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Optimizing autoinjector devices using physics-based simulations and Gaussian processes.

Vivek Sree¹, Xiaoxu Zhong¹, Ilias Bilionis¹

¹School of Mechanical Engineering, Purdue University, West Lafayette, USA.

Journal of the Mechanical Behavior of Biomedical Materials

|February 5, 2023

Summary

This summary is machine-generated.

This study introduces a Bayesian framework to optimize autoinjector devices by modeling tissue biomechanics. This approach improves drug delivery by accounting for tissue properties and device parameters, ensuring robust subcutaneous injections.

Keywords:

Fracture mechanics Machine learning Nonlinear finite element methods Skin biomechanics Subcutaneous tissue biomechanics Uncertainty quantification

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Area of Science:

Biomedical Engineering
Drug Delivery Systems
Computational Mechanics

Background:

Autoinjectors are crucial for subcutaneous drug delivery, requiring robust autonomous function for optimal bioavailability.
Current autoinjector designs often overlook the complex interplay between device dynamics and the biomechanical properties of subcutaneous tissue.
Understanding this coupling is essential for enhancing the reliability and efficacy of autoinjector devices.

Purpose of the Study:

To develop a Bayesian framework for optimizing autoinjector device design.
To incorporate coupled autoinjector-tissue biomechanics and tissue property uncertainties into the optimization process.
To identify key tissue and device parameters influencing subcutaneous injection performance.

Main Methods:

Utilized a Bayesian optimization framework to model and optimize autoinjector performance.
Replaced high-fidelity tissue insertion models with a computationally efficient Gaussian Process (GP) model.
Conducted a sensitivity analysis to determine the impact of various parameters on injection dynamics and drug delivery.

Main Results:

Identified tissue fracture toughness and shear modulus as critical factors influencing injection depth and crack formation.
Demonstrated that drug viscosity and autoinjector spring force significantly affect drug delivery timing and location.
Showcased how spring force impacts penetration depth and acceleration, while viscosity influences delivery timing and required force.

Conclusions:

The proposed Bayesian framework effectively optimizes autoinjector design by considering uncertain tissue biomechanics.
This approach enables the development of more reliable medical devices that require optimization under material uncertainty.
The findings provide critical insights for designing next-generation autoinjectors for enhanced subcutaneous drug delivery.