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Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes
Linkoping Univ, Dept Biomed Engn, Linkoping, Sweden.;CVMD iMed DMPK AstraZeneca R&D, Gothenburg, Sweden..
Eindhoven Univ Technol, Dept Biomed Engn, POB 513, NL-5600 MB Eindhoven, Netherlands..
Pharmaceut LP, AstraZeneca, Quantitat Clin Pharmacol, Waltham, MA USA..
AstraZeneca, Quantitat Clin Pharmacol, Gothenburg, Sweden..
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2016 (English)In: Interface Focus, ISSN 2042-8898, E-ISSN 2042-8901, Vol. 6, no 2, 20150075Article, review/survey (Refereed) PublishedText
Abstract [en]

We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decisionsupport systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

Place, publisher, year, edition, pages
2016. Vol. 6, no 2, 20150075
Keyword [en]
mathematical modelling, systems pharmacology, disease progression, decision-support type 2 diabetes, anti-diabetic treatment
National Category
Endocrinology and Diabetes Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-299389DOI: 10.1098/rsfs.2015.0075ISI: 000375410900001PubMedID: 27051506OAI: oai:DiVA.org:uu-299389DiVA: diva2:949289
Funder
Swedish Research CouncilSwedish Diabetes AssociationEU, FP7, Seventh Framework Programme, FP7-HEALTH-305707AstraZeneca
Available from: 2016-07-18 Created: 2016-07-18 Last updated: 2016-07-18Bibliographically approved

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Kjellsson, Maria C.
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