1. We could know the results before the trial starts… JG Chase Centre for Bio-Engineering University of Canterbury New Zealand T Desaive Cardiovascular Research Center University of Liege Belgium

2. 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.

3. 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

4. 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

5. The Model Physiologically Relevant Model Normal T2DM Limited to 1-16U/hour

6.  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

7. Virtual Trials

8. 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 S I )

9. 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) 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

10. Virtual Trials Per-Patient Results Median % Difference Per-Patient (Self Validation) Variation due to model and compliance errors – 95% less than 15% error Median BG is within 10% for 85-95% of patients

11. 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)

12. 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)

13. 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.

14. Conclusion Model-based methods can be used to develop safely and quickly BEFORE trials so… … We know the outcome ahead of time…

15. Acknowledgments Fatanah Suhaimi Normy Razak Ummu Jamaludin Chris Pretty Aaron Le Compte Geoff Chase Geoff Shaw Jessica Lin The Belgians 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,...