Adequate design of pharmacokinetic-pharmacodynamic studies will help optimize tuberculosis treatment for the future

Effect of NAT2, GSTM1 and CYP2E1 genetic polymorphisms on plasma concentration of isoniazid and its metabolites in patients with tuberculosis, and the assessment of exposure-response relationships

Objectives: Isoniazid is a key drug in the chemotherapy of tuberculosis (TB), however, interindividual variability in pharmacokinetic parameters and drug plasma levels may affect drug responses including drug induced hepatotoxicity. The current study investigated the relationships between isoniazid exposure and isoniazid metabolism-related genetic factors in the context of occurrence of drug induced hepatotoxicity and TB treatment outcomes. Methods: Demographic characteristics and clinical information were collected in a prospective TB cohort study in Latvia (N = 34). Time to sputum culture conversion (tSCC) was used as a treatment response marker. Blood plasma concentrations of isoniazid (INH) and its metabolites acetylisoniazid (AcINH) and isonicotinic acid (INA) were determined at three time points (pre-dose (0 h), 2 h and 6 h after drug intake) using liquid chromatography-tandem mass spectrometry. Genetic variations of three key INH-metabolizing enzymes (NAT2, CYP2E1, and GSTM1) were investigated by application PCR- and Next-generation sequencing-based methods. Depending on variables, group comparisons were performed by Student's t-test, one-way ANOVA, Mann-Whitney-Wilcoxon, and Kruskal-Wallis tests. Pearson correlation coefficient was calculated for the pairs of normally distributed variables; model with rank transformations were used for non-normally distributed variables. Time-to-event analysis was performed to analyze the tSCC data. The cumulative probability of tSCC was obtained using Kaplan-Meier estimators. Cox proportional hazards models were fitted to estimate hazard rate ratios of successful tSCC. Results: High TB treatment success rate (94.1%) was achieved despite the variability in INH exposure. Clinical and demographic factors were not associated with either tSCC, hepatotoxicity, or INH pharmacokinetics parameters. Correlations between plasma concentrations of INH and its metabolites were NAT2 phenotype-dependent, while GSTM1 genetic variants did not showed any effects. CYP2E1*6 (T > A) allelic variant was associated with INH pharmacokinetic parameters. Decreased level of AcINH was associated with hepatotoxicity, while decreased values of INA/INH and AcINH/INH were associated with month two sputum culture positivity. Conclusion: Our findings suggest that CYP2E1, but not GSTM1, significantly affects the INH pharmacokinetics along with NAT2. AcINH plasma level could serve as a biomarker for INH-related hepatotoxicity, and the inclusion of INH metabolite screening in TB therapeutic drug monitoring could be beneficial in clinical studies for determination of optimal dosing strategies.

Determination of plasma concentrations of levofloxacin by high performance liquid chromatography for use at a multidrug-resistant tuberculosis hospital in Tanzania

Therapeutic drug monitoring may improve multidrug-resistant tuberculosis (MDR-TB) treatment outcomes. Levofloxacin demonstrates significant individual pharmacokinetic variability. Thus, we sought to develop and validate a high-performance liquid chromatography (HPLC) method with ultraviolet (UV) detection for levofloxacin in patients on MDR-TB treatment. The HPLC-UV method is based on a solid phase extraction (SPE) and a direct injection into the HPLC system. The limit of quantification was 0.25 μg/mL, and the assay was linear over the concentration range of 0.25—15 μg/mL (y = 0.5668x—0.0603, R² = 0.9992) for the determination of levofloxacin in plasma. The HPLC-UV methodology achieved excellent accuracy and reproducibility along a clinically meaningful range. The intra-assay RSD% of low, medium, and high quality control samples (QC) were 1.93, 2.44, and 1.90, respectively, while the inter-assay RSD% were 3.74, 5.65, and 3.30, respectively. The mean recovery was 96.84%. This method was then utilized to measure levofloxacin concentrations from patients’ plasma samples from a retrospective cohort of consecutive enrolled subjects treated for MDR-TB at the national TB hospital in Tanzania during 5/3/2013–8/31/2015. Plasma was collected at 2 hours after levofloxacin administration, the time of estimated peak concentration (eC_max) treatment. Forty-one MDR-TB patients had plasma available and 39 had traceable programmatic outcomes. Only 13 (32%) patients had any plasma concentration that reached the lower range of the expected literature derived C_max with the median eC_max being 5.86 (3.33–9.08 μg/ml). Using Classification and Regression Tree analysis, an eC_max ≥7.55 μg/mL was identified as the threshold which best predicted cure. Analyzing this CART derived threshold on treatment outcome, the time to sputum culture conversion was 38.3 ± 22.7 days vs. 47.8 ± 26.5 days (p = 0.27) and a greater proportion were cured, in 10 out of 15 (66.7%) vs. 6 out of 18 (33.3%) (p = 0.06) respectively. Furthermore, one patient with an eC_max/minimum inhibitory concentration (MIC) of only 1.13 acquired extensively drug resistant (XDR)-TB while undergoing treatment. The individual variability of levofloxacin concentrations in MDR-TB patients from Tanzania supports further study of the application of onsite therapeutic drug monitoring and MIC testing.

Optimizing treatment outcome of first-line anti-tuberculosis drugs: the role of therapeutic drug monitoring

INTRODUCTION

Tuberculosis (TB) remains one of the world's deadliest communicable diseases. Although cure rates of the standard four-drug (rifampicin, isoniazid, pyrazinamide, ethambutol) treatment schedule can be as high as 95-98 % under clinical trial conditions, success rates may be much lower in less well resourced countries. Unsuccessful treatment with these first-line anti-TB drugs may lead to the development of multidrug resistant and extensively drug resistant TB. The intrinsic interindividual variability in the pharmacokinetics (PK) of the first-line anti-TB drugs is further exacerbated by co-morbidities such as HIV infection and diabetes.

METHODS

Therapeutic drug monitoring has been proposed in an attempt to optimize treatment outcome and reduce the development of drug resistance. Several studies have shown that maximum plasma concentrations (C max), especially of rifampicin and isoniazid, are well below the proposed target C max concentrations in a substantial fraction of patients being treated with the standard four-drug treatment schedule, even though treatment's success rate in these studies was typically at least 85 %.

DISCUSSION

The proposed target C max concentrations are based on the concentrations of these agents achieved in healthy volunteers and patients receiving the standard doses. Estimation of C max based on one or two sampling times may not have the necessary accuracy since absorption rate, especially for rifampicin, may be highly variable. In addition, minimum inhibitory concentration (MIC) variability should be taken into account to set clinically meaningful susceptibility breakpoints. Clearly, there is a need to better define the key target PK and pharmacodynamic (PD) parameters for therapeutic drug monitoring (TDM) of the first-line anti-TB drugs to be efficacious, C max (or area under the curve (AUC)) and C max/MIC (or AUC/MIC).

CONCLUSION

Although TDM of first-line anti-TB drugs has been successfully used in a limited number of specialized centers to improve treatment outcome in slow responders, a better characterization of the target PK and/or PK/PD parameters is in our opinion necessary to make it cost-effective.

Emerging drugs and alternative possibilities in the treatment of tuberculosis

INTRODUCTION

Tuberculosis (TB) remains a global health problem. Drug resistance, treatment duration, complexity, and adverse drug reactions associated with anti-TB regimens are associated with treatment failure, prolonged infectiousness and relapse. With the current set of anti-TB drugs the goal to end TB has not been met. New drugs and new treatment regimens are needed to eradicate TB.

AREAS COVERED

Literature was explored to select publications on drugs currently in phase II and phase III trials. These include new chemical entities, immunotherapy, established drugs in new treatment regimens and vaccines for the prophylaxis of TB.

EXPERT OPINION

Well designed trials, with detailed pharmacokinetic/pharmacodynamic analysis, in which information on drug exposure and drug susceptibility of the entire anti-TB regimen is included, in combination with long-term follow-up will provide relevant data to optimize TB treatment. The new multi arm multistage trial design could be used to test new combinations of compounds, immunotherapy and therapeutic vaccines. This new approach will both reduce the number of patients exposed to inferior treatment and the financial burden. Moreover, it will speed up drug evaluation. Considering the investments involved in development of new drugs it is worthwhile to thoroughly investigate existing, non-TB drugs in new regimens.

Serum Levels of Antituberculosis Drugs and Their Effect on Tuberculosis Treatment Outcome

Therapeutic drug monitoring in tuberculosis remains controversial. We evaluated the relationship between antituberculosis drug levels in blood and clinical outcome. Serum concentrations of first-line antituberculosis drugs were measured in tuberculosis patients between March 2006 and April 2013. Venous blood was drawn 2 h after drug ingestion and was analyzed using high-performance liquid chromatography-tandem mass spectrometry. We retrospectively reviewed the data and determined the association of serum drug levels with clinical outcome. Among 413 patients, the prevalences of low serum concentrations of isoniazid (INH), rifampin (RMP), ethambutol (EMB), and pyrazinamide (PZA) were 59.9%, 27.8%, 12.8%, and 8.7%, respectively. The low INH group had a greater percentage of patients with a history of tuberculosis treatment (19.2% versus 11.0%; P = 0.026) and was more likely to present with drug-resistant strains (17.6% versus 8.8%; P = 0.049) than the normal INH group; however, low levels of INH, RMP, EMB, and PZA were not related to treatment outcome. Low INH level had a tendency to be associated with 2-month culture positivity, but it was not statistically significant (P = 0.072) in multivariate analysis. Seventeen (4.1%) patients experienced a recurrence. However, the recurrence rate was not statistically different between the low and normal INH groups. Low serum INH may play a role in recurrence and in acquired drug resistance. However, the serum level of INH was not directly related to either treatment response or recurrence rate. The role and usefulness of therapeutic drug monitoring should be evaluated in further prospective studies.

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.