to succeed.

Dose optimization of TBI-223 for enhanced therapeutic benefit compared to linezolid in antituberculosis regimen

TBI-223, a novel oxazolidinone for tuberculosis, is designed to provide improved efficacy and safety compared to linezolid in combination with bedaquiline and pretomanid (BPaL). We aim to optimize the dosing of TBI-223 within the BPaL regimen for enhanced therapeutic outcomes. TBI-223 is investigated in preclinical monotherapy, multidrug therapy, and lesion penetration experiments to describe its efficacy and safety versus linezolid. A translational platform incorporating linezolid and BPaL data from preclinical experiments and 4 clinical trials (NCT00396084, NCT02333799, NCT03086486, NCT00816426) is developed, enabling validation of the framework. TBI-223 preclinical and Phase 1 data (NCT03758612) are applied to the translational framework to predict clinical outcomes and optimize TBI-223 dosing in combination with bedaquiline and pretomanid. Results indicate that daily doses of 1200-2400 mg TBI-223 may achieve efficacy comparable to the BPaL regimen, with >90% of patients predicted to reach culture conversion by two months.

Emergence of antibiotic-specific Mycobacterium tuberculosis phenotypes during prolonged treatment of mice

A major challenge in tuberculosis (TB) therapeutics is that antibiotic exposure leads to changes in the physiologic state of M. tuberculosis (Mtb) which may enable the pathogen to withstand treatment. While antibiotic-treated Mtb have been evaluated in short-term in vitro experiments, it is unclear if and how long-term in vivo treatment with diverse antibiotics with varying treatment-shortening activity (sterilizing activity) affect Mtb physiologic states differently. Here, we used SEARCH-TB, a pathogen-targeted RNA-sequencing platform, to characterize the Mtb transcriptome in the BALB/c high-dose aerosol infection mouse model following 4-week treatment with three sterilizing and three non-sterilizing antibiotics. Certain transcriptional changes were concordant among most antibiotics, including decreased expression of genes associated with protein synthesis and metabolism, and the induction of certain genes associated with stress responses. However, the magnitude of this concordant response differed between antibiotics. Sterilizing antibiotics rifampin, pyrazinamide, and bedaquiline generated a more quiescent Mtb state than did non-sterilizing antibiotics isoniazid, ethambutol, and streptomycin, as indicated by decreased expression of genes associated with translation, transcription, secretion of immunogenic proteins, metabolism, and cell wall synthesis. Additionally, we identified distinguishing transcriptional effects specific to each antibiotic, indicating that different mechanisms of action induce distinct patterns of cellular injury. In addition to elucidating Mtb physiologic changes associated with antibiotic stress, this study demonstrates the value of SEARCH-TB as a highly granular pharmacodynamic assay that reveals antibiotic effects that are not apparent based on culture alone.

The use of Mycobacterium tuberculosis H37Ra-infected immunocompetent mice as an in vivo model of persisters

Persistence of Mycobacterium tuberculosis (Mtb) is one of the challenges to successful treatment of tuberculosis (TB). In vitro models of non-replicating Mtb are used to test the efficacy of new molecules against Mtb persisters. The H37Ra strain is attenuated for growth in macrophages and mice. We validated H37Ra-infected immunocompetent mice for testing anti-TB molecules against slow/non-replicating Mtb in vivo. Swiss mice were infected intravenously with H37Ra and monitored for CFU burden and histopathology for a period of 12 weeks. The bacteria multiplied at a slow pace reaching a maximum load of ∼106 in 8-12 weeks depending on the infection dose, accompanied by time and dose-dependent histopathological changes in the lungs. Surprisingly, four-weeks of treatment with isoniazid-rifampicin-ethambutol-pyrazinamide combination caused only 0.4 log10 and 1 log10 reduction in CFUs in lungs and spleen respectively. The results show that ∼40 % of the H37Ra bacilli in lungs are persisters after 4 weeks of anti-TB therapy. Isoniazid/rifampicin monotherapy also showed similar results. A combination of bedaquiline and isoniazid reduced the CFU counts to <200 (limit of detection), compared to ∼5000 CFUs by isoniazid alone. The study demonstrates an in vivo model of Mtb persisters for testing new leads using a BSL-2 strain.

A brief overview of mathematical modeling of the within-host dynamics of Mycobacterium tuberculosis

Tuberculosis (TB), a disease caused by bacteria Mycobacterium tuberculosis (Mtb), remains one of the major infectious diseases of humans with 10 million TB cases and 1.5 million deaths due to TB worldwide yearly. Upon exposure of a new host to Mtb, bacteria typically infect one local site in the lung, but over time, Mtb disseminates in the lung and in some cases to extrapulmonary sites. The contribution of various host components such as immune cells to Mtb dynamics in the lung, its dissemination in the lung and outside of the lung, remains incompletely understood. Here we overview different types of mathematical models used to gain insights in within-host dynamics of Mtb; these include models based on ordinary or partial differential equations (ODEs and PDEs), stochastic simulation models based on ODEs, agent-based models (ABMs), and hybrid models (ODE-based models linked to ABMs). We illustrate results from several of such models and identify areas for future resesarch.

Translational predictions of phase 2a first-in-patient efficacy studies for antituberculosis drugs

BACKGROUND

Phase 2a trials in tuberculosis typically use early bactericidal activity (EBA), the decline in sputum CFU over 14 days, as the primary end-point for testing the efficacy of drugs as monotherapy. However, the cost of phase 2a trials can range from USD 7 million to USD 19.6 million on average, while >30% of drugs fail to progress to phase 3. Better utilising pre-clinical data to predict and prioritise the most likely drugs to succeed will thus help to accelerate drug development and reduce costs. We aim to predict clinical EBA using pre-clinical in vivo pharmacokinetic (PK)-pharmacodynamic (PD) data and a model-based translational pharmacology approach.

METHODS AND FINDINGS

First, mouse PK, PD and clinical PK models were compiled. Second, mouse PK-PD models were built to derive an exposure-response relationship. Third, translational prediction of clinical EBA studies was performed using mouse PK-PD relationships and informed by clinical PK models and species-specific protein binding. Presence or absence of clinical efficacy was accurately predicted from the mouse model. Predicted daily decreases of CFU in the first 2 days of treatment and between day 2 and day 14 were consistent with clinical observations.

CONCLUSION

This platform provides an innovative solution to inform or even replace phase 2a EBA trials, to bridge the gap between mouse efficacy studies and phase 2b and phase 3 trials, and to substantially accelerate drug development.

In vitro and in vivo local tolerability of a synergistic anti-tuberculosis drug combination intended for pulmonary delivery

A drug combination, vancomycin (VAN) plus tetrahydrolipstatin (THL), has demonstrated an effective synergistic action in vitro against Mycobacterium tuberculosis (Mtb). The poor oral bioavailability of VAN and THL and the predominant tropism of Mtb infection to the lungs make their pulmonary administration very attractive. To evaluate their local tolerability, bronchial cells, alveolar cells and monocytes were exposed to concentrations around and above their minimal inhibitory concentration (MIC). The VAN had no inhibitory activity on the tested human cell lines, even at a concentration 125 times higher than its MIC, whereas the THL, alone or in combination with VAN, presented a cytostatic action. Monolayer epithelium showed no significant irreversible damage at concentrations up to 100 times the combination MIC. BALB/cAnNRj mice exposed to concentration of 50 times the combination MIC delivered endotracheally 3 times a week for 3 weeks showed no clinical signs or significant weight loss. The increase of proinflammatory biomarkers (i.e., IL-1, IL-6, TNF-α and proportion of inflammatory cells) and cytotoxicity in bronchoalveolar lavage fluid (BALF) were non-significant. Lung histopathology did not show significant tissue damage. The VAN/THL combination at doses up to 50 times the combination MIC is found to be thus well tolerated by pulmonary route. This study is a promising result and encouraging further investigations of pulmonary administration of VAN/THL combination as dry powder for anti-tuberculosis treatment.

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.

Model-Based Exposure-Response Assessment for Spectinamide 1810 in a Mouse Model of Tuberculosis

Despite decades of research, tuberculosis remains a leading cause of death from a single infectious agent. Spectinamides are a promising novel class of antituberculosis agents, and the lead spectinamide 1810 has demonstrated excellent efficacy, safety, and drug-like properties in numerous in vitro and in vivo assessments in mouse models of tuberculosis. In the current dose ranging and dose fractionation study, we used 29 different combinations of dose level and dosing frequency to characterize the exposure-response relationship for spectinamide 1810 in a mouse model of Mycobacterium tuberculosis infection and in healthy animals. The obtained data on 1810 plasma concentrations and counts of CFU in lungs were analyzed using a population pharmacokinetic/pharmacodynamic (PK/PD) approach as well as classical anti-infective PK/PD indices. The analysis results indicate that there was no difference in the PK of 1810 in infected compared to healthy, uninfected animals. The PK/PD index analysis showed that bacterial killing of 1810 in mice was best predicted by the ratio of maximum free drug concentration to MIC (fC_max/MIC) and the ratio of the area under the free concentration-time curve to the MIC (fAUC/MIC) rather than the cumulative percentage of time that the free drug concentration is above the MIC (f%T_MIC). A novel PK/PD model with consideration of postantibiotic effect could adequately describe the exposure-response relationship for 1810 and supports the notion that the in vitro observed postantibiotic effect of this spectinamide also translates to the in vivo situation in mice. The obtained results and pharmacometric model for the exposure-response relationship of 1810 provide a rational basis for dose selection in future efficacy studies of this compound against M. tuberculosis.

Emerging Therapeutics, Technologies, and Drug Development Strategies to Address Patient Nonadherence and Improve Tuberculosis Treatment

Imperfect medication adherence remains the biggest predictor of treatment failure for patients with tuberculosis. Missed doses during treatment lead to relapse, tuberculosis resistance, and further spread of disease. Understanding individual patient phenotypes, population pharmacokinetics, resistance development, drug distribution to tuberculosis lesions, and pharmacodynamics at the site of infection is necessary to fully measure the impact of adherence on patient outcomes. To decrease the impact of expected variability in drug intake on tuberculosis outcomes, an improvement in patient adherence and new forgiving regimens that protect against missed doses are needed. In this review, we summarize emerging technologies to improve medication adherence in clinical practice and provide suggestions on how digital adherence technologies can be incorporated in clinical trials and practice and the drug development pipeline that will lead to more forgiving regimens and benefit patients suffering from tuberculosis. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Mycobacterium tuberculosis precursor rRNA as a measure of treatment-shortening activity of drugs and regimens

There is urgent need for new drug regimens that more rapidly cure tuberculosis (TB). Existing TB drugs and regimens vary in treatment-shortening activity, but the molecular basis of these differences is unclear, and no existing assay directly quantifies the ability of a drug or regimen to shorten treatment. Here, we show that drugs historically classified as sterilizing and non-sterilizing have distinct impacts on a fundamental aspect of Mycobacterium tuberculosis physiology: ribosomal RNA (rRNA) synthesis. In culture, in mice, and in human studies, measurement of precursor rRNA reveals that sterilizing drugs and highly effective drug regimens profoundly suppress M. tuberculosis rRNA synthesis, whereas non-sterilizing drugs and weaker regimens do not. The rRNA synthesis ratio provides a readout of drug effect that is orthogonal to traditional measures of bacterial burden. We propose that this metric of drug activity may accelerate the development of shorter TB regimens.

The Treatment of Tuberculosis

Tuberculosis (TB) remains a leading cause of infectious death worldwide, and poverty is a major driver. Clinically, TB presents as "latent" TB and active TB disease, and the treatment for each is different. TB drugs can display "early bactericidal activity (EBA)" and / or "sterilizing activity" (clearing persisters). Isoniazid is excellent at the former, and rifampin is excellent at the latter. Pyrazinamide and ethambutol complete the first-line regimen for drug-susceptible TB, each playing a specific role. Drug-resistant TB is an increasing concern, being met, in part, with repurposed drugs (including moxifloxacin, levofloxacin, linezolid, clofazimine, and beta-lactams) and new drugs (including bedaquiline, pretomanid, and delamanid). One challenge is to select drugs without overlapping adverse drug reaction profiles. QTc interval prolongation is one such concern, but to date, it has been manageable. Drug penetration into organism sanctuaries, such as the central nervous system, bone, and pulmonary TB cavities remain important challenges. The pharmacodynamics of most TB drugs can be described by the area under the curve (AUC) divided by the minimal inhibitory concentration (MIC). The hollow fiber infection model (HFIM) and various animal models (especially mouse and macaque) allow for sophisticated pharmacokinetic/pharmacodynamic experiments. These experiments may hasten the selection of the most potent, shortest possible regimens to treat even extremely drug resistant TB. These findings can be translated to humans by optimizing drug exposure in each patient, using therapeutic drug monitoring and dose individualization.

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.