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

Author:

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

Abstract

AbstractThe 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.

Funder

JDRF

Novo Nordisk Hartwell Foundation

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary

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