The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Jihao Zhou - One of the best experts on this subject based on the ideXlab platform.
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non Compartment Model to Compartment Model pharmacokinetics transformation meta analysis a multivariate nonlinear mixed Model
BMC Systems Biology, 2010Co-Authors: Zhiping Wang, Seongho Kim, Sara K Quinney, Jihao ZhouAbstract:Background: To fulfill the Model based drug development, the very first step is usually a Model establishment from published literatures. Pharmacokinetics Model is the central piece of Model based drug development. This paper proposed an important approach to transform published non-Compartment Model pharmacokinetics (PK) parameters into Compartment Model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed Model. A conditional first-order linearization approach was developed for statistical estimation and inference. Results: Using MDZ as an example, we showed that this approach successfully transformed 6 non-Compartment Model PK parameters from 10 publications into 5 Compartment Model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed Model had little relative bias (<1%) in estimating Compartment Model PK parameters if all non-Compartment PK parameters were reported in every study. If there missing nonCompartment PK parameters existed in some published literatures, the relative bias of Compartment Model PK parameter was still small (<3%). The 95% coverage probabilities of these PK parameter estimates were above 85%. Conclusions: This non-Compartment Model PK parameter transformation into Compartment Model meta-analysis approach possesses valid statistical inference. It can be routinely used for Model based drug development.
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Non-Compartment Model to Compartment Model pharmacokinetics transformation meta-analysis--a multivariate nonlinear mixed Model.
BMC systems biology, 2010Co-Authors: Zhiping Wang, Seongho Kim, Sara K Quinney, Jihao ZhouAbstract:To fulfill the Model based drug development, the very first step is usually a Model establishment from published literatures. Pharmacokinetics Model is the central piece of Model based drug development. This paper proposed an important approach to transform published non-Compartment Model pharmacokinetics (PK) parameters into Compartment Model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed Model. A conditional first-order linearization approach was developed for statistical estimation and inference. Using MDZ as an example, we showed that this approach successfully transformed 6 non-Compartment Model PK parameters from 10 publications into 5 Compartment Model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed Model had little relative bias (<1%) in estimating Compartment Model PK parameters if all non-Compartment PK parameters were reported in every study. If there missing non-Compartment PK parameters existed in some published literatures, the relative bias of Compartment Model PK parameter was still small (<3%). The 95% coverage probabilities of these PK parameter estimates were above 85%. This non-Compartment Model PK parameter transformation into Compartment Model meta-analysis approach possesses valid statistical inference. It can be routinely used for Model based drug development.
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Non-Compartment Model to Compartment Model pharmacokinetics transformation meta-analysis – a multivariate nonlinear mixed Model
BMC Systems Biology, 2010Co-Authors: Zhiping Wang, Seongho Kim, Sara K Quinney, Jihao ZhouAbstract:Abstract Background To fulfill the Model based drug development, the very first step is usually a Model establishment from published literatures. Pharmacokinetics Model is the central piece of Model based drug development. This paper proposed an important approach to transform published non-Compartment Model pharmacokinetics (PK) parameters into Compartment Model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed Model. A conditional first-order linearization approach was developed for statistical estimation and inference. Results Using MDZ as an example, we showed that this approach successfully transformed 6 non-Compartment Model PK parameters from 10 publications into 5 Compartment Model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed Model had little relative bias (
Andrew R Hoy - One of the best experts on this subject based on the ideXlab platform.
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optimization of a free water elimination two Compartment Model for diffusion tensor imaging
NeuroImage, 2014Co-Authors: Andrew R Hoy, Cheng Guan Koay, Steven Kecskemeti, Andrew L AlexanderAbstract:Abstract Diffusion tensor imaging is used to measure the diffusion of water in tissue. The diffusion properties carry information about the relative organization and structure of the underlying tissue. In the case of a single voxel containing both tissue and a fast diffusing component such as free water, a single diffusion tensor is no longer appropriate. A two-tensor free water elimination Model has previously been proposed to correct for the case of volume mixing. Here, this Model was implemented in a straightforward but novel manner without the use of spatial constraints. The optimal acquisition parameters were investigated through Monte Carlo simulations and human brain imaging studies. At a signal-to-noise ratio of 40 with 64 diffusion-weighted encoding images, the most accurate estimates of fast diffusion signal were obtained with two diffusion-weighted shells (b-value in s/mm2 × number of directions) of 500 × 32 and 1500 × 32. The potential bias in fractional anisotropy induced by this two-Compartment Model was more than an order of magnitude less than the error of using the single diffusion tensor Model in the presence of partial volume effects with free water. This strategy may be useful for characterizing the diffusion of tissues adjacent to cerebral spinal fluid (CSF), tissues affected by edema, and removing artifacts from blurring and ghosting of the CSF signal.
Zhiping Wang - One of the best experts on this subject based on the ideXlab platform.
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non Compartment Model to Compartment Model pharmacokinetics transformation meta analysis a multivariate nonlinear mixed Model
BMC Systems Biology, 2010Co-Authors: Zhiping Wang, Seongho Kim, Sara K Quinney, Jihao ZhouAbstract:Background: To fulfill the Model based drug development, the very first step is usually a Model establishment from published literatures. Pharmacokinetics Model is the central piece of Model based drug development. This paper proposed an important approach to transform published non-Compartment Model pharmacokinetics (PK) parameters into Compartment Model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed Model. A conditional first-order linearization approach was developed for statistical estimation and inference. Results: Using MDZ as an example, we showed that this approach successfully transformed 6 non-Compartment Model PK parameters from 10 publications into 5 Compartment Model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed Model had little relative bias (<1%) in estimating Compartment Model PK parameters if all non-Compartment PK parameters were reported in every study. If there missing nonCompartment PK parameters existed in some published literatures, the relative bias of Compartment Model PK parameter was still small (<3%). The 95% coverage probabilities of these PK parameter estimates were above 85%. Conclusions: This non-Compartment Model PK parameter transformation into Compartment Model meta-analysis approach possesses valid statistical inference. It can be routinely used for Model based drug development.
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Non-Compartment Model to Compartment Model pharmacokinetics transformation meta-analysis--a multivariate nonlinear mixed Model.
BMC systems biology, 2010Co-Authors: Zhiping Wang, Seongho Kim, Sara K Quinney, Jihao ZhouAbstract:To fulfill the Model based drug development, the very first step is usually a Model establishment from published literatures. Pharmacokinetics Model is the central piece of Model based drug development. This paper proposed an important approach to transform published non-Compartment Model pharmacokinetics (PK) parameters into Compartment Model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed Model. A conditional first-order linearization approach was developed for statistical estimation and inference. Using MDZ as an example, we showed that this approach successfully transformed 6 non-Compartment Model PK parameters from 10 publications into 5 Compartment Model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed Model had little relative bias (<1%) in estimating Compartment Model PK parameters if all non-Compartment PK parameters were reported in every study. If there missing non-Compartment PK parameters existed in some published literatures, the relative bias of Compartment Model PK parameter was still small (<3%). The 95% coverage probabilities of these PK parameter estimates were above 85%. This non-Compartment Model PK parameter transformation into Compartment Model meta-analysis approach possesses valid statistical inference. It can be routinely used for Model based drug development.
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Non-Compartment Model to Compartment Model pharmacokinetics transformation meta-analysis – a multivariate nonlinear mixed Model
BMC Systems Biology, 2010Co-Authors: Zhiping Wang, Seongho Kim, Sara K Quinney, Jihao ZhouAbstract:Abstract Background To fulfill the Model based drug development, the very first step is usually a Model establishment from published literatures. Pharmacokinetics Model is the central piece of Model based drug development. This paper proposed an important approach to transform published non-Compartment Model pharmacokinetics (PK) parameters into Compartment Model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed Model. A conditional first-order linearization approach was developed for statistical estimation and inference. Results Using MDZ as an example, we showed that this approach successfully transformed 6 non-Compartment Model PK parameters from 10 publications into 5 Compartment Model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed Model had little relative bias (
Andrew L Alexander - One of the best experts on this subject based on the ideXlab platform.
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optimization of a free water elimination two Compartment Model for diffusion tensor imaging
NeuroImage, 2014Co-Authors: Andrew R Hoy, Cheng Guan Koay, Steven Kecskemeti, Andrew L AlexanderAbstract:Abstract Diffusion tensor imaging is used to measure the diffusion of water in tissue. The diffusion properties carry information about the relative organization and structure of the underlying tissue. In the case of a single voxel containing both tissue and a fast diffusing component such as free water, a single diffusion tensor is no longer appropriate. A two-tensor free water elimination Model has previously been proposed to correct for the case of volume mixing. Here, this Model was implemented in a straightforward but novel manner without the use of spatial constraints. The optimal acquisition parameters were investigated through Monte Carlo simulations and human brain imaging studies. At a signal-to-noise ratio of 40 with 64 diffusion-weighted encoding images, the most accurate estimates of fast diffusion signal were obtained with two diffusion-weighted shells (b-value in s/mm2 × number of directions) of 500 × 32 and 1500 × 32. The potential bias in fractional anisotropy induced by this two-Compartment Model was more than an order of magnitude less than the error of using the single diffusion tensor Model in the presence of partial volume effects with free water. This strategy may be useful for characterizing the diffusion of tissues adjacent to cerebral spinal fluid (CSF), tissues affected by edema, and removing artifacts from blurring and ghosting of the CSF signal.
Yuto Nakahara - One of the best experts on this subject based on the ideXlab platform.
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Noninvasive estimation of quantitative myocardial blood flow with Tc-99m MIBI by a Compartment Model analysis in rat
Journal of Nuclear Cardiology, 2020Co-Authors: Atsutaka Okizaki, Michihiro Nakayama, Kaori Nakajima, Osuke Fujimoto, Shinobu Oshikiri, Miho Koike-satake, Yuto NakaharaAbstract:Background We aimed to investigate the use of dynamic cardiac planar images to estimate myocardial blood flow (MBF) by a Compartment Model analysis using time-to-peak (TP) map and compared it by the microsphere technique in rat. Positron emission tomography is considered the gold standard method, but is not available everywhere. By contrast, although myocardial perfusion imaging (MPI) with single-photon tracers is more widely available, it may be difficult to obtain adequate region of interest (ROI) settings. We proposed using the TP map to set the ROI, and hypothesized that this method could facilitate the measurement of absolute MBF by MPI in rat. Methods Twenty-one normal rats were studied. Dynamic planar images with Tc-99m MIBI were obtained, and input function and cardiac ROIs were set using the obtained TP map. MBF was estimated by a one-Compartment Model analysis with the Renkin-Crone Model and by the microsphere technique. Results The MBFs from these two methods were significantly correlated. A negative proportional bias was observed, but no significant difference was observed between the mean MBFs calculated with each method. Conclusions MBF estimation by a Compartment Model analysis using TP map could facilitate absolute MBF measurement in rats.