we could know the results before the trial starts… jg chase centre for bio-engineering university...
TRANSCRIPT
We could know the results before the trial starts…
JG ChaseCentre for Bio-Engineering
University of CanterburyNew Zealand
T DesaiveCardiovascular Research CenterUniversity of LiegeBelgium
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
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Blood glucose, (BG), [mmol/L]
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-hep
atic
insu
lin s
ecre
tion,
(U
en),
[mU
/hr]
ND dataND modelT2DM dataT2DM model
The Model
Physiologically Relevant Model
Normal
T2DM
Limited to 1-16U/hour
Model-based SI “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
Model
Brain
Othercells
Insulin losses (liver, kidneys)
Glucose
Insulin
Liver
BloodGlucose
Liver
Insulin sensitiv
ity
Insulin sensitiv
ity
Effective insulin
PlasmaInsulin
Pancreas
Brain
Othercells
Insulin losses (liver, kidneys)
Glucose
Insulin
Liver
BloodGlucose
Liver
Insulin sensitiv
ity
Insulin sensitiv
ity
Effective insulin
PlasmaInsulin
Pancreas
Self & Cross Validation
The Glucontrol study randomised patients to two arms:
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)
Group A Virtual patients
Glucontrol A Control Protocol Simulation Code
Group B Virtual patients
Glucontrol B Control Protocol Simulation Code
Group A Clinical Data
Self Validation Cross
Validation Cross
Validation
Group A Clinical Data
Group B Clinical Data
Group B Clinical Data
Virtual Trials Repeat Whole Trial Results
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)
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BG [mmol/L]
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ulat
ive
Freq
uenc
y
Protocol A on Population A clinical dataProtocol A on Population A simulated using clinical timingProtocol A on Population A simulated using protocol timingProtocol B on Population B clinical dataProtocol B on Population B simulated using clinical timingProtocol B on Population B simulated using protocol timingProtocol A on Population B simulated using protocol timingProtocol B on Population A simulated using protocol timing
Excellent correlation and thus, the Virtual patients are very good for tight control where Insulin and safety risks are higher
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
Model assumptions have no effect on A case where exogenous inputs are higher and impact is thus less
Virtual Trials Per-Patient Results
Median % Difference Per-Patient (Self Validation)Variation due to model and compliance errors – 95% less than 15% error
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Error [%]
Cum
ulat
ive
Freq
uenc
y
Protocol A on Population A simulated using clinical timingProtocol A on Population A simulated using protocol timingProtocol B on Population B simulated using clinical timingProtocol B on Population B simulated using protocol timing
Median BG is within 10% for 85-95% of patients
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)
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Blood Glucose (mg/dL)
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ulat
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Accurate Glycaemic Control with STAR
STAR Pilot Trial Results
STAR Virtual Trial Results
SPRINT Clinical Results
Virtual Trials Predicted Outcome: STAR
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)
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
Fatanah Suhaimi Normy RazakUmmu JamaludinChris Pretty
Aaron Le Compte
Geoff Chase
Geoff Shaw
Jessica Lin
The Belgians
Dr Thomas Desaive
Dr Jean-CharlesPreiser Sophie Penning
The Hungarians: Dr Balazs Benyo, Dr Levente Kovacs, Mr Peter Szalay and Mr Tamas Ferenci, Dr Attila Ilyes, Dr Noemi Szabo, ...