PubMed Health⌕ Search

Biomedical subjects

Ene I Ette

Publications and source records attributed to Ene I Ette.

10 recordsLinked to original sources
PubMed2005

A pragmatic approach to the design of population pharmacokinetic studies.

The publication of a seminal article on nonlinear mixed-effect modeling led to a revolution in pharmacokinetics (PKs) with the introduction of the population approach. Since then, interest in obtaining accurate and precise estimates of population PK parameters has led to work on population PK study design that extended previous work on optimal sampling designs for individual PK parameter estimation. The issues and developments in the design of population PK studies are reviewed as a prelude to investigating, via simulation, the performance of 2 approaches (population Fisher information matrix D-optimal design and informative block [profile] randomized [IBR] design) for designing population PK studies. The results of our simulation study indicate that the designs based on the 2 approaches yielded efficient parameter estimates. The designs based on the 2 approaches performed similarly, and in some cases designs based on the IBR approach were slightly better. The ease with which the IBR designs can be generated makes them preferable in drug development, where pragmatism and time are of great consideration. We, therefore, refer to the IBR designs as pragmatic designs. Pragmatic designs that achieve high efficiency in the estimation parameters should be used in the design of population PK studies, and simulation should be used to determine the efficiency of the designs.

Animals↗
PubMed2005

A random sampling approach for robust estimation of tissue-to-plasma ratio from extremely sparse data.

his study was performed to develop a new nonparametric approach for the estimation of robust tissue-to-plasma ratio from extremely sparsely sampled paired data (ie, one sample each from plasma and tissue per subject). Tissue-to-plasma ratio was estimated from paired/unpaired experimental data using independent time points approach, area under the curve (AUC) values calculated with the naïve data averaging approach, and AUC values calculated using sampling based approaches (eg, the pseudoprofile-based bootstrap [PpbB] approach and the random sampling approach [our proposed approach]). The random sampling approach involves the use of a 2-phase algorithm. The convergence of the sampling/resampling approaches was investigated, as well as the robustness of the estimates produced by different approaches. To evaluate the latter, new data sets were generated by introducing outlier(s) into the real data set. One to 2 concentration values were inflated by 10% to 40% from their original values to produce the outliers. Tissue-to-plasma ratios computed using the independent time points approach varied between 0 and 50 across time points. The ratio obtained from AUC values acquired using the naive data averaging approach was not associated with any measure of uncertainty or variability. Calculating the ratio without regard to pairing yielded poorer estimates. The random sampling and pseudoprofile-based bootstrap approaches yielded tissue-to-plasma ratios with uncertainty and variability. However, the random sampling approach, because of the 2-phase nature of its algorithm, yielded more robust estimates and required fewer replications. Therefore, a 2-phase random sampling approach is proposed for the robust estimation of tissue-to-plasma ratio from extremely sparsely sampled data.

Algorithms↗
PubMed2005

Data supplementation: a pharmacokinetic/pharmacodynamic knowledge creation approach for characterizing an unexplored region of the response surface.

PURPOSE: To develop a data supplementation [i.e., a pharmacokinetic/pharmacodynamics (PK/PD) knowledge creation] approach for generating supplemental data to be used in characterizing a targeted unexplored segment of the response surface. METHODS: The procedure for data supplementation can be summarized as follows: 1) statement of the objective of data supplementation for PK/PD knowledge creation, 2) performance of PK knowledge discovery, 3) PK data synthesis for target dose group(s), 4) covariate data synthesis for virtual subjects in the target dose group(s), 5) discovery of hidden knowledge from real data set to which supplemental data will be added, 6) implementation of a data supplementation methodology, and 7) discovery and communication of the created knowledge. A nonparametric approximate Bayesian multiple supplementation and its modification, structure-based multiple supplementation, which is an adaptation of the approximate Bayesian bootstrap, is proposed as a method of data supplementation for PK/PD knowledge creation. The structured-based multiple supplementation methodology was applied to characterize the effect of a target dose of 100 mg that was unexplored in a previously concluded study that investigated the effect of 200- and 600-mg doses on biomarker response. RESULTS: The target dose of 100 mg was found to produce a response comparable with that of the 200 mg and better than that obtained with the 600 mg. CONCLUSIONS: Implementation of the PK/PD knowledge creation process through data supplementation resulted in gaining knowledge about a targeted region of a response surface (i.e., the effect of a target dose) that was not previously studied in a completed study without expending resources in conducting a new study.

Algorithms↗
PubMed2004

Population pharmacokinetics III: design, analysis, and application of population pharmacokinetic Studies.

OBJECTIVE: To present a framework within which population pharmacokinetic (PPK) studies should be designed and analyzed and discuss the application of developed PPK models. METHODS: Information on PPK was retrieved from a MEDLINE search (1979-December 2003) of the literature and a bibliographic evaluation of review articles and books. This information is used in conjunction with experience to explain the design and analysis of PPK studies. Also, examples are included to demonstrate the usefulness of PPK. SYNTHESIS: A great deal of thought must be given to the design and analysis of PPK studies (ie, development of PPK models). Models are of 2 primary types--descriptive and predictive--and the process applied to these models is necessarily different. An approach that ensures model applicability is presented. CONCLUSIONS: PPK models have great utility, and the applications are many. They are very different from single-subject pharmacokinetic models and therefore require different approaches to model estimation.

Humans↗
PubMed2004

Population pharmacokinetics II: estimation methods.

OBJECTIVE: To present, compare, and contrast the various approaches to estimating population pharmacokinetic (PPK) models with respect to the mathematical foundation, statistical aspects, software programs for implementation, and underlying assumptions. DATA SOURCES: Information on PPK was retrieved from a MEDLINE search (1977-August 2004) of literature and a bibliographic review of review articles and books. This information is used in conjunction with experience to explain the various methodologic approaches to PPK. STUDY SELECTION AND DATA EXTRACTION: All articles indentified from data sources were evaluated and relevant information was included in this review. DATA SYNTHESIS: Over 80 articles dealing with PPK estimation methods and/or their implementation were identified and reviewed. Sixty-four of these were chosen for their direct relevance to the subject of this article. Different estimation methods ranging from the naive averaging and naive pooled approaches through the standard two-stage approach to the nonlinear mixed-effects modeling approaches for estimating PPK are reviewed with their advantages and limitations. CONCLUSIONS: PPK estimation methods that rely on the characterizing of mixed (fixed and random) effects are known to produce PPK parameter estimates that are less biased than those obtained using the naive and standard two-stage approaches. The NONMEM software is the most widely used software for the characterization of PPK.

Bayes Theorem↗
PubMed2004

Population pharmacokinetics I: background, concepts, and models.

OBJECTIVE: To present and emphasize the background, foundations, utility, and conceptual underlying theory of the population pharmacokinetic (PPK) approach with an examination of the advantages when compared with other approaches of pharmacokinetic modeling. DATA SOURCES: Information on PPK was retrieved from a MEDLINE search (1979-June 2002) of literature and a bibliographic review of review articles and books. STUDY SELECTION AND DATA EXTRACTION: All articles identified from data sources were evaluated and relevant information was included in this review. DATA SYNTHESIS: PPK plays a pivotal role in developing dosing strategies for direct patient care and in drug development. PPK is valuable because it targets the patient group that will eventually receive the drug of interest, quantitates pharmacokinetic variability at several levels, and seeks to explain those sources of variability. CONCLUSIONS: PPK models have great utility and the applications are many. They are very different from single-subject pharmacokinetic models and therefore require different approaches to model development.

Humans↗
PubMed2003

Model appropriateness and population pharmacokinetic modeling.

The purpose of this study was to define model appropriateness, identifying the individual elements thereof, and to set out a framework within which model appropriateness could be determined for population pharmacokinetic (PPK) models. Model appropriateness was defined by stating the problem to be solved, with the intended use of the model being the pivotal event. The elements of model appropriateness were identified with the type of model (descriptive vs. predictive) determining which elements of model appropriateness need to be executed. An example is presented to show how model appropriateness is determined for the optimal application of PPK models. It was determined that PPK models are developed to solve problems. Model appropriateness depends on identifying the problem, as well as stating the intended use of the model, and requires evaluation of the model for goodness of fit, reliability, and stability if intended for descriptive purposes; for predictive models, validation would be an additional requirement. Descriptive models are used to explain variability in the pharmacokinetics (PK) of a drug, while predictive models are developed to extrapolate beyond the immediate study population. For those models used for predictive purposes, strong assumptions are made about the relationship to the underlying population from which the data were collected. As an example of determining model appropriateness, a PPK model for 5-fluorocytosine was developed, using NONMEM, version IV. The model was evaluated and validated by the process of percentile bootstrapping. From the PPK model, the range of expected serum concentrations based on two widely used dosing methods (Sanford and the University of California at San Diego [UCSD]) was simulated (Pharsight Trial Designer software). These results indicated that the UCSD method performed well and has the advantage of recommending convenient dosing intervals. In conclusion, considering and applying the principles of model appropriateness to PPK models will result in models that can be applied for their intended use with confidence. Model appropriateness was efficiently established and determined to address the problem of comparing competing dosing strategies.

Adult↗
PubMed2002

Safety and efficacy of the multidrug-resistance inhibitor biricodar (VX-710) with concurrent doxorubicin in patients with anthracycline-resistant advanced soft tissue sarcoma.

PURPOSE: Incel (biricodar, VX-710) restores drug sensitivity to P-glycoprotein and multidrug resistance-associated protein-1-expressing cells. This Phase I/II study evaluated the safety/tolerability, pharmacokinetics, and efficacy of VX-710 plus doxorubicin in patients with inoperable, locally advanced or metastatic, anthracycline-resistant/refractory, soft tissue sarcoma. EXPERIMENTAL DESIGN: In Phase I, i.v. bolus doxorubicin at 60, 75, or 67.5 mg/m(2) was administered 8 h after initiation of a 72-h continuous i.v. (CIV) infusion of VX-710 (120 mg/m(2)/h) to cohorts of patients to establish a maximum tolerated dose. For efficacy evaluations in Phase II, eligible patients had inoperable, locally advanced or metastatic, anthracycline-resistant/refractory soft tissue sarcoma; < or =225 mg/m(2) cumulative prior doxorubicin; and adequate hematological, liver, and kidney function. Cycles were repeated every 3 weeks. RESULTS: Fourteen patients were enrolled in Phase I. Myelosuppression was the dose-limiting toxicity with 75 and then 67.5 mg/m(2) doxorubicin, and the maximum tolerated dose was established at 60 mg/m(2) with VX-710, 120 mg/m(2)/h, 72-h CIV. VX-710 had no apparent effect on doxorubicin pharmacokinetics. Twenty-nine patients enrolled in Phase II were treated with VX-710, 120 mg/m(2)/h 72-h CIV, and 60 mg/m(2) doxorubicin. Among 26 evaluable patients, minimal activity was noted among 11 patients with gastrointestinal stromal tumors (GISTs); however, in 15 patients with anthracycline-resistant sarcomas of other histologies, 2 achieved partial responses and 7 patients had disease stabilization with an overall median progression-free interval of 3.4 months. CONCLUSION: Anthracycline resistance in GISTs appears to be independent of P-glycoprotein or multidrug resistance-associated protein-1 resistance mechanisms. However, the combination of VX-710 and doxorubicin resulted in objective responses or disease stabilization in patients with strictly defined anthracycline-refractory non-GIST sarcomas, which warrants further evaluation.

ATP Binding Cassette Transporter, Subfamily B, Mem↗
PubMedDate not supplied

Estimating inestimable standard errors in population pharmacokinetic studies: the bootstrap with Winsorization.

A simulation study was performed to determine how inestimable standard errors could be obtained when population pharmacokinetic analysis is performed with the NONMEM software on data from small sample size phase I studies. Plausible sets of concentration-time data for nineteen subjects were simulated using an incomplete longitudinal population pharmacokinetic study design, and parameters of a drug in development that exhibits two compartment linear pharmacokinetics with single dose first order input. They were analyzed with the NONMEM program. Standard errors for model parameters were computed from the simulated parameter values to serve as true standard errors of estimates. The nonparametric bootstrap approach was used to generate replicate data sets from the simulated data and analyzed with NONMEM. Because of the sensitivity of the bootstrap to extreme values, winsorization was applied to parameter estimates. Winsorized mean parameters and their standard errors were computed and compared with their true values as well as the non-winsorized estimates. Percent bias was used to judge the performance of the bootstrap approach (with or without winsorization) in estimating inestimable standard errors of population pharmacokinetic parameters. Winsorized standard error estimates were generally more accurate than non-winsorized estimates because the distribution of most parameter estimates were skewed, sometimes with heavy tails. Using the bootstrap approach combined with winsorization, inestimable robust standard errors can be obtained for NONMEM estimated population pharmacokinetic parameters with > or = 150 bootstrap replicates. This approach was also applied to a real data set and a similar outcome was obtained. This investigation provides a structural framework for estimating inestimable standard errors when NONMEM is used for population pharmacokinetic modeling involving small sample sizes.

Clinical Trials, Phase I as Topic↗