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**Harvard**

Trägårdh, M., Chappell, M., Evans, N., Jirstrand, M., Gabrielsson, J. och Gennemark, P. (2014) *Input estimation in nonlinear dynamical systems for model – based drug discovery using optimal control techniques*.

** BibTeX **

@conference{

Trägårdh2014,

author={Trägårdh, Magnus and Chappell, Michael and Evans, Niel and Jirstrand, Mats and Gabrielsson, Johan and Gennemark, Peter},

title={Input estimation in nonlinear dynamical systems for model – based drug discovery using optimal control techniques},

booktitle={In proceedings of PKUK 2014},

abstract={Background: In many pharmacokinetic (PK) applications, it is of interest to determine the input to a dynamical system, based only on sparse and noisy measurements. One typical case is when a drug is administered orally, and where there is a known PK model, but the absorption process is not well understood. When the model of the system is linear and time invariant, input estimation is referred to as deconvolution. Traditional deconvolution methods based on regularised regression can easily be solved in closed form for linear models. However, many PK models are nonlinear, e.g. as a result of saturable elimination. Therefore, being able to handle only the linear case is a severe restriction. Besides, input estimation methods have a much wider applicability than PK, and can be used in any pharmacodynamic (PD) or disease modelling problem where the input to a linear or nonlinear model needs to be determined. As an example, these methods are being considered for use in body weight modelling, estimating the energy intake from body weight measurements.
Aim: To investigate, implement and benchmark techniques for input estimation (deconvolution) for the case when the underlying dynamical system is nonlinear.
Methods: Key techniques from optimal control theory were applied: multiple shooting and collocation in combination with sensitivity analysis and automatic differentiation. The techniques were benchmarked on a previously published dataset measuring the plasma concentration of eflornithine in 26 rats after oral administration. Two choices of regularisation functions were used: Tikhonov (ridge regression) and Maximum Entropy.
Results: The investigated methods worked robustly on the benchmark dataset, even when starting from very poor initial guesses. The multiple shooting methods needed 15 seconds on a standard workstation for a typical dataset, while collocation methods needed about 5 seconds.
Conclusion: Optimal control methods make it possible to use traditional deconvolution methods even when the system is nonlinear.},

year={2014},

}

** RefWorks **

RT Conference Proceedings

SR Print

ID 210772

A1 Trägårdh, Magnus

A1 Chappell, Michael

A1 Evans, Niel

A1 Jirstrand, Mats

A1 Gabrielsson, Johan

A1 Gennemark, Peter

T1 Input estimation in nonlinear dynamical systems for model – based drug discovery using optimal control techniques

YR 2014

T2 In proceedings of PKUK 2014

AB Background: In many pharmacokinetic (PK) applications, it is of interest to determine the input to a dynamical system, based only on sparse and noisy measurements. One typical case is when a drug is administered orally, and where there is a known PK model, but the absorption process is not well understood. When the model of the system is linear and time invariant, input estimation is referred to as deconvolution. Traditional deconvolution methods based on regularised regression can easily be solved in closed form for linear models. However, many PK models are nonlinear, e.g. as a result of saturable elimination. Therefore, being able to handle only the linear case is a severe restriction. Besides, input estimation methods have a much wider applicability than PK, and can be used in any pharmacodynamic (PD) or disease modelling problem where the input to a linear or nonlinear model needs to be determined. As an example, these methods are being considered for use in body weight modelling, estimating the energy intake from body weight measurements.
Aim: To investigate, implement and benchmark techniques for input estimation (deconvolution) for the case when the underlying dynamical system is nonlinear.
Methods: Key techniques from optimal control theory were applied: multiple shooting and collocation in combination with sensitivity analysis and automatic differentiation. The techniques were benchmarked on a previously published dataset measuring the plasma concentration of eflornithine in 26 rats after oral administration. Two choices of regularisation functions were used: Tikhonov (ridge regression) and Maximum Entropy.
Results: The investigated methods worked robustly on the benchmark dataset, even when starting from very poor initial guesses. The multiple shooting methods needed 15 seconds on a standard workstation for a typical dataset, while collocation methods needed about 5 seconds.
Conclusion: Optimal control methods make it possible to use traditional deconvolution methods even when the system is nonlinear.

LA eng

OL 30