New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response

Use of multiple pharmacodynamic measures to deconstruct the Nix-TB regimen in a short-course murine model of tuberculosis

A major challenge for tuberculosis (TB) drug development is to prioritize promising combination regimens from a large and growing number of possibilities. This includes demonstrating individual drug contributions to the activity of higher-order combinations. A BALB/c mouse TB infection model was used to evaluate the contributions of each drug and pairwise combination in the clinically relevant Nix-TB regimen [bedaquiline-pretomanid-linezolid (BPaL)] during the first 3 weeks of treatment at human equivalent doses. The rRNA synthesis (RS) ratio, an exploratory pharmacodynamic (PD) marker of ongoing Mycobacterium tuberculosis rRNA synthesis, together with solid culture CFU counts and liquid culture time to positivity (TTP) were used as PD markers of treatment response in lung tissue; and their time-course profiles were mathematically modeled using rate equations with pharmacologically interpretable parameters. Antimicrobial interactions were quantified using Bliss independence and Isserlis formulas. Subadditive (or antagonistic) and additive effects on bacillary load, assessed by CFU and TTP, were found for bedaquiline-pretomanid and linezolid-containing pairs, respectively. In contrast, subadditive and additive effects on rRNA synthesis were found for pretomanid-linezolid and bedaquiline-containing pairs, respectively. Additionally, accurate predictions of the response to BPaL for all three PD markers were made using only the single-drug and pairwise effects together with an assumption of negligible three-way drug interactions. The results represent an experimental and PD modeling approach aimed at reducing combinatorial complexity and improving the cost-effectiveness of in vivo systems for preclinical TB regimen development.

Therapeutic developments for tuberculosis and nontuberculous mycobacterial lung disease

Tuberculosis (TB) drug discovery and development has undergone nothing short of a revolution over the past 20 years. Successful public-private partnerships and sustained funding have delivered a much-improved understanding of mycobacterial disease biology and pharmacology and a healthy pipeline that can tolerate inevitable attrition. Preclinical and clinical development has evolved from decade-old concepts to adaptive designs that permit rapid evaluation of regimens that might greatly shorten treatment duration over the next decade. But the past 20 years also saw the rise of a fatal and difficult-to-cure lung disease caused by nontuberculous mycobacteria (NTM), for which the drug development pipeline is nearly empty. Here, we discuss the similarities and differences between TB and NTM lung diseases, compare the preclinical and clinical advances, and identify major knowledge gaps and areas of cross-fertilization. We argue that applying paradigms and networks that have proved successful for TB, from basic research to clinical trials, will help to populate the pipeline and accelerate curative regimen development for NTM disease.

A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis

Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework. Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b. Results: The model has been successfully tested and validated against 11 anti-TB compounds-rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices. Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.

Use of Multiple Pharmacodynamic Measures to Deconstruct the Nix-TB Regimen in a Short-Course Murine Model of Tuberculosis

A major challenge for tuberculosis (TB) drug development is to prioritize promising combination regimens from a large and growing number of possibilities. This includes demonstrating individual drug contributions to the activity of higher-order combinations. A BALB/c mouse TB infection model was used to evaluate the contributions of each drug and pairwise combination in the clinically relevant Nix-TB regimen (bedaquiline-pretomanid-linezolid [BPaL]) during the first three weeks of treatment at human equivalent doses. RS ratio, an exploratory pharmacodynamic (PD) marker of ongoing Mycobacterium tuberculosis rRNA synthesis, to-gether with solid culture CFU and liquid culture time to positivity (TTP) were used as PD markers of treatment response in lung tissue; and their time course profiles were mathematically modeled using rate equations with pharmacologically interpretable parameters. Antimicrobial interactions were quantified using Bliss independence and Isserlis formulas. Subadditive (or antagonistic) and additive effects on bacillary load, assessed by CFU and TTP, were found for bedaquiline-pretomanid and linezolid-containing pairs, respectively. In contrast, subadditive and additive effects on rRNA synthesis were found for pretomanid-linezolid and bedaquiline-containing pairs, respectively. Additionally, accurate predictions of the response to BPaL for all three PD markers were made using only the single-drug and pairwise effects together with an assumption of negligible three-way drug interactions. The results represent an experimental and PD modeling approach aimed at reducing combinatorial complexity and improving the cost-effectiveness of in vivo systems for preclinical TB regimen development.

Pharmacokinetics and pharmacodynamics of anti-tuberculosis drugs: An evaluation of in vitro, in vivo methodologies and human studies

There has been an increased interest in pharmacokinetics and pharmacodynamics (PKPD) of anti-tuberculosis drugs. A better understanding of the relationship between drug exposure, antimicrobial kill and acquired drug resistance is essential not only to optimize current treatment regimens but also to design appropriately dosed regimens with new anti-tuberculosis drugs. Although the interest in PKPD has resulted in an increased number of studies, the actual bench-to-bedside translation is somewhat limited. One of the reasons could be differences in methodologies and outcome assessments that makes it difficult to compare the studies. In this paper we summarize most relevant in vitro, in vivo, in silico and human PKPD studies performed to optimize the drug dose and regimens for treatment of tuberculosis. The in vitro assessment focuses on MIC determination, static time-kill kinetics, and dynamic hollow fibre infection models to investigate acquisition of resistance and killing of Mycobacterium tuberculosis populations in various metabolic states. The in vivo assessment focuses on the various animal models, routes of infection, PK at the site of infection, PD read-outs, biomarkers and differences in treatment outcome evaluation (relapse and death). For human PKPD we focus on early bactericidal activity studies and inclusion of PK and therapeutic drug monitoring in clinical trials. Modelling and simulation approaches that are used to evaluate and link the different data types will be discussed. We also describe the concept of different studies, study design, importance of uniform reporting including microbiological and clinical outcome assessments, and modelling approaches. We aim to encourage researchers to consider methods of assessing and reporting PKPD of anti-tuberculosis drugs when designing studies. This will improve appropriate comparison between studies and accelerate the progress in the field.

A case report of persistent drug-sensitive pulmonary tuberculosis after treatment completion

Background

Mycobacterium tuberculosis (Mtb) has been found to persist within cavities in patients who have completed their anti-tuberculosis therapy. The clinical implications of Mtb persistence after therapy include recurrence of disease and destructive changes within the lungs. Data on residual changes in patients who completed anti-tuberculosis therapy are scarce. This case highlights the radiological and pathological changes that persist after anti-tuberculosis therapy completion and the importance of achieving sterilization of cavities in order to prevent these changes.

Case presentation

This is a case report of a 33 year old female with drug-sensitive pulmonary tuberculosis who despite successfully completing standard 6-month treatment had persistent changes in her lungs on radiological imaging. The patient underwent multiple adjunctive surgeries to resect cavitary lesions, which were culture positive for Mtb. After surgical treatment, the patient’s chest radiographies improved, symptoms subsided, and she was given a definition of cure.

Conclusions

Medical therapy alone, in the presence of severe cavitary lung lesions may not be able to achieve sterilizing cure in all cases. Cavities can not only cause reactivation but also drive inflammatory changes and subsequent lung damage leading to airflow obstruction, bronchiectasis, and fibrosis. Surgical removal of these foci of bacilli can be an effective adjunctive treatment necessary for a sterilizing cure and improved long term lung health.

Anti-tuberculosis treatment strategies and drug development: challenges and priorities

Despite two decades of intensified research to understand and cure tuberculosis disease, biological uncertainties remain and hamper progress. However, owing to collaborative initiatives including academia, the pharmaceutical industry and non-for-profit organizations, the drug candidate pipeline is promising. This exceptional success comes with the inherent challenge of prioritizing multidrug regimens for clinical trials and revamping trial designs to accelerate regimen development and capitalize on drug discovery breakthroughs. Most wanted are markers of progression from latent infection to active pulmonary disease, markers of drug response and predictors of relapse, in vitro tools to uncover synergies that translate clinically and animal models to reliably assess the treatment shortening potential of new regimens. In this Review, we highlight the benefits and challenges of 'one-size-fits-all' regimens and treatment duration versus individualized therapy based on disease severity and host and pathogen characteristics, considering scientific and operational perspectives.

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

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

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Evaluate the large drug combination space for potential tuberculosis treatments

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

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

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Simple rules articulate drug combination design principles for tuberculosis

Integrative analysis of clinical health records, imaging and pathogen genomics identifies personalized predictors of disease prognosis in tuberculosis

Tuberculosis (TB) afflicts over 10 million people every year and its global burden is projected to increase dramatically due to multidrug-resistant TB (MDR-TB). The Covid-19 pandemic has resulted in reduced access to TB diagnosis and treatment, reversing decades of progress in disease management globally. It is thus crucial to analyze real-world multi-domain information from patient health records to determine personalized predictors of TB treatment outcome and drug resistance. We conduct a retrospective analysis on electronic health records of 5060 TB patients spanning 10 countries with high burden of MDR-TB including Ukraine, Moldova, Belarus and India available on the NIAID-TB portals database. We analyze over 200 features across multiple host and pathogen modalities representing patient social demographics, disease presentations as seen in cChest X rays and CT scans, and genomic records with drug susceptibility features of the pathogen strain from each patient. Our machine learning model, built with diverse data modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 81% and AUC of 0.768. We determine robust predictors across countries that are associated with unsuccessful treatmentclinical outcomes, and validate our predictions on new patient data from TB Portals. Our analysis of drug regimens and drug interactions suggests that synergistic drug combinations and those containing the drugs Bedaquiline, Levofloxacin, Clofazimine and Amoxicillin see more success in treating MDR and XDR TB. Features identified via chest imaging such as percentage of abnormal volume, size of lung cavitation and bronchial obstruction are associated significantly with pathogen genomic attributes of drug resistance. Increased disease severity was also observed in patients with lower BMI and with comorbidities. Our integrated multi-modal analysis thus revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities, providing a deeper understanding of personalized responses to aid in the clinical management of TB.

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 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.

Host-directed therapies for tuberculosis: quantitative systems pharmacology approaches

Host-directed therapies (HDTs) that modulate host-pathogen interactions offer an innovative strategy to combat Mycobacterium tuberculosis (Mtb) infections. When combined with tuberculosis (TB) antibiotics, HDTs could contribute to improving treatment outcomes, reducing treatment duration, and preventing resistance development. Translation of the interplay of host-pathogen interactions leveraged by HDTs towards therapeutic outcomes in patients is challenging. Quantitative understanding of the multifaceted nature of the host-pathogen interactions is vital to rationally design HDT strategies. Here, we (i) provide an overview of key Mtb host-pathogen interactions as basis for HDT strategies; and (ii) discuss the components and utility of quantitative systems pharmacology (QSP) models to inform HDT strategies. QSP models can be used to identify and optimize treatment targets, to facilitate preclinical to human translation, and to design combination treatment strategies.

Precision-Enhancing Risk Stratification Tools for Selecting Optimal Treatment Durations in Tuberculosis Clinical Trials

Rationale: No evidence-based tools exist to enhance precision in the selection of patient-specific optimal treatment durations to study in tuberculosis clinical trials. Objectives: To develop risk stratification tools that assign patients with tuberculosis into risk groups of unfavorable outcome and inform selection of optimal treatment duration for each patient strata to study in clinical trials. Methods: Publicly available data from four phase 3 trials, each evaluating treatment duration shortening from 6 to 4 months, were used to develop parametric time-to-event models that describe unfavorable outcomes. Regimen, baseline, and on-treatment characteristics were evaluated as predictors of outcomes. Exact regression coefficients of predictors were used to assign risk groups and predict optimal treatment durations. Measurements and Main Results: The parametric model had an area under the receiver operating characteristic curve of 0.72. A six-item risk score (HIV status, smear grade, sex, cavitary disease status, body mass index, and Month 2 culture status) successfully grouped participants into low (1,060/3,791; 28%), moderate (1,740/3,791; 46%), and high (991/3,791; 26%) risk, requiring treatment durations of 4, 6, and greater than 6 months, respectively, to reach a target cure rate of 93% when receiving standard-dose rifamycin-containing regimens. With current one-duration-fits-all approaches, high-risk groups have a 3.7-fold (95% confidence interval, 2.7-5.1) and 2.4-fold (1.9-2.9) higher hazard risk of unfavorable outcomes compared with low- and moderate-risk groups, respectively. Four-month regimens were noninferior to the standard 6-month regimen in the low-risk group. Conclusions: Our model discrimination was modest but consistent with current models of unfavorable outcomes. Our results showed that stratified medicine approaches are feasible and may achieve high cure rates in all patients with tuberculosis. An interactive risk stratification tool is provided to facilitate decision-making in the regimen development pathway.

Reproducibility of the Ribosomal RNA Synthesis Ratio in Sputum and Association with Markers of Mycobacterium tuberculosis Burden

There is a critical need for improved pharmacodynamic markers for use in human tuberculosis (TB) drug trials. Pharmacodynamic monitoring in TB has conventionally used culture or molecular methods to enumerate the burden of Mycobacterium tuberculosis organisms in sputum. A recently proposed assay called the rRNA synthesis (RS) ratio measures a fundamentally novel property, how drugs impact ongoing bacterial rRNA synthesis. Here, we evaluated RS ratio as a potential pharmacodynamic monitoring tool by testing pretreatment sputa from 38 Ugandan adults with drug-susceptible pulmonary TB. We quantified the RS ratio in paired pretreatment sputa and evaluated the relationship between the RS ratio and microbiologic and molecular markers of M. tuberculosis burden. We found that the RS ratio was highly repeatable and reproducible in sputum samples. The RS ratio was independent of M. tuberculosis burden, confirming that it measures a distinct new property. In contrast, markers of M. tuberculosis burden were strongly associated with each other. These results indicate that the RS ratio is repeatable and reproducible and provides a distinct type of information from markers of M. tuberculosis burden. IMPORTANCE This study takes a major next step toward practical application of a novel pharmacodynamic marker that we believe will have transformative implications for tuberculosis. This article follows our recent report in Nature Communications that an assay called the rRNA synthesis (RS) ratio indicates the treatment-shortening of drugs and regimens. Distinct from traditional measures of bacterial burden, the RS ratio measures a fundamentally novel property, how drugs impact ongoing bacterial rRNA synthesis.

Bacterial growth dynamics and pharmacokinetic-pharmacodynamic relationships of rifampicin and bedaquiline in BALB/c mice

BACKGROUND AND PURPOSE

Translational efforts in the evaluation of novel anti-tubercular drugs demand better integration of pharmacokinetic-pharmacodynamic data arising from preclinical protocols. However, parametric approaches that discriminate drug effect from the underlying bacterial growth dynamics have not been fully explored, making it difficult to translate and/or extrapolate preclinical findings to humans. This analysis aims to develop a drug-disease model that allows distinction between drug- and system-specific properties.

EXPERIMENTAL APPROACH

Given their clinical relevance, rifampicin and bedaquiline were used as test compounds. A two-state model was used to describe bacterial growth dynamics. The approach assumes the existence of fast- and slow-growing bacterial populations. Drug effect on the growth dynamics of each subpopulation was characterised in terms of potency (EC₅₀ -F and EC₅₀ -S) and maximum killing rate.

KEY RESULTS

The doubling time of the fast- and slow-growing population was estimated to be 25 h and 42 days, respectively. Rifampicin was more potent against the fast-growing (EC₅₀ -F = 4.8 mg·L^-1 ), as compared with the slow-growing population (EC₅₀ -S = 60.2 mg·L^-1 ). Bedaquiline showed higher potency than rifampicin (EC₅₀ -F = 0.19 mg·L^-1 ; EC₅₀ -S = 3.04 mg·L^-1 ). External validation procedures revealed an effect of infection route on the apparent potency of rifampicin.

CONCLUSION AND IMPLICATIONS

Model parameter estimates suggest that nearly maximum killing rate is achieved against fast-growing, but not against slow-growing populations at the tested doses. Evidence of differences in drug potency for each subpopulation may facilitate the translation of preclinical findings and improve the dose rationale for anti-tubercular drugs in humans.

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.

Visualizing the dynamics of tuberculosis pathology using molecular imaging

Nearly 140 years after Robert Koch discovered Mycobacterium tuberculosis, tuberculosis (TB) remains a global threat and a deadly human pathogen. M. tuberculosis is notable for complex host-pathogen interactions that lead to poorly understood disease states ranging from latent infection to active disease. Additionally, multiple pathologies with a distinct local milieu (bacterial burden, antibiotic exposure, and host response) can coexist simultaneously within the same subject and change independently over time. Current tools cannot optimally measure these distinct pathologies or the spatiotemporal changes. Next-generation molecular imaging affords unparalleled opportunities to visualize infection by providing holistic, 3D spatial characterization and noninvasive, temporal monitoring within the same subject. This rapidly evolving technology could powerfully augment TB research by advancing fundamental knowledge and accelerating the development of novel diagnostics, biomarkers, and therapeutics.

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.

Measurement of the Intracellular Mycobacterium tuberculosis Drug Effect and Prediction of the Clinical Dose-Response Relationship Using Intracellular Pharmacodynamic Modeling (PDi)

The human disease tuberculosis (TB) is the leading cause of death from a single infectious agent. A quarter of the world's population is estimated to be latently infected. Drug development and screening is slow and costly. We have developed a physiologically relevant assay to screen drugs against TB when inside immune cells. This chapter will describe a newly developed preclinical drug screening assay for TB, using high-content imaging and pharmacokinetic/pharmacodynamic modeling.

Anti-tuberculosis effect of isoniazid scales accurately from zebrafish to humans

Background and Purpose

There is a clear need for innovation in anti‐tuberculosis drug development. The zebrafish larva is an attractive disease model in tuberculosis research. To translate pharmacological findings to higher vertebrates, including humans, the internal exposure of drugs needs to be quantified and linked to observed response.

Experimental Approach

In zebrafish studies, drugs are usually dissolved in the external water, posing a challenge to quantify internal exposure. We developed experimental methods to quantify internal exposure, including nanoscale blood sampling, and to quantify the bacterial burden, using automated fluorescence imaging analysis, with isoniazid as the test compound. We used pharmacokinetic–pharmacodynamic modelling to quantify the exposure–response relationship responsible for the antibiotic response. To translate isoniazid response to humans, quantitative exposure–response relationships in zebrafish were linked to simulated concentration–time profiles in humans, and two quantitative translational factors on sensitivity to isoniazid and stage of infection were included.

Key Results

Blood concentration was only 20% of the external drug concentration. The bacterial burden increased exponentially, and an isoniazid dose corresponding to 15 mg·L⁻¹ internal concentration (minimum inhibitory concentration) leads to bacteriostasis of the mycobacterial infection in the zebrafish. The concentration–effect relationship was quantified, and based on that relationship and the translational factors, the isoniazid response was translated to humans, which correlated well with observed data.

Conclusions and Implications

This proof of concept study confirmed the potential of zebrafish larvae as tuberculosis disease models in translational pharmacology and contributes to innovative anti‐tuberculosis drug development, which is very clearly needed.

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

Tuberculosis (TB) drug, regimen, and vaccine development rely heavily on preclinical animal experiments, and quantification of bacterial and immune response dynamics is essential for understanding drug and vaccine efficacy. A mechanism-based model was built to describe Mycobacterium tuberculosis H37Rv infection over time in BALB/c and athymic nude mice, which consisted of bacterial replication, bacterial death, and adaptive immune effects. The adaptive immune effect was best described by a sigmoidal function on both bacterial load and incubation time. Applications to demonstrate the utility of this baseline model showed (i) the important influence of the adaptive immune response on pyrazinamide (PZA) drug efficacy, (ii) a persistent adaptive immune effect in mice relapsing after chemotherapy cessation, and (iii) the protective effect of vaccines after M. tuberculosis challenge. These findings demonstrate the utility of our model for describing M. tuberculosis infection and corresponding adaptive immune dynamics for evaluating the efficacy of TB drugs, regimens, and vaccines.

Advances in clinical trial design: Weaving tomorrow's TB treatments

Christian Lienhardt and co-authors discuss the conclusions of the PLOS Medicine Collection on advances in clinical trial design for development of new tuberculosis treatments.

Small Animal Models for Human Immunodeficiency Virus (HIV), Hepatitis B, and Tuberculosis: Proceedings of an NIAID Workshop

The main advantage of animal models of infectious diseases over in vitro studies is the gain in the understanding of the complex dynamics between the immune system and the pathogen. While small animal models have practical advantages over large animal models, it is crucial to be aware of their limitations. Although the small animal model at least needs to be susceptible to the pathogen under study to obtain meaningful data, key elements of pathogenesis should also be reflected when compared to humans. Well-designed small animal models for HIV, hepatitis viruses and tuberculosis require, additionally, a thorough understanding of the similarities and differences in the immune responses between humans and small animals and should incorporate that knowledge into the goals of the study. To discuss these considerations, the NIAID hosted a workshop on 'Small Animal Models for HIV, Hepatitis B, and Tuberculosis' on May 30, 2019. Highlights of the workshop are outlined below.

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

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

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

Modeling and Simulation of Pretomanid Pharmacodynamics in Pulmonary Tuberculosis Patients

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

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

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

Intracellular PD Modelling (PDi) for the Prediction of Clinical Activity of Increased Rifampicin Dosing

Increasing rifampicin (RIF) dosages could significantly reduce tuberculosis (TB) treatment durations. Understanding the pharmacokinetic-pharmacodynamics (PK-PD) of increasing RIF dosages could inform clinical regimen selection. We used intracellular PD modelling (PD_i) to predict clinical outcomes, primarily time to culture conversion, of increasing RIF dosages. PD_i modelling utilizes in vitro-derived measurements of intracellular (macrophage) and extracellular Mycobacterium tuberculosis sterilization rates to predict the clinical outcomes of RIF at increasing doses. We evaluated PD_i simulations against recent clinical data from a high dose (35 mg/kg per day) RIF phase II clinical trial. PD_i-based simulations closely predicted the observed time-to-patient culture conversion status at eight weeks (hazard ratio: 2.04 (predicted) vs. 2.06 (observed)) for high dose RIF-based treatments. However, PD_i modelling was less predictive of culture conversion status at 26 weeks for high-dosage RIF (99% predicted vs. 81% observed). PD_i-based simulations indicate that increasing RIF beyond 35 mg/kg/day is unlikely to significantly improve culture conversion rates, however, improvements to other clinical outcomes (e.g., relapse rates) cannot be ruled out. This study supports the value of translational PD_i-based modelling in predicting culture conversion rates for antitubercular therapies and highlights the potential value of this platform for the improved design of future clinical trials.

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.

Forecasting Clinical Dose-Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin

A crucial step for accelerating tuberculosis drug development is bridging the gap between preclinical and clinical trials. In this study, we developed a preclinical model-informed translational approach to predict drug effects across preclinical systems and early clinical trials using the in vitro-based Multistate Tuberculosis Pharmacometric (MTP) model using rifampicin as an example. The MTP model predicted rifampicin biomarker response observed in 1) a hollow-fiber infection model, 2) a murine study to determine pharmacokinetic/pharmacodynamic indices, and 3) several clinical phase IIa early bactericidal activity (EBA) studies. In addition, we predicted rifampicin biomarker response at high doses of up to 50 mg/kg, leading to an increased median EBA0-2 days (90% prediction interval) of 0.513 log CFU/mL/day (0.310; 0.701) compared to the standard dose of 10 mg/kg of 0.181 log/CFU/mL/day (0.076; 0.483). These results suggest that the translational approach could assist in the selection of drugs and doses in early-phase clinical tuberculosis trials.