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Helmsley Charitable Trust","doi-asserted-by":"publisher","award":["2202\u201005781"],"award-info":[{"award-number":["2202\u201005781"]}],"id":[{"id":"10.13039\/100007028","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Adv Healthcare Materials"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Glucose\u2010responsive glucagon (GRG) therapeutics are a promising technology for reducing the risk of severe hypoglycemia as a complication of diabetes mellitus. Herein, the performance of candidate GRGs in the literature by modeling the kinetics of activation and connecting them as input into physiological glucoregulatory models is evaluated and projected the two distinct GRG designs, experimental results reported in Wu et\u00a0al. (GRG\u2010I) and Webber et\u00a0al. (GRG\u2010II) is considered. Both are evaluated using a multi\u2010compartmental glucoregulatory model (IMPACT) and used to compare in\u2010vivo experimental data of therapeutic performance in rats and mice. For GRG\u2010I and GRG\u2010II, the total integrated glucose material balances are overestimated by 41.5% \u00b1 14% and underestimated by 24.8% \u00b1 16% compared to in\u2010vivo time\u2010course data, respectively. These large differences to the relatively simple computational descriptions of glucagon dynamics in the model, which underscores the urgent need for improved glucagon models is attributed. Additionally, therapeutic insulin and glucagon infusion pumps are modeled for type 1 diabetes mellitus (T1DM) human subjects to extend the results to additional datasets. These observations suggest that both the representative physiological and non\u2010physiological models considered in this work require additional refinement to successfully describe clinical data that involve simultaneous, coupled insulin, glucose, and glucagon dynamics.<\/jats:p>","DOI":"10.1002\/adhm.202401410","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T03:22:50Z","timestamp":1724901770000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Analysis of Glucose Responsive Glucagon Therapeutics using Computational Models of the Glucoregulatory System"],"prefix":"10.1002","volume":"13","author":[{"given":"Ali A.","family":"Alizadehmojarad","sequence":"first","affiliation":[{"name":"Department of Chemical Engineering Massachusetts Institute of Technology  Cambridge MA 02139 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungyun","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering Massachusetts Institute of Technology  Cambridge MA 02139 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering Massachusetts Institute of Technology  Cambridge MA 02139 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2944-808X","authenticated-orcid":false,"given":"Michael S.","family":"Strano","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering Massachusetts Institute of Technology  Cambridge MA 02139 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,8,29]]},"reference":[{"key":"e_1_2_8_1_1","first-page":"141","volume":"12","author":"Banting F. 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