Prediction of area under the curve for a p-glycoprotein, a CYP3A4 and a CYP2C9 substrate using a single time point strategy: assessment using fexofenadine, itraconazole and losartan and metabolites

Fexofenadine Plasma Concentrations to Estimate Systemic Exposure in Healthy Adults Using a Limited Sampling Strategy with a Population Pharmacokinetic Approach

BACKGROUND

Fexofenadine is a recommended in vivo probe drug for phenotyping P-glycoprotein (P-gp) and organic anion transporting polypeptide (OATP) 1B1/3 transporter activities. This study evaluated a limited sampling strategy using a population pharmacokinetic approach to estimate plasma fexofenadine exposure as an index of P-gp and OATP activities.

METHODS

In a previous study, a single oral dose of fexofenadine (120 mg) was administered alone or in combination with grapefruit juice, Panax ginseng , or Echinacea purpurea to healthy adult participants. Serial plasma samples were collected up to 72 hours after administration and fexofenadine concentrations were measured. A population pharmacokinetic model was developed using nonlinear mixed-effects modeling. Limited sampling models (LSMs) using single and 2-timepoint fexofenadine concentrations were compared with full profiles from intense sampling using empirical Bayesian post hoc estimations of systemic exposure derived from the population pharmacokinetic model. Predefined criteria for LSM selection and validation included a coefficient of determination (R 2 ) ≥ 0.90, relative percent mean prediction error ≥ -5 to ≤5%, relative percent mean absolute error ≤ 10%, and relative percent root mean square error ≤ 15%.

RESULTS

Fexofenadine concentrations (n = 1520) were well described using a 2-compartment model. Grapefruit juice decreased the relative oral bioavailability of fexofenadine by 25%, whereas P. ginseng and E. purpurea had no effect. All the evaluated single timepoint fexofenadine LSMs showed unacceptable percent mean prediction error, percent mean absolute error, and/or percent root mean square error. Although adding a second time point improved precision, the predefined criteria were not met.

CONCLUSIONS

Identifying novel fexofenadine LSMs to estimate P-gp and OATP1B1/3 activities in healthy adults for future transporter-mediated drug-drug interaction studies remains elusive.

Critical Assessment of Phenotyping Cocktails for Clinical Use in an African Context

Interethnic and interindividual variability in in vivo cytochrome P450 (CYP450)-dependent metabolism and altered drug absorption via expressed transport channels such as P-glycoprotein (P-gp) contribute to the adverse drug reactions, drug-drug interaction and therapeutic failure seen in clinical practice. A cost-effective phenotyping approach could be advantageous in providing real-time information on in vivo phenotypes to assist clinicians with individualized drug therapy, especially in resource-constrained countries such as South Africa. A number of phenotyping cocktails have been developed and the aim of this study was to critically assess the feasibility of their use in a South African context. A literature search on library databases (including AccessMedicine, BMJ, ClinicalKey, MEDLINE (Ovid), PubMed, Scopus and TOXLINE) was limited to in vivo cocktails used in the human population to phenotype phase I metabolism and/or P-gp transport. The study found that the implementation of phenotyping in clinical practice is currently limited by multiple administration routes, the varying availability of probe drugs, therapeutic doses eliciting side effects, the interaction between probe drugs and extensive sampling procedures. Analytical challenges include complicated sample workup or extraction assays and impractical analytical procedures with low detection limits, analyte sensitivity and specificity. It was concluded that a single time point, non-invasive capillary sampling, combined with a low-dose probe drug cocktail, to simultaneously quantify in vivo drug and metabolite concentrations, would enhance the feasibility and cost-effectiveness of routine phenotyping in clinical practice; however, future research is needed to establish whether the quantitative bioanalysis of drugs in a capillary whole-blood matrix correlates with that of the standard plasma/serum matrixes used as a reference in the current clinical environment.

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.

Applicability of a Single Time Point Strategy for the Prediction of Area Under the Concentration Curve of Linezolid in Patients: Superiority of Ctrough- over Cmax-Derived Linear Regression Models

Background and Objectives

Linezolid, a oxazolidinone, was the first in class to be approved for the treatment of bacterial infections arising from both susceptible and resistant strains of Gram-positive bacteria. Since overt exposure of linezolid may precipitate serious toxicity issues, therapeutic drug monitoring (TDM) may be required in certain situations, especially in patients who are prescribed other co-medications.

Methods

Using appropriate oral pharmacokinetic data (single dose and steady state) for linezolid, both maximum plasma drug concentration (C_max) versus area under the plasma concentration–time curve (AUC) and minimum plasma drug concentration (C_min) versus AUC relationship was established by linear regression models. The predictions of the AUC values were performed using published mean/median C_max or C_min data and appropriate regression lines. The quotient of observed and predicted values rendered fold difference calculation. The mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), and the goodness of the AUC fold prediction were used to evaluate the two models.

Results

The C_max versus AUC and trough plasma concentration (C_trough) versus AUC models displayed excellent correlation, with r values of >0.9760. However, linezolid AUC values were predicted to be within the narrower boundary of 0.76 to 1.5-fold by a higher percentage by the C_trough (78.3 %) versus C_max model (48.2 %). The C_trough model showed superior correlation of predicted versus observed values and RMSE (r = 0.9031; 28.54 %, respectively) compared with the C_max model (r = 0.5824; 61.34 %, respectively).

Conclusions

A single time point strategy of using C_trough level is possible as a prospective tool to measure the AUC of linezolid in the patient population.

Interspecies scaling of excretory amounts using allometry - retrospective analysis with rifapentine, aztreonam, carumonam, pefloxacin, miloxacin, trovafloxacin, doripenem, imipenem, cefozopran, ceftazidime, linezolid for urinary excretion and rifapentine, cabotegravir, and dolutegravir for fecal excretion

1. Interspecies allometry scaling for prediction of human excretory amounts in urine or feces was performed for numerous antibacterials. Antibacterials used for urinary scaling were: rifapentine, pefloxacin, trovafloxacin (Gr1/low; <10%); miloxacin, linezolid, PNU-142300 (Gr2/medium; 10-40%); aztreonam, carumonam, cefozopran, doripenem, imipenem, and ceftazidime (Gr3/high; >50%). Rifapentine, cabotegravir, and dolutegravir was used for fecal scaling (high; >50%). 2. The employment of allometry equation: Y = aW(b) enabled scaling of urine/fecal amounts from animal species. Corresponding predicted amounts were converted into % recovery by considering the respective human dose. Comparison of predicted/observed values enabled fold difference and error calculations (mean absolute error [MAE] and root mean square error [RMSE]). Comparisons were made for urinary/fecal data; and qualitative assessment was made amongst Gr1/Gr2/Gr3 for urine. 3. Average correlation coefficient for the allometry scaling was >0.995. Excretory amount predictions were largely within 0.75- to 1.5-fold differences. Average MAE and RMSE were within ±22% and 23%, respectively. Although robust predictions were achieved for higher urinary/fecal excretion (>50%), interspecies scaling was applicable for low/medium excretory drugs. 4. Based on the data, interspecies scaling of urine or fecal excretory amounts may be potentially used as a tool to understand the significance of either urinary or fecal routes of elimination in humans in early development.