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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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

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Insulin losses (liver, kidneys)

Glucose

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Virtual Trials

Virtual Trials

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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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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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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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, ...