Common noncompartmental pharmacokinetic variables: are they normally or log-normally distributed?

Lacey, Larry F.; Keene, Oliver N.; Pritchard, J. Frederick; Bye, Alan

doi:10.1080/10543409708835177

Cited by 113 publications

(70 citation statements)

References 12 publications

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“…Data preprocessing comprised of (i) log transformation, (ii) age correction, (iii) uniform scaling and (iv) imputation of missing data. Specifically, (i) as quantile-quantile plots pointed at log-normal distribution of the data, which is in line with general observations in blood-derived concentration data [26], data was zero invariant log-transformed, except for the two endocannabinoids (AE and OEA) for which the plots suggested to prefer the original linear scaling. Subsequently, (ii) the influences of age on the lipid marker plasma concentrations were reduced by applying corrections based on robust linear regression using the Levenberg-Marquardt nonlinear leastsquares algorithm implemented in the R library "minpack.lm" (https://cran.r-project.…”

Section: Data Prepossessingmentioning

confidence: 69%

Identification of disease-distinct complex biomarker patterns by means of unsupervised machine-learning using an interactive R toolbox (Umatrix)

et al. 2018

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Background: Unsupervised machine-learned analysis of cluster structures, applied using the emergent self-organizing feature maps (ESOM) combined with the unified distance matrix (U-matrix) has been shown to provide an unbiased method to identify true clusters. It outperforms classical hierarchical clustering algorithms that carry a considerable tendency to produce erroneous results. To facilitate the application of the ESOM/U-matrix method in biomedical research, we introduce the interactive R-based bioinformatics tool "Umatrix", which enables valid identification of a biologically meaningful cluster structure in the data by training a Kohonen-type self-organizing map followed by interface-guided interactive clustering on the emergent U-matrix map. Results: The ability to detect clinical relevant subgroups was applied to a data set comprising plasma concentrations of d = 25 lipid markers including endocannabinoids, lysophosphatidic acids, ceramides and sphingolipids acquired from n = 100 patients with Parkinson's disease and n = 100 controls. Following ESOM training, clear data structures in the high-dimensional data space were observed on the U-matrix, allowing separation of patients from controls almost perfectly. When the data structure was destroyed by Monte-Carlo random resampling, the U-matrix became unstructured and patients and controls were mixed. Obtained results are biologically plausible and supported by empirical evidence of a regulation of several classes of lipids in Parkinson's disease. Conclusions: Sophisticated analysis of structures in biomedical data provides a basis for the mechanistic interpretation of the observations and facilitates subsequent analyses focusing on hypothesis testing. The freely available R library "Umatrix" provides an interactive tool for broader application of unsupervised machine learning on complex biomedical data.

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Section: Data Prepossessingmentioning

confidence: 69%

Identification of disease-distinct complex biomarker patterns by means of unsupervised machine-learning using an interactive R toolbox (Umatrix)

et al. 2018

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“…Total plasma clearance (CL) was calculated as the dose divided by the AUC. Mean values of the pharmacokinetic variables were calculated as the geometric mean of the individual patient values (5,6). SDs for the geometric mean values were estimated by the jackknife method (7).…”

Section: Methodsmentioning

confidence: 99%

Phase I Clinical Trial of the Farnesyltransferase Inhibitor BMS-214662 Given as a 1-Hour Intravenous Infusion in Patients with Advanced Solid Tumors

Ryan

Eder

Puchlaski

et al. 2004

Clinical Cancer Research

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Purpose: BMS-214662 is a nonsedating benzodiazepine derivative that exhibits broad spectrum cytotoxicity against human solid tumor cell lines and potently inhibits farnesylation of the H-ras and K-ras oncogenic proteins. This report describes the initial Phase I clinical trial of the compound. The main objective of the study was to determine the doselimiting toxicities and the maximum tolerated dose of BMS-214662 when administered as a single dose i.v. over 1 h every 21 days to patients with advanced solid tumors.Experimental Design: Patients with advanced solid tumors and adequate organ function were eligible for the study. The dose was escalated according to a modified Fibonacci schedule after evaluating groups of at least three patients for toxicity during the first cycle of therapy at each dose level. Pharmacokinetic and pharmacodynamic studies were performed after administration of the two initial doses.Results

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“…Skewness and kurtosis may also be utilized along with these tests. Unless there is direct evidence that the log-normality is not valid, log-transformed AUC and Cmax should be used for statistical analysis with the parametric method (Lacey et al, 1997). If data do not show a normal distribution even after transformation, nonparametric methods are needed.…”

Section: Discussionmentioning

confidence: 99%

“…Because AUC and Cmax are positive and right-skewed, they have been regarded as log normally-distributed (Midha et al, 1993;Chow, 2003). Nonparametric methods may be indicated for data which do not follow a normal distribution even after some transformation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Parametric and Bootstrap Method in Bioequivalence Test

Ahn

2009

Korean J Physiol Pharmacol

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The estimation of 90% parametric confidence intervals (CIs) of mean AUC and Cmax ratios in bioequivalence (BE) tests are based upon the assumption that formulation effects in log-transformed data are normally distributed. To compare the parametric CIs with those obtained from nonparametric methods we performed repeated estimation of bootstrap-resampled datasets. The AUC and Cmax values from 3 archived datasets were used. BE tests on 1,000 resampled datasets from each archived dataset were performed using SAS (Enterprise Guide Ver.3). Bootstrap nonparametric 90% CIs of formulation effects were then compared with the parametric 90% CIs of the original datasets. The 90% CIs of formulation effects estimated from the 3 archived datasets were slightly different from nonparametric 90% CIs obtained from BE tests on resampled datasets. Histograms and density curves of formulation effects obtained from resampled datasets were similar to those of normal distribution. However, in 2 of 3 resampled log (AUC) datasets, the estimates of formulation effects did not follow the Gaussian distribution. Bias-corrected and accelerated (BCa) CIs, one of the nonparametric CIs of formulation effects, shifted outside the parametric 90% CIs of the archived datasets in these 2 non-normally distributed resampled log (AUC) datasets. Currently, the 80∼ 125% rule based upon the parametric 90% CIs is widely accepted under the assumption of normally distributed formulation effects in log-transformed data. However, nonparametric CIs may be a better choice when data do not follow this assumption.

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Common noncompartmental pharmacokinetic variables: are they normally or log-normally distributed?

Cited by 113 publications

References 12 publications

Identification of disease-distinct complex biomarker patterns by means of unsupervised machine-learning using an interactive R toolbox (Umatrix)

Identification of disease-distinct complex biomarker patterns by means of unsupervised machine-learning using an interactive R toolbox (Umatrix)

Phase I Clinical Trial of the Farnesyltransferase Inhibitor BMS-214662 Given as a 1-Hour Intravenous Infusion in Patients with Advanced Solid Tumors

Comparison of Parametric and Bootstrap Method in Bioequivalence Test

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