Autoinjector optimization through cavitation response and severity minimization.

Area of Science:

• Biomedical Engineering
• Fluid Dynamics
• Materials Science

Background:

• Autoinjector (AI) operation can cause abrupt syringe acceleration, leading to cavitation—the formation and collapse of vapor bubbles.
• Cavitation generates shock waves, potentially damaging the device, causing protein aggregation, and compromising drug delivery.
• Optimizing AI design is crucial to mitigate cavitation severity and ensure reliable drug delivery.

Purpose of the Study:

• To optimize autoinjector (AI) design for minimizing cavitation severity using an experimentally validated computational model.
• To identify AI configurations that balance device performance, patient comfort, and reduced risk of damage and cavitation.
• To explore the influence of various design parameters on AI performance and cavitation dynamics.

Main Methods:

• Developed and validated computational models for autoinjector kinematics and cavitation dynamics.
• Employed a multi-objective optimization framework, incorporating a deep neural network surrogate model for efficiency.
• Conducted variance-based sensitivity analysis to identify key design parameters influencing cavitation and performance.

Main Results:

• Identified critical design parameters affecting syringe acceleration, injection time, and cavitation severity.
• Located over 300 successful AI design candidates through multi-objective optimization.
• Utilized uncertainty analysis to pinpoint three promising candidates suitable for various drug viscosities.

Conclusions:

• The developed methodology effectively optimizes autoinjector design to minimize cavitation and enhance performance.
• The study provides a framework for hypothesis testing and discovering novel autoinjector configurations.
• Optimized designs ensure reliable drug delivery while improving patient safety and device longevity.