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J R Nedelman

Publications and source records attributed to J R Nedelman.

11 recordsLinked to original sources
PubMed1997

Description of blood pressure changes in patients beginning cyclosporin A therapy.

Cyclosporin A (CyA) is the primary immunosuppressive agent for the prophylaxis of rejection episodes in renal, cardiac, liver, and other transplants. Recently, its use in autoimmune diseases has been investigated as well. Although several studies have produced promising results, nephrotoxicity and hypertension can result from CyA treatment, and their development must be understood in order to facilitate patient management. This article describes the diastolic blood pressure (DBP) responses in two populations of patients during three months of CyA therapy. Study A involved psoriasis patients and Study B involved postoperative renal transplant patients. The relationship between blood pressure and systemic CyA exposure and other covariates was evaluated using linear mixed effects modeling. Temporal patterns of blood pressure changes with varying duration of CyA exposure were investigated. In Study A, the psoriasis patients showed transient exposure-related increases in DBP on CyA. These elevations, while statistically significant, were clinically insignificant. In Study B, the renal transplant patients showed no CyA-related rises in DBP. In neither study was there evidence for a difference in effect on DBP between Sandimmune and Neoral, the two formulations of CyA presently approved for marketing by the Food and Drug Administration, after differences in CyA exposure were taken into account.

Adult↗
PubMed1996

Pharmacokinetics and pharmacodynamics of multiple-dose terbinafine.

Data from clinical trials of terbinafine for the treatment of onychomycosis were analyzed with the following two objectives: 1) to identify demographic predictors of the duration and extent of systemic drug exposure; and 2) to explore whether increased systemic exposure or demographic predictors of increased exposure were associated with altered safety or efficacy. Demographic predictors of exposure were identified by a model-free, nonparametric approach applied to the sparse pharmacokinetic data from the onychomycosis studies. Those covariates were then incorporated into a multicompartmental nonlinear mixed effects model. Post hoc parameter estimates from the nonlinear mixed effects model provided individual measures of exposure. Safety scores were derived for adverse events that were frequently attributed to drug exposure and for liver function tests. Terbinafine was found to have an average terminal half-life (t1/2) of approximately 3 weeks. That terminal elimination phase contributed so little to the total exposure, however, that average concentrations accumulated only approximately two-fold at steady state with once daily dosing. Age and concomitant hypertension were predictors of higher plasma concentrations of terbinafine; smokers had lower levels than nonsmokers. Although some statistically significant associations between adverse events and systemic exposure were found, in all cases the actual frequency of the adverse events and systemic exposure were found, in all cases the actual frequency of the adverse events was low, and there were no trends in severity with respect to exposure. Above-normal levels of gamma-glutamyl transferase were associated with exposure, but there was no trend in severity with respect to exposure. No other liver function test abnormalities were associated with exposure, nor were there any significant associations between adverse events or liver function abnormalities and demographic subgroups that differed with respect to exposure. Among patients taking the active drug there were no significant associations between exposure levels and efficacy, nor were there differences in efficacy between demographic subgroups that differed with respect to exposure.

Adult↗
PubMed1996

Serial versus sparse sampling in toxicokinetic studies.

PURPOSE: Sparse sampling in rodent toxicokinetics usually involves the collection of a single blood sample on a given study day from each animal in a treatment group. The samples are allocated to different time points, often allowing some replicates, and statistical inferences are then made about the concentration-time behavior of the test compound. The present study compared the results of one such analysis with those obtained from serial sampling as might be applied using satellite animals. METHODS: Ten rats each received a single oral dose of tritium-labeled compound X. Blood concentrations in each rat at 10 time points post-dose were determined by liquid scintillation counting. Individual peak concentrations of blood radioactivity (Cmax) and peak times (tmax) were recorded, and area-under-curve (AUC) values were calculated by trapezoidal rule. The mean AUC and Cmax of all 10 animals over all 10 time points, referred to as AUCtrue and Cmax,true, were used as points of reference. These values were then estimated using subsets of the data that simulated satellite-animal or sparse-sampling designs. First, several different sampling schedules of 5 bleeding times were stimulated by taking subsets of the full data set. For analysis using satellite animals, serial blood concentrations from subsets of 3 or 4 rats were used to calculate point and confidence-interval estimates of AUCtrue and Cmax,true by standard methods; all possible subsets of 3 or 4 of the 10 rats were considered. For sparse data analysis, a single concentration from each of the 10 rats was used to calculate both point and confidence-interval estimates of AUCtrue by the Bailer-Satterthwaite method, and point estimates of Cmax,true, according to several different designs of replication. Animals were randomly assigned to time points, and 1000 of over 50000 possible combinations were evaluated for each bleeding schedule. The average percent absolute errors of the point estimates were computed and, for the 95% confidence intervals, average widths were determined. RESULTS: For point estimates of AUCtrue, sparse sampling yielded average percent absolute errors of 7-13%. Percent errors for 3 and 4 satellite animals were 6-12% and 5-10%, respectively. For 95% confidence intervals, sparse sampling yielded widths of 24-90% of AUCtrue, whereas for 3 and 4 satellite animals widths were 37-50% and 24-34%, respectively. For Cmax,true point estimates from sparse-sampling and satellite-animal approaches had average percent absolute errors of 5-12% and 3-8%, respectively. The confidence-interval widths for Cmax,true from the satellite-animal approach were 15-24% of Cmax,true, but coverage did not achieve the nominal 95% for some choices of sampling times. CONCLUSIONS: By using proper study designs, one can limit the number of samples and the amount of blood drawn so as not to affect the animals' health status, yet still achieve the customary pharmacokinetic objectives in a toxicity study.

Animals↗
PubMed1996

Errors in time in pharmacokinetic studies.

Pharmacokinetic models with errors in the time variable were developed and explored by simulation in NONMEM. The precision of estimated parameters decreased with increasing error magnitude. The usual asymptotic standard error estimates were biased low by 10-50%. Accuracy and precision were not greatly diminished by ignoring errors in time when fitting models.

Computer Simulation↗
PubMed1995

A nonlinear mixed-effects pharmacokinetic model comparing two formulations of cyclosporine in stable renal transplant patients.

A nonlinear mixed-effects model simultaneously modeled two pharmacokinetic (PK) variables in patients administered cyclosporine twice daily: (i) concentration of drug in blood at the end of the 12-hr dosing interval (C12) and (ii) area under the concentration-time curve within the dosing interval (AUC). For two formulations (Neoral and Sandimmune), the model assessed the following: nonlinearity with respect to dose, interoccasion (intraindividual) variability, interindividual variability, and within- and across-individual correlation between C12 and AUC. Data were pooled from six clinical studies in stable renal transplant patients administered each formulation. PK samples on two occasions were taken usually for each formulation. Each individual's random effect was eight-dimensional consisting of two PK variables for each formulation on two occasions. An ANOVA-like partitioning worked well and reduced the variance matrix for the random effect to a known function of 13 parameters to be estimated, thereby making a numerically intensive computation feasible. Simulations were used to check the model fit, to compute standard errors, and to account for peculiarities in the residual analysis. Outcomes of tests comparing formulations, most of which were statistically significant, favored Neoral (dose proportional, lower interoccasion variability, lower interindividual variability, and higher correlation between C12 and AUC).

Computer Simulation↗
PubMed1995

Perspectives in pharmacokinetics. Physiologically based pharmacokinetic modeling as a tool for drug development.

Since the pioneering work of Haggard and Teorell in the first half of the 20th century, and of Bischoff and Dedrick in the late 1960s, physiologically based pharmacokinetic (PBPK) modeling has gone through cycles of general acceptance, and of healthy skepticism. Recently, however, the trend in the pharmaceuticals industry has been away from PBPK models. This is understandable when one considers the time and effort necessary to develop, test, and implement a typical PBPK model, and the fact that in the present-day environment for drug development, efficacy and safety must be demonstrated and drugs brought to market more rapidly. Although there are many modeling tools available to the pharmacokineticist today, many of which are preferable to PBPK modeling in most circumstances, there are several situations in which PBPK modeling provides distinct benefits that outweigh the drawbacks of increased time and effort for implementation. In this Commentary, we draw on our experience with this modeling technique in an industry setting to provide guidelines on when PBPK modeling techniques could be applied in an industrial setting to satisfy the needs of regulatory customers. We hope these guidelines will assist researchers in deciding when to apply PBPK modeling techniques. It is our contention that PBPK modeling should be viewed as one of many modeling tools for drug development.

Animals↗
PubMed1995

Applying Bailer's method for AUC confidence intervals to sparse sampling.

Bailer developed a method for constructing confidence intervals for areas under the concentration-vs-time curve (AUC's) with only one sample per subject but with multiple subjects sampled at each of several time points post dose. We have modified this method to account for estimation of the variances. How the need to estimate variances affects study design is discussed. An extension of Bailer's method is proposed where variances are modeled as a function of the means, in order to get more precise estimates of variances. The modified and extended methods are applied to a rat toxicokinetic study with only two rats per time point per treatment group.

Animals↗
PubMed1993

Assessing drug exposure in rodent toxicity studies without satellite animals.

Five major objectives for pharmacokinetic investigations in support of toxicity studies are identified as follows: Assess whether animals exhibited measurable blood concentrations in a dose-dependent manner; estimate average area under the concentration-time curve (AUC) and maximal concentration (Cmax) for each treatment group; elucidate general patterns in the concentration-time (CxT) profile, and summarize relationships between CxT and treatment group; determine CxT dependence on day into study; and judge interanimal variability and identify any animals with unusual concentration response. Such objectives are generally addressed in rodent toxicity studies by including "satellite" animals in the study. Satellite animals are extra animals dosed as per protocol but not subjected to toxicological and pathological observations and tests. Instead, they are used exclusively for the evaluation of pharmacokinetic characteristics of the test compound. In this paper, methods are described for achieving the five listed pharmacokinetic objectives in rodent toxicity studies without the use of satellite animals. A rat toxicity study is presented as an example.

Animals↗
PubMedDate not supplied

Inferring systemic exposure from a pharmacokinetic screen: model-free and model-based approaches.

To infer patterns of average systemic exposure and to estimate individual exposures in phase III clinical trials of a new anxiolytic, two statistical methodologies were applied and compared: non-linear mixed-effect modelling, and a model-free approach based on quartiles of dose-normalized plasma concentrations of the drug. Although the model-based approach provides more quantitative insight about relationships between average exposure and demographic covariates, the model-free approach provides qualitatively similar results about average clearance and quantitatively similar results about individual exposures, and the model-free approach is easy and inexpensive to implement.

Adult↗