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.

Rituximab pharmacokinetic and pharmacokinetic-pharmacodynamic evaluation based on a study in diffuse large B-cell lymphoma: Influence of tumor size on pharmacokinetic and assessment of pharmacokinetic similarity.

Dr. Reddy's Laboratories rituximab (DRL_RI; Dr. Reddy's Laboratories SA, Basel, Switzerland) is under development as a rituximab biosimilar. Study RI-01-002 (Clinical Trials Registry - India/2012/11/003129), comparing DRL_RI to the reference medicinal product (RMP) MabThera® (Roche, Grenzach-Wyhlen, Germany), demonstrated pharmacokinetic (PK) equivalence and showed comparable pharmacodynamic, efficacy, safety, and immunogenicity profiles. We used data from the same study to perform population PK and PK-pharmacodynamic analyses: first exploring possible factors influencing the PK similarity assessment between products and then performing simulations to investigate the impact of tumor size on rituximab PK. Nonlinear mixed-effects models for PK, tumor size, tumor size-PK, and tumor response were developed independently. The final PK model included drug product as a dose-scaling parameter and predicted a 6.75% higher dose reaching the system in RMP-treated patients. However, when tumor size was included in the tumor size-PK model, the drug product effect was no longer observed. The model rather indicated that patients with larger tumor size have higher clearance. Further simulations confirmed that higher baseline tumor size is associated to slightly lower rituximab exposure. Tumor response, described by a continuous-time Markov model, did not differ between drug products. Both had higher effects during the first 20 weeks of treatment. Also, the model described a subpopulation of nonresponders to treatment (42%) with faster transitions to a worse state. The different rituximab exposure initially detected between drug products (6.75%) was shown using PK/PK-pharmacodynamic analysis to be attributed to a tumor size imbalance between treatment groups. PK/PK-pharmacodynamic analyses may contribute to PK similarity assessments.

Efficient and relevant stepwise covariate model building for pharmacometrics.

Covariate modeling is an important opportunity for pharmacometrics to influence decision making in drug development. The stepwise covariate model (SCM) building procedure is the most common method for covariate model development. Despite its advantages, the traditional SCM method is known to have long runtimes and the suboptimal ability to select relevant covariates, especially in more complex phase III settings. In this work, two alternative approaches are presented: SCM+, which introduces the "adaptive scope reduction" and changes to general estimation settings, and "stage-wise filtering," which groups covariates into categories based on their importance (mechanistic, structural, and exploratory). The three methods (SCM, SCM+, and SCM+ with stage-wise filtering) are applied to data from a simulated phase III population pharmacokinetic study and are compared in terms of efficiency and relevance. The two SCM+ methods were considerably more efficient than the traditional SCM: the number of function evaluations was reduced by 70% for SCM+ and by 76% for SCM+ with stage-wise filtering compared to SCM; the corresponding number of executed models was reduced by 44% for SCM+ and 70% for SCM+ with stage-wise filtering. In addition, among the three methods, SCM+ with stage-wise filtering selected the highest number of relevant covariates. Given the improved efficiency and ability to select relevant covariates shown in this work, the use of SCM+ and stage-wise filtering can greatly increase the efficiency of covariate modeling in drug development, which will ultimately facilitate more timely support for decision making.

Population Pharmacokinetics and Exposure-Response Relationships of Astegolimab in Patients With Severe Asthma.

Astegolimab is a fully human immunoglobulin G2 monoclonal antibody that binds to the ST2 receptor and blocks the interleukin-33 signaling. It was evaluated in patients with uncontrolled severe asthma in the phase 2b study (Zenyatta) at doses of 70, 210, and 490 mg subcutaneously every 4 weeks for 52 weeks. This work aimed to characterize astegolimab pharmacokinetics, identify influential covariates contributing to its interindividual variability, and make a descriptive assessment of the exposure-response relationships. A population pharmacokinetic model was developed using data from 368 patients in the Zenyatta study. Predicted average steady-state concentration was used in the subsequent exposure-response analyses, which evaluated efficacy (asthma exacerbation rate) and biomarker end points including forced expiratory volume in 1 second, fraction exhaled nitric oxide, blood eosinophils, and soluble ST2. A 2-compartment disposition model with first-order elimination and first-order absorption best described the astegolimab pharmacokinetics. The relative bioavailability for the 70-mg dose was 15.3% lower. Baseline body weight, estimated glomerular filtration rate, and eosinophils were statistically correlated with pharmacokinetic parameters, but only body weight had a clinically meaningful influence on the steady-state exposure (ratios exceeding 0.8-1.25). The exposure-response of efficacy and biomarkers were generally flat with a weak trend in favor of the highest dose/exposure. This study characterized astegolimab pharmacokinetics in patients with asthma and showed typical pharmacokinetic behavior as a monoclonal antibody-based drug. The exposure-response analyses suggested the highest dose tested in the Zenyatta study (490 mg every 4 weeks) performed close to the maximum effect, and no additional response may be expected above it.

Population repeated time-to-event analysis of exacerbations in asthma patients: A novel approach for predicting asthma exacerbations based on biomarkers, spirometry, and diaries/questionnaires.

Identification of covariates, including biomarkers, spirometry, and diaries/questionnaires, that predict asthma exacerbations would allow better clinical predictions, shorter phase II trials and inform decisions on phase III design, and/or initiation (go/no-go). The objective of this work was to characterize asthma-exacerbation hazard as a function of baseline and time-varying covariates. A repeated time-to-event (RTTE) model for exacerbations was developed using data from a 52-week phase IIb trial, including 502 patients with asthma randomized to placebo or 70 mg, 210 mg, or 490 mg astegolimab every 4 weeks. Covariate analysis was performed for 20 baseline covariates using the full random effects modeling approach, followed by time-varying covariate analysis of nine covariates using the stepwise covariate model (SCM) building procedure. Following the SCM, an astegolimab treatment effect was explored. Diary-based symptom score (difference in objective function value [dOFV] of -83.7) and rescue medication use (dOFV = -33.5), and forced expiratory volume in 1 s (dOFV = -14.9) were identified as significant time-varying covariates. Of note, time-varying covariates become more useful with more frequent measurements, which should favor the daily diary scores over others. The most influential baseline covariates were exacerbation history and diary-based symptom score (i.e., symptom score was important as both time-varying and baseline covariate). A (nonsignificant) astegolimab treatment effect was included in the final model because the limited data set did not allow concluding the remaining effect size as irrelevant. Without time-varying covariates, the treatment effect was statistically significant (p < 0.01). This work demonstrated the utility of a population RTTE approach to characterize exacerbation hazard in patients with severe asthma.

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 MPNrpf 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 MPNrpf 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.

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. 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.

Rifampicin Can Be Given as Flat-Dosing Instead of Weight-Band Dosing.

BACKGROUND: The weight-band dosing in tuberculosis treatment regimen has been implemented in clinical practice for decades. Patients will receive different number of fixed dose combination tablets according to their weight-band. However, some analysis has shown that weight was not the best covariate to explain variability of rifampicin exposure. Furthermore, the rationale for using weight-band dosing instead of flat-dosing becomes questionable. Therefore, this study aimed to compare the average and the variability of rifampicin exposure after weight-band dosing and flat-dosing. METHODS: Rifampicin exposure were simulated using previously published population pharmacokinetics model at dose 10-40 mg/kg for weight-band dosing and dose 600-2400 mg for flat-dosing. The median area under the curve (AUC0-24 h) after day 7 and 14 were compared as well as the variability of each dose group between weight-band and flat-dosing. RESULTS: The difference of median AUC0-24 h of all dose groups between flat-dosing and weight-band dosing were considered low (< 20%) except for the lowest dose. At the dose of 10 mg/kg (600 mg for flat-dosing), flat-dosing resulted in higher median AUC0-24h compared to the weight-band dosing. A marginal decrease in between-patient variability was predicted for weight-band dosing compared to flat-dosing. CONCLUSIONS: Weight-band dosing yields a small and non-clinically relevant decrease in variability of AUC0-24h.

Individualised dosing algorithm and personalised treatment of high-dose rifampicin for tuberculosis.

AIMS: To propose new exposure targets for Bayesian dose optimisation suited for high-dose rifampicin and to apply them using measured plasma concentrations coupled with a Bayesian forecasting algorithm allowing predictions of future doses, considering rifampicin's auto-induction, saturable pharmacokinetics and high interoccasion variability. METHODS: Rifampicin exposure targets for Bayesian dose optimisation were defined based on literature data on safety and anti-mycobacterial activity in relation to rifampicin's pharmacokinetics i.e. highest plasma concentration up to 24 hours and area under the plasma concentration-time curve up to 24 hours (AUC0-24h ). Targets were suggested with and without considering minimum inhibitory concentration (MIC) information. Individual optimal doses were predicted for patients treated with rifampicin (10 mg/kg) using the targets with Bayesian forecasting together with sparse measurements of rifampicin plasma concentrations and baseline rifampicin MIC. RESULTS: The suggested exposure target for Bayesian dose optimisation was a steady state AUC0-24h of 181-214 h × mg/L. The observed MICs ranged from 0.016-0.125 mg/L (mode: 0.064 mg/L). The predicted optimal dose in patients using the suggested target ranged from 1200-3000 mg (20-50 mg/kg) with a mode of 1800 mg (30 mg/kg, n = 24). The predicted optimal doses when taking MIC into account were highly dependent on the known technical variability of measured individual MIC and the dose was substantially lower compared to when using the AUC0-24h -only target. CONCLUSIONS: A new up-to-date exposure target for Bayesian dose optimisation suited for high-dose rifampicin was derived. Using measured plasma concentrations coupled with Bayesian forecasting allowed prediction of the future dose whilst accounting for the auto-induction, saturable pharmacokinetics and high between-occasion variability of rifampicin.

Model-Based Relationship between the Molecular Bacterial Load Assay and Time to Positivity in Liquid Culture.

The molecular bacterial load (MBL) assay is a new tuberculosis biomarker which provides results in ∼4 hours. The relationship between MBL and time-to-positivity (TTP) has not been thoroughly studied, and predictive models do not exist. We aimed to develop a model for MBL and identify the MBL-TTP relationship in patients. The model was developed on data from 105 tuberculosis patients from Malawi, Mozambique, and Tanzania with joint MBL and TTP observations quantified from patient sputum collected for 12 weeks. MBL was quantified using PCR of mycobacterial RNA and TTP using the mycobacterial growth indicator tube (MGIT) 960 system. Treatment consisted of isoniazid, pyrazinamide, and ethambutol in standard doses together with rifampin 10 or 35 mg/kg of body weight. The developed MBL-TTP model included several linked submodels, a component describing decline of bacterial load in sputum, another component describing growth in liquid culture, and a hazard model translating bacterial growth into a TTP signal. Additional components for contaminated and negative TTP samples were included. Visual predictive checks performed using the developed model gave good description of the observed data. The model predicted greater total sample loss for TTP than MBL due to contamination and negative samples. The model detected an increase in bacterial killing for 35 versus 10 mg/kg rifampin (P = 0.002). In conclusion, a combined model for MBL and TTP was developed that described the MBL-TTP relationship. The full MBL-TTP model or each submodel was used separately. Second, the model can be used to predict biomarker response for MBL given TTP data or vice versa in historical or future trials.

The Potential for Treatment Shortening With Higher Rifampicin Doses: Relating Drug Exposure to Treatment Response in Patients With Pulmonary Tuberculosis.

Background: Tuberculosis remains a huge public health problem and the prolonged treatment duration obstructs effective tuberculosis control. Higher rifampicin doses have been associated with better bactericidal activity, but optimal dosing is uncertain. This analysis aimed to characterize the relationship between rifampicin plasma exposure and treatment response over 6 months in a recent study investigating the potential for treatment shortening with high-dose rifampicin. Methods: Data were analyzed from 336 patients with pulmonary tuberculosis (97 with pharmacokinetic data) treated with rifampicin doses of 10, 20, or 35 mg/kg. The response measure was time to stable sputum culture conversion (TSCC). We derived individual exposure metrics with a previously developed population pharmacokinetic model of rifampicin. TSCC was modeled using a parametric time-to-event approach, and a sequential exposure-response analysis was performed. Results: Higher rifampicin exposures increased the probability of early culture conversion. No maximal limit of the effect was detected within the observed range. The expected proportion of patients with stable culture conversion on liquid medium at week 8 was predicted to increase from 39% (95% confidence interval, 37%-41%) to 55% (49%-61%), with the rifampicin area under the curve increasing from 20 to 175 mg/L·h (representative for 10 and 35 mg/kg, respectively). Other predictors of TSCC were baseline bacterial load, proportion of culture results unavailable, and substitution of ethambutol for either moxifloxacin or SQ109. Conclusions: Increasing rifampicin exposure shortened TSCC, and the effect did not plateau, indicating that doses >35 mg/kg could be yet more effective. Optimizing rifampicin dosage while preventing toxicity is a clinical priority.

Greater Early Bactericidal Activity at Higher Rifampicin Doses Revealed by Modeling and Clinical Trial Simulations.

Background: The currently recommended rifampicin dose (10 mg/kg) for treating tuberculosis is suboptimal. The PanACEA HIGHRIF1 trial evaluated the pharmacokinetics and early bactericidal activity of rifampicin doses of up to 40 mg/kg. Conventional statistical analyses revealed no significant exposure-response relationship. Our objectives were to explore the exposure-response relationship for high-dose rifampicin by using pharmacokinetic-pharmacodynamic modeling and to predict the early bactericidal activity of 50 mg/kg rifampicin. Methods: Data included time to Mycobacterium tuberculosis positivity of liquid cultures of sputum specimens from 83 patients with tuberculosis who were treated with 10 mg/kg rifampicin (n = 8; reference arm) or 20, 25, 30, 35, or 40 mg/kg rifampicin (n = 15/arm) for 7 days. We used a semimechanistic time-to-event approach to model the time-to-positivity data. Rifampicin exposure and baseline time to culture positivity were explored as covariates. Results: The baseline time to culture positivity was a significant covariate on the predicted initial bacterial load, and rifampicin exposure was a significant covariate on the bacterial kill rate in sputum resulting in increased early bactericidal activity. The 90% prediction interval for the predicted median day 7 increase in time to positivity for 50 mg/kg rifampicin was 7.25-10.3 days. Conclusions: A significant exposure-response relationship was found between rifampicin exposure and early bactericidal activity. Clinical trial simulations showed greater early bactericidal activity for 50 mg/kg rifampicin. Clinical Trials Registration: NCT01392911.

Improving treatment outcome assessment in a mouse tuberculosis model.

Preclinical treatment outcome evaluation of tuberculosis (TB) occurs primarily in mice. Current designs compare relapse rates of different regimens at selected time points, but lack information about the correlation between treatment length and treatment outcome, which is required to efficiently estimate a regimens' treatment-shortening potential. Therefore we developed a new approach. BALB/c mice were infected with a Mycobacterium tuberculosis Beijing genotype strain and were treated with rifapentine-pyrazinamide-isoniazid-ethambutol (RpZHE), rifampicin-pyrazinamide-moxifloxacin-ethambutol (RZME) or rifampicin-pyrazinamide-moxifloxacin-isoniazid (RZMH). Treatment outcome was assessed in n = 3 mice after 9 different treatment lengths between 2-6 months. Next, we created a mathematical model that best fitted the observational data and used this for inter-regimen comparison. The observed data were best described by a sigmoidal Emax model in favor over linear or conventional Emax models. Estimating regimen-specific parameters showed significantly higher curative potentials for RZME and RpZHE compared to RZMH. In conclusion, we provide a new design for treatment outcome evaluation in a mouse TB model, which (i) provides accurate tools for assessment of the relationship between treatment length and predicted cure, (ii) allows for efficient comparison between regimens and (iii) adheres to the reduction and refinement principles of laboratory animal use.

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.

A Population Pharmacokinetic Model Incorporating Saturable Pharmacokinetics and Autoinduction for High Rifampicin Doses.

Accumulating evidence suggests that increasing doses of rifampicin may shorten tuberculosis treatment. The PanACEA HIGHRIF1 trial assessed safety, pharmacokinetics, and antimycobacterial activity of rifampicin at doses up to 40 mg/kg. Eighty-three pulmonary tuberculosis patients received 10, 20, 25, 30, 35, or 40 mg/kg rifampicin daily over 2 weeks, supplemented with standard doses of isoniazid, pyrazinamide, and ethambutol in the second week. This study aimed at characterizing rifampicin pharmacokinetics observed in HIGHRIF1 using nonlinear mixed effects modeling. The final population pharmacokinetic model included an enzyme turnover model accounting for time-dependent elimination due to autoinduction, concentration-dependent clearance, and dose-dependent bioavailability. The relationship between clearance and concentration was characterized by a Michaelis-Menten relationship. The relationship between bioavailability and dose was described using an Emax relationship. The model will be key in determining exposure-response relationships for rifampicin and should be considered when designing future trials and when treating future patients with high-dose rifampicin.

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.

Limiting resources in sessile systems: food enhances diversity and growth of suspension feeders despite available space.

Much of our understanding of competition comes trom onservations in sessue systems, such as rainforests and marine invertebrate communities. In terrestrial systems, sessile species often compete for multiple limiting resources (i.e., space, light, and nutrients), but in marine systems, space is viewed as the primary or sole limiting resource. Competition theory, on the other hand, suggests that competition for a single limiting resource is unlikely to maintain high species diversity, but manipulative tests of competition for other resources in marine benthic systems are exceedingly rare. Here, we manipulate the availability of food for a classic system, marine sessile invertebrate communities, and investigate the effects on species diversity, abundance, and composition during early succession as well as on the growth of bryozoan populations in the field. We found the number of species to be greater, available space to be lower, and the community composition to be different in assemblages subjected to increased food availability compared to controls. Similarly, laboratory-settled bryozoans deployed into the field grew more in the presence of enhanced food. Our results suggest that food can act as a limiting resource, affecting both diversity and abundance, even when bare space is still available in hard-substratum communities. Consequently, broadening the view of resource limitation beyond solely space may increase our understanding and predictability of marine sessile systems.