A Novel Method for Generation of in Silico Subjects with Type 2 Diabetes

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Fältnamn	Indikatorer	Metadata
000		03653naa a2200385 4500
001		oai:research.chalmers.se:ea7ad3fd-772c-43e2-aed8-c528e053e9f4
003		SwePub
008		220225s2021 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://research.chalmers.se/publication/5288282 URI
024	7	a https://doi.org/10.1109/EMBC46164.2021.96296782 DOI
040		a (SwePub)cth
041		a engb eng
042		9 SwePub
072	7	a kon2 swepub-publicationtype
072	7	a ref2 swepub-contenttype
100	1	a Visentin, Robertou Università Degli Studi di Padova,University of Padua4 aut
245	1 0	a A Novel Method for Generation of in Silico Subjects with Type 2 Diabetes
264	1	c 2021
520		a A type 2 diabetes (T2D) simulator has been recently proposed for supporting drug development and treatment optimization. This tool consists of a physiological model of glucose/insulin/C-peptide dynamics and a virtual cohort of T2D subjects (i.e., random extractions of model parameterizations from a joint parameter distribution) well describing both average and variability realistic T2D dynamics. However, the state-of-art procedure to get a reliable virtual population requires some post-processing after subject extraction, in order to discard implausible behaviors. We propose an improved method for virtual subjects' generation to overcome this burdensome task. To do so, we first assessed a refined joint parameter distribution, from which extracting a number of subjects, greater than the target population size. Then, target-size subsets are undersampled from the large cohort. The final virtual population is selected among the subsets as the one maximizing the similarity with T2D data and model parameter distribution, by means of measurement' outcome metrics and Euclidian distance (Δ), respectively. In the final population, almost all the outcome metrics are statistically identical to the clinical counterparts (p-value>0.05) and model parameters' distribution differs by ~5-10% from that derived from data. The methodology described here is flexible, thus resulting suitable for different T2D stages and type 1 diabetes, as well.Clinical Relevance - A straightforward subjects' generation would ease the availability of tailored in silico trials for testing diabetes treatment in a specific population.
650	7	a MEDICIN OCH HÄLSOVETENSKAPx Medicinska och farmaceutiska grundvetenskaperx Farmaceutiska vetenskaper0 (SwePub)301012 hsv//swe
650	7	a MEDICAL AND HEALTH SCIENCESx Basic Medicinex Pharmaceutical Sciences0 (SwePub)301012 hsv//eng
650	7	a NATURVETENSKAPx Data- och informationsvetenskapx Bioinformatik0 (SwePub)102032 hsv//swe
650	7	a NATURAL SCIENCESx Computer and Information Sciencesx Bioinformatics0 (SwePub)102032 hsv//eng
650	7	a NATURVETENSKAPx Matematikx Sannolikhetsteori och statistik0 (SwePub)101062 hsv//swe
650	7	a NATURAL SCIENCESx Mathematicsx Probability Theory and Statistics0 (SwePub)101062 hsv//eng
653		a Diabetes Mellitus, Type 2
653		a Blood Glucose
653		a Insulin
653		a Humans
653		a Diabetes Mellitus, Type 1
700	1	a de Lazzari, Mattia,d 1996u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)lazzari
710	2	a Università Degli Studi di Padovab Chalmers tekniska högskola4 org
773	0	t Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBSg , s. 1380-1383q <1380-1383x 1557-170X
856	4 8	u https://research.chalmers.se/publication/528828
856	4 8	u https://doi.org/10.1109/EMBC46164.2021.9629678

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