Population Pharmacokinetics Modeling of Lamotrigine in Jordanian Epileptic Patients Using Dried Blood Spot Sampling

Aims To characterize the population pharmacokinetics of lamotrigine in Jordanian epileptic patients and to identify factors affecting therapeutic parameters. Patients and Methods A population pharmacokinetics model for lamotrigine was established based on a prospectively collected data of 52 steady-state concentrations from 38 adult and pediatric patients with epilepsy. Lamotrigine concentrations were determined by a dried blood spot liquid chromatography method. Data were analyzed according to a one-compartment model with first-order absorption and elimination using the nonlinear mixed effect modeling program. The covariates effect of total body weight, gender, age, and co-medication with topiramate, carbamazepine, phenytoin, phenobarbital, and valproic acid on lamotrigine clearance were investigated using a stepwise forward addition followed by a stepwise backward elimination. Results The final population pharmacokinetics model for lamotrigine clearance was as follows: CL/Fpop=θ1*exp (θ3*age)*exp (θ5*carbamazepine)*exp (θ6*valproic acid) , where θ1 is the relative clearance (L/hr) estimated, and θ3, θ5, and θ6 are the fixed parameters relating to age and co-medication with carbamazepine and valproic acid, respectively.The population mean value of lamotrigine total clearance generated in the final model (with covariates) was 2.12 L/hr. Inter-individual variability and residual unexplained variability expressed as the coefficient of variation was 37.1 and 26.1%, respectively. Conclusion Lamotrigine total clearance in the Jordanian patients is comparable to that reported by others for Caucasian patients. Age and concomitant therapy with carbamazepine and valproic acid significantly affected lamotrigine clearance, and accounted for 48% of its inter-individual variability.

Estimation of renal perfusion based on measurement of rubidium-82 clearance by PET/CT scanning in healthy subjects

Background Changes in renal blood flow (RBF) may play a pathophysiological role in hypertension and kidney disease. However, RBF determination in humans has proven difficult. We aimed to confirm the feasibility of RBF estimation based on positron emission tomography/computed tomography (PET/CT) and rubidium-82 (82Rb) using the abdominal aorta as input function in a 1-tissue compartment model. Methods Eighteen healthy subjects underwent two dynamic 82Rb PET/CT scans in two different fields of view (FOV). FOV-A included the left ventricular blood pool (LVBP), the abdominal aorta (AA) and the majority of the kidneys. FOV-B included AA and the kidneys in their entirety. In FOV-A, an input function was derived from LVBP and from AA, in FOV-B from AA. One-tissue compartmental modelling was performed using tissue time activity curves generated from volumes of interest (VOI) contouring the kidneys, where the renal clearance of 82Rb is represented by the K1 kinetic parameter. Total clearance for both kidneys was calculated by multiplying the K1 values with the volume of VOIs used for analysis. Intra-assay coefficients of variation and inter-observer variation were calculated. Results For both kidneys, K1 values derived from AA did not differ significantly from values obtained from LVBP, neither were significant differences seen between AA in FOV-A and AA in FOV-B, nor between the right and left kidneys. For both kidneys, the intra-assay coefficients of variation were low (~ 5%) for both input functions. The measured K1 of 2.80 ml/min/cm3 translates to a total clearance for both kidneys of 766 ml/min/1.73 m2. Conclusion Measurement of renal perfusion based on PET/CT and 82Rb using AA as input function in a 1-tissue compartment model is feasible in a single FOV. Based on previous studies showing 82Rb to be primarily present in plasma, the measured K1 clearance values are most likely representative of effective renal plasma flow (ERPF) rather than estimated RBF values, but as the accurate calculation of total clearance/flow is very much dependent on the analysed volume, a standardised definition for the employed renal volumes is needed to allow for proper comparison with standard ERPF and RBF reference methods.

MO703URINE VOLUME AS A MARKER OF RESIDUAL KIDNEY FUNCTION IN PERITONEAL DIALYSIS PATIENTS: QUANTITATIVE ASSESSMENT

Background and Aims In dialysis patients, urine volume is an easy-to-obtain marker of residual kidney function but information is lacking of its potential value as an estimate of the renal contribution to total clearance of small solutes. We explored whether correlations of urine volume with different estimations of the residual renal function for urea, creatinine, and phosphorus, could be used to assess renal solute clearances and renal mass removal for investigated solutes. Method In an observational study of 94 non-anuric (urine output ≥ 100 mL per 24 hours) patients (54% men, median age 59 [45 - 68] year, BMI 25.8 [21.4 - 27.7] kg/m2) undergoing automated (n = 59) or continuous ambulatory (n = 35) peritoneal dialysis (PD), we evaluated renal, peritoneal and total (renal plus peritoneal) solute removal (g/week) and clearance (L/week) in relation to urine volume (L/day). Urine volume, renal clearances, ratio of urine solute to serum solute concentration, removed mass of each solute and the ratio of mass removed by urine (renal clearance) over total mass removed by urine and dialysate (total clearance) for urea, creatinine and phosphorus were estimated from 24 h collections of urine and dialysate and determination of solute concentrations in serum, urine and dialysate. Statistical dependence between variables was tested using Spearman’s correlation coefficient (rho). Data are expressed as median with interquartile range. Results Median 24-hour urine output was 560 [323 – 938] mL. Renal mass removal for urea, creatinine and phosphorus was 10.1 [4.5 – 17.1], 3.5 [1.8 – 5.6] and 1.0 [0.4 – 1.7] g/week, respectively. The average contribution of residual renal removal to the total mass removed was 28% [17% - 41%] for urea, 56% [30% - 72%] for creatinine and 44% [24% - 58%] for phosphorus. Serum creatinine correlated weakly and negatively with urine volume (rho = -0.26, p < 0.05), but no such relationship was observed for urea and phosphorus. Only urine concentration of creatinine correlated weakly with urine volume with rho = -0.28 and p < 0.01. Urine concentration over plasma concentration did not correlate with urine volume for any solute. Renal urea clearance (20.1 [11.4 - 35.7] L/week) correlated positively with creatinine renal clearance (43.0 [18.9 - 75.1] L/week), (rho = 0.92), and with phosphorus renal clearance (17.3 [7.6 - 32.9] L/week), (rho = 0.89, p < 0.001; Fig. 1A), while renal creatinine clearance correlated positively with phosphorus renal clearance (rho = 0.86, p<0.001). Urine volume correlated positively with urea, creatinine and phosphorus clearances at rho 0.78, 0.63 and 0.73, respectively (all p< 0.001), and with renal removal of mass of urea, creatinine, and phosphorus with rho= 0.83, 0.68 and 0.74 (Fig. 1B), respectively; all p<0.001. Conclusion In PD patients, solute renal clearances and renal mass removal for urea, creatinine and phosphorus may be predicted from urine volume. Among renal clearances for urea, creatinine, and phosphorus two of them may be assessed based on measurements of the third one.