We could know the results
before the trial starts…
T Desaive
Cardiovascular Research Center
University of Liege
Belgium
JG Chase
Centre for Bio-Engineering
University of Canterbury
New Zealand
The problem (1)

Critically ill patients can be defined by the high variability in
response to care and treatment.

Variability in outcome arises from



variability in care
variability in the patient-specific response to care.
The greater the variability, the more difficult the patient’s
management and the more likely a lesser outcome becomes.
The problem (2)
Recent increase in importance of protocolized care to minimize
the iatrogenic component due to variability in care.
BUT: protocols are potentially most applicable to groups with
well-known clinical pathways and limited comorbidities, where
a “ one size fits all” approach can be effective.
Those outside this group may receive lesser care and
outcomes compared with the greater number receiving benefit.
Need to try to reduce the component due to inter- and
intra-patient variability in response to treatment.
Model-based methods to provide patient-specific care
A Well Known Story
 Application: Tight glycaemic control (TGC)
 TGC can improve outcomes BUT difficult to achieve without
hypoglycemia
 In-silico simulated clinical trials (“Virtual trials”) can
increase safety and save time + cost

Enable the rapid testing of new TGC intervention protocols and analysing control protocol
performance

Used to simulate a TGC protocol using a virtual patient profile identified from clinical data and
different insulin and nutrition inputs.
 Virtual trials can help predicting outcomes of both individual
intervention and overall trial cohort
The Model
Physiologically Relevant Model
17000
ND data
ND model
T2DM data
T2DM model
16000
15000
Pre-hepatic insulin secretion, (Uen), [mU/hr]

14000
Normal
13000
12000
T2DM
11000
10000
Limited to 1-16U/hour
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Blood glucose, (BG), [mmol/L]
Model
 Model-based SI
Insulin
Brain

“Whole-body” insulin sensitivity

Overall metabolic balance, including net effect of :


Altered endogenous glucose production

Peripheral and hepatic insulin mediated
glucose uptake

Endogenous insulin secretion
Has been used to guide model-based TGC in
several studies

Provides a means to analyse the evolution and
hour-to-hour variability of SI in critically ill patients

Enables prediction of variability in future
Insulin losses
(liver, kidneys)
Blood
Glucose
Glucose
Effective
insulin
ty
ivi
sit
n
se
lin
u
Ins
Liver
Liver
Other
cells
Plasma
Insulin
Pancreas
Virtual Trials

Virtual Trials
Self & Cross Validation

Group A
Group B
Virtual patients
Virtual patients
The Glucontrol study randomised
patients to two arms:


Glucontrol A
Glucontrol B
Control Protocol
Simulation Code
Control Protocol
Simulation Code
Group A
Group A
Group B
Group B
Clinical Data
Clinical Data
Clinical Data
Clinical Data
Cross
Validation
Self Validation
Cross
Validation
Self Validation: expect to generate original clinical data with
differences being due to compliance or model errors
Cross Validation: expect to generate clinical data of one
Group A: Treated with Protocol A
(intensive insulin protocol)
Group B: Treated with Protocol B
(conventional insulin protocol)

Two clinically matched cohorts
that received different insulin
treatments.

Test the assumption of
independence of clinical inputs
(insulin) and patient state (insulin
sensitivity parameter SI)
Virtual Trials Repeat Whole Trial
Results
1
Protocol A on Population A clinical data
Protocol A on Population A simulated using clinical timing
Protocol A on Population A simulated using protocol timing
Protocol B on Population B clinical data
Protocol B on Population B simulated using clinical timing
Protocol B on Population B simulated using protocol timing
Protocol A on Population B simulated using protocol timing
Protocol B on Population A simulated using protocol timing
0.9
Cumulative Frequency
0.8
0.7
Excellent correlation and thus,
the Virtual patients are very
good for tight control where
Insulin and safety risks are
higher
0.6
0.5
0.4
Very good match. Small 0.1-0.2 mmol/L shift
due to several factors:
• B patients often receive zero insulin
• Model assumptions on endog insulin
• Model assumptions on EGP
• Protocol non-compliance clinically
0.3
0.2
Model assumptions have no effect on A case
where exogenous inputs are higher and
impact is thus less
0.1
0
0
2
4
6
8
10
12
14
BG [mmol/L]

CDFs of BG for clinical Glucontrol data and virtual trials on a (whole cohort)

Validates the idea that virtual patients can INDEPENDENTLY capture effects
of different treatment (cross validation results)
Virtual Trials Per-Patient Results
1
0.9
Cumulative Frequency
0.8
0.7
Median BG is within 10% for 85-95% of patients
0.6
0.5
0.4
0.3
0.2
Protocol A on Population A simulated using clinical timing
Protocol A on Population A simulated using protocol timing
Protocol B on Population B simulated using clinical timing
Protocol B on Population B simulated using protocol timing
0.1
0
0
5
10
15
20
25
Error [%]
Median % Difference Per-Patient (Self Validation)
Variation due to model and compliance errors – 95% less than 15% error
Virtual Trials Predicted Outcome:
SPRINT

SPRINT was simulated first in to show efficacy

Clinical & virtual results are almost identical

Other protocols were simulated for
comparison

Shows ability to “know the answer first” or at
least have a lot of confidence
Virtual trials of ~160 patients vs first 160 clinical patients (~20k hours)
Virtual Trials Predicted Outcome:
STAR
Accurate Glycaemic Control with STAR
100
STAR Pilot Trial Results
STAR Virtual Trial Results
SPRINT Clinical Results
90
Cumulative Density (%)
80

Virtual Trials on 371 virtual patients from
SPRINT data but given STAR model-based
protocol

Clinical & virtual results are almost
identical for first 2000 hours

Virtual trials done before clinical data for
first 15 patients shown here

Improvements using STAR and models is
evident compared to SPRINT

Shows ability to optimise with confidence
in silico (safely and first)
70
60
50
40
30
20
10
0
0
20
40
60
80 100 120
Blood Glucose (mg/dL)
140
160
180
Summary

Virtual patients are effective and accurate portrayals of outcome, regardless
of input used to make them.



For a whole cohort
For a specific patient
Virtual patients and in-silico virtual trial methods are validated with cross
validation with independent Glucontrol data
Overall, we have a highly effective and physiologically representative
model for design, analysis and real-time application of TGC protocols, in
silico before they are implemented clinically!
Methods readily extensible to other drug delivery problems to help
predicting trials outcomes.
Conclusion
Model-based methods can be used to develop safely and quickly
BEFORE trials so…
… We know the outcome ahead of time…
Acknowledgments
Geoff Chase
Jessica Lin
Fatanah Suhaimi
Chris Pretty
Ummu Jamaludin
Normy Razak
The Belgians
Geoff Shaw
Aaron Le Compte
Dr Thomas Desaive
Dr Jean-Charles
Preiser
Sophie Penning
The Hungarians: Dr Balazs Benyo, Dr Levente Kovacs, Mr Peter Szalay
and Mr Tamas Ferenci, Dr Attila Ilyes, Dr Noemi Szabo, ...