eprintid: 2208
rev_number: 9
eprint_status: archive
userid: 6
dir: disk0/00/00/22/08
datestamp: 2014-06-19 08:09:43
lastmod: 2014-09-02 09:28:54
status_changed: 2014-06-19 08:09:43
type: article
metadata_visibility: show
creators_name: Sopasakis, Pantelis
creators_name: Patrinos, Panagiotis
creators_name: Sarimveis, Haralambos
creators_id: pantelis.sopasakis@imtlucca.it
creators_id: panagiotis.patrinos@imtlucca.it
creators_id: 
title: Robust Model Predictive Control for optimal continuous drug administration
ispublished: pub
subjects: QA75
subjects: TJ
divisions: CSA
full_text_status: none
keywords: Drug Administration Control; Drug Dosing; PBPK Modelling; Model Predictive Control
abstract: In this paper the Model Predictive Control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular,
for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a
discrete-time state-space model. Only plasma measurements are assumed to be measured online. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modeling errors, disturbances and noise. A case study on a PBPK model with 7 compartments, constraints on 5 tissues and a variable drug concentration set-point illustrates the efficiency of the methodology in drug dosing control applications. The proposed methodology is also tested in an uncertain setting
and proves successful in presence of modelling errors and inaccurate measurements. 
date: 2014-10
date_type: published
publication: Computer Methods and Programs in Biomedicine
volume: 116
number: 3
publisher: Elsevier
pagerange: 193-204
id_number: doi:10.1016/j.cmpb.2014.06.003
refereed: TRUE
issn: 0169-2607
official_url: http://dx.doi.org/10.1016/j.cmpb.2014.06.003
citation:   Sopasakis, Pantelis and Patrinos, Panagiotis and Sarimveis, Haralambos  Robust Model Predictive Control for optimal continuous drug administration.  Computer Methods and Programs in Biomedicine, 116 (3).  pp. 193-204.  ISSN 0169-2607      (2014)