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E I Ette

Publications and source records attributed to E I Ette.

At least 19 recordsLinked to original sources
PubMed2001

The process of knowledge discovery from large pharmacokinetic data sets.

The advent of statistical software with powerful graphical and modeling capabilities has revolutionized the manner in which pharmacokinetic and pharmacodynamic analyses are performed. Knowledge discovery from a large (population) pharmacokinetic data set incorporates all steps taken from data assembly to the development of a population pharmacokinetic model and the communication of the results thereof. The process can be formalized into a number of steps: (1) creation of a data set for pharmacokinetic knowledge discovery, (2) data quality analysis, (3) data structure analysis (exploratory examination of raw data), (4) determination of the basic pharmacokinetic model that best describes the data and generating post hoc empiric individual Bayesian parameter estimates, (5) the search for patterns and relationships between parameters and parameters and covariates by visualization, (6) the use of modern statistical modeling techniques for data structure revelation and covariate selection, (7) consolidation of the discovered knowledge into irreducible form (i.e., developing a population pharmacokinetic model), (8) the determination of model robustness (determination of the reliability of model parameter estimates), and (9) the communication and integration of the discovered pharmacokinetic knowledge. This process is discussed, and a motivating example is presented. The use of modern graphical, modeling, and statistical techniques for knowledge discovery from large pharmacokinetic data sets has given the data analyst the freedom to choose statistical methodology appropriate to the problem at hand with the maximization of information extraction, rather than on the basis of mathematical/statistical tractability.

Adolescent↗
PubMed2000

The role of population pharmacokinetics in drug development in light of the Food and Drug Administration's 'Guidance for Industry: population pharmacokinetics'.

Population pharmacokinetics (PPK) has evolved from a discipline primarily applied to therapeutic drug monitoring to one that plays a significant role in clinical pharmacology in general and drug development in particular. In February 1999 the US Food and Drug Administration issued a 'Guidance for Industry: Population Pharmacokinetics' that sets out the mechanisms and philosophy of PPK and outlines its role in drug development. The application of PPK to the drug development process plays an important role in the efficient development of safe and effective drugs. PPK knowledge is essential for mapping the response surface, explaining subgroup differences, developing and evaluating competing dose administration strategies, and as an aid in designing future studies. The mapping of the response surface is done to maximise the benefit-risk ratio, so that the impact of the input profile and dose magnitude on beneficial and harmful pharmacological effects can be understood and applied to individual patients. PPK combined with simulation methods provides a tool for estimating the expected range of concentrations from competing dose administration strategies. Once extracted, this knowledge can be applied to labelling or used to assess various future study designs. PPK should be implemented across all phases of drug development. For preclinical studies, PPK can be applied to allometric scaling and toxicokinetic analyses, and is useful for determining 'first time in man' doses and explaining toxicological results. Phase I studies provide initial understanding of the structural model and the effect of possible covariates, and may later be used to evaluate PPK differences between patients and healthy individuals. Phase II studies provide the greatest opportunity to map the response surface. With these PPK models it is possible to gain an improved understanding of the role of the dose on the response surface and of the range of expected responses. In phase III and IV studies, PPK is implemented to further refine the PPK model and to explain unexpected responses. Planning for the implementation of PPK across all phases of drug development is necessary, as well as planning for individual PPK studies. Planning should include: defining important questions, identifying covariates and drug-drug interactions that need to be investigated, and identifying the applications and intended use of the model(s). The plan for each project must have a strategy for data management, data collection, data quality assurance, staff training for data collection, data analysis and model validation.

Animals↗
PubMed1999

Population pharmacokinetics. A regulatory perspective.

The application of population approaches to drug development is recommended in several US Food and Drug Administration (FDA) guidance documents. Population pharmacokinetic (and pharmacodynamic) techniques enable identification of the sources of inter- and intra-individual variability that impinge upon drug safety and efficacy. This article briefly discusses the 2-stage approach to the estimation of population pharmacokinetic parameters, which requires serial multiple measurements on each participant, and comprehensively reviews the nonlinear mixed-effects modelling approach, which can be applied in situations where extensive sampling is not done on all or any of the participants. Certain preliminary information, such as the compartment model used in describing the pharmacokinetics of the drug, is required for a population pharmacokinetic study. The practical design considerations of the location of sampling times, number of samples/participants and the need to sample an individual more than once should be borne in mind. Simulation may be useful for choosing the study design that will best meet study objectives. The objectives of the population pharmacokinetic study can be secondary to the objectives of the primary clinical study (in which case an add-on population pharmacokinetic protocol may be needed) or primary (when a stand-alone protocol is required). Having protocols for population pharmacokinetic studies is an integral part of 'good pharmacometric practice'. Real-time data assembly and analysis permit an ongoing evaluation of site compliance with the study protocol and provide the opportunity to correct violations of study procedures. Adequate policies and procedures should be in place for study blind maintenance. Real-time data assembly creates the opportunity for detecting and correcting errors in concentration-time data, drug administration history and covariate data. Population pharmacokinetic analyses may be undertaken in 3 interwoven steps: exploratory data analysis, model development and model validation (i.e. predictive performance). Documentation for regulatory purposes should include a complete inventory of key runs in the analyses undertaken (with flow diagrams if possible), accompanied by articulation of objectives, assumptions and hypotheses. Use of diagnostic analyses of goodness of fit as evidence of reliability of results is advised. Finally, the use of stability testing or model validation may be warranted to support label claims. The opinions expressed in this article were revised by incorporating comments from various sources and published by the FDA as 'Guidance for Industry: Population Pharmacokinetics' (see the FDA home page http:/(/)www.fda.gov for further information).

Data Interpretation, Statistical↗
PubMed1998

Ignorability and parameter estimation in longitudinal pharmacokinetic studies.

In the analysis of longitudinal pharmacokinetic data, both balanced (equal number of samples per subject) and unbalanced data are used. It is implicitly assumed that the process that caused the missing data can be ignored. A simulation study was performed to determine the effect of ignoring the missing data (i.e., "ignorability") on the accuracy and precision of parameter estimation in longitudinal pharmacokinetic studies. A two-compartment model with multiple intravenous bolus inputs was assumed. Subjects with balanced data sets had six samples, and those with unbalanced data had 1 to 5 samples missing (i.e., supplied in a decreasing order from 5 to 1 samples). The proportion of subjects with 1 to 5 samples missing varied from 25% to 75% in a fixed sample size of 100. The effect of ignorability was studied at intersubject variability ranging from 15% to 60% for a drug assumed to be dosed at its elimination half-life. One hundred replicate data sets of 100 subjects each were simulated for each missing data scenario. The accuracy of parameter estimation was not significantly affected by the amount of ignorable missing data at any given level of variability. However, the precision of parameter estimation was affected by the degree of "missingness."

Humans↗
PubMed1998

Balanced designs in longitudinal population pharmacokinetic studies.

A simulation study was performed using a balanced design to determine the sample size required for accurate and precise estimation of a parameter at a given level of intersubject variability in a longitudinal population pharmacokinetic study. A two-compartment model parameterized in terms of clearance (Cl), volumes of the central (V1) and peripheral (V2) compartments, and intercompartmental clearance (Q) with multiple intravenous bolus inputs was assumed. Six samples were obtained from each subject using the informative profile (block) randomized design. Variability (in terms of coefficient of variation, CV) in model parameters was varied between 30% and 100%, and residual variability was fixed at 15%. Sample sizes ranging from 30 to 1,000 subjects were studied, and a hundred replicate data sets were generated and analyzed with NONMEM for each sample size at each CV. A sample size of 30 was required for accurate and precise estimation of structural model parameters when CV < or = 75%, except for Cl where it is adequate for CV < or = 100%. A sample size of 80 was required for intersubject variability estimation with CV < or = 60%. Robust estimates of variability in Cl were obtained with sample sizes of 30 (CV < or = 45%), 60 (CV 60-75%), and 100 (CV > or = 75%). Positively biased estimates of residual variability were obtained irrespective of sample size at > or = 60% CV. This indicates that estimates of residual variability obtained in study situations where CV > or = 60% should be interpreted with caution. In such situations model misspecification may not be the issue, because in this simulation study concentration-time profiles were generated and analyzed with the same model. Although these results should be interpreted within the context of the study, they provide a framework for addressing the issue of sample size in longitudinal population pharmacokinetic study with a balanced sampling design. The result of a population pharmacokinetic study can be anticipated by comparing the results of several simulations in which the various input factors have been varied.

Computer Simulation↗
PubMed1997

Stability and performance of a population pharmacokinetic model.

This study aimed to determine the stability (in terms of covariate selection) of a population pharmacokinetic model and evaluate its performance in the absence of a test data set. Data from 88 full-term infants, 11 of whom were human immunodeficiency virus (HIV)-seropositive, taking an antiinfective agent were analyzed using exploratory data analysis methods and the nonlinear mixed-effects modeling (NONMEM) program to obtain the final population pharmacokinetic model. The stability of the population pharmacokinetic model was tested using the nonparametric bootstrap approach in four steps: 1) with the base pharmacokinetic model, 100 bootstrap replicates of the original data were generated by sampling with replacement; 2) ascertainment that each bootstrap data replicate was described by the basic structural model using the NONMEM objective function; 3) generalized additive modeling (GAM) applied to empiric Bayesian estimates for covariate selection at alpha = 0.05 and a frequency (f) cutoff value of 0.50; and 4) NONMEM population model building using covariates selected in the third step with alpha = 0.005. Performance of the population pharmacokinetic model was evaluated using 200 additional bootstrap replicates of the data by fitting the model obtained in step 4 to them. Parameters obtained were compared with those obtained in the model stability step, and improved prediction error, a measure of predictive accuracy as an index of internal validation, was computed. The reciprocal of serum creatinine (RSC; f = 0.73) and HIV (f = 0.70) were selected by GAM as predictors of clearance (Cl). The population pharmacokinetic model obtained without the determination of model stability included RSC as a predictor of Cl, but the final model from the model stability step included both HIV and RSC as predictors of Cl. Final population pharmacokinetic parameters were obtained with this model fitted to the original data; however, the 95% confidence interval on the HIV status regression coefficient included zero, indicating no significance. The mean parameter estimates obtained with the additional 200 bootstrap replicates of data were within 15% of those obtained with the final model at the regression stability step. Bootstrap resampling procedure is useful for evaluating the stability and performance of a population model by repeatedly fitting it to the bootstrap samples when there is no test data set.

Adolescent↗
PubMed1996

On the recording of sample times and parameter estimation from repeated measures pharmacokinetic data.

A pharmacokinetic screen has been advocated for the characterization of the population pharmacokinetics of drugs during Phase 3 clinical trials. A common perception encountered in the collection of such data is that the accuracy of sampling times relative to dose is inadequate. A prospective simulation study was carried out to evaluate the effect of error in the recording of sampling times on the accuracy and precision of population parameter estimates from repeated measures pharmacokinetic data. A two-compartment model with intravenous bolus input(s) (single and multiple doses) was assumed. Random and systematic error in sampling times ranging from 5-50% using profile (block) randomized design were introduced. Sampling times were simulated in EXCEL while concentration data simulation and analysis were done in NONMEM. The effect of error in sampling times was studied at levels of variability ranging from 15-45% for a drug assumed to be dosed at its elimination half-life. One hundred replicate data sets of 100 subjects each were simulated for each case. Although estimates of clearance (CL) and variability in clearance were robust for most of the sampling time errors, there was an increase in bias and imprecision in overall parameter estimation as intersubject variability was increased. If there is interest in parameters other than CL, then the design of prospective population studies should include procedures for minimizing the error in the recording of sample times relative to dosing history.

Computer Simulation↗
PubMed1996

Comparing non-hierarchical models: application to non-linear mixed effects modeling.

There is no method available to compare the fit of two non-hierarchical non-linear mixed effects models, although the common practice is to select the model with the lower objective function. Bootstrapping the log-likelihood differences (LLDs) of non-hierarchical models and constructing a bootstrap confidence interval on the LLDs is proposed for comparing the goodness-of-fit of such models. This is illustrated with different parameterizations of clearance models for an anti-infective agent in a longitudinal pharmacokinetic study which are compared. Additive and exponential models of creatinine clearance as a predictor of clearance are used as examples.

Adult↗
PubMed1995

Analysis of animal pharmacokinetic data: performance of the one point per animal design.

A simulation study was carried out to determine the impact of various design factors on the accuracy and precision with which population pharmacokinetic parameters are estimated in preclinical pharmacokinetic studies. A drug given by intravenous bolus injection and having mono-exponential disposition characteristics was assumed. The factors investigated were (i) number of animals sampled at specified times with one observation taken per animal, (ii) error in observed concentration measurements, and (iii) doubling the number of observations per animal while varying the number of animals. Data were analyzed with the NONMEM program, and the least number of animals per time point (where each animal supplied one concentration-time point) required for accurate and precise parameter estimation was determined. The one observation per animal design yielded biased and imprecise estimates of variability, and residual variability could not be estimated. Increasing the error in the concentration measurement led to a significant deterioration in the accuracy and precision with which variability was estimated. Obtaining a second sample from each animal practically eliminated bias and facilitated the partitioning of interanimal variability and residual intraanimal variability, by introducing information about the latter. Doubling the total number of observations per animal required using half (i.e., 50) the total number of animals required for accurate and precise parameter estimation with the one sample per animal design.

Animals↗
PubMed1995

Population pharmacokinetic modeling: the importance of informative graphics.

PURPOSE: The usefulness of several modelling methods were examined in the development of a population pharmacokinetics model for cefepime. METHODS: The analysis was done in six steps: (1) exploratory data analysis to examine distributions and correlations among covariates, (2) determination of a basic pharmacokinetic model using the NON-MEM program and obtaining Bayesian individual parameter estimates, (3) examination of the distribution of parameter estimates, (4) multiple linear regression (MLR) with case deletion diagnostics, generalized additive modelling (GAM), and tree-based modelling (TBM) for the selection of covariates and revealing structure in the data, (5) final NONMEM modelling to determine the population PK model, and (6) the evaluation of final parameter estimates. RESULTS: An examination of the distribution of individual clearance (CL) estimates suggested bimodality. Thus, the mixture model feature in NONMEM was used for the separation of subpopulations. MLR and GAM selected creatinine clearance (CRCL) and age, while TBM selected both of these covariates and weight as predictors of CL. The final NONMEM model for CL included only a linear relationship with CRCL. However, two subpopulations were identified that differed in slope and intercept. CONCLUSIONS: The findings suggest that using informative graphical and statistical techniques enhance the understanding of the data structure and lead to an efficient analysis of the data.

Adolescent↗
PubMed1995

Experimental design and efficient parameter estimation in preclinical pharmacokinetic studies.

Monte Carlo simulation technique used to evaluate the effect of the arrangement of concentrations on the efficiency of estimation of population pharmacokinetic parameters in the preclinical setting is described. Although the simulations were restricted to the one compartment model with intravenous bolus input, they provide the basis of discussing some structural aspects involved in designing a destructive ("quantic") preclinical population pharmacokinetic study with a fixed sample size as is usually the case in such studies. The efficiency of parameter estimation obtained with sampling strategies based on the three and four time point designs were evaluated in terms of the percent prediction error, design number, individual and joint confidence intervals coverage for parameter estimates approaches, and correlation analysis. The data sets contained random terms for both inter- and residual intra-animal variability. The results showed that the typical population parameter estimates for clearance and volume were efficiently (accurately and precisely) estimated for both designs, while interanimal variability (the only random effect parameter that could be estimated) was inefficiently (inaccurately and imprecisely) estimated with most sampling schedules of the two designs. The exact location of the third and fourth time point for the three and four time point designs, respectively, was not critical to the efficiency of overall estimation of all population parameters of the model. However, some individual population pharmacokinetic parameters were sensitive to the location of these times.

Animals↗
PubMed1993

Interpretation of simulation studies for efficient estimation of population pharmacokinetic parameters.

OBJECTIVE: To develop new approaches for evaluating results obtained from simulation studies used to determine sampling strategies for efficient estimation of population pharmacokinetic parameters. METHODS: One-compartment kinetics with intravenous bolus injection was assumed and the simulated data (one observation made on each experimental unit [human subject or animal]), were analyzed using NONMEM. Several approaches were used to judge the efficiency of parameter estimation. These included: (1) individual and joint confidence intervals (CIs) coverage for parameter estimates that were computed in a manner that would reveal the influence of bias and standard error (SE) on interval estimates; (2) percent prediction error (%PE) approach; (3) the incidence of high pair-wise correlations; and (4) a design number approach. The design number (phi) is a new statistic that provides a composite measure of accuracy and precision (using SE). RESULTS: The %PE approach is useful only in examining the efficiency of estimation of a parameter considered independently. The joint CI coverage approach permitted assessment of the accuracy and reliability of all model parameter estimates. The phi approach is an efficient method of achieving an accurate estimate of parameter(s) with good precision. Both the phi for individual parameter estimation and the overall phi for the estimation of model parameters led to optimal experimental design. CONCLUSIONS: Application of these approaches to the analyses of the results of the study was found useful in determining the best sampling design (from a series of two sampling times designs within a study) for efficient estimation of population pharmacokinetic parameters.

Bias↗
PubMed1990

Ivermectin: a long-acting microfilaricidal agent.

Ivermectin is a macrocyclic lactone (fermentation) product and actinomycete (Streptomyces avermitilis) that possesses an unusually broad spectrum of potent activity against several species of nematodes, arachnids, and insects that parasitize domestic animals. From clinical trials in humans it has been found to be microfilaricidal, killing microfilariae of Onchocerca volvulus (the parasite causing onchocerciasis), and interrupting its transmission by the black fly vector. Dermal microfilariae density in patients are reduced to near zero levels for 6-12 months after a single oral dose of ivermectin 0.15-0.2 mg/kg. Its precise mechanism of action is unknown. It has a time to maximum concentration of 2.7-4.3 h, and an elimination half-life of 28 +/- 10 h. When compared with an oral solution the tablet dosage form has a relative bioavailability of approximately 60 percent. Not much is known about its metabolism in humans, and the unchanged drug is not detected in the urine. Controlled clinical trials have shown ivermectin to be associated with milder side effects than diethylcarbamazine, the current drug of choice for onchocerciasis therapy. It does not cause the severe Mazzoti-type (anaphylactoid) reactions that are associated with diethylcarbamazine use. Ivermectin is effective, safer, and more tolerable than diethylcarbamazine. It should, therefore, replace diethylcarbamazine as the drug of choice for onchocerciasis therapy.

Animals↗
PubMed1989

Determination of saliva: total plasma chloroquine levels relationship by high performance liquid chromatography.

The use of saliva chloroquine concentrations measurement as a noninvasive technique in the evaluation of the pharmacokinetics of the drug was investigated. Chloroquine concentrations in saliva and plasma were measured in eight healthy volunteers after a single oral dose of two tablets of chloroquine sulfate. The saliva: total plasma concentrations (S/P) ratio was found to be approximately constant in the absorption (0.4 +/- 0.07), distribution (0.47 +/- 0.08), and elimination (0.46 +/- 0.05) phases. Thus, saliva sampling for chloroquine concentrations was found to be a useful noninvasive technique for the estimation of all the pharmacokinetic parameters of the drug and hence, for chloroquine pharmacokinetic studies.

Adult↗
PubMed1989

Pharmacokinetics of chloroquine and some of its metabolites in healthy volunteers: a single dose study.

Eight healthy volunteers who had not taken chloroquine 2 to 12 months previously participated in a single dose study designed to evaluate the pharmacokinetics of chloroquine and some of its metabolites. Each subject received two tablets of chloroquine sulfate (300 mg base) only. Blood and urine samples were collected just before and periodically after chloroquine administration. These samples were assayed for chloroquine and its N-dealkylated metabolites (monodesethylchloroquine, didesethylchloroquine, 7-chloro-4-aminoquinoline), chloroquine side chain N-oxide and chloroquine di-N-oxide using a high performance liquid chromatographic method. Residual levels of chloroquine and its N-oxidation metabolites were found in all subjects. 7-chloro-4-aminoquinoline was eliminated more slowly (t1/2z = 126.48 +/- 20.13 h) than the other metabolites and the unchanged drug (t1/2z = 106.43 +/- 10.13 h). Also, 7-chloro-4-aminoquinoline had a significantly faster (Student's t-test, P less than 0.05) formation clearance when compared with the other metabolites. The plasma concentration of 7-chloro-4-aminoquinoline was about twice that of the unchanged drug while the plasma concentration of monodesethylchloroquine was about 46% that of the unchanged drug. In order to investigate whether the metabolites were produced from the same binding sites or closely related sites on the cytochrome P-450 system, their formation clearances were correlated. The best correlation (r2 = 0.83) was observed for didesethylchloroquine and monodesethylchloroquine, and a fair correlation (r2 = 0.59) was observed for monodesethylchloroquine and 7-chloro-4-aminoquinoline. Formation clearances of the other metabolites were poorly correlated.(ABSTRACT TRUNCATED AT 250 WORDS)

Administration, Oral↗
PubMed1988

Passage of chloroquine into semen.

The passage of chloroquine into semen was investigated in four healthy men following the oral administration of four chloroquine sulphate tablets (600 mg base) to each subject. Chloroquine was found to be excreted into semen with a slow transfer rate constant of 0.0002 min-1, and the semen/plasma ratio based on regression analysis was 0.40 +/- 0.06 (mean +/- s.d.). It is concluded that the passage of chloroquine from plasma to semen occurs by passive diffusion.

Adult↗
PubMed1988

Chloroquine-induced acute dystonic reactions in the presence of metronidazole.

A 30-year-old woman underwent laparotomy and was placed on a seven-day course of metronidazole and ampicillin postoperatively. Chloroquine therapy for malaria was instituted on the sixth day and the patient developed acute dystonic reactions after a single dose. Diphenhydramine therapy before chloroquine administration did not prevent the development of the dystonic reactions. The extrapyramidal symptoms subsided upon diazepam administration and chloroquine withdrawal even though metronidazole therapy was continued. The mechanism of this adverse drug reaction based on the pharmacodynamic interaction between chloroquine and metronidazole is discussed. It is suggested that the combination of pyrimethamine and sulfadoxine be used in place of chloroquine for malaria chemotherapy in patients on metronidazole therapy.

Adult↗