Limited sampling strategies to predict the area under the concentration-time curve for rifampicin

Limited sampling strategies for therapeutic drug monitoring of anti-tuberculosis medications: A systematic review of their feasibility and clinical utility

Therapeutic drug monitoring (TDM) is recommended for medications with high inter-individual variability, narrow therapeutic index drugs, possible drug-drug interactions, drug toxicity, and subtherapeutic concentrations, as well as to assess noncompliance. The area under the plasma concentration-time curve (AUC) is a significant pharmacokinetic parameter since it calculates the drug's total systematic exposure in the body. However, multiple blood samples from the patient are required to calculate the area under the curve, which is inconvenient for both the patient and the healthcare professional. To alleviate the issue, the limited sampling strategy (LSS) was devised, in which sampling is minimized while obtaining complete and precise findings to anticipate the area under the curve. One can reduce costs, labor, and discomfort for patients and healthcare workers by applying this limited sampling strategy. This article examines a systematic evaluation of all the limited sampling done in anti-tuberculosis (anti-TB) medications resulting from the literature search of several research papers. This article also briefly describes the two methodologies: Multiple regression analysis (MRA) and the Bayesian approach used to develop a limited sampling strategy model. Anti-TB medications have been found to have considerable inter-individual variability, and isoniazid has a narrow therapeutic index, both of which are criteria for therapeutic drug monitoring. To avoid multi-drug resistance and therapy failure, it is proposed that limited sampling strategy-based therapeutic drug monitoring of anti-TB medications be undertaken to generate an individualized dose regimen, particularly for individuals at high risk of treatment failure or delayed response.

Has the Time Come for Systematic Therapeutic Drug Monitoring of First-Line and WHO Group A Antituberculosis Drugs?

ABSTRACT

Tuberculosis (TB) is a major global health issue, with approximately 10 million people being infected each year, and is the leading cause of mortality from infectious disease, with 1.5 million deaths a year. Optimal TB treatment requires a combination of drugs for an adequate treatment duration owing to persistent organisms, hardly accessible infection sites, and a high risk of resistance selection. Long-term therapy increases the risk of patients' loss of adherence, adverse drug reactions, and drug-drug interactions, potentially leading to treatment failure. The high interpatient variability of TB drug exposure is another point eliciting interest in therapeutic drug monitoring (TDM) to optimize treatment. Studies reporting clinically relevant exposure thresholds, which might be proposed as targets toward treatment personalization, are discussed. Practical TDM strategies have also been reported to circumvent issues related to delayed drug absorption and the need for multiple samples when evaluating the area under the curve of drug concentrations. The need for treatment individualization is further emphasized because of the development of multidrug-resistant TB or extensively drug-resistant TB. Finally, the willingness to shorten the treatment duration while maintaining success is also a driver for ensuring adequate exposure to TB drugs with TDM. The aim of the present review was to underline the role of TDM in drug-susceptible TB and World Health Organization group A TB drugs.

Optimal Sampling Strategies for Therapeutic Drug Monitoring of First-Line Tuberculosis Drugs in Patients with Tuberculosis

Background

The 24-h area under the concentration–time curve (AUC₂₄)/minimal inhibitory concentration ratio is the best predictive pharmacokinetic/pharmacodynamic (PK/PD) parameter of the efficacy of first-line anti-tuberculosis (TB) drugs. An optimal sampling strategy (OSS) is useful for accurately estimating AUC₂₄; however, OSS has not been developed in the fed state or in the early phase of treatment for first-line anti-TB drugs.

Methods

An OSS for the prediction of AUC₂₄ of isoniazid, rifampicin, ethambutol and pyrazinamide was developed for TB patients starting treatment. A prospective, randomized, crossover trial was performed during the first 3 days of treatment in which first-line anti-TB drugs were administered either intravenously or in fasting or fed conditions. The PK data were used to develop OSS with best subset selection multiple linear regression. The OSS was internally validated using a jackknife analysis and externally validated with other patients from different ethnicities and in a steady state of treatment.

Results

OSS using time points of 2, 4 and 8 h post-dose performed best. Bias was < 5% and imprecision was < 15% for all drugs except ethambutol in the fed condition. External validation showed that OSS_2-4-8 cannot be used for rifampicin in steady state conditions.

Conclusion

OSS at 2, 4 and 8 h post-dose enabled an accurate and precise prediction of AUC₂₄ values of first-line anti-TB drugs in this population.

Trial Registration

ClinicalTrials.gov (NCT02121314).

A population approach of rifampicin pharmacogenetics and pharmacokinetics in Mexican patients with tuberculosis

The aim of this study was to develop a population pharmacokinetic model of rifampicin (RMP) in Mexican patients with tuberculosis (TB) to evaluate the influence of anthropometric and clinical covariates, as well as genotypic variants associated with MDR1 and OATP1B1 transporters. A prospective study approved by Research Ethics Committee was performed at Hospital Central in San Luis Potosí, Mexico. TB patients under DOTS scheme and who signed informed consent were consecutively included. Anthropometric and clinical information was retrieved from medical records. Single nucleotide polymorphisms in MDR1 (C3435T) and SLCO1B1 (A388G and T521C) genes were evaluated. RMP plasma concentrations and time data were assessed with NONMEM software. A total of 71 Mexican TB patients from 18 to 72 years old were included for RMP quantification from 0.3 to 12 h after dose; 329 and 97 plasma concentrations were available for model development and validation, respectively. Sequential process includes a typical lag time of 0.25 h prior to absorption start with a Ka of 1.24 h^-1 and a zero-order absorption of 0.62 h to characterize the gradual increase in RMP plasma concentrations. Final model includes total body weight in volume of distribution (0.7 L/kg, CV = 26.8%) and a total clearance of 5.96 L/h (CV = 38.5%). Bioavailability was modified according to time under treatment and generic formulation administration. In conclusion, a population pharmacokinetic model was developed to describe the variability in RMP plasma concentrations in Mexican TB patients. Genetic variants evaluated did not showed significant influence on pharmacokinetic parameters. Final model will allow therapeutic drug monitoring at early stages.

Personalized Tuberculosis Treatment Through Model-Informed Dosing of Rifampicin

BACKGROUND AND OBJECTIVE

This study proposes a model-informed approach for therapeutic drug monitoring (TDM) of rifampicin to improve tuberculosis (TB) treatment.

METHODS

Two datasets from pulmonary TB patients were used: a pharmacokinetic study (34 patients, 373 samples), and TDM data (96 patients, 391 samples) collected at Radboud University Medical Center, The Netherlands. Nine suitable population pharmacokinetic models of rifampicin were identified in the literature and evaluated on the datasets. A model developed by Svensson et al. was found to be the most suitable based on graphical goodness of fit, residual diagnostics, and predictive performance. Prediction of individual area under the concentration-time curve from time zero to 24 h (AUC₂₄) and maximum concentration (C_max) employing various sampling strategies was compared with a previously established linear regression TDM strategy, using sampling at 2, 4, and 6 h, in terms of bias and precision (mean error [ME] and root mean square error [RMSE]).

RESULTS

A sampling strategy using 2- and 4-h blood collection was selected to be the most suitable. The bias and precision of the two strategies were comparable, except that the linear regression strategy was more biased in prediction of the AUC₂₄ than the model-informed approach (ME of 9.9% and 1.5%, respectively). A comparison of resulting dose advice, using predictions on a simulated dataset, showed no significant difference in sensitivity or specificity between the two methods. The model was successfully implemented in the InsightRX precision dosing platform.

CONCLUSION

Blood sampling at 2 and 4 h, combined with model-based prediction, can be used instead of the currently used linear regression strategy, shortening the sampling by 2 h and one sampling point without performance loss while simultaneously offering flexibility in sampling times.

What the lab can and cannot do: clinical interpretation of drug testing results

Urine drug testing is one of the objective tools available to assess adherence. To monitor adherence, quantitative urinary results can assist in differentiating "new" drug use from "previous" (historical) drug use. "Spikes" in urinary concentration can assist in identifying patterns of drug use. Coupled chromatographic-mass spectrometric methods are capable of identifying very small amounts of analyte and can make clinical interpretation rather challenging, specifically for drugs that have a longer half-life. Polypharmacy is common in treatment and rehabilitation programs because of co-morbidities. Medications prescribed for comorbidities can cause drug-drug interaction and phenoconversion of genotypic extensive metabolizers into phenotypic poor metabolizers of the treatment drug. This can have significant impact on both pharmacokinetic (PK) and pharmacodynamic properties of the treatment drug. Therapeutic drug monitoring (TDM) coupled with PKs can assist in interpreting the effects of phenoconversion. TDM-PKs reflects the cumulative effects of pathophysiological changes in the patient as well as drug-drug interactions and should be considered for treatment medications/drugs used to manage pain and treat substance abuse. Since only a few enzyme immunoassays for TDM are available, this is a unique opportunity for clinical laboratory scientists to develop TDM-PK protocols that can have a significant impact on patient care and personalized medicine. Interpretation of drug screening results should be done with caution while considering pharmacological properties and the presence or absence of the parent drug and its metabolites. The objective of this manuscript is to review and address the variables that influence interpretation of different drugs analyzed from a rehabilitation and treatment programs perspective.

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 (AUC_0-24h ). 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 AUC_0-24h 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 AUC_0-24h -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.

A simultaneous population pharmacokinetic analysis of rifampicin in Malawian adults and children

AIMS

Low rifampicin plasma concentrations can lead to treatment failure and increased risk of developing drug resistant tuberculosis. The objectives of this study were to characterize the population pharmacokinetics (popPK) of rifampicin in Malawian children and adults with tuberculosis, simulate exposures under revised WHO dosing guidelines that aim to reduce the risk of low exposures of rifampicin and examine predicted exposures using weight- and age-based dosing bands under new dosing recommendations.

METHODS

Patients were recruited at least two weeks after initiation of the intensive phase of treatment and received RIF in FDC of anti-TB drugs. A total of 5-6 rich and 1-2 sparse samples were collected. nonmem (v7.2) was used to build a population-PK model.

RESULTS

A 165 TB patients, 115 adults and 50 children, aged 7 months to 65 years and weighing 4.8 to 87 kg, were included in the one compartment model with first order absorption best described the data. The mean population estimate for CL/F was 23.9 (l h(-1)  70 kg(-1) ) with inter-individual variability of 46.6%. Exposure was unaffected by HIV status. Relative bioavailability in children was estimated at 49% lower compared to adults (100% relative bioavailability). Simulations showed significantly lower rifampicin exposure in children vs. adults. In children average AUC was 13.5 mg l(-1) h, which was nearly half that was observed in adults (26.3 mg l(-1)  h). Using age as a surrogate for weight in dosing bands gave similar results compared with the weight bands. Increasing dose to approximately 15 mg kg(-1) , increased AUC in children to an average of 22 mgl(-1)  h. bringing expected exposures in children closer to those predicted for adults.

CONCLUSION

The popPK model developed can be used to optimize rifampicin exposures through dosing simulations. WHO dosing recommendations may not be achieved using currently licensed fixed dose combination formulations of TB therapy.

Pharmacokinetic Modeling and Optimal Sampling Strategies for Therapeutic Drug Monitoring of Rifampin in Patients with Tuberculosis

Rifampin, together with isoniazid, has been the backbone of the current first-line treatment of tuberculosis (TB). The ratio of the area under the concentration-time curve from 0 to 24 h (AUC0-24) to the MIC is the best predictive pharmacokinetic-pharmacodynamic parameter for determinations of efficacy. The objective of this study was to develop an optimal sampling procedure based on population pharmacokinetics to predict AUC0-24 values. Patients received rifampin orally once daily as part of their anti-TB treatment. A one-compartmental pharmacokinetic population model with first-order absorption and lag time was developed using observed rifampin plasma concentrations from 55 patients. The population pharmacokinetic model was developed using an iterative two-stage Bayesian procedure and was cross-validated. Optimal sampling strategies were calculated using Monte Carlo simulation (n = 1,000). The geometric mean AUC0-24 value was 41.5 (range, 13.5 to 117) mg · h/liter. The median time to maximum concentration of drug in serum (Tmax) was 2.2 h, ranging from 0.4 to 5.7 h. This wide range indicates that obtaining a concentration level at 2 h (C2) would not capture the peak concentration in a large proportion of the population. Optimal sampling using concentrations at 1, 3, and 8 h postdosing was considered clinically suitable with an r(2) value of 0.96, a root mean squared error value of 13.2%, and a prediction bias value of -0.4%. This study showed that the rifampin AUC0-24 in TB patients can be predicted with acceptable accuracy and precision using the developed population pharmacokinetic model with optimal sampling at time points 1, 3, and 8 h.