Limited sampling strategies for determining the area under the plasma concentration–time curve for isoniazid might be a valuable approach for optimizing treatment in adult patients with tuberculosis

Variability in plasma rifampicin concentrations and role of SLCO1B1, ABCB1, AADAC2 and CES2 genotypes in Ethiopian patients with tuberculosis

BACKGROUND

Rifampicin, a key drug against tuberculosis (TB), displays wide between-patient pharmacokinetics variability and concentration-dependent antimicrobial effect. We investigated variability in plasma rifampicin concentrations and the role of SLCO1B1, ABCB1, arylacetamide deacetylase (AADAC) and carboxylesterase 2 (CES-2) genotypes in Ethiopian patients with TB.

METHODS

We enrolled adult patients with newly diagnosed TB (n = 119) who had received 2 weeks of rifampicin-based anti-TB therapy. Venous blood samples were obtained at three time points post-dose. Genotypes for SLCO1B1 (c.388A > G, c.521T > C), ABCB1 (c.3435C > T, c.4036A > G), AADACc.841G > A and CES-2 (c.269-965A > G) were determined. Rifampicin plasma concentration was quantified using LC-MS/MS. Predictors of rifampicin Cmax and AUC0-7 h were analysed.

RESULTS

The median rifampicin Cmax and AUC0-7 were 6.76 µg/mL (IQR 5.37-8.48) and 17.05 µg·h/mL (IQR 13.87-22.26), respectively. Only 30.3% of patients achieved the therapeutic efficacy threshold (Cmax>8 µg/mL). The allele frequency for SLCO1B1*1B (c.388A > G), SLCO1B1*5 (c.521T > C), ABCB1 c.3435C > T, ABCB1c.4036A > G, AADAC c.841G > A and CES-2 c.269-965A > G were 2.2%, 20.2%, 24.4%, 14.6%, 86.1% and 30.6%, respectively. Sex, rifampicin dose and ABCB1c.4036A > G, genotypes were significant predictors of rifampicin Cmax and AUC0-7. AADACc.841G > A genotypes were significant predictors of rifampicin Cmax. There was no significant influence of SLCO1B1 (c.388A > G, c.521T > C), ABCB1c.3435C > T and CES-2 c.269-965A > G on rifampicin plasma exposure variability.

CONCLUSIONS

Subtherapeutic rifampicin plasma concentrations occurred in two-thirds of Ethiopian TB patients. Rifampicin exposure varied with sex, dose and genotypes. AADACc.841G/G and ABCB1c.4036A/A genotypes and male patients are at higher risk of lower rifampicin plasma exposure. The impact on TB treatment outcomes and whether high-dose rifampicin is required to improve therapeutic efficacy requires further investigation.

Influence of sex on the exposure to isoniazid in patients with pulmonary tuberculosis

Isoniazid is a key component of tuberculosis treatment. Adequate exposure is a determinant for therapeutic success; however, considerable inter- and intraindividual variations in drug plasma levels can lead to unfavorable outcomes. While some predictors of isoniazid levels are well-known, others, such as sex, yield controversial results, requiring further investigation to optimize exposure. This study investigates whether the sex of patients influences the dose administered and the concentrations of isoniazid in plasma. Levels of isoniazid were associated with the N-acetyltransferase 2 phenotypes. A total of 76 male and 58 female patients were included. Isoniazid was measured by high-performance liquid chromatography, and N-acetyltransferase 2 phenotypes were assessed using molecular techniques. The results show that the dose administered, expressed in mg/kg, was higher in females, but the plasma levels were similar between both sexes. Among patients, 46.2%, 38.8%, and 15% were slow, intermediate, and fast acetylators, respectively. As expected, isoniazid levels were associated with the acetylation phenotypes, with higher concentrations in the slow acetylators. Thus, sex-related difference in isoniazid levels is due to the body weight of patients, and the optimized dose regimen based on patient weight and acetylator phenotypes can improve the treatment outcomes.

Correlation of N-acetyltransferase 2 genotype and acetylation status with plasma isoniazid concentration and its metabolic ratio in ethiopian tuberculosis patients

Unfavorable treatment outcomes for tuberculosis (TB) treatment might result from altered plasma exposure to antitubercular drugs in TB patients. The present study investigated the distribution of the N-Acetyltransferase 2 (NAT2) genotype, isoniazid acetylation status, genotype-phenotype concordance of NAT2, and isoniazid plasma exposure among Ethiopian tuberculosis patients. Blood samples were collected from newly diagnosed TB patients receiving a fixed dose combination of first-line antitubercular drugs daily. Genotyping of NAT2 was done using TaqMan drug metabolism assay. Isoniazid and its metabolite concentration were determined using validated liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 120 patients (63 male and 57 female) were enrolled in this study. The mean daily dose of isoniazid was 4.71 mg/kg. The frequency of slow, intermediate, and fast NAT2 acetylators genotypes were 74.2%, 22.4%, and 3.3% respectively. The overall median isoniazid maximum plasma concentration (Cmax) was 4.77 µg/mL and the AUC0-7 h was 11.21 µg.h/mL. The median Cmax in slow, intermediate, and fast acetylators were 5.65, 3.44, and 2.47 μg/mL, respectively. The median AUC0-7 h hour in slow, intermediate, and fast acetylators were 13.1, 6.086, and 3.73 mg•h/L, respectively. The majority (87.5%) of the study participants achieved isoniazid Cmax of above 3 µg/mL, which is considered a lower limit for a favorable treatment outcome. There is 85% concordance between the NAT2 genotype and acetylation phenotypes. NAT2 genotype, female sex, and dose were independent predictors of Cmax and AUC0-7 h (p < 0.001). Our finding revealed that there is a high frequency of slow NAT2 genotypes. The plasma Cmax of isoniazid was higher in the female and slow acetylators genotype group. The overall target plasma isoniazid concentrations in Ethiopian tuberculosis patients were achieved in the majority of the patients. Therefore, it is important to monitor adverse drug reactions and the use of a higher dose of isoniazid should be closely monitored.

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.

Population Pharmacokinetics and Pharmacodynamics of Isoniazid and its Metabolite Acetylisoniazid in Chinese Population

Objective: We aimed to establish a population pharmacokinetic (PPK) model for isoniazid (INH) and its major metabolite Acetylisoniazid (AcINH) in healthy Chinese participants and tuberculosis patients and assess the role of the NAT2 genotype on the transformation of INH to AcINH. We also sought to estimate the INH exposure that would achieve a 90% effective concentration (EC₉₀) efficiency for patients with various NAT2 genotypes.

Method: A total of 45 healthy participants and 157 tuberculosis patients were recruited. For healthy subjects, blood samples were collected 0–14 h after administration of 300 mg or 320 mg of the oral dose of INH; for tuberculosis patients who received at least seven days therapy with INH, blood samples were collected two and/or six hours after administration. The plasma concentration of INH and AcINH was determined by the reverse-phase HPLC method. NAT2 genotypes were determined by allele-specific amplification. The integrated PPK model of INH and AcINH was established through nonlinear mixed-effect modeling (NONMEM). The effect of NAT2 genotype and other covariates on INH and AcINH disposition was evaluated. Monte Carlo simulation was performed for estimating EC90 of INH in patients with various NAT2 genotypes.

Results: The estimated absorption rate constant (K_a), oral clearance (CL/F), and apparent volume of distribution (V₂/F) for INH were 3.94 ± 0.44 h⁻¹, 18.2 ± 2.45 L⋅h⁻¹, and 56.8 ± 5.53 L, respectively. The constant of clearance (K₃₀) and the volume of distribution (V₃/F) of AcINH were 0.33 ± 0.11 h⁻¹ and 25.7 ± 1.30 L, respectively. The fraction of AcINH formation (F_M) was 0.81 ± 0.076. NAT2 genotypes had different effects on the CL/F and F_M. In subjects with only one copy of NAT2 *5, *6, and *7 alleles, the CL/F values were approximately 46.3%, 54.9%, and 74.8% of *4/*4 subjects, respectively. The F_M values were approximately 48.7%, 63.8%, and 86.9% of *4/*4 subjects, respectively. The probability of target attainment of INH EC₉₀ in patients with various NAT2 genotypes was different.

Conclusion: The integrated parent-metabolite PPK model accurately characterized the disposition of INH and AcINH in the Chinese population sampled, which may be useful in the individualized therapy of INH.

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 of a limited sampling strategy for the estimation of isoniazid exposure considering N-acetyltransferase 2 genotypes in Korean patients with tuberculosis

A limited sampling strategy (LSS) to estimate the exposure to isoniazid was developed considering N-acetyltransferase 2 (NAT2) genotypes in Korean patients with tuberculosis. The influence of the genotypes on the pharmacokinetics of isoniazid was also evaluated. A total of 33 participants participated in the study and received isoniazid 300 mg once daily. Evaluable participants consist of ten slow (SA), fourteen intermediate (IA) and six rapid acetylators (RA). As expected, isoniazid exposure was higher (mean AUC, 28.4 versus 7.6 mg*h/L) and systemic clearance lower (mean apparent clearance, 14.8 versus 50.6 L/h) in SAs than RAs. The formulas to estimate isoniazid exposure were constructed using one or more concentration-time points that correlate with the area under the concentration-time curve (AUC). The LSS using a formula of single concentration-time point at 4 h post dose (C₄) is applicable for all acetylators to the therapeutic drug monitoring (TDM) of isoniazid in patients with tuberculosis when evaluated using the Deming regression and Bland-Altman plot (AUC = 1.53 + 10.03*C₄, adjusted r² = 0.95, p < 0.001). Considering that SAs are more prone to adverse effects, pre-dose NAT2 genotyping would be valuable for optimal isoniazid dosing in conjunction with TDM.

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).

Limited sampling strategy for predicting isoniazid exposure in patients with extrapulmonary tuberculosis

WHAT IS KNOWN AND OBJECTIVE

Limited sampling strategies (LSS), using few sampling times after dosing, have been used to reliably predict the isoniazid area under the 24-hour concentration-time curve (AUC). Experience with isoniazid is very limited, and no LSS has been developed in south-Mediterranean populations. Hence, we aimed to develop an accurate and convenient LSS for predicting isoniazid AUC in Tunisian patients with extrapulmonary tuberculosis.

METHODS

Pharmacokinetic profiles consisting of six blood samples each, collected during the 24-hour dosing interval, were obtained from 25 (6 men and 19 women) Tunisian patients with extrapulmonary tuberculosis. The AUC was calculated according to the linear trapezoidal rule. The isoniazid concentrations at each sampling time were correlated by a linear regression analysis with the measured AUC. We analysed all the developed models for their ability to estimate the isoniazid AUC. Error indices including the percentage of Mean Absolute Prediction Error (%MAE) and the percentage of Root Mean Squared Prediction Error (%RMSE) were used to evaluate the predictive performance. The agreement between predicted and measured AUCs was investigated using Bland and Altman and mountain plot analyses.

RESULTS AND DISCUSSION

Among the 1-time-point estimations, the C₃ -predicted AUC showed the highest correlation with the measured one (r²  = .906, %MAE = 10.45% and %RMSE = 2.69%). For the 2-time-point estimations, the model including the C₂ and C₆ provided the highest correlation between predicted and measured isoniazid AUC (r²  = .960, %MAE = 8.02% and %RMSE = 1.75%). The C₀ /C₃ LSS model provided satisfactory correlation and agreement (r²  = .930, %MAE = 10.19% and %RMSE = 2.32%). The best multilinear regression model for predicting the full isoniazid AUC was found to be the combination of 3 time points: C₀ , C₁ and C₆ (r²  = .992, %MAE = 4.06% and %RMSE = 0.80%). The use of a 2-time-point LSS to predict AUC in our population could be sufficient. C₂ /C₆ combination has shown the best correlation but the use of the C₀ /C₃ combination could be more practical with an accurate prediction. Therapeutic drug monitoring of isoniazid based on the C₃ can be used also in daily clinical practice in view of its reliability and practicality.

WHAT IS NEW AND CONCLUSION

The LSS using C₀ and C₃ is reliable, accurate and practical to estimate the AUC of isoniazid. A 1-time-point LSS including C₃ had acceptable correlation coefficient and prediction error indicators could be used alternatively.

Pharmacokinetics of Selenium in Healthy Piglets After Different Routes of Administration: Application of Pharmacokinetic Data to the Risk Assessment of Selenium

Selenium (Se) is a trace element in the environment. Although it is a necessary trace element for human and animal health, excessive Se can also pollute the environment and show toxic effects on humans and animals. Since the safe dose range of Se is narrow, it is important to study the pharmacokinetics of Se in order to make better use of the biological effects of Se. In the present study, we investigated the pharmacokinetic process of sodium selenate in healthy piglet plasma after either intramuscular injection or oral administrations, and examined dynamic changes of antioxidant system in healthy piglets after Se supplementation. The results showed that the Se reached the peak concentration of (0.2451 ± 0.0123) μg mL^-1 at (0.4237 ± 0.0185) h following intramuscular injection administration and (0.1781 ± 0.0142) μg mL^-1 at (2.1517 ± 0.1806) h following oral administration in the plasma. The average AUC of sodium selenite following intramuscular injection and oral administrations was (31.7260 ± 1.3574) and (75.1460 ± 3.4127) mg L^-1 h^-1, respectively. Total antioxidant capacity (T-AOC), glutathione peroxidase (GPx), and superoxide dismutase (SOD) generally show an upward trend and malondialdehyde (MDA) shows a downward trend, regardless of intramuscular injection or oral sodium selenite. An increased concentration of Se was observed in the serum of healthy piglets after intramuscular injection and oral sodium selenite. Our results indicated that the pharmacokinetic process of sodium selenate in healthy piglet blood conforms to the two-chamber open model. Its pharmacokinetic properties are rapid absorption and slow excretion. Antioxidant systems in healthy piglets vary with Se levels, but there is a significant lag period compared with the latter. Our current findings will provide a more complete understanding of clinical rational Se supplementation and avoid contamination of the environment by overdose.

Population Pharmacokinetic Model and Limited Sampling Strategies for Personalized Dosing of Levofloxacin in Tuberculosis Patients

Levofloxacin is an antituberculosis drug with substantial interindividual pharmacokinetic variability; therapeutic drug monitoring (TDM) could therefore be helpful to improve treatment results. TDM would be more feasible with limited sampling strategies (LSSs), a method to estimate the area under the concentration curve for the 24-h dosing interval (AUC0-24) by using a limited number of samples. This study aimed to develop a population pharmacokinetic (popPK) model of levofloxacin in tuberculosis patients, along with LSSs using a Bayesian and multiple linear regression approach. The popPK model and Bayesian LSS were developed using data from 30 patients and externally validated with 20 patients. The LSS based on multiple linear regression was internally validated using jackknife analysis. Only clinically suitable LSSs (maximum time span, 8 h; minimum interval, 1 h; 1 to 3 samples) were tested. Performance criteria were root-mean-square error (RMSE) of <15%, mean prediction error (MPE) of <5%, and r2 value of >0.95. A one-compartment model with lag time best described the data while only slightly underestimating the AUC0-24 (mean, -7.9%; standard error [SE], 1.7%). The Bayesian LSS using 0- and 5-h postdose samples (RMSE, 8.8%; MPE, 0.42%; r2 = 0.957) adequately estimated the AUC0-24, with a mean underestimation of -4.4% (SE, 2.7%). The multiple linear regression LSS using 0- and 4-h postdose samples (RMSE, 7.0%; MPE, 5.5%; r2 = 0.977) was internally validated, with a mean underestimation of -0.46% (SE, 2.0%). In this study, we successfully developed a popPK model and two LSSs that could be implemented in clinical practice to assist TDM of levofloxacin. (This study has been registered at ClinicalTrials.gov under identifier NCT01918397.).