Jerry Nedelman

Pharmacodynamics of single doses of the novel immunosuppressant FTY720 in stable renal transplant patients.

FTY720, a new and potent immunosuppressant, causes in animal models a rapid, reversible reduction of all subsets of peripheral blood lymphocytes, inducing their migration to secondary lymphoid organs. In this human phase I trial, the pharmacodynamics of single oral doses of FTY720 were evaluated. A randomized, double‐blind, placebo‐controlled, time‐lagged study of six different single ascending oral doses of FTY720 ranging from 0.25 to 3.5 mg was conducted in stable renal transplant patients receiving a cyclosporine‐based regimen. Absolute and subset lymphocyte counts, as well as absolute differential leukocyte counts, were determined by differential blood counts and flow cytometry at screening and multiple intervals thereafter. A pharmacodynamic model was established. Twenty‐four single doses of FTY720 that were administered caused a transient, reversible pan‐lymphopenia within 4 h. Lymphocyte subgroup analysis revealed that almost all subsets declined, with CD4‐ and CD45RA‐positive cells being affected the most. Natural killer cells, granulocytes and monocytes were not influenced by FTY720. The lymphocyte count returned to baseline within 72 h in all dosing cohorts except the highest. Pharmacokinetik/pharmacodynamic modelling revealed a nonlinear dose effect and resulted in a good fit with observed values. These data show that FTY720 is highly effective in humans, with single oral doses of FTY720 ranging from 0.25 to 3.5 mg causing a reversible selective panlymphopenia.

Pharmacokinetic‐pharmacodynamic comparison of a novel multiligand somatostatin analog, SOM230, with octreotide in patients with acromegaly

Acromegaly is a serious hormonal disorder resulting from a pituitary adenoma causing excess growth hormone (GH) production. Somatostatin analogs such as octreotide have been the medical treatment of choice. SOM230, a novel somatostatin analog, was compared with octreotide with respect to pharmacokinetic (PK) profiles and inhibition of GH secretion in acromegalic patients.

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.

Correlating changes in lung function with patient outcomes in chronic obstructive pulmonary disease: a pooled analysis

BackgroundRelationships between improvements in lung function and other clinical outcomes in chronic obstructive pulmonary disease (COPD) are not documented extensively. We examined whether changes in trough forced expiratory volume in 1 second (FEV1) are correlated with changes in patient-reported outcomes.MethodsPooled data from three indacaterol studies (n = 3313) were analysed. Means and responder rates for outcomes including change from baseline in Transition Dyspnoea Index (TDI), St. Georges Respiratory Questionnaire (SGRQ) scores (at 12, 26 and 52 weeks), and COPD exacerbation frequency (rate/year) were tabulated across categories of ΔFEV1. Also, generalised linear modelling was performed adjusting for covariates such as baseline severity and inhaled corticosteroid use.ResultsWith increasing positive ΔFEV1, TDI and ΔSGRQ improved at all timepoints, exacerbation rate over the study duration declined (P < 0.001). Individual-level correlations were 0.03-0.18, but cohort-level correlations were 0.79-0.95. At 26 weeks, a 100 ml increase in FEV1 was associated with improved TDI (0.46 units), ΔSGRQ (1.3-1.9 points) and exacerbation rate (12% decrease). Overall, adjustments for baseline covariates had little impact on the relationship between ΔFEV1 and outcomes.ConclusionsThese results suggest that larger improvements in FEV1 are likely to be associated with larger patient-reported benefits across a range of clinical outcomes.Trial RegistrationClinicalTrials.gov NCT00393458, NCT00463567, and NCT00624286

An extension of satterth waite's approximation applied to pharmacokinetics

Satterthwaites approximation for the degrees of freedom of a linear combination of independent mean squares is extended to the case that the mean squares are correlated. The mean squares are sample variances where some of the experimental units have been used in more than one sample. The motivation for such an extension comes from pharmacokinetics. The observations, taken at different time points from a set of animals, are blood drug concentrations. Some animals were sampled at more than one time point. A linear combination of sample means provides an estimate of the population mean area under the concentration-versus-time curve, which is an indicator of drug exposure. An associated linear combination of sample variances provides an estimate of the variance of the area estimator. The behavior of confidence intervals based on the approximation was studied by simulation. The confidence interval for the population mean, constructed by assuming that the variance estimator has a chi-square distribution with the computed degrees of freedom, achieved close to its nominal 95% coverage, justifying the extension of Satterthwaites approximation.

A therapeutic drug monitoring algorithm for refining the imatinib trough level obtained at different sampling times.

Background: Correlation analyses have demonstrated that maintaining an adequate imatinib (IM) trough concentration would be important for clinical response in patients with chronic myeloid leukemia (CML) and Kit-positive gastrointestinal stromal tumors. The objectives of the current work were to use a pharmacokinetic model to refine the trough levels obtained at different sampling times and to propose a therapeutic drug monitoring algorithm and an acceptable sampling time window for imatinib trough sampling. Methods: The pharmacokinetics of IM in patients (pts) with CML were characterized based on historical data from a Phase III study. In the elimination phase the concentration of IM (C(t)) follows a mono-exponential decline, and the standardized trough concentration (Cmin,std = C(τ)) can be described by a simple algorithm Cmin,std = C(t)* exp(ke · Δt), where Δt = t − τ, and τ is 24 hours for qd or 12 hours for bid dosing and ke is the elimination rate constant. The percent deviation of C(t) from Cmin,std was simulated for different Δt and ke values to define a sampling time window Δt, within which the percent deviation is <20%. Results: Simulation analysis shows that C(t) is largely dependent on Δt and ke. The percent deviation of C(t) at 3 hours before or after τ from Cmin,std will be 7.1%, 13.1%, and 23.4% for pts with low, typical, and high ke values, 0.023/hour, 0.041/hour, and 0.070/hour, respectively. However, if a correction is made for C(t) by the algorithm using the typical ke value of 0.041 per hour, the percent deviation at 3 hours will be reduced to 5.3%, 0%, and 9.1% for pts with low, typical, and high ke values, respectively. Even if the sampling window is extended to ±6 hours, the corresponding percent deviation will still be reasonable: 10.2%, 0%, and 19.0%, respectively. Conclusion: By using the algorithm, the pharmacokinetic sampling window can be extended to a wider window to make the trough sampling easy to implement in the clinical setting, provided that the sampling time and dosing time are accurately recorded.

Application of Sparse Sampling Approaches in Rodent Toxicokinetics: A Prospective View

In toxicology experiments in rodents, separate animals in satellite groups are routinely incorporated to assess the pharmacokinetic (toxicokinetic) characteristics of the drug. This approach has two major drawbacks: 1. limited or no individual exposure estimates are obtained for the animals used in the toxicological evaluation, preventing a quantitative assessment of the concentration/(tox)effect relationship, and 2. the use of satellite animals increases the number of animals and workload involved in a toxicological study. The combined use of sparse sampling and mixed-effects modeling is proposed to overcome these disadvantages. Depending on the specific objectives of the study, a pharmacokinetic-model-independent or a pharmacokinetic-model-dependent approach can be used. The former is based on appropriate transformation of the concentration data. A general linear model type of methodology, relating the main study variables such as dose level, time, and gender directly to the transformed concentrations, is then applied. The model-dependent approach uses nonlinear mixed-effects modeling with the NONMEM software: a pharmacokinetic (PK)-model is used to describe the concentration data in which the study variables (covariates) influence the structural parameters of the model. Both approaches were applied to a four-week and a 26-week oral toxicity study in rodents. No satellite animals were used and sampling was performed on the animals which were evaluated for toxicity as well. Both approaches were able to summarize average concentration versus time behavior and to quantify sources of intra- and interanimal variation. The PK-model-dependent approach offers the possibility for elucidation of pharmacokinetic processes and for individualization by means of Bayesian regression. In the four-week study, the calculation of the post hoc conditional estimates in NONMEM IV yielded the individual structural PK parameters, allowing the reconstruction of the individual concentration-time profiles and the calculation of exposure measures. This option offers the possibility of directly correlating pharmacokinetic characteristics with the observed toxicological findings. Future methodological development should focus on providing and optimizing the tools necessary for establishing and describing this relationship.

Bias in the Wagner-Nelson estimate of the fraction of drug absorbed.

AbstractPurpose. To examine and quantify bias in the Wagner-Nelson estimate of the fraction of drug absorbed resulting from the estimation error of the elimination rate constant (k), measurement error of the drug concentration, and the truncation error in the area under the curve. Methods. Bias in the Wagner-Nelson estimate was derived as a function of post-dosing time (t), k, ratio of absorption rate constant to k (r), and the coefficient of variation for estimates of k (CVk), or CVc for the observed concentration, by assuming a one-compartment model and using an independent estimate of k. The derived functions were used for evaluating the bias with r= 0.5, 3, or 6; k= 0.1 or 0.2; CVc = 0.2 or 0.4; and CVk =0.2 or 0.4; for t= 0 to 30 or 60. Results. Estimation error of k resulted in an upward bias in the Wagner-Nelson estimate that could lead to the estimate of the fraction absorbed being greater than unity. The bias resulting from the estimation error of k inflates the fraction of absorption vs. time profiles mainly in the early post-dosing period. The magnitude of the bias in the Wagner-Nelson estimate resulting from estimation error of k was mainly determined by CVk. The bias in the Wagner-Nelson estimate resulting from to estimation error in k can be dramatically reduced by use of the mean of several independent estimates of k, as in studies for development of an in vivo-in vitro correlation. The truncation error in the area under the curve can introduce a negative bias in the Wagner-Nelson estimate. This can partially offset the bias resulting from estimation error of k in the early post-dosing period. Measurement error of concentration does not introduce bias in the Wagner-Nelson estimate. Conclusions. Estimation error of k results in an upward bias in the Wagner-Nelson estimate, mainly in the early drug absorption phase. The truncation error in AUC can result in a downward bias, which may partially offset the upward bias due to estimation error of k in the early absorption phase. Measurement error of concentration does not introduce bias. The joint effect of estimation error of k and truncation error in AUC can result in a non-monotonic fraction-of-drug-absorbed-vs-time profile. However, only estimation error of k can lead to the Wagner-Nelson estimate of fraction of drug absorbed greater than unity.

On some “disadvantages” of the population approach

In a seminal article on population pharmacokinetic modeling, researchers demonstrated how means and variances of pharmacokinetic parameters for a patient population could be inferred from sparse data collected under conditions of routine patient care. But they also identified 4 potential concerns about their methodology: unobserved confounding variables may bias the inferences; conditions under which data are collected may lead to inaccuracies of reporting or recording; correlations among important predictor variables may reduce statistical efficiency; and costs cannot be controlled by principles of study design. Experiences are reviewed that related to those potential disadvantages. A method is presented for diagnosing the possible presence of confounding. A model is constructed and applied that captures the influences of data inaccuracies. An example of selecting from among correlated covariates is summarized. Finally, a methodology for optimal study design is reviewed and applied to an example.

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 betweenC12 andAUC. 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 individuals 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 betweenC12 andAUC).