Clinical Data for Parametrization of In Silico Bone Models Incorporating Cell-Cytokine Dynamics: A Systematic Review of Literature

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dc.contributor.authorLedoux, Charlesen
dc.contributor.authorBoaretti, Danieleen
dc.contributor.authorSachan, Akankshaen
dc.contributor.authorMüller, Ralphen
dc.contributor.authorCollins, Caitlyn J.en
dc.date.accessioned2022-07-13T19:51:24Zen
dc.date.available2022-07-13T19:51:24Zen
dc.date.issued2022-07-12en
dc.date.updated2022-07-13T18:21:05Zen
dc.description.abstractIn silico simulations aim to provide fast, inexpensive, and ethical alternatives to years of costly experimentation on animals and humans for studying bone remodeling, its deregulation during osteoporosis and the effect of therapeutics. Within the varied spectrum of in silico modeling techniques, bone cell population dynamics and agent-based multiphysics simulations have recently emerged as useful tools to simulate the effect of specific signaling pathways. In these models, parameters for cell and cytokine behavior are set based on experimental values found in literature; however, their use is currently limited by the lack of clinical in vivo data on cell numbers and their behavior as well as cytokine concentrations, diffusion, decay and reaction rates. Further, the settings used for these parameters vary across research groups, prohibiting effective cross-comparisons. This review summarizes and evaluates the clinical trial literature that can serve as input or validation for in silico models of bone remodeling incorporating cells and cytokine dynamics in post-menopausal women in treatment, and control scenarios. The GRADE system was used to determine the level of confidence in the reported data, and areas lacking in reported measures such as binding site occupancy, reaction rates and cell proliferation, differentiation and apoptosis rates were highlighted as targets for further research. We propose a consensus for the range of values that can be used for the cell and cytokine settings related to the RANKL-RANK-OPG, TGF-β and sclerostin pathways and a Levels of Evidence-based method to estimate parameters missing from clinical trial literature.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier901420 (Article number)en
dc.identifier.citationLedoux C, Boaretti D, Sachan A, Müller R and Collins CJ (2022) Clinical Data for Parametrization of In Silico Bone Models Incorporating Cell-Cytokine Dynamics: A Systematic Review of Literature. Front. Bioeng. Biotechnol. 10:901720. doi: 10.3389/fbioe.2022.901720en
dc.identifier.doihttps://doi.org/10.3389/fbioe.2022.901720en
dc.identifier.issn2296-4185en
dc.identifier.orcidCollins, Caitlyn [0000-0003-0181-878X]en
dc.identifier.urihttp://hdl.handle.net/10919/111235en
dc.identifier.volume10en
dc.language.isoenen
dc.publisherFrontiersen
dc.relation.urihttps://doi.org/10.3389/fbioe.2022.901720en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectBoneen
dc.subjectOsteoporosisen
dc.subjectAgingen
dc.subjectAgent-based modelingen
dc.subjectCell population dynamicsen
dc.subjectCytokineen
dc.subjectParametrization approachen
dc.titleClinical Data for Parametrization of In Silico Bone Models Incorporating Cell-Cytokine Dynamics: A Systematic Review of Literatureen
dc.title.serialFrontiers in Bioengineering and Biotechnologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Biomedical Engineering and Mechanicsen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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