A predictive computational platform for optimizing the design of bioartificial pancreas devices.
Nat Commun
; 13(1): 6031, 2022 10 13.
Article
em En
| MEDLINE
| ID: mdl-36229614
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Transplante das Ilhotas Pancreáticas
/
Ilhotas Pancreáticas
/
Diabetes Mellitus Tipo 1
/
Insulinas
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Nat Commun
Assunto da revista:
BIOLOGIA
/
CIENCIA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos