Sree Vivek, Zhong Xiaoxu, Bilionis Ilias, Ardekani Arezoo, Tepole Adrian Buganza
School of Mechanical Engineering, Purdue University, West Lafayette, USA.
School of Mechanical Engineering, Purdue University, West Lafayette, USA; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, USA.
J Mech Behav Biomed Mater. 2023 Apr;140:105695. doi: 10.1016/j.jmbbm.2023.105695. Epub 2023 Jan 30.
Autoinjectors are becoming a primary drug delivery option to the subcutaneous space. These devices need to work robustly and autonomously to maximize drug bio-availability. However, current designs ignore the coupling between autoinjector dynamics and tissue biomechanics. Here we present a Bayesian framework for optimization of autoinjector devices that can account for the coupled autoinjector-tissue biomechanics and uncertainty in tissue mechanical behavior. The framework relies on replacing the high fidelity model of tissue insertion with a Gaussian process (GP). The GP model is accurate yet computationally affordable, enabling a thorough sensitivity analysis that identified tissue properties, which are not part of the autoinjector design space, as important variables for the injection process. Higher fracture toughness decreases the crack depth, while tissue shear modulus has the opposite effect. The sensitivity analysis also shows that drug viscosity and spring force, which are part of the design space, affect the location and timing of drug delivery. Low viscosity could lead to premature delivery, but can be prevented with smaller spring forces, while higher viscosity could prevent premature delivery while demanding larger spring forces and increasing the time of injection. Increasing the spring force guarantees penetration to the desired depth, but it can result in undesirably high accelerations. The Bayesian optimization framework tackles the challenge of designing devices with performance metrics coupled to uncertain tissue properties. This work is important for the design of other medical devices for which optimization in the presence of material behavior uncertainty is needed.
自动注射器正成为皮下给药的主要选择。这些设备需要强大且自主地工作，以最大限度地提高药物生物利用度。然而，目前的设计忽略了自动注射器动力学与组织生物力学之间的耦合。在此，我们提出了一个用于优化自动注射器设备的贝叶斯框架，该框架可以考虑自动注射器与组织的耦合生物力学以及组织力学行为的不确定性。该框架依赖于用高斯过程（GP）替代组织插入的高保真模型。GP模型准确且计算成本低，能够进行全面的敏感性分析，从而确定组织特性（这些特性并非自动注射器设计空间的一部分）是注射过程的重要变量。更高的断裂韧性会减小裂纹深度，而组织剪切模量则有相反的效果。敏感性分析还表明，作为设计空间一部分的药物粘度和弹簧力会影响药物输送的位置和时间。低粘度可能导致过早给药，但可以通过较小的弹簧力来防止，而高粘度可以防止过早给药，但需要更大的弹簧力并增加注射时间。增加弹簧力可确保穿透到所需深度，但可能会导致过高的加速度。贝叶斯优化框架解决了设计与不确定组织特性相关性能指标的设备这一挑战。这项工作对于其他需要在材料行为存在不确定性的情况下进行优化的医疗设备设计具有重要意义。
