Comparison of three estimators for determining cyclosporine dosing in infants after liver transplantation
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OBJECTIVE: To compare and analyze how allometrically- and linearly-scaled daily doses of cyclosporine could affect the therapeutic drug monitoring concentrations when applied to 8 infants with liver transplants. MATERIALS AND METHODS: Eight infants who underwent liver transplantations were put on twice-daily oral cyclosporine immunosuppressive regimens. After starting therapy, the adjustments of individual daily doses were determined by using therapeutic monitoring of plasma cyclosporine levels by measuring trough concentrations (C0) and concentrations at 2 hours after drug administration (C2). These doses were analyzed and compared with the hypothetical doses estimated by allometric and linear scaling in order to compare which of the two methods would yield closer estimates to the actual doses applied. RESULTS: The median therapeutic drug monitoring (TDM)-based dose (n = 53) was 70.00 mg (10.9 mg/kg/day) (5.00 - 190.00 mg), whereas the median allometric (n = 53) and linear (n = 53) doses were 65.21 mg (10.11 mg/kg/day) (57.17 - 79.25 mg) and 35.63 mg (5.52 mg/kg/day) (29.89 - 46.20 mg), respectively. The median allometric dose was significantly different than the median linear dose (p < 0.0001), whereas there was no statistical difference between the median TDM-based dose and median allometric dose (p = 0.72). CONCLUSIONS: The allometric approach, when used to estimate cyclosporine doses in this cohort of liver transplant infants, yielded closer estimates to actually applied daily doses in comparison to linear scaling. Allometric scaling could be employed in calculating starting doses for drugs that lack specific dosing recommendations for infants, in order to achieve therapeutic levels faster, lowering the need for constant monitoring and dose adjustment.
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Parametric and nonparametric population methods: their comparative performance in analysing a clinical dataset and two Monte Carlo simulation studies.

BACKGROUND AND OBJECTIVES: This study examined parametric and nonparametric population modelling methods in three different analyses. The first analysis was of a real, although small, clinical dataset from 17 patients receiving intramuscular amikacin. The second analysis was of a Monte Carlo simulation study in which the populations ranged from 25 to 800 subjects, the model parameter distributions were Gaussian and all the simulated parameter values of the subjects were exactly known prior to the analysis. The third analysis was again of a Monte Carlo study in which the exactly known population sample consisted of a unimodal Gaussian distribution for the apparent volume of distribution (V(d)), but a bimodal distribution for the elimination rate constant (k(e)), simulating rapid and slow eliminators of a drug. METHODS: For the clinical dataset, the parametric iterative two-stage Bayesian (IT2B) approach, with the first-order conditional estimation (FOCE) approximation calculation of the conditional likelihoods, was used together with the nonparametric expectation-maximisation (NPEM) and nonparametric adaptive grid (NPAG) approaches, both of which use exact computations of the likelihood. For the first Monte Carlo simulation study, these programs were also used. A one-compartment model with unimodal Gaussian parameters V(d) and k(e) was employed, with a simulated intravenous bolus dose and two simulated serum concentrations per subject. In addition, a newer parametric expectation-maximisation (PEM) program with a Faure low discrepancy computation of the conditional likelihoods, as well as nonlinear mixed-effects modelling software (NONMEM), both the first-order (FO) and the FOCE versions, were used. For the second Monte Carlo study, a one-compartment model with an intravenous bolus dose was again used, with five simulated serum samples obtained from early to late after dosing. A unimodal distribution for V(d) and a bimodal distribution for k(e) were chosen to simulate two subpopulations of 'fast' and 'slow' metabolisers of a drug. NPEM results were compared with that of a unimodal parametric joint density having the true population parameter means and covariance. RESULTS: For the clinical dataset, the interindividual parameter percent coefficients of variation (CV%) were smallest with IT2B, suggesting less diversity in the population parameter distributions. However, the exact likelihood of the results was also smaller with IT2B, and was 14 logs greater with NPEM and NPAG, both of which found a greater and more likely diversity in the population studied. For the first Monte Carlo dataset, NPAG and PEM, both using accurate likelihood computations, showed statistical consistency. Consistency means that the more subjects studied, the closer the estimated parameter values approach the true values. NONMEM FOCE and NONMEM FO, as well as the IT2B FOCE methods, do not have this guarantee. Results obtained by IT2B FOCE, for example, often strayed visibly away from the true values as more subjects were studied. Furthermore, with respect to statistical efficiency (precision of parameter estimates), NPAG and PEM had good efficiency and precise parameter estimates, while precision suffered with NONMEM FOCE and IT2B FOCE, and severely so with NONMEM FO. For the second Monte Carlo dataset, NPEM closely approximated the true bimodal population joint density, while an exact parametric representation of an assumed joint unimodal density having the true population means, standard deviations and correlation gave a totally different picture. CONCLUSIONS: The smaller population interindividual CV% estimates with IT2B on the clinical dataset are probably the result of assuming Gaussian parameter distributions and/or of using the FOCE approximation. NPEM and NPAG, having no constraints on the shape of the population parameter distributions, and which compute the likelihood exactly and estimate parameter values with greater precision, detected the more likely greater diversity in the parameter values in the population studied. In the first Monte Carlo study, NPAG and PEM had more precise parameter estimates than either IT2B FOCE or NONMEM FOCE, as well as much more precise estimates than NONMEM FO. In the second Monte Carlo study, NPEM easily detected the bimodal parameter distribution at this initial step without requiring any further information. Population modelling methods using exact or accurate computations have more precise parameter estimation, better stochastic convergence properties and are, very importantly, statistically consistent. Nonparametric methods are better than parametric methods at analysing populations having unanticipated non-Gaussian or multimodal parameter distributions.

Nonparametric expectation maximisation (NPEM) population pharmacokinetic analysis of caffeine disposition from sparse data in adult caucasians: systemic caffeine clearance as a biomarker for cytochrome P450 1A2 activity.

OBJECTIVE: To explore the ability of the nonparametric expectation maximisation (NPEM) method of population pharmacokinetic modelling to deal with sparse data in estimating systemic caffeine clearance for monitoring and evaluation of cytochrome P450 (CYP) 1A2 activity. DESIGN AND PARTICIPANTS: Nonblind, single-dose clinical investigation in 34 non-related adult Bulgarian Caucasians (18 women and 16 men, aged between 18 and 62 years) with normal and reduced renal function. METHODS: Each participant received oral caffeine 3 mg/kg. Two blood samples per individual were taken according to the protocol for measuring caffeine plasma concentrations. A total of 67 measured concentrations were used to obtain NPEM estimates of caffeine clearance. Paraxanthine/caffeine plasma ratios were calculated and correlated with clearance estimates. Graphical methods and tests for normality were applied and parametric and nonparametric statistical tests were used for comparison. RESULTS: NPEM median estimates of caffeine absorption and elimination rate constants, k(a) = 4.54 h(-1) and k(el) = 0.139 h(-1), as well as of fractional volume of distribution and plasma clearance, V(S1) = 0.58 L/kg and CL(S1) = 0.057 L/h/kg, agreed well with reported values from more 'data rich' studies. Significant correlations were observed between paraxanthine/caffeine ratios at 3, 8 and 10 hours and clearance (Spearman rank correlation coefficients, r(s), >0.74, p

Pharmacokinetics and comparative bioavailability of two metformin formulations after single-dose administration in healthy subjects.

OBJECTIVE: To assess the average bioequivalence of two formulations of metformin after single-dose administration of each treatment to healthy subjects under fasting conditions by assessing the pharmacokinetic measures of systemic exposure and evaluating the confidence intervals (CIs) for each pharmacokinetic parameter. DESIGN: Randomised, comparative, single-dose, open-label, balanced, two-period, two-treatment, crossover study under fasting conditions. PARTICIPANTS: 20 healthy volunteers (ten men and ten women) took part in the study. METHODS: Subjects were investigated after a single dose of 850mg after a washout period of 7 days. Plasma samples were taken at regular time intervals according to the study protocol for measuring plasma metformin concentrations. Systemic exposure was estimated with the use of pharmacokinetic parameters (area under the curve of the plasma drug concentrations from time zero to the last sampling time [AUC(0-36)], area under the curve of the plasma drug concentrations from time zero to infinity [AUC(0-)(infinity)], time to peak drug concentration [t(max)], partial area under the concentration-time curve with a cut-off point at the t(max) of the reference product [AUC(p)], peak plasma drug concentration [C(max)], the ratio C(max)/AUC(0-)(infinity), and mean residence time [MRT]). The point estimates of pharmacokinetic parameters (geometric means of the ratios test [T]/reference [R] and the 90% CIs for the ratios of geometric means [T]/[R]), estimated by parametric and nonpara-metric analysis, were used in the statistical analysis. RESULTS: The point estimates and the 90% CIs after parametric analysis of AUC(0-)(infinity) were 0.98 and (0.96-1.21), and after nonparametric analysis were 1.06 and (0.95-1.207), respectively. The two drug products were considered to be bioequivalent and with significant variability across subjects for the pharmaco-kinetic parameters AUC(0-36), AUC(0-)(infinity), C(max) and MRT according to analysis of variance of log-transformed data. CONCLUSIONS: The two studied formulations of metformin were found to be bioequivalent. They showed similar extents and rates of absorption and similar exposure. However, analysis of variance of logarithmically transformed data revealed significant variability among individuals in AUC(0-36), AUC(0-)(infinity) and C(max), making careful individualisation of the metformin dosage important.