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@PHDTHESIS{Schaller:464366,
author = {Schaller, Stephan},
othercontributors = {Schuppert, Andreas and Mitsos, Alexander},
title = {{A}utomated optimal glycaemic control using a physiology
based pharmacokinetic, pharmacodynamic model},
school = {Aachen, Techn. Hochsch.},
type = {Diss.},
address = {Aachen},
publisher = {Publikationsserver der RWTH Aachen University},
reportid = {RWTH-CONV-207067},
pages = {163 S. : Ill., graph. Darst.},
year = {2015},
note = {Aachen, Techn. Hochsch., Diss., 2014},
abstract = {After decades of research, Automated Glucose Control (AGC)
is still out of reach for everyday control of blood glucose.
The inter- and intra-individual variability of glucose
dynamics largely arising from variability in insulin
absorption, distribution, and action, and related
physiological lag-times remain a core problem in the
development of suitable control algorithms. Over the years,
model predictive control (MPC) has established itself as the
gold standard in AGC systems in research. Models of glucose
metabolism are a core element of MPC control schemes. The
standard two- or three-compartmental models, i.e. the
“Minimal-Model” [1], represent little biological detail,
hampering the integration of multi-scale data, thus
confining capabilities of model extrapolation. To overcome
remaining challenges, a new approach to MPC AGC is developed
here. The MPC uses, for the first time, an individualizable
generic whole-body physiology-based
pharmacokinetic/pharmacodynamic (PBPK/PD) model of the
glucose-insulin-glucagon regulatory system. The model
reflects detailed physiological properties of healthy
populations and type 1 diabetes individuals expressed in the
respective parameterizations. The model features a detailed
representation of absorption models for oral glucose,
subcutaneous insulin and glucagon, and an insulin receptor
model relating pharmacokinetic properties to pharmacodynamic
effects. Model development and validation is based on
literature data. The quality of predictions is high and
captures relevant observed inter- and intra-individual
variability, thus improving model long-term predictions.
This significantly strengthens the rationale for the use of
MPC. To increase robustness vs. uncertainties (closed-loop
stability), model kernels were updated with growing patient
data and the MPC was integrated in a control cascade with a
proportional, integrative, derivative (PID) based
offset-control. Both, model and control algorithm, were
validated and evaluated within an in-silico environment
before testing the control approach within two 30-h clinical
trials. The trials were each conducted in ten subjects with
type 1 diabetes without endogenous insulin secretion. Blood
glucose was controlled by subcutaneous delivery of insulin
based on plasma glucose (PG, in trial #1) and continuous
blood glucose monitors (CGMs, subcutaneous sampling, trial
#2) measurements in 15 min intervals. Meal information, but
no priming bolus (pre-meal insulin), was given to the
controller at start of each meal. For the first clinical
trial, the overall mean (n=10) PG was 156 mg/dL, with $74\%$
time of PG values in the target range of 70–180 mg/dL.
With 2 incidents during 240 h of closed-loop control,
hypoglycemia (PG < 60 mg/dL) was rare. During nighttime
control, prior to model adaptation (adaptation was slow if
successful at all), mean PG was elevated (149 mg/dL, with
$38\%$ time in target 70–140 mg/dL). For the second
clinical trial, control performance improved significantly
due to an improved workflow and faster (earlier) model
adaptation with an overall mean (n=10) PG of 127 mg/dL, with
$76\%$ time of PG values in the target range of 70–180
mg/dL. With 9 incidents during 240 h of closed-loop control,
hypoglycemia (PG < 60 mg/dL) was slightly increased.
Nighttime control improved the most with a mean PG exactly
on target (110 mg/dL, with $78\%$ time in target 70–140
mg/dL). Retrospective analysis of insulin and glucagon
measurements collected during the trial, revealed
significant glucagon surges, which were observed
postprandial and coincided with severe morning insulin
resistance for some patients. Whereas a consistent
interpretation of the observed behavior is outstanding, the
modeling framework allowed a structural mode-of-action
evaluation to shed new light on the role of glucagon and
nutrition (i.e. coffee) in the “dawn-effect” in
Diabetes. This work shows that large-scale in-silico models
of the glucose metabolism can provide a framework to improve
diabetes research, the development of automatic control
strategies for diabetes and ultimately every day diabetes
management. The algorithm for the integrated closed-loop
control system was benchmarked both, within in-silico
clinical trials as well as within clinical feasibility
studies. Once the relevance of (postprandial) glucagon in
T1DM has been analyzed, fully understood and captured by
PBPK/PD modeling, future trials testing the improved system
seem very promising.},
keywords = {Prädiktive Regelung (SWD) / MPC (SWD) / Blutzucker (SWD) /
Glukose (SWD) / Diabetes mellitus (SWD) / Systembiologie
(SWD)},
cin = {416210},
ddc = {620},
cid = {$I:(DE-82)416210_20140620$},
typ = {PUB:(DE-HGF)11},
urn = {urn:nbn:de:hbz:82-opus-53083},
url = {https://publications.rwth-aachen.de/record/464366},
}
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