scholarly journals Pharmacokinetic Models for FcRn-Mediated IgG Disposition

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Jim J. Xiao
Keyword(s):  
Binding Capacity ◽  
Simulated Data ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Pharmacokinetic Models ◽  
Turnover Rates ◽  
Lysosomal Degradation ◽  
Pbpk Models ◽  
Physiologically Based ◽  
Parameter Values

The objectives were to review available PK models for saturable FcRn-mediated IgG disposition, and to explore an alternative semimechanistic model. Most available empirical and mechanistic PK models assumed equal IgG concentrations in plasma and endosome in addition to other model-specific assumptions. These might have led to inappropriate parameter estimates and model interpretations. Some physiologically based PK (PBPK) models included FcRn-mediated IgG recycling. The nature of PBPK models requires borrowing parameter values from literature, and subtle differences in the assumptions may render dramatic changes in parameter estimates related to the IgG recycling kinetics. These models might have been unnecessarily complicated to address FcRn saturation and nonlinear IgG PK especially in the IVIG setting. A simple semimechanistic PK model (cutoff model) was developed that assumed a constant endogenous IgG production rate and a saturable FcRn-binding capacity. The FcRn-binding capacity was defined as MAX, and IgG concentrations exceeding MAX in endosome resulted in lysosomal degradation. The model parameters were estimated using simulated data from previously published models. The cutoff model adequately described the rat and mouse IgG PK data simulated from published models and allowed reasonable estimation of endogenous IgG turnover rates.

scholarly journals A computational investigation of the ventilation structure and maximum rate of metabolism for a physiologically based pharmacokinetic (PBPK) model of inhaled xylene

BIOMATH ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 1901067
Author(s):  
Karen A Yokley ◽  
Jaclyn Ashcraft ◽  
Nicholas S Luke
Keyword(s):  
Inverse Problems ◽  
Blood Concentration ◽  
Maximum Rate ◽  
Pbpk Model ◽  
Simulated Data ◽  
Parameter Estimates ◽  
Computational Investigation ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

Physiologically based pharmacokinetic (PBPK) models are systems of ordinary differential equations that estimate internal doses following exposure to toxicants. Most PBPK models use standard equations to describe inhalation and concentrations in blood. This study extends previous work investigating the effect of the structure of air and blood concentration equations on PBPK predictions. The current study uses an existing PBPK model of xylene to investigate if different values for the maximum rate of toxicant metabolism can result in similar compartmental predictions when used with different equations describing inhalation. Simulations are performed using values based on existing literature. Simulated data is also used to determine specific values that result in similar predictions from different ventilation structures. Differences in ventilation equation structure may affect parameter estimates found through inverse problems, although further investigation is needed with more complicated models.

scholarly journals Leave-One-Trial-Out (LOTO): A general approach to link single-trial parameters of cognitive models to neural data

2018 ◽  
Author(s):  
Sebastian Gluth ◽  
Nachshon Meiran
Keyword(s):  
Cognitive Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Single Trial ◽  
Neural Data ◽  
Brain Signals ◽  
Complete Dataset ◽  
Advance Research ◽  
The Difference ◽  
Parameter Values

AbstractIt has become a key goal of model-based neuroscience to estimate trial-by-trial fluctuations of cognitive model parameters for linking these fluctuations to brain signals. However, previously developed methods were limited by being difficulty to implement, time-consuming, or model-specific. Here, we propose an easy, efficient and general approach to estimating trial-wise changes in parameters: Leave-One-Trial-Out (LOTO). The rationale behind LOTO is that the difference between the parameter estimates for the complete dataset and for the dataset with one omitted trial reflects the parameter value in the omitted trial. We show that LOTO is superior to estimating parameter values from single trials and compare it to previously proposed approaches. Furthermore, the method allows distinguishing true variability in a parameter from noise and from variability in other parameters. In our view, the practicability and generality of LOTO will advance research on tracking fluctuations in latent cognitive variables and linking them to neural data.

An approach to computer analysis of the ligand binding assay data on example of radioligand assay data

2020 ◽  
Vol 18 (02) ◽  
pp. 2050014
Author(s):  
S. N. Fedotov
Keyword(s):  
Least Squares Method ◽  
Main Idea ◽  
Simulated Data ◽  
Real Data ◽  
Model Parameters ◽  
Nonlinear Method ◽  
Ligand Interaction ◽  
Receptor Ligand ◽  
Parameter Values ◽  
True Values

As a rule, receptor-ligand assay data are fitted by logistic functions (4PL model, 5PL model, Feldman’s model). The preparation of the initial estimates for parameters of these functions is an important problem for processing receptor-ligand interaction data. This study represents a new mathematical approach to calculate the initial estimates more closely to the true values of parameters. The main idea of this approach is in using the modified linear least squares method for calculations of the parameters for the 4PL model and the Feldman’s model. In this study, the convergence of model parameters to true values is verified for the simulated data with different statistical scatter. Also, the results of processing real data for the 4PL model and the Feldman’s model are presented. A comparison is made of the parameter values calculated by the presented and a nonlinear method. The developed approach has demonstrated its efficiency in calculating the parameters of the complex Feldman”s models up to 4 ligands and 4 sites.

Physiological Parameter Values for Physiologically Based Pharmacokinetic Models

1997 ◽  
Vol 13 (4) ◽  
pp. 407-484 ◽  
Author(s):  
Ronald P. Brown ◽  
Michael D. Delp ◽  
Stan L. Lindstedt ◽  
Lorenz R. Rhomberg ◽  
Robert P. Beliles
Keyword(s):  
Physiological Parameter ◽  
Pharmacokinetic Models ◽  
Physiologically Based Pharmacokinetic Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Parameter Values

Improving Model Parameters in Vibrating Systems Using Neumann Series

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Alyssa T. Liem ◽  
J. Gregory McDaniel ◽  
Andrew S. Wixom
Keyword(s):  
Longitudinal Vibration ◽  
Dynamic Stiffness ◽  
Neumann Series ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Experimental Measurements ◽  
Resonance Frequencies ◽  
Model Predictions ◽  
Parameter Values ◽  
Vibrating Systems

A method is presented to improve the estimates of material properties, dimensions, and other model parameters for linear vibrating systems. The method improves the estimates of a single model parameter of interest by finding parameter values that bring model predictions into agreement with experimental measurements. A truncated Neumann series is used to approximate the inverse of the dynamic stiffness matrix. This approximation avoids the need to directly solve the equations of motion for each parameter variation. The Neumman series is shown to be equivalent to a Taylor series expansion about nominal parameter values. A recursive scheme is presented for computing the associated derivatives, which are interpreted as sensitivities of displacements to parameter variations. The convergence of the Neumman series is studied in the context of vibrating systems, and it is found that the spectral radius is strongly dependent on system resonances. A homogeneous viscoelastic bar in longitudinal vibration is chosen as a test specimen, and the complex-valued Young's modulus is chosen as an uncertain parameter. The method is demonstrated on simulated experimental measurements computed from the model. These demonstrations show that parameter values estimated by the method agree with those used to simulate the experiment when enough terms are included in the Neumann series. Similar results are obtained for the case of an elastic plate with clamped boundary conditions. The method is also demonstrated on experimental data, where it produces improved parameter estimates that bring the model predictions into agreement with the measured response to within 1% at a point on the bar across a frequency range that includes three resonance frequencies.

scholarly journals On the Measurement of Bias in Geographically Weighted Regression Models

2019 ◽  
Author(s):  
Hanchen Yu ◽  
Alexander Stewart Fotheringham ◽  
Ziqi Li ◽  
Taylor M. Oshan ◽  
Levi John Wolf
Keyword(s):  
Geographically Weighted Regression ◽  
Regression Models ◽  
Simulated Data ◽  
Information Criterion ◽  
Weighted Regression ◽  
Parameter Estimates ◽  
Local Parameter ◽  
Data Set ◽  
Trade Off ◽  
Parameter Values

Under the realization that Geographically Weighted Regression (GWR) is a data-borrowing technique, this paper derives expressions for the amount of bias introduced to local parameter estimates by borrowing data from locations where the processes might be different from those at the regression location. This is done for both GWR and Multiscale GWR (MGWR). We demonstrate the accuracy of our expressions for bias through a comparison with empirically derived estimates based on a simulated data set with known local parameter values. By being able to compute the bias in both models we are able to demonstrate the superiority of MGWR. We then demonstrate the utility of a corrected Akaike Information Criterion statistic in finding optimal bandwidths in both GWR and MGWR as a trade-off between minimizing both bias and uncertainty. We further show how bias in one set of local parameter estimates can affect the bias in another set of local estimates. The bias derived from borrowing data from other locations appears to be very small.

scholarly journals A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions

2021 ◽  
Author(s):  
Ranik Raaen Wahlstrøm ◽  
Florentina Paraschiv ◽  
Michael Schürle
Keyword(s):  
Yield Curve ◽  
Initial Parameter ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Treasury Bills ◽  
Treasury Securities ◽  
Parsimonious Models ◽  
The Stability ◽  
Parameter Values ◽  
Over Time

AbstractWe shed light on computational challenges when fitting the Nelson-Siegel, Bliss and Svensson parsimonious yield curve models to observed US Treasury securities with maturities up to 30 years. As model parameters have a specific financial meaning, the stability of their estimated values over time becomes relevant when their dynamic behavior is interpreted in risk-return models. Our study is the first in the literature that compares the stability of estimated model parameters among different parsimonious models and for different approaches for predefining initial parameter values. We find that the Nelson-Siegel parameter estimates are more stable and conserve their intrinsic economical interpretation. Results reveal in addition the patterns of confounding effects in the Svensson model. To obtain the most stable and intuitive parameter estimates over time, we recommend the use of the Nelson-Siegel model by taking initial parameter values derived from the observed yields. The implications of excluding Treasury bills, constraining parameters and reducing clusters across time to maturity are also investigated.

scholarly journals Parameter Estimation of the Macroscopic Fundamental Diagram: A Maximum Likelihood Approach

2021 ◽  
Author(s):  
Rafegh Aghamohammadi ◽  
Jorge Laval
Keyword(s):  
Maximum Likelihood ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Fundamental Diagram ◽  
Urban Networks ◽  
Loop Detectors ◽  
Unsignalized Intersections ◽  
Macroscopic Fundamental Diagram ◽  
Parameter Values

This paper extends the Stochastic Method of Cuts (SMoC) to approximate of the Macroscopic Fundamental Diagram (MFD) of urban networks and uses Maximum Likelihood Estimation (MLE) method to estimate the model parameters based on empirical data from a corridor and 30 cities around the world. For the corridor case, the estimated values are in good agreement with the measured values of the parameters. For the network datasets, the results indicate that the method yields satisfactory parameter estimates and graphical fits for roughly 50\% of the studied networks, where estimations fall within the expected range of the parameter values. The satisfactory estimates are mostly for the datasets which (i) cover a relatively wider range of densities and (ii) the average flow values at different densities are approximately normally distributed similar to the probability density function of the SMoC. The estimated parameter values are compared to the real or expected values and any discrepancies and their potential causes are discussed in depth to identify the challenges in the MFD estimation both analytically and empirically. In particular, we find that the most important issues needing further investigation are: (i) the distribution of loop detectors within the links, (ii) the distribution of loop detectors across the network, and (iii) the treatment of unsignalized intersections and their impact on the block length.

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