Dataset and code files for the article "Development and comparison of machine learning models for in-vitro drug permeation prediction from microneedle patch"
Description
The dataset was gleaned from literature to train and evaluate different machine learning (ML) models, such as stacking regressor model, artificial neural network (ANN) model, and voting regressor model. In this study, models were developed to improve the prediction accuracy of the in-vitro drug release amount from the hydrogel-type microneedle patch and the in-vitro drug permeation amount through the micropores created by solid microneedles on the skin. We compared the performance of these models using various metrics, including R-squared score (R2 score), root mean squared error (RMSE), and mean absolute error (MAE). Voting regressor model performed better with drug permeation percentage as an outcome feature having RMSE value of 3.24. The value of permeation amount calculated from the predicted percentage is found to be more accurate with RMSE of 654.94 than direct amount prediction, having a RMSE of 669.69. All our models have performed far better than the previously developed model before this research, which had a RMSE of 4447.23. We then optimized voting regressor model’s hyperparameter and cross-validated its performance. Furthermore, it was deployed in a webapp using Flask framework, showing a way to develop an application to allow other users to easily predict drug permeation amount from the microneedle patch at a particular time period. All code files and deployment files are available on the provided GitHub repository link. Altogether, this project demonstrates the potential of ML to facilitate the development of microneedle patch and other drug delivery systems.