Dosage of isoniazid and rifampicin poorly predicts drug exposure in tuberculosis patients

State of the art of real-life concentration monitoring of rifampicin and its implementation contextualized in resource-limited settings: the Tanzanian case

The unique medical and socio-economic situation in each country affected by TB creates different epidemiological contexts, thus providing exploitable loopholes for the spread of the disease. Country-specific factors such as comorbidities, health insurance, social stigma or the rigidity of the health system complicate the management of TB and the overall outcome of each patient. First-line TB drugs are administered in a standardized manner, regardless of patient characteristics other than weight. This approach does not consider patient-specific conditions such as HIV infection, diabetes mellitus and malnutrition, which can affect the pharmacokinetics of TB drugs, their overall exposure and response to treatment. Therefore, the 'one-size-fits-all' approach is suboptimal for dealing with the underlying inter-subject variability in the pharmacokinetics of anti-TB drugs, further complicated by the recent increased dosing regimen of rifampicin strategies, calling for a patient-specific methodology. In this context, therapeutic drug monitoring (TDM), which allows personalized drug dosing based on blood drug concentrations, may be a legitimate solution to address treatment failure. This review focuses on rifampicin, a critical anti-TB drug, and examines its suitability for TDM and the socio-economic factors that may influence the implementation of TDM in clinical practice in resource-limited settings, illustrated by Tanzania, thereby contributing to the advancement of personalized TB treatment.

Population Pharmacokinetic Modelling and Limited Sampling Strategies for Therapeutic Drug Monitoring of Pyrazinamide in Patients with Tuberculosis

Pyrazinamide is one of the first-line antituberculosis drugs. The efficacy of pyrazinamide is associated with the ratio of 24-h area under the concentration-time curve (AUC₂₄) to MIC. The objective of this study was to develop and validate a limited sampling strategy (LSS) based on a population pharmacokinetic (popPK) model to predict AUC₂₄. A popPK model was developed using an iterative two-stage Bayesian procedure and was externally validated. Using data from 20 treatment-naive adult tuberculosis (TB) patients, a one compartment model with transit absorption and first-order elimination best described pyrazinamide pharmacokinetics and fed state was the only significant covariate for absorption rate constant (k_a). External validation, using data from 26 TB patients, showed that the popPK model predicted AUC₂₄ with a slight underestimation of 2.1%. LSS were calculated using Monte Carlo simulation (n = 10,000). External validation showed LSS with time points 0 h, 2 h, and 6 h performed best with RMSE of 9.90% and bias of 0.06%. Food slowed absorption of pyrazinamide, but did not affect bioavailability, which may be advantageous in case of nausea or vomiting in which food can be used to diminish these effects. In this study, we successfully developed and validated a popPK model and LSS, using 0 h, 2 h, and 6 h postdose samples, that could be used to perform therapeutic drug monitoring (TDM) of pyrazinamide in TB patients.

Development of a population pharmacokinetic model and Bayesian estimators for isoniazid in Tunisian tuberculosis patients

This study aimed to develop a population pharmacokinetic model using full pharmacokinetic (PK) profiles of isoniazid (INH) taking into account demographic and genetic covariates and to develop Bayesian estimators for predicting INH area under the curve (AUC) in Tunisian tuberculosis patients. The INH concentrations in the building data set were fitted using a one- to three-compartment model. The impact of the different covariates was assessed on the PK parameters of the best model. The best limited sampling strategy (LSS) for estimating the INH AUC was selected by comparing the predicted values to an independent data set. INH PK was best described using a three-compartment model with lag-time absorption. The different studied covariates did not have any impact on the PK parameters of the building model. The Bayesian estimation using one-point concentrations gave the lowest values of prediction errors for the C₃ LSS model. This model could be sufficient in routine activity for INH monitoring in this population.

Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia

BACKGROUND AND OBJECTIVES

Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time-consuming. Furthermore, GeneXpert for rapid diagnosis is not widely available in Indonesia. This study aims to develop and evaluate the CUHAS-ROBUST model performance, an artificial-intelligence-based RR-TB screening tool.

METHODS

A cross-sectional study involved suspected all type of RR-TB patients with complete sputum Lowenstein Jensen DST (reference) and 19 clinical, laboratory, and radiology parameter results, retrieved from medical records in hospitals under the Faculty of Medicine, Hasanuddin University Indonesia, from January 2015-December 2019. The Artificial Neural Network (ANN) models were built along with other classifiers. The model was tested on participants recruited from January 2020-October 2020 and deployed into CUHAS-ROBUST (index test) application. Sensitivity, specificity, and accuracy were obtained for assessment.

RESULTS

A total of 487 participants (32 Multidrug-Resistant/MDR 57 RR-TB, 398 drug-sensitive) were recruited for model building and 157 participants (23 MDR and 21 RR) in prospective testing. The ANN full model yields the highest values of accuracy (88% (95% CI 85-91)), and sensitivity (84% (95% CI 76-89)) compare to other models that show sensitivity below 80% (Logistic Regression 32%, Decision Tree 44%, Random Forest 25%, Extreme Gradient Boost 25%). However, this ANN has lower specificity among other models (90% (95% CI 86-93)) where Logistic Regression demonstrates the highest (99% (95% CI 97-99)). This ANN model was selected for the CUHAS-ROBUST application, although still lower than the sensitivity of global GeneXpert results (87.5%).

CONCLUSION

The ANN-CUHAS ROBUST outperforms other AI classifiers model in detecting all type of RR-TB, and by deploying into the application, the health staff can utilize the tool for screening purposes particularly at the primary care level where the GeneXpert examination is not available.

TRIAL REGISTRATION

NCT04208789.

A Model-Informed Method for the Purpose of Precision Dosing of Isoniazid in Pulmonary Tuberculosis

Background and Objective

This study aimed to develop and evaluate a population pharmacokinetic model and limited sampling strategy for isoniazid to be used in model-based therapeutic drug monitoring.

Methods

A population pharmacokinetic model was developed based on isoniazid and acetyl-isoniazid pharmacokinetic data from seven studies with in total 466 patients from three continents. Three limited sampling strategies were tested based on the available sampling times in the dataset and practical considerations. The tested limited sampling strategies sampled at 2, 4, and 6 h, 2 and 4 h, and 2 h after dosing. The model-predicted area under the concentration–time curve from 0 to 24 h (AUC₂₄) and the peak concentration from the limited sampling strategies were compared to predictions using the full pharmacokinetic curve. Bias and precision were assessed using the mean error (ME) and the root mean square error (RMSE), both expressed as a percentage of the mean model-predicted AUC₂₄ or peak concentration on the full pharmacokinetic curve.

Results

Performance of the developed model was acceptable and the uncertainty in parameter estimations was generally low (the highest relative standard error was 39% coefficient of variation). The limited sampling strategy with sampling at 2 and 4 h was determined as most suitable with an ME of 1.1% and RMSE of 23.4% for AUC₂₄ prediction, and ME of 2.7% and RMSE of 23.8% for peak concentration prediction. For the performance of this strategy, it is important that data on both isoniazid and acetyl-isoniazid are used. If only data on isoniazid are available, a limited sampling strategy using 2, 4, and 6 h can be employed with an ME of 1.7% and RMSE of 20.9% for AUC₂₄ prediction, and ME of 1.2% and RMSE of 23.8% for peak concentration prediction.

Conclusions

A model-based therapeutic drug monitoring strategy for personalized dosing of isoniazid using sampling at 2 and 4 h after dosing was successfully developed. Prospective evaluation of this strategy will show how it performs in a clinical therapeutic drug monitoring setting.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40262-020-00971-2.

Drug exposure of first-line anti-tuberculosis drugs in China: A prospective pharmacological cohort study

AIM

Exploring the need for optimization of drug exposure to improve tuberculosis (TB) treatment outcome is of great importance. We aimed to describe drug exposure at steady state as well as the population pharmacokinetics (PK) of rifampicin (RIF), isoniazid (INH) and pyrazinamide (PZA) in Chinese TB patients.

METHODS

A prospective multicentre PK study of RIF, INH and PZA was conducted in China between January 2015 and December 2017. Six blood samples were collected from each subject for drug concentration measurement. Nonlinear mixed effect analyses were used to develop population PK models.

RESULTS

In total, 217 patients were included. Positive correlations between body weight, clearance and volume of distribution were identified for RIF and PZA, whereas body weight only influenced clearance for INH. In addition, males had higher RIF clearance and thus lower RIF exposure than women. Acetylator status was significantly associated with INH clearance as INH exposure in intermediate and fast acetylators was significantly lower than in slow acetylators, especially in low-weight bands. Simulations also showed significantly lower drug exposures in low-weight bands for all three drugs. Patients weighing <38 kg were respectively exposed to 30.4%, 45.9% and 18.0% lower area under the concentration-time curve of RIF, INH and PZA than those weighing ≥70 kg. Higher doses by addition of one fixed-dose combination tablet or 150 mg INH were simulated and found to be effective in improving INH drug exposures, especially in low-weight bands.

CONCLUSION

PK variability of first-line anti-TB drugs is common in Chinese TB patients. The developed population PK models can be used to optimize drug exposures in Chinese patients. Moreover, standard dosing needs to be adjusted to increase target attainment.

Amikacin Dosing for MDR Tuberculosis: A Systematic Review to Establish or Revise the Current Recommended Dose for Tuberculosis Treatment

Background

Amikacin has been used for over 40 years in multidrug resistant tuberculosis (MDR-TB), but there is still debate on the right dose. The aim of this review was to search relevant pharmacokinetic (PK) and pharmacodynamic (PD) literature for the optimal dose and dosing frequency of amikacin in MDR-TB regimens trying to optimize efficacy while minimizing toxicity.

Methods

A systematic review on the value of amikacin as second-line drug in the treatment of MDR-TB was performed.

Results

Five articles were identified with data on PK, hollow-fiber system model for TB and or early bactericidal activity of amikacin. Despite the long period in which amikacin has been available for the treatment of MDR-TB, very little PK data is available. This highlights the need for more research.

Conclusions

Maximum concentration (Cmax) of amikacin related to MIC proved to be the most important PK/PD index for efficacy. The target Cmax/MIC ratio should be 10 at site of infection. Cumulative area under the concentration-time curve (AUC) corresponding with cumulative days of treatment was associated with an increased risk of toxicity.

The cursed duet today: Tuberculosis and HIV-coinfection

The tuberculosis (TB) and HIV syndemic continues to rage and are a major public health concern worldwide. This deadly association raises complexity and represent a significant barrier towards TB elimination. TB continues to be the leading cause of death amongst HIV-infected people. This paper reports the challenges that lay ahead and outlines some of the current and future strategies that may be able to address this co-epidemic efficiently. Improved diagnostics, cheaper and more effective drugs, shorter treatment regimens for both drug-sensitive and drug-resistant TB are discussed. Also, special topics on drug interactions, TB-IRIS and TB relapse are also described. Notwithstanding the defeats and meagre investments, diagnosis and management of the two diseases have seen significant and unexpected improvements of late. On the HIV side, expansion of ART coverage, development of new updated guidelines aimed at the universal treatment of those infected, and the increasing availability of newer, more efficacious and less toxic drugs are an essential element to controlling the two epidemics. On the TB side, diagnosis of MDR-TB is becoming easier and faster thanks to the new PCR-based technologies, new anti-TB drugs active against both sensitive and resistant strains (i.e. bedaquiline and delamanid) have been developed and a few more are in the pipeline, new regimens (cheaper, shorter and/or more effective) have been introduced (such as the "Bangladesh regimen") or are being tested for MDR-TB and drug-sensitive-TB. However, still more resources will be required to implement an integrated approach, install new diagnostic tests, and develop simpler and shorter treatment regimens.