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Pharmacology and Therapeutics

Connecting Rodent and Human Pharmacokinetic Models for the Design and Translation of Glucose-Responsive Insulin

  1. Jing Fan Yang1,
  2. Xun Gong1,
  3. Naveed A. Bakh1,
  4. Kelley Carr2,
  5. Nelson F.B. Phillips2,
  6. Faramarz Ismail-Beigi2,
  7. Michael A. Weiss3 and
  8. Michael S. Strano1⇑
  1. 1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA
  2. 2Department of Biochemistry, Case Western Reserve University, Cleveland, OH
  3. 3Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
  1. Corresponding author: Michael S. Strano, strano{at}mit.edu
Diabetes 2020 Aug; 69(8): 1815-1826. https://doi.org/10.2337/db19-0879
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Abstract

Despite considerable progress, development of glucose-responsive insulins (GRIs) still largely depends on empirical knowledge and tedious experimentation—especially on rodents. To assist the rational design and clinical translation of the therapeutic, we present a Pharmacokinetic Algorithm Mapping GRI Efficacies in Rodents and Humans (PAMERAH) built upon our previous human model. PAMERAH constitutes a framework for predicting the therapeutic efficacy of a GRI candidate from its user-specified mechanism of action, kinetics, and dosage, which we show is accurate when checked against data from experiments and literature. Results from simulated glucose clamps also agree quantitatively with recent GRI publications. We demonstrate that the model can be used to explore the vast number of permutations constituting the GRI parameter space and thereby identify the optimal design ranges that yield desired performance. A design guide aside, PAMERAH more importantly can facilitate GRI’s clinical translation by connecting each candidate’s efficacies in rats, mice, and humans. The resultant mapping helps to find GRIs that appear promising in rodents but underperform in humans (i.e., false positives). Conversely, it also allows for the discovery of optimal human GRI dynamics not captured by experiments on a rodent population (false negatives). We condense such information onto a “translatability grid” as a straightforward, visual guide for GRI development.

Footnotes

  • This article contains supplementary material online at https://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db19-0879/-/DC1.

  • See accompanying article, p. 1608.

  • Received September 3, 2019.
  • Accepted February 8, 2020.
  • © 2020 by the American Diabetes Association
http://www.diabetesjournals.org/content/license

Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.

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August 2020, 69(8)
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Connecting Rodent and Human Pharmacokinetic Models for the Design and Translation of Glucose-Responsive Insulin
Jing Fan Yang, Xun Gong, Naveed A. Bakh, Kelley Carr, Nelson F.B. Phillips, Faramarz Ismail-Beigi, Michael A. Weiss, Michael S. Strano
Diabetes Aug 2020, 69 (8) 1815-1826; DOI: 10.2337/db19-0879

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Connecting Rodent and Human Pharmacokinetic Models for the Design and Translation of Glucose-Responsive Insulin
Jing Fan Yang, Xun Gong, Naveed A. Bakh, Kelley Carr, Nelson F.B. Phillips, Faramarz Ismail-Beigi, Michael A. Weiss, Michael S. Strano
Diabetes Aug 2020, 69 (8) 1815-1826; DOI: 10.2337/db19-0879
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