ML-augmented bayesian optimization of pain induced by microneedles

Abstract

Microneedles (MNs) have emerged as a promising solution for drug delivery and extraction of body fluids. Pain is an important physiological attribute to be examined when designing MNs. There is no known representation of pain with geometric features of a MN despite the focus on experimental work. This study focuses on optimizing MN designs with the aim of minimizing pain through means of machine learning, finite element analysis, and optimization tools. Three distinct approaches are proposed. The first approach involves training multiple regression models on data obtained through finite element analysis in COMSOL. The second approach uses COMSOL's built-in nonlinear optimization solver. Finally, the third approach utilizes the LiveLink interface between COMSOL and MATLAB, combined with Bayesian optimization. Each approach presents unique strengths and challenges, with the third approach demonstrating significant promise due to its efficiency, practicality, and time-saving. A machine learning (ML)-augmented Bayesian framework is described in the article number by Ahmed Choukri Abdullah and Savas Tasoglu to optimize and minimize pain induced by microneedles. Introduction of ML-based optimization frameworks into microfabrication processes can pave the way for a much more effective and customized designs of minimally invasive microneedles.