Population pharmacokinetics of pregabalin in healthy subjects and patients with post-herpetic neuralgia or diabetic peripheral neuropathy

Abstract

Aim: Pregabalin, a chemical analogue of the mammalian neurotransmitter γ-aminobutyric acid, has been approved in many countries for partial-onset seizures, generalized anxiety disorder and various other pain disorders, including neuropathic pain associated with post-herpetic neuralgia and diabetic peripheral neuropathy and fibromyalgia. The aim of this study was to develop a population pharmacokinetic model and quantify the influence of covariates on the parameters.

Methods: This pregabalin population pharmacokinetic analysis was conducted on data from 14 clinical trials involving healthy subjects, subjects with impaired renal function and patients with post-herpetic neuralgia or diabetic peripheral neuropathy (n= 616). The data analysis was performed using nonlinear mixed effects modelling methodology as implemented by NONMEM.

Results: A one-compartment model with first-order absorption and elimination adequately described pregabalin pharmacokinetics. The model indicated that pregabalin apparent clearance (CL/F) was proportional to estimated creatinine clearance (CL(cr) ). The pregabalin systemic exposure in patients with lower renal function who received pregabalin 150 mg twice daily was almost equal to that of patients with normal renal function administered pregabalin 300 mg twice daily. The systemic exposure stratified by lower or normal renal function was similar between patients with post-herpetic neuralgia and diabetic peripheral neuropathy.

Conclusion: The developed model identified CL(cr) and ideal body weight as clinically influential covariates on CL/F and volume of distribution, respectively. This study indicates that renal function accounts for variability in the apparent clearance of pregabalin which is consistent with what is known about the elimination of this drug.

Figure 1

Scatter plots of pregabalin apparent clearance (CL/F) and estimated creatinine clearance (CL_cr). Circles in the left panel represent individual observed CL/F from noncompartment analysis (NCA; observed CL/F in NCA) vs. CL_cr (Note: the number of observed CL/F values were limited owing to the fact that NCA results were available only in healthy subjects and subjects with impaired renal function). Circles in the centre panel represent individual CL/F from post hoc Bayesian estimates in the initial base model without CL_cr effect on CL/F vs. CL_cr. Circles in the right panel represent the individual CL/F estimated from the final base model vs. CL_cr. Each solid line indicates a nonparametric regression with robust local linear fits. Each dotted line indicates the relationship between population mean CL/F vs. CL_cr in the final base model. Note: Several CL_cr values at different visits in the same subject (CL/F) are included in each plot

Figure 2

Scatter plots of individual random effects on pregabalin apparent clearance (CL/F) and volume of distribution (V/F) in the base model and covariates. ETA.CL, random effect on CL/F; ETA.V, random effect on V/F; AGE, age; WT, total body weight; BMI, body mass index; IBW, ideal body weight; CL_cr, estimated creatinine clearance; SEX (male = 0, female = 1); ETHN, ethnicity (non-Japanese = 0, Japanese = 1); TYPE (healthy = 0, patient = 1)

Figure 3

(A) Scatter plots of observed pregabalin concentrations (DV) vs. population predicted mean concentrations (PRED) and DV vs. individual predicted concentrations (IPRED) for the final model stratified by subject type (healthy subjects or patients) and renal function (estimated creatinine clearance [CL_cr] <60 or CL_cr≥60 ml min⁻¹). Each dotted line represents concordance line (Y = X). Each solid line indicates a nonparametric regression with robust local linear fits. (B) Scatter plots of residuals normalized by the SD of the data (conditional weighted residuals [CWRES]) vs. PRED and CWRES vs. time after the last dose (Time) for the final model stratified by subject type (healthy subjects or patients) and renal function (CL_cr <60 or CL_cr≥60 ml min⁻¹). Dotted lines represent CWRES = 0, −6 and +6, respectively. Each line indicates a nonparametric regression with robust local linear fits

Figure 4

Visual predictive check plots representing pregabalin concentration vs. time after the most recent administration (up to 12 h) stratified by pregabalin doses (25, 50, 100, 150, 200 and 300 mg) and renal function (estimated creatinine clearance [CL_cr]≥90 ml min⁻¹, 60 ml min⁻¹≤ CL_cr <90 ml min⁻¹, 30 ml min⁻¹≤ CL_cr <60 ml min⁻¹, CL_cr <30 ml min⁻¹). Each panel shows observed pregabalin concentrations (circles), the observed 2.5, 50 and 97.5 percentile points (dotted lines) and the predicted (n = 500) 2.5, 50 and 97.5 percentile points (solid lines). Some plots in the lower renal functions are skipped owing to limited data

Figure 5

Scatter plots of the empirical Bayesian estimates in the final model in healthy subjects (left panel; ETA.CL.HV =η_CL/F, ETA.V.HV =η_V/F and ETA.k_a.HV =η_ka) and patients (right panel; ETA.CL.PT =η_CL/F, ETA.V.PT =η_V/F and ETA.k_a.PT =η_ka). Each line indicates a nonparametric regression with robust local linear fit