Marios Spanakis(1), Emmanouil G. Spanakis(1), Dimitris Kafetzopoulos(2), Vangelis Sakkalis(1), Manolis Tsiknakis(1,3), Kostas Marias(1), Feng Dong(4)
(1) Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Crete, Greece; (2) Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology - Hellas, Heraklion, Crete, Greece; (3) Department of Informatics and Engineering, Technological Educational Institute of Crete, Heraklion, Crete, Greece; (4) Department of Computer Science and Technology, University of Bedforshire, Luton, Bedforshire UK
Objectives: MyHealthAvatar (MHA) platform aims towards a collaborative partnership among patients and healthcare providers [1]. Nowadays, in silico clinical trials (ISCTs), population pharmacokinetics, pharmacogenomics and information communication technologies have provided several tools towards stratified and personalized medicine approaches [2-4]. In this work a methodology of potential fitting of results generated through ISCTs with real life patients through virtual profiles of MHA is presented. To this respect, we use a simple example of discontinuation of warfarin administration during pre-operative period for a 55 year’s old male patient with a MHA profile.
Methods: MHA’s architecture is based on integration of multiscale data gained from several sources (i.e. demographic, biomedical, genomics, lifestyle) and transform them into a representation of health status as a “virtual twin” or avatar [1]. The integration of these information from different avatars can lead in a creation of a virtual population profile (i.e. patients follow anti-coagulating treatment). The population pharmacokinetics in this example are based on the results from simulation of S-warfarin administration in a virtual population through Simcyp® population based simulator [5].
Results: The results from the Simcyp® simulations generated a PK/PD profile of S-warfarin during and after the discontinuation of the treatment in a virtual population with different characteristics regarding demographic, physiology and genomic data. The data output from MHA platform allow also the generation of a virtual cohort with characteristics regarding demographic, physiology and pharmacogenomics (i.e. CYP2C9 polymorphism). To this respect, the best fit of data between these two virtual profiles, e.g. based on patient’s demographics and genomic information, finally leads in generation of information regarding our real-life patient (in this example, the 55 years old male) serving as additional information tool regarding the schedule of the operation.
Conclusions: MHA aims to serve as an innovative representation of the health status for citizens whereas for clinicians MHA potentially could support clinical decisions by extrapolating and/or fitting profiles with simulation models (i.e. population pharmacokinetics) and visual analytics [6]. The potential interconnection with in silico tools can provide novel approaches towards implementation of stratified and/or personalized medicine [7].
References:
[1] Spanakis EG, Yang, P, Deng Z, Sakkalis V, Kafetzopoulos D., Marias, K, Tsiknakis MN, Dong, F.(2014). MyHealthAvatar: personalized and empowerment health services through Internet of Things technologies”, 4th International Conference on Wireless Mobile Communication and Healthcare, November 3-5, 2014, Athens, Greece.
[2] Rostami-Hodjegan A. “Physiologically based pharmacokinetics joined with in vitro-in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology.” Clin Pharmacol Ther 92(1): 50-61.
[3] Van de Waterbeemd H. and Gifford E. (2003). “ADMET in silico modelling: towards prediction paradise?” Nat Rev Drug Discov 2(3): 192-204.
[4] Spat S, Höll B, Beck P, Chiarurgi F,Kontogiannis V, Spanakis EG, Manousos D, Pieber TR. (2012) “A Mobile Android-Based Application for In-hospital Glucose Management in Compliance with the Medical Device Directive for Software”Wireless Mobile Communication and Healthcare, 211-216.
[5] Jamei M., Dickinson G. L., Rostami-Hodjegan A. (2009). “A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: A tale of ‘bottom-up’ vs ‘top-down’ recognition of covariates”.
[6] Maniadi E, Kondylakis H, Spanakis EG, Spanakis M, Tsiknakis M, Marias K, Dong F.(2013) “Designing a digital patient avatar in the context of the MyHealthAvatar project initiative” IEEE 13th International Conference on BioInformatics and BioEngineering (BIBE) 2013.
[7] Spanakis M, Papadaki E, Kafetzopoulos D, Karantanas A, Maris TG, Sakkalis V, Marias K (2013) “Exploitation of patient avatars towards stratified medicine through the development of in silico clinical trials approaches.” IEEE 13th International Conference on BioInformatics and BioEngineering (BIBE) 2013.
Reference: PAGE 24 (2015) Abstr 3678 [www.page-meeting.org/?abstract=3678]
Poster: Methodology - Other topics