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 and Minglin Ma ()
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Alexander U. Ernst: Cornell University
Long-Hai Wang: Cornell University
Scott C. Worland: Cornell University
Braulio A. Marfil-Garza: University of Alberta
Xi Wang: Cornell University
Wanjun Liu: Cornell University
Alan Chiu: Cornell University
Tatsuya Kin: University of Alberta
Doug O’Gorman: University of Alberta
Scott Steinschneider: Cornell University
Ashim K. Datta: Cornell University
Klearchos K. Papas: University of Arizona
A. M. James Shapiro: University of Alberta
Minglin Ma: Cornell University

Nature Communications, 2022, vol. 13, issue 1, 1-18

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-33760-5

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