scholarly journals A nonlinear mixed-effect mixture model for functional mapping of dynamic traits

Heredity ◽  
2008 ◽  
Vol 101 (4) ◽  
pp. 321-328 ◽  
Author(s):  
W Hou ◽  
H Li ◽  
B Zhang ◽  
M Huang ◽  
R Wu
Keyword(s):  
Mixture Model ◽  
Functional Mapping ◽  
Mixed Effect ◽  
Nonlinear Mixed Effect ◽  
Dynamic Traits

scholarly journals Pharmacokinetics of Efavirenz at a High Dose of 25 Milligrams per Kilogram per Day in Children 2 to 3 Years Old

2017 ◽  
Vol 61 (7) ◽  
Author(s):  
Claire Pressiat ◽  
Madeleine Amorissani-Folquet ◽  
Caroline Yonaba ◽  
Jean-Marc Treluyer ◽  
Désiré Lucien Dahourou ◽  
...  
Keyword(s):  
Antiretroviral Treatment ◽  
Recommended Dose ◽  
Pharmacokinetic Parameters ◽  
High Dose ◽  
Age Group ◽  
Mixed Effect ◽  
Sleeping Disorders ◽  
Nonlinear Mixed Effect ◽  
Nonlinear Mixed Effect Modeling ◽  
Mixed Effect Modeling

ABSTRACT The MONOD ANRS 12206 trial was designated to assess simplification of a successful lopinavir (LPV)-based antiretroviral treatment in HIV-infected children younger than 3 years of age using efavirenz (EFV; 25 mg/kg of body weight/day) to preserve the class of protease inhibitors for children in that age group. In this substudy, EFV concentrations were measured to check the consistency of an EFV dose of 25 mg/kg and to compare it with the 2016 FDA recommended dose. Fifty-two children underwent blood sampling for pharmacokinetic study at 6 months and 12 months after switching to EFV. We applied a Bayesian approach to derive EFV pharmacokinetic parameters using the nonlinear mixed-effect modeling (NONMEM) program. The proportion of midinterval concentrations 12 h after drug intake (C 12 h) corresponding to the EFV therapeutic pharmacokinetic thresholds (1 to 4 mg/liter) was assessed according to different dose regimens (25 mg/kg in the MONOD study versus the 2016 FDA recommended dose). With both the 25 mg/kg/day dose and the 2016 FDA recommended EFV dose, simulations showed that the majority of C 12 h values were within the therapeutic range (62.6% versus 62.8%). However, there were more children underexposed with the 2016 FDA recommended dose (11.6% versus 1.2%). Conversely, there were more concentrations above the threshold of toxicity with the 25 mg/kg dose (36.2% versus 25.6%), with C 12 h values of up to 15 mg/liter. Only 1 of 52 children was switched back to LPV because of persistent sleeping disorders, but his C 12 h value was within therapeutic ranges. A high EFV dose of 25 mg/kg per day in children under 3 years old achieved satisfactory therapeutic effective levels. However, the 2016 FDA recommended EFV dose appeared to provide more acceptable safe therapeutic profiles. (This study has been registered at ClinicalTrials.gov under identifier NCT01127204.)

Spectral Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect

2004 ◽  
Vol 101 (1) ◽  
pp. 34-42 ◽  
Author(s):  
Ann L.G. Vanluchene ◽  
Hugo Vereecke ◽  
Olivier Thas ◽  
Eric P. Mortier ◽  
Steven L. Shafer ◽  
...  
Keyword(s):  
Drug Effect ◽  
Rank Correlation ◽  
Burst Suppression ◽  
Spectral Entropy ◽  
Anesthetic Drug ◽  
Mixed Effect ◽  
Nonlinear Mixed Effect ◽  
Prediction Probability ◽  
Nonlinear Mixed Effect Modeling ◽  
Mixed Effect Modeling

Background The authors compared the behavior of two calculations of electroencephalographic spectral entropy, state entropy (SE) and response entropy (RE), with the A-Line ARX Index (AAI) and the Bispectral Index (BIS) and as measures of anesthetic drug effect. They compared the measures for baseline variability, burst suppression, and prediction probability. They also developed pharmacodynamic models relating SE, RE, AAI, and BIS to the calculated propofol effect-site concentration (Ceprop). Methods With institutional review board approval, the authors studied 10 patients. All patients received 50 mg/min propofol until either burst suppression greater than 80% or mean arterial pressure less than 50 mmHg was observed. SE, RE, AAI, and BIS were continuously recorded. Ceprop was calculated from the propofol infusion profile. Baseline variability, prediction of burst suppression, prediction probability, and Spearman rank correlation were calculated for SE, RE, AAI, and BIS. The relations between Ceprop and the electroencephalographic measures of drug effect were estimated using nonlinear mixed effect modeling. Results Baseline variability was lowest when using SE and RE. Burst suppression was most accurately detected by spectral entropy. Prediction probability and individualized Spearman rank correlation were highest for BIS and lowest for SE. Nonlinear mixed effect modeling generated reasonable models relating all four measures to Ceprop. Conclusions Compared with BIS and AAI, both SE and RE seem to be useful electroencephalographic measures of anesthetic drug effect, with low baseline variability and accurate burst suppression prediction. The ability of the measures to predict Ceprop was best for BIS.

On fitting the k-C* first order model to batch loaded sub-surface treatment wetlands

2007 ◽  
Vol 56 (3) ◽  
pp. 93-99 ◽  
Author(s):  
O.R. Stein ◽  
B.W. Towler ◽  
P.B. Hook ◽  
J.A. Biederman
Keyword(s):  
Time Series ◽  
Plug Flow ◽  
Typha Latifolia ◽  
Statistical Technique ◽  
Species Variation ◽  
Order Model ◽  
Residual Concentration ◽  
Mixed Effect ◽  
First Order ◽  
Nonlinear Mixed Effect

The k-C* first order model was fit to time-series COD data collected from batch-loaded model wetlands. Four replicates of four plant species treatments; Carex utriculata (sedge), Schoenoplectus acutus (bulrush), Typha latifolia (cattail) and unplanted controls were compared. Temperature was varied by 4 °C from 24 °C to 4 °C to 24 °C over a year-long period. One mathematical fit was made for each wetland replicate at each temperature setting (192 fits). Temperature effects on both parameters were subsequently estimated by fitting the Arrhenius relationship to the estimated coefficients. Inherent interactions between k and C* make values dependent on sample timing and statistical technique for either time series (batch load) or distance profile (plug flow) data. Coefficients calibrated using the Levenberg–Marquardt method are compared to values previously reported using a nonlinear mixed effect regression technique. Overall conclusions are similar across approaches: (a) the magnitude of the coefficients varies strongly by species; (b) the rate constant k decreases with increasing temperature; and (c) temperature and species variation in the residual concentration C* is greater than the variation in k, such that variation in k alone is a poor predictor of performance. However, the magnitudes of the coefficients, especially the rate parameter k, vary between the statistical techniques, highlighting the need to better document the statistical routines used to calibrate the k-C* model.

scholarly journals E-Index for Differentiating Complex Dynamic Traits

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Jiandong Qi ◽  
Jianfeng Sun ◽  
Jianxin Wang
Keyword(s):  
Functional Mapping ◽  
Developmental Trajectory ◽  
Model Parameters ◽  
Nucleotide Polymorphisms ◽  
Complex Dynamic ◽  
Index Vector ◽  
Underlying Network ◽  
Dynamic Trait ◽  
Daunting Challenge ◽  
Dynamic Traits

While it is a daunting challenge in current biology to understand how the underlying network of genes regulates complex dynamic traits, functional mapping, a tool for mapping quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs), has been applied in a variety of cases to tackle this challenge. Though useful and powerful, functional mapping performs well only when one or more model parameters are clearly responsible for the developmental trajectory, typically being a logistic curve. Moreover, it does not work when the curves are more complex than that, especially when they are not monotonic. To overcome this inadaptability, we therefore propose a mathematical-biological concept and measurement,E-index (earliness-index), which cumulatively measures the earliness degree to which a variable (or a dynamic trait) increases or decreases its value. Theoretical proofs and simulation studies show thatE-index is more general than functional mapping and can be applied to any complex dynamic traits, including those with logistic curves and those with nonmonotonic curves. Meanwhile,E-index vector is proposed as well to capture more subtle differences of developmental patterns.

scholarly journals 3FunMap: full-sib family functional mapping of dynamic traits

2011 ◽  
Vol 27 (14) ◽  
pp. 2006-2008 ◽  
Author(s):  
Chunfa Tong ◽  
Zhong Wang ◽  
Bo Zhang ◽  
Jisen Shi ◽  
Rongling Wu
Keyword(s):  
Functional Mapping ◽  
Dynamic Traits
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