References. Competing interests

Acknowledgements

Competing interests

Conclusions

This paper presented the analysis and validation of an in silico virtual patient and model-based virtual trials methodology. The validation approach, as presented, is readily generalized. It takes advantage of a set of independent clinical data comprised of two clinically matched cohorts treated with two different TGC protocols with two different glycemic targets. Three main conclusions can be drawn:

• Self validation indicated a clinically insignificant error in these virtual patient methods due to model and/or clinical compliance. They also showed the impact of some non-compliance independent of model error.

• Cross validation clearly showed that the virtual patient methods and models enabled by patient-specific S_I(t) profiles are effective and the assumption that the S_I(t) profiles are independent of the clinical inputs used to generate them holds.

• Thus, the virtual patients and in silico virtual trial methods presented are validated in their ability to accurately simulate, in advance, the clinical results of an independent TGC protocol, directly enabling rapid design and optimisation of safe and effective TGC protocols with high confidence of clinical success.

Overall, this study further shows the potential and capability of model-based, data driven in silico methods to aid protocol design, as well as the potential for models to provide accurate, safe and effective real-time TGC.

The authors declare that they have no competing interests.

Authors' contributions

JGC, FS, GS, ALC and JL conceived and developed the models and this analysis. FS, SP and ALC did most of the computational analysis with input from JGC, CGP, TD and KTM. J-CP supplied the data and Glucontrol protocol information. JGC, FS, ALC drafted the manuscript primarily although all authors made contributions. All authors approved the final manuscript.

Financial support provided by:

Aaron LE COMPTE: New Zealand Tertiary Education Commission and NZ Foundation for Research Science and Technology Post-Doctoral Fellowship Grant

Jessica LIN: NZ Foundation for Research Science and Technology Post-Doctoral Fellowship Grant

Sophie PENNING: FNRS (Fonds Nationale de la Recherche Scientifique) Research Fellow

Katherine MOORHEAD: University of Liege Post-Doctoral Fellowship Grant

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