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

Author

Listed:
  • Alexander U. Ernst

    (Cornell University)

  • Long-Hai Wang

    (Cornell University
    University of Science and Technology of China)

  • 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
    University of Alberta)

  • Doug O’Gorman

    (University of Alberta
    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
    University of Alberta)

  • Minglin Ma

    (Cornell University)

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.

Suggested Citation

  • 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. , 2022. "A predictive computational platform for optimizing the design of bioartificial pancreas devices," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33760-5
    DOI: 10.1038/s41467-022-33760-5
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    References listed on IDEAS

    as
    1. Long-Hai Wang & Alexander Ulrich Ernst & Duo An & Ashim Kumar Datta & Boris Epel & Mrignayani Kotecha & Minglin Ma, 2021. "A bioinspired scaffold for rapid oxygenation of cell encapsulation systems," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
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