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

Evaluation of critical parameters in the hollow-fibre system for tuberculosis: A case study of moxifloxacin

Aims

The hollow‐fibre system for tuberculosis (HFS‐TB) is a preclinical model qualified by the European Medicines Agency to underpin the anti‐TB drug development process. It can mimic in vivo pharmacokinetic (PK)–pharmacodynamic (PD) attributes of selected antimicrobials, which could feed into in silico models to inform the design of clinical trials. However, historical data and published protocols are insufficient and omit key information to allow experiments to be reproducible. Therefore, in this work, we aim to optimize and standardize various HFS‐TB operational procedures.

Methods

First, we characterized bacterial growth dynamics with different types of hollow‐fibre cartridges, Mycobacterium tuberculosis strains and media. Second, we mimicked a moxifloxacin PK profile within hollow‐fibre cartridges, in order to check drug–fibres compatibility. Lastly, we mimicked the moxifloxacin total plasma PK profile in human after once daily oral dose of 400 mg to assess PK–PD after different sampling methods, strains, cartridge size and bacterial adaptation periods before drug infusion into the system.

Results

We found that final bacterial load inside the HFS‐TB was contingent on the studied variables. Besides, we demonstrated that drug–fibres compatibility tests are critical preliminary HFS‐TB assays, which need to be properly reported. Lastly, we uncovered that the sampling method and bacterial adaptation period before drug infusion significantly impact actual experimental conclusions.

Conclusion

Our data contribute to the necessary standardization of HFS‐TB experiments, draw attention to multiple aspects of this preclinical model that should be considered when reporting novel results and warn about critical parameters in the HFS‐TB currently overlooked.

Model-based dose optimization framework for bedaquiline, pretomanid and linezolid for the treatment of drug-resistant tuberculosis

AIMS

Bedaquiline, pretomanid and linezolid (BPaL) combination treatment against Mycobacterium tuberculosis is promising, yet safety and adherence concerns exist that motivate exploration of alternative dosing regimens. We developed a mechanistic modelling framework to compare the efficacy of the current and alternative BPaL treatment strategies.

METHODS

Pharmacodynamic models for each drug in the BPaL combination treatment were developed using in vitro time-kill data. These models were combined with pharmacokinetic models, incorporating body weight, lesion volume, site-of-action distribution, bacterial susceptibility and pharmacodynamic interactions to assemble the framework. The model was qualified by comparing the simulations against the observed clinical data. Simulations were performed evaluating bedaquiline and linezolid approved (bedaquiline 400 mg once daily [QD] for 14 days followed by 200 mg three times a week, linezolid 1200 mg QD) and alternative dosing regimens (bedaquiline 200 mg QD, linezolid 600 mg QD).

RESULTS

The framework adequately described the observed antibacterial activity data in patients following monotherapy for each drug and approved BPaL dosing. The simulations suggested a minor difference in median time to colony forming unit (CFU)-clearance state with the bedaquiline alternative compared to the approved dosing and the linezolid alternative compared to the approved dosing. Median time to non-replicating-clearance state was predicted to be 15 days from the CFU-clearance state.

CONCLUSIONS

The model-based simulations suggested that comparable efficacy can be achieved using alternative bedaquiline and linezolid dosing, which may improve safety and adherence in drug-resistant tuberculosis patients. The framework can be utilized to evaluate treatment optimization approaches, including dosing regimen and duration of treatment predictions to eradicate both replicating- and non-replicating bacteria from lung and lesions.

Characterization of spectinamide 1599 efficacy against different mycobacterial phenotypes

Spectinamides are a novel series of spectinomycin analogs being developed for the treatment of tuberculosis. The preclinical lead spectinamide 1599 is an antituberculosis drug that possesses robust in vivo efficacy, good pharmacokinetic properties, and excellent safety profiles in rodents. In individuals infected with Mycobacterium tuberculosis or Mycobacterium bovis, causative agents of tuberculosis, the host immune system is capable of restraining these mycobacteria within granulomatous lesions. The harsh microenvironmental conditions of these granuloma lead to phenotypic transformation of mycobacteria. Phenotypically transformed bacteria display suboptimal growth, or complete growth arrest and are frequently associated with drug tolerance. Here we quantified the effect of spectinamide 1599 on log-phase and phenotypically tolerant isoforms of Mycobacterium bovis BCG using various in vitro approaches as a first indicator of spectinamide 1599 activity against various mycobacterial isoforms. We also used the hollow fiber infection model to establish time-kill curves and deployed pharmacokinetic/pharmacodynamic modeling to characterize the activity differences of spectinamide 1599 towards the different phenotypic subpopulations. Our results indicate that spectinamide 1599 is more efficacious against log phase bacteria when compared to its activity against other phenotypically tolerant forms such as acid phase bacteria and hypoxic phase bacteria, a behavior similar to the established antituberculosis drug isoniazid.

Standards for model-based early bactericidal activity analysis and sample size determination in tuberculosis drug development

Background: A critical step in tuberculosis (TB) drug development is the Phase 2a early bactericidal activity (EBA) study which informs if a new drug or treatment has short-term activity in humans. The aim of this work was to present a standardized pharmacometric model-based early bactericidal activity analysis workflow and determine sample sizes needed to detect early bactericidal activity or a difference between treatment arms. Methods: Seven different steps were identified and developed for a standardized pharmacometric model-based early bactericidal activity analysis approach. Non-linear mixed effects modeling was applied and different scenarios were explored for the sample size calculations. The sample sizes needed to detect early bactericidal activity given different TTP slopes and associated variability was assessed. In addition, the sample sizes needed to detect effect differences between two treatments given the impact of different TTP slopes, variability in TTP slope and effect differences were evaluated. Results: The presented early bactericidal activity analysis approach incorporates estimate of early bactericidal activity with uncertainty through the model-based estimate of TTP slope, variability in TTP slope, impact of covariates and pharmacokinetics on drug efficacy. Further it allows for treatment comparison or dose optimization in Phase 2a. To detect early bactericidal activity with 80% power and at a 5% significance level, 13 and 8 participants/arm were required for a treatment with a TTP-EBA0-14 as low as 11 h when accounting for variability in pharmacokinetics and when variability in TTP slope was 104% [coefficient of variation (CV)] and 22%, respectively. Higher sample sizes are required for smaller early bactericidal activity and when pharmacokinetics is not accounted for. Based on sample size determinations to detect a difference between two groups, TTP slope, variability in TTP slope and effect difference between two treatment arms needs to be considered. Conclusion: In conclusion, a robust standardized pharmacometric model-based EBA analysis approach was established in close collaboration between microbiologists, clinicians and pharmacometricians. The work illustrates the importance of accounting for covariates and drug exposure in EBA analysis in order to increase the power of detecting early bactericidal activity for a single treatment arm as well as differences in EBA between treatments arms in Phase 2a trials of TB drug development.

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

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

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.

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.

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.

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.

Pharmacokinetics and Pharmacodynamics of Posaconazole

Posaconazole is typically used for preventing invasive yeast and mold infections such as invasive aspergillosis in high-risk immunocompromised patients. The oral suspension was the first released formulation and many pharmacokinetic and pharmacodynamic studies of this formulation have been published. Erratic absorption profiles associated with this formulation were widely reported. Posaconazole exposure was found to be significantly influenced by food and many gastrointestinal conditions, including pH and motility. As a result, low posaconazole plasma concentrations were obtained in large groups of patients. These issues of erratic absorption urged the development of the subsequently marketed delayed-release tablet, which proved to be associated with higher and more stable exposure profiles. Shortly thereafter, an intravenous formulation was released for patients who are not able to take oral formulations. Both new formulations require a loading dose on day 1 to achieve high posaconazole concentrations more quickly, which was not possible with the oral suspension. So far, there appears to be no evidence of increased toxicity correlated to the higher posaconazole exposure achieved with the regimen for these formulations. The higher systemic availability of posaconazole for the delayed-release tablet and intravenous formulation have resulted in these two formulations being preferable for both prophylaxis and treatment of invasive fungal disease. This review aimed to integrate the current knowledge on posaconazole pharmacokinetics, pharmacodynamics, major toxicity, existing resistance, clinical experience in special populations, and new therapeutic strategies in order to get a clear understanding of the clinical use of this drug.

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.

Can phenotypic data complement our understanding of antimycobacterial effects for drug combinations?

Objectives

To demonstrate how phenotypic cell viability data can provide insight into antimycobacterial effects for the isoniazid/rifampicin treatment backbone.

Methods

Data from a Mycobacterium komossense hollow-fibre infection model comprising a growth control group, rifampicin at three different exposures (C_max = 0.14, 0.4 and 1.47 mg/L with t_½ = 1.57 h and τ = 8 h) and rifampicin plus isoniazid (C_max rifampicin = 0.4 mg/L and C_max isoniazid = 1.2 mg/L with t_½ = 1.57 h and τ = 8 h) were used for this investigation. A non-linear mixed-effects modelling approach was used to fit conventional cfu data, quantified using solid-agar plating. Phenotypic proportions of respiring (alive), respiring but with damaged cell membrane (injured) and ‘not respiring’ (dead) cells data were quantified using flow cytometry and Sytox Green™ (Sigma–Aldrich, UK) and resazurin sodium salt staining and fitted using a multinomial logistic regression model.

Results

Isoniazid/rifampicin combination therapy displayed a decreasing overall antimicrobial effect with time (θTime1/2 = 438 h) on cfu data, in contrast to rifampicin monotherapy where this trend was absent. In the presence of isoniazid a phenotype associated with cell injury was displayed, whereas with rifampicin monotherapy a pattern of phenotypic cell death was observed. Bacterial killing onset time on cfu data correlated negatively (θTime50 = 28.9 h, θLAGRIF50 = 0.132 mg/L) with rifampicin concentration up to 0.165 mg/L and this coincided with a positive relationship between rifampicin concentration and the probability of phenotypic cell death.

Conclusions

Cell viability data provide structured information on the pharmacodynamic interaction between isoniazid and rifampicin that complements the understanding of the antibacillary effects of this mycobacterial treatment backbone.

Difference in persistent tuberculosis bacteria between in vitro and sputum from patients: implications for translational predictions

This study aimed to investigate the number of persistent bacteria in sputum from tuberculosis patients compared to in vitro and to suggest a model-based approach for accounting for the potential difference. Sputum smear positive patients (n = 25) provided sputum samples prior to onset of chemotherapy. The number of cells detected by conventional agar colony forming unit (CFU) and most probable number (MPN) with Rpf supplementation were quantified. Persistent bacteria was assumed to be the difference between MPN_rpf and CFU. The difference in persistent bacteria between in vitro and human sputum prior to chemotherapy was quantified using different model-based approaches. The persistent bacteria in sputum was 17% of the in vitro levels, suggesting a difference in phenotypic resistance, whereas no difference was found for multiplying bacterial subpopulations. Clinical trial simulations showed that the predicted time to 2 log fall in MPN_rpf in a Phase 2a setting using in vitro pre-clinical efficacy information, would be almost 3 days longer if drug response was predicted ignoring the difference in phenotypic resistance. The discovered phenotypic differences between in vitro and humans prior to chemotherapy could have implications on translational efforts but can be accounted for using a model-based approach for translating in vitro to human drug response.

Advanced Quantification Methods To Improve the 18b Dormancy Model for Assessing the Activity of Tuberculosis Drugs In Vitro

One of the reasons for the lengthy tuberculosis (TB) treatment is the difficulty to treat the nonmultiplying mycobacterial subpopulation. In order to assess the ability of (new) TB drugs to target this subpopulation, we need to incorporate dormancy models in our preclinical drug development pipeline. In most available dormancy models, it takes a long time to create a dormant state, and it is difficult to identify and quantify this nonmultiplying condition. The Mycobacterium tuberculosis 18b strain might overcome some of these problems, because it is dependent on streptomycin for growth and becomes nonmultiplying after 10 days of streptomycin starvation but still can be cultured on streptomycin-supplemented culture plates. We developed our 18b dormancy time-kill kinetics model to assess the difference in the activity of isoniazid, rifampin, moxifloxacin, and bedaquiline against log-phase growth compared to the nonmultiplying M. tuberculosis subpopulation by CFU counting, including a novel area under the curve (AUC)-based approach as well as time-to-positivity (TTP) measurements. We observed that isoniazid and moxifloxacin were relatively more potent against replicating bacteria, while rifampin and high-dose bedaquiline were equally effective against both subpopulations. Moreover, the TTP data suggest that including a liquid culture-based method could be of additional value, as it identifies a specific mycobacterial subpopulation that is nonculturable on solid media. In conclusion, the results of our study underline that the time-kill kinetics 18b dormancy model in its current form is a useful tool to assess TB drug potency and thus has its place in the TB drug development pipeline.

Longitudinal Pharmacokinetic-Pharmacodynamic Biomarkers Correlate With Treatment Outcome in Drug-Sensitive Pulmonary Tuberculosis: A Population Pharmacokinetic-Pharmacodynamic Analysis

Background

This study aims to explore relationships between baseline demographic covariates, plasma antibiotic exposure, sputum bacillary load, and clinical outcome data to help improve future tuberculosis (TB) treatment response predictions.

Methods

Data were available from a longitudinal cohort study in Malawian drug-sensitive TB patients on standard therapy, including steady-state plasma antibiotic exposure (154 patients), sputum bacillary load (102 patients), final outcome (95 patients), and clinical details. Population pharmacokinetic and pharmacokinetic-pharmacodynamic models were developed in the software package NONMEM. Outcome data were analyzed using univariate logistic regression and Cox proportional hazard models in R, a free software for statistical computing.

Results

Higher isoniazid exposure correlated with increased bacillary killing in sputum (P < .01). Bacillary killing in sputum remained fast, with later progression to biphasic decline, in patients with higher rifampicin area under the curve (AUC)_0-24 (P < .01). Serial sputum colony counting negativity at month 2 (P < .05), isoniazid C_MAX (P < .05), isoniazid C_MAX/minimum inhibitory concentration ([MIC] P < .01), and isoniazid AUC_0-24/MIC (P < .01) correlated with treatment success but not with remaining free of TB. Slower bacillary killing (P < .05) and earlier progression to biphasic bacillary decline (P < .01) both correlate with treatment failure. Posttreatment recurrence only correlated with slower bacillary killing (P < .05).

Conclusions

Patterns of early bacillary clearance matter. Static measurements such as month 2 sputum conversion and pharmacokinetic parameters such as C_MAX/MIC and AUC_0-24/MIC were predictive of treatment failure, but modeling of quantitative longitudinal data was required to assess the risk of recurrence. Pooled individual patient data analyses from larger datasets are needed to confirm these findings.

A model-based analysis identifies differences in phenotypic resistance between in vitro and in vivo: implications for translational medicine within tuberculosis

Proper characterization of drug effects on Mycobacterium tuberculosis relies on the characterization of phenotypically resistant bacteria to correctly establish exposure–response relationships. The aim of this work was to evaluate the potential difference in phenotypic resistance in in vitro compared to murine in vivo models using CFU data alone or CFU together with most probable number (MPN) data following resuscitation with culture supernatant. Predictions of in vitro and in vivo phenotypic resistance i.e. persisters, using the Multistate Tuberculosis Pharmacometric (MTP) model framework was evaluated based on bacterial cultures grown with and without drug exposure using CFU alone or CFU plus MPN data. Phenotypic resistance and total bacterial number in in vitro natural growth observations, i.e. without drug, was well predicted by the MTP model using only CFU data. Capturing the murine in vivo total bacterial number and persisters during natural growth did however require re-estimation of model parameter using both the CFU and MPN observations implying that the ratio of persisters to total bacterial burden is different in vitro compared to murine in vivo. The evaluation of the in vitro rifampicin drug effect revealed that higher resolution in the persister drug effect was seen using CFU and MPN compared to CFU alone although drug effects on the other bacterial populations were well predicted using only CFU data. The ratio of persistent bacteria to total bacteria was predicted to be different between in vitro and murine in vivo. This difference could have implications for subsequent translational efforts in tuberculosis drug development.

Drug Effect of Clofazimine on Persisters Explains an Unexpected Increase in Bacterial Load in Patients

Antituberculosis (anti-TB) drug development is dependent on informative trials to secure the development of new antibiotics and combination regimens. Clofazimine (CLO) and pyrazinamide (PZA) are important components of recommended standard multidrug treatments of TB. Paradoxically, in a phase IIa trial aiming to define the early bactericidal activity (EBA) of CLO and PZA monotherapy over the first 14 days of treatment, no significant drug effect was demonstrated for the two drugs using traditional statistical analysis.

Antituberculosis (anti-TB) drug development is dependent on informative trials to secure the development of new antibiotics and combination regimens. Clofazimine (CLO) and pyrazinamide (PZA) are important components of recommended standard multidrug treatments of TB. Paradoxically, in a phase IIa trial aiming to define the early bactericidal activity (EBA) of CLO and PZA monotherapy over the first 14 days of treatment, no significant drug effect was demonstrated for the two drugs using traditional statistical analysis. Using a model-based analysis, we characterized the statistically significant exposure-response relationships for both drugs that could explain the original findings of an increase in the numbers of CFU with CLO treatment and no effect with PZA. Sensitive analyses are crucial for exploring drug effects in early clinical trials to make the right decisions for advancement to further development. We propose that this quantitative semimechanistic approach provides a rational framework for analyzing phase IIa EBA studies and can accelerate anti-TB drug development.

Quantification of Natural Growth of Two Strains of Mycobacterium Marinum for Translational Antituberculosis Drug Development

The zebrafish infected with Mycobacterium marinum (M. marinum) is an attractive tuberculosis disease model, showing similar pathogenesis to Mycobacterium tuberculosis (M. tuberculosis) infections in humans. To translate pharmacological findings from this disease model to higher vertebrates, a quantitative understanding of the natural growth of M. marinum in comparison to the natural growth of M. tuberculosis is essential. Here, the natural growth of two strains of M. marinum, E11 and M^USA, is studied over an extended period using an established model‐based approach, the multistate tuberculosis pharmacometric (MTP) model, for comparison to that of M. tuberculosis. Poikilotherm‐derived strain E11 and human‐derived strain M^USA were grown undisturbed up to 221 days and viability of cultures (colony forming unit (CFU)/mL) was determined by plating at different time points. Nonlinear mixed effects modeling using the MTP model quantified the bacterial growth, the transfer among fast, slow, and non‐multiplying states, and the inoculi. Both strains showed initial logistic growth, reaching a maximum after 20–25 days for E11 and M^USA, respectively, followed by a decrease to a new plateau. Natural growth of both E11 and M^USA was best described with Gompertz growth functions. For E11, the inoculum was best described in the slow‐multiplying state, for M^USA in the fast‐multiplying state. Natural growth of E11 was most similar to that of M. tuberculosis, whereas M^USA showed more aggressive growth behavior. Characterization of natural growth of M. marinum and quantitative comparison with M. tuberculosis brings the zebrafish tuberculosis disease model closer to the quantitative translational pipeline of antituberculosis drug development.

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

Translational Model-Informed Approach for Selection of Tuberculosis Drug Combination Regimens in Early Clinical Development

The development of optimal treatment regimens in tuberculosis (TB) remains challenging due to the need of combination therapy and possibility of pharmacodynamic (PD) interactions. Preclinical information about PD interactions needs to be used more optimally when designing early bactericidal activity (EBA) studies. In this work, we developed a translational approach which can allow for forward translation to predict efficacy of drug combination in EBA studies using the Multistate Tuberculosis Pharmacometric (MTP) and the General Pharmacodynamic Interaction (GPDI) models informed by in vitro static time-kill data. These models were linked with translational factors to account for differences between the in vitro system and humans. Our translational MTP-GPDI model approach was able to predict the EBA_{0-2 days} , EBA_{0-5 days} , and EBA_{0-14 days} from different EBA studies of rifampicin and isoniazid in monotherapy and combination. Our translational model approach can contribute to an optimal dose selection of drug combinations in early TB clinical trials.

Re-growth of Mycobacterium tuberculosis populations exposed to antibiotic combinations is due to the presence of isoniazid and not bacterial growth rate

Modulation of the growth rate in Mycobacterium tuberculosis is key to its survival in the host, particularly with regard to its adaptation during chronic infection, when the growth rate is very slow. The resulting physiological changes influence the way in which this pathogen interacts with the host and responds to antibiotics. Therefore, it is important that we understand how the growth rate impacts antibiotic efficacy, particularly with respect to recovery/relapse.

Modulation of the growth rate in Mycobacterium tuberculosis is key to its survival in the host, particularly with regard to its adaptation during chronic infection, when the growth rate is very slow. The resulting physiological changes influence the way in which this pathogen interacts with the host and responds to antibiotics. Therefore, it is important that we understand how the growth rate impacts antibiotic efficacy, particularly with respect to recovery/relapse. This is the first study that has asked how growth rates influence the mycobacterial responses to combinations of the frontline antimycobacterials, isoniazid (INH), rifampin (RIF), and pyrazinamide (PZA), using continuous cultures. The time course profiles of log-transformed total viable counts for cultures, controlled at either a fast growth rate (mean generation time [MGT], 23.1 h) or a slow growth rate (MGT, 69.3 h), were analyzed by the fitting of a mathematical model by nonlinear regression that accounted for the dilution rate in the chemostat and profiled the kill rates and recovery in culture. Using this approach, we show that populations growing more slowly were generally less susceptible to all treatments. We observed a faster kill rate associated with INH than with RIF or PZA and the appearance of regrowth. In line with this observation, regrowth was not observed with RIF exposure, which provided a slower bactericidal response. The sequential additions of RIF and PZA did not eliminate regrowth. We consider here that faster, early bactericidal activity is not what is required for the successful sterilization of M. tuberculosis, but instead, slower elimination of the bacilli followed by reduced recovery of the bacterial population is required.

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.

A model-informed preclinical approach for prediction of clinical pharmacodynamic interactions of anti-TB drug combinations

Background

Identification of pharmacodynamic interactions is not reasonable to carry out in a clinical setting for many reasons. The aim of this work was to develop a model-informed preclinical approach for prediction of clinical pharmacodynamic drug interactions in order to inform early anti-TB drug development.

Methods

In vitro time–kill experiments were performed with Mycobacterium tuberculosis using rifampicin, isoniazid or ethambutol alone as well as in different combinations at clinically relevant concentrations. The multistate TB pharmacometric (MTP) model was used to characterize the natural growth and exposure–response relationships of each drug after mono exposure. Pharmacodynamic interactions during combination exposure were characterized by linking the MTP model to the general pharmacodynamic interaction (GPDI) model with successful separation of the potential effect on each drug’s potency (EC₅₀) by the combining drug(s).

Results

All combinations showed pharmacodynamic interactions at cfu level, where all combinations, except isoniazid plus ethambutol, showed more effect (synergy) than any of the drugs alone. Using preclinical information, the MTP-GPDI modelling approach was shown to correctly predict clinically observed pharmacodynamic interactions, as deviations from expected additivity.

Conclusions

With the ability to predict clinical pharmacodynamic interactions, using preclinical information, the MTP-GPDI model approach outlined in this study constitutes groundwork for model-informed input to the development of new and enhancement of existing anti-TB combination regimens.

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.

Utilization of data below the analytical limit of quantitation in pharmacokinetic analysis and modeling: promoting interdisciplinary debate

Traditionally, bioanalytical laboratories do not report actual concentrations for samples with results below the LOQ (BLQ) in pharmacokinetic studies. BLQ values are outside the method calibration range established during validation and no data are available to support the reliability of these values. However, ignoring BLQ data can contribute to bias and imprecision in model-based pharmacokinetic analyses. From this perspective, routine use of BLQ data would be advantageous. We would like to initiate an interdisciplinary debate on this important topic by summarizing the current concepts and use of BLQ data by regulators, pharmacometricians and bioanalysts. Through introducing the limit of detection and evaluating its variability, BLQ data could be released and utilized appropriately for pharmacokinetic research.

A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice

BACKGROUND

The yeast-like fungi Pneumocystis, resides in lung alveoli and can cause a lethal infection known as Pneumocystis pneumonia (PCP) in hosts with impaired immune systems. Current therapies for PCP, such as trimethoprim-sulfamethoxazole (TMP-SMX), suffer from significant treatment failures and a multitude of serious side effects. Novel therapeutic approaches (i.e. newly developed drugs or novel combinations of available drugs) are needed to treat this potentially lethal opportunistic infection. Quantitative Systems Pharmacological (QSP) models promise to aid in the development of novel therapies by integrating available pharmacokinetic (PK) and pharmacodynamic (PD) knowledge to predict the effects of new treatment regimens.

RESULTS

In this work, we constructed and independently validated PK modules of a number of drugs with available pharmacokinetic data. Characterized by simple structures and well constrained parameters, these PK modules could serve as a convenient tool to summarize and predict pharmacokinetic profiles. With the currently accepted hypotheses on the life stages of Pneumocystis, we also constructed a PD module to describe the proliferation, transformation, and death of Pneumocystis. By integrating the PK module and the PD module, the QSP model was constrained with observed levels of asci and trophic forms following treatments with multiple drugs. Furthermore, the temporal dynamics of the QSP model were validated with corresponding data.

CONCLUSIONS

We developed and validated a QSP model that integrates available data and promises to facilitate the design of future therapies against PCP.

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

Background

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

Objectives

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

Methods

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

Results

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

Conclusions

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

Comparisons of Analysis Methods for Assessment of Pharmacodynamic Interactions Including Design Recommendations

Quantitative evaluation of potential pharmacodynamic (PD) interactions is important in tuberculosis drug development in order to optimize Phase 2b drug selection and ultimately to define clinical combination regimens. In this work, we used simulations to (1) evaluate different analysis methods for detecting PD interactions between two hypothetical anti-tubercular drugs in in vitro time-kill experiments, and (2) provide design recommendations for evaluation of PD interactions. The model used for all simulations was the Multistate Tuberculosis Pharmacometric (MTP) model linked to the General Pharmacodynamic Interaction (GPDI) model. Simulated data were re-estimated using the MTP-GPDI model implemented in Bliss Independence or Loewe Additivity, or using a conventional model such as an Empirical Bliss Independence-based model or the Greco model based on Loewe Additivity. The GPDI model correctly characterized different PD interactions (antagonism, synergism, or asymmetric interaction), regardless of the underlying additivity criterion. The commonly used conventional models were not able to characterize asymmetric PD interactions, i.e., concentration-dependent synergism and antagonism. An optimized experimental design was developed that correctly identified interactions in ≥ 94% of the evaluated scenarios using the MTP-GPDI model approach. The MTP-GPDI model approach was proved to provide advantages to other conventional models for assessing PD interactions of anti-tubercular drugs and provides key information for selection of drug combinations for Phase 2b evaluation.

Improved power for TB Phase IIa trials using a model-based pharmacokinetic-pharmacodynamic approach compared with commonly used analysis methods

Background: The demand for new anti-TB drugs is high, but development programmes are long and costly. Consequently there is a need for new strategies capable of accelerating this process.

Objectives: To explore the power to find statistically significant drug effects using a model-based pharmacokinetic–pharmacodynamic approach in comparison with the methods commonly used for analysing TB Phase IIa trials.

Methods: Phase IIa studies of four hypothetical anti-TB drugs (labelled A, B, C and D), each with a different mechanism of action, were simulated using the multistate TB pharmacometric (MTP) model. cfu data were simulated over 14 days for patients taking once-daily monotherapy at four different doses per drug and a reference (10 mg/kg rifampicin). The simulated data were analysed using t-test, ANOVA, mono- and bi-exponential models and a pharmacokinetic–pharmacodynamic model approach (MTP model) to establish their respective power to find a drug effect at the 5% significance level.

Results: For the pharmacokinetic–pharmacodynamic model approach, t-test, ANOVA, mono-exponential model and bi-exponential model, the sample sizes needed to achieve 90% power were: 10, 30, 75, 20 and 30 (drug A); 30, 75, 245, 75 and 105 (drug B); 70, >1250, 315, >1250 and >1250 (drug C); and 30, 110, 710, 170 and 185 (drug D), respectively.

Conclusions: A model-based design and analysis using a pharmacokinetic–pharmacodynamic approach can reduce the number of patients required to determine a drug effect at least 2-fold compared with current methodologies. This could significantly accelerate early-phase TB drug development.

Assessing Pharmacodynamic Interactions in Mice Using the Multistate Tuberculosis Pharmacometric and General Pharmacodynamic Interaction Models

The aim of this study was to investigate pharmacodynamic (PD) interactions in mice infected with Mycobacterium tuberculosis using population pharmacokinetics (PKs), the Multistate Tuberculosis Pharmacometric (MTP) model, and the General Pharmacodynamic Interaction (GPDI) model. Rifampicin, isoniazid, ethambutol, or pyrazinamide were administered in monotherapy for 4 weeks. Rifampicin and isoniazid showed effects in monotherapy, whereas the animals became moribund after 7 days with ethambutol or pyrazinamide alone. No PD interactions were observed against fast‐multiplying bacteria. Interactions between rifampicin and isoniazid on killing slow and non‐multiplying bacteria were identified, which led to an increase of 0.86 log₁₀ colony‐forming unit (CFU)/lungs at 28 days after treatment compared to expected additivity (i.e., antagonism). An interaction between rifampicin and ethambutol on killing non‐multiplying bacteria was quantified, which led to a decrease of 2.84 log₁₀ CFU/lungs at 28 days after treatment (i.e., synergism). These results show the value of pharmacometrics to quantitatively assess PD interactions in preclinical tuberculosis drug development.

The role of the time-kill kinetics assay as part of a preclinical modeling framework for assessing the activity of anti-tuberculosis drugs

Novel treatment strategies for tuberculosis are urgently needed. Many different preclinical models assessing anti-tuberculosis drug activity are available, but it is yet unclear which combination of models is most predictive of clinical treatment efficacy. The aim of this study was to determine the role of our in vitro time kill-kinetics assay as an asset to a predictive preclinical modeling framework assessing anti-tuberculosis drug activity. The concentration- and time-dependent mycobacterial killing capacities of six anti-tuberculosis drugs were determined during exposure as single drugs or in dual, triple and quadruple combinations towards a Mycobacterium tuberculosis Beijing genotype strain and drug resistance was assessed. Streptomycin, rifampicin and isoniazid were most active against fast-growing M. tuberculosis. Isoniazid with rifampicin or high dose ethambutol were the only synergistic drug combinations. The addition of rifampicin or streptomycin to isoniazid prevented isoniazid resistance. In vitro ranking showed agreement with early bactericidal activity in tuberculosis patients for some but not all anti-tuberculosis drugs. The time-kill kinetics assay provides important information on the mycobacterial killing dynamics of anti-tuberculosis drugs during the early phase of drug exposure. As such, this assay is a valuable component of the preclinical modeling framework.

The multistate tuberculosis pharmacometric model: a semi-mechanistic pharmacokinetic-pharmacodynamic model for studying drug effects in an acute tuberculosis mouse model

The Multistate Tuberculosis Pharmacometric (MTP) model, a pharmacokinetic-pharmacodynamic disease model, has been used to describe the effects of rifampicin on Mycobacterium tuberculosis (M. tuberculosis) in vitro. The aim of this work was to investigate if the MTP model could be used to describe the rifampicin treatment response in an acute tuberculosis mouse model. Sixty C57BL/6 mice were intratracheally infected with M. tuberculosis H37Rv strain on Day 0. Fifteen mice received no treatment and were sacrificed on Days 1, 9 and 18 (5 each day). Twenty-five mice received oral rifampicin (1, 3, 9, 26 or 98 mg·kg⁻¹·day⁻¹; Days 1–8; 5 each dose level) and were sacrificed on Day 9. Twenty mice received oral rifampicin (30 mg·kg⁻¹·day⁻¹; up to 8 days) and were sacrificed on Days 2, 3, 4 and 9 (5 each day). The MTP model was linked to a rifampicin population pharmacokinetic model to describe the change in colony forming units (CFU) in the lungs over time. The transfer rates between the different bacterial states were fixed to estimates from in vitro data. The MTP model described well the change in CFU over time after different exposure levels of rifampicin in an acute tuberculosis mouse model. Rifampicin significantly inhibited the growth of fast-multiplying bacteria and stimulated the death of fast- and slow-multiplying bacteria. The data did not support an effect of rifampicin on non-multiplying bacteria possibly due to the short duration of the study. The pharmacometric modelling framework using the MTP model can be used to perform investigations and predictions of the efficacy of anti-tubercular drugs against different bacterial states.

Translational PK/PD of anti-infective therapeutics

Translational PK/PD modeling has emerged as a critical technique for quantitative analysis of the relationship between dose, exposure and response of antibiotics. By combining model components for pharmacokinetics, bacterial growth kinetics and concentration-dependent drug effects, these models are able to quantitatively capture and simulate the complex interplay between antibiotic, bacterium and host organism. Fine-tuning of these basic model structures allows to further account for complicating factors such as resistance development, combination therapy, or host responses. With this tool set at hand, mechanism-based PK/PD modeling and simulation allows to develop optimal dosing regimens for novel and established antibiotics for maximum efficacy and minimal resistance development.

Application of the Multistate Tuberculosis Pharmacometric Model in Patients With Rifampicin-Treated Pulmonary Tuberculosis

This is the first clinical implementation of the Multistate Tuberculosis Pharmacometric (MTP) model describing fast-, slow-, and nonmultiplying bacterial states of Mycobacterium tuberculosis. Colony forming unit data from 19 patients treated with rifampicin were analyzed. A previously developed rifampicin population pharmacokinetic (PK) model was linked to the MTP model previously developed using in vitro data. Drug effect was implemented as exposure-response relationships tested at several effect sites, both alone and in combination. All MTP model parameters were fixed to in vitro estimates except Bmax . Drug effect was described by an on/off effect inhibiting growth of fast-multiplying bacteria in addition to linear increase of the stimulation of the death rate of slow- and nonmultiplying bacteria with increasing drug exposure. Clinical trial simulations predicted well three retrospective clinical trials using the final model that confirmed the potential utility of the MTP model in antitubercular drug development.