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Updated: Jan 8, 2026

Author Spotlight: Innovative Microneedle-Based Strategies for Enhanced Exosome Delivery and Stability

Author Spotlight: Innovative Microneedle-Based Strategies for Enhanced Exosome Delivery and Stability

Published on: July 12, 2024

Machine learning-driven insights into the mechanical performance of polymeric microneedle array patches.

Xue Yao Liew¹, Jing Yi Lee², Jia Yee Ong²

¹School of Computer Sciences, Universiti Sains Malaysia, 11800, USM, Penang, Malaysia.

Journal of Controlled Release : Official Journal of the Controlled Release Society

|December 11, 2025

Summary

Machine learning models predict microneedle array patch mechanical performance using literature data. This approach streamlines development for transdermal drug delivery systems by identifying key formulation factors.

Keywords:

In vitro skin insertion Machine learning Mechanical strength Microneedles Polymer

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Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing

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Published on: June 1, 2012

Area of Science:

Pharmaceutical Sciences
Materials Science
Biomedical Engineering

Background:

Microneedle array patches (MAP) are crucial for transdermal drug delivery, requiring robust mechanical properties for skin penetration.
Predicting MAP mechanical performance is essential but challenging, necessitating efficient methods for formulation development.
Artificial intelligence and machine learning (ML) offer promising solutions for accelerating pharmaceutical research and reducing experimental efforts.

Purpose of the Study:

To apply ML algorithms to predict the mechanical properties of polymeric MAP.
To leverage literature-mined data for developing predictive models of MAP mechanical performance.
To identify key formulation parameters influencing the mechanical behavior of MAP.

Main Methods:

Extracted data from 148 publications, encompassing 313 unique formulations.
Applied various ML algorithms, including gradient boosting, to predict mechanical properties like failure force and skin insertion.
Focused on predicting in vitro skin insertion due to limited ex vivo data availability.

Main Results:

Gradient boosting emerged as the most effective ML model for predicting failure force (±0.467 N error) and in vitro skin insertion (±7.781% error).
Key predictors identified include microneedle dimensions (inter-needle spacing, height, volume), polymer content, and molecular weight.
The study successfully demonstrated the predictive power of ML models using existing literature data.

Conclusions:

ML models can accurately predict the mechanical properties of polymeric MAP from literature data.
This approach significantly aids in streamlining the development of effective transdermal drug delivery systems.
Understanding the influence of microneedle design and material properties is critical for optimizing MAP performance.