A predictive computational platform for optimizing the design of bioartificial pancreas devices

Alexander U. Ernst, Long Hai Wang, Scott C. Worland, Braulio A. Marfil-Garza, Xi Wang, Wanjun Liu, Alan Chiu, Tatsuya Kin, Doug O’Gorman, Scott Steinschneider, Ashim K. Datta, Klearchos K. Papas, A. M. James Shapiro, Minglin Ma

Research output: Contribution to journalArticlepeer-review

Abstract

The delivery of encapsulated islets or stem cell-derived insulin-producing cells (i.e., bioartificial pancreas devices) may achieve a functional cure for type 1 diabetes, but their efficacy is limited by mass transport constraints. Modeling such constraints is thus desirable, but previous efforts invoke simplifications which limit the utility of their insights. Herein, we present a computational platform for investigating the therapeutic capacity of generic and user-programmable bioartificial pancreas devices, which accounts for highly influential stochastic properties including the size distribution and random localization of the cells. We first apply the platform in a study which finds that endogenous islet size distribution variance significantly influences device potency. Then we pursue optimizations, determining ideal device structures and estimates of the curative cell dose. Finally, we propose a new, device-specific islet equivalence conversion table, and develop a surrogate machine learning model, hosted on a web application, to rapidly produce these coefficients for user-defined devices.

Original languageEnglish (US)
Article number6031
JournalNature communications
Volume13
Issue number1
DOIs
StatePublished - Dec 2022
Externally publishedYes

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • General
  • Physics and Astronomy(all)

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