scholarly journals Practical unidentifiability of receptor density in target mediated drug disposition models can lead to over-interpretation of drug concentration data

2017 ◽  
Author(s):  
Andrew M Stein
Keyword(s):  
Monoclonal Antibodies ◽  
Drug Development ◽  
Drug Concentration ◽  
Drug Disposition ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Receptor Density ◽  
Concentration Data ◽  
Human Dose ◽  
Target Mediated Drug Disposition

AbstractFor monoclonal antibodies, mathematical models of target mediated drug disposition (TMDD) are often fit to data in order to estimate key physiological parameters of the system. These parameter estimates can then be used to support drug development by assisting with the assessment of whether the target is druggable and what the first in human dose should be. The TMDD model is almost always over-parameterized given the available data, resulting in the practical unidentifiability of some of the model parameters, including the target receptor density. In particular, when only PK data is available, the receptor density is almost always practically unidentifiable. However, because practical identifiability is not regularly assessed, incorrect interpretation of model fits to the data can be made. This issue is illustrated using two case studies from the literature.

Practical identifiability of model parameters by combined respirometric-titrimetric measurements

2001 ◽  
Vol 43 (7) ◽  
pp. 347-355 ◽  
Author(s):  
B. Petersen ◽  
K. Gernaey ◽  
P. A. Vanrolleghem
Keyword(s):  
Fisher Information Matrix ◽  
Information Matrix ◽  
Accurate Estimation ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Concentration Data ◽  
Sensitivity Functions ◽  
Practical Identifiability ◽  
Lower Accuracy ◽  
Accuracy Parameter

An earlier study on theoretical identifiability of parameters for a two-step nitrification model showed that a unique estimation of the yield YA1 is possible with combined respirometric-titrimetric data, contrary to the case where only one type of measurement is available. Here, the practical identifiability of model parameters was investigated via evaluation of the output sensitivity functions and the corresponding Fisher Information Matrix (FIM). It appeared that the FIM was not sufficiently powerful to predict the practical identifiability of this case with combined measurements as parameters could indeed be identified despite the fact that the FIM became singular. The accuracy of parameter estimates based on respirometric and titrimetric data and combination thereof was also investigated. Estimation on titrimetric data (Hp) was very accurate and a fast convergence of the objective function towards a minimum was obtained. The latter also holds for estimation on oxygen uptake rate data (rO), however with a lower accuracy. Parameter estimation based on oxygen concentration data (SO) was more complex but resulted in a higher accuracy. Thus, when the highest accuracy is needed it is recommended to estimate parameters initially on Hp and/or rO data, and to subsequently use these parameters as initial values for final, and more accurate estimation on SO data.

scholarly journals Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Stability ◽  
Small Uncertainty ◽  
Best Fit ◽  
Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

Multi-day flow forecasting using the Kalman filter

1991 ◽  
Vol 18 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Murray A. Fitch ◽  
Edward A. McBean
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Linear Prediction ◽  
Measured Data ◽  
System Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Forecasting Performance ◽  
Optimal Forecasting ◽  
Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

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