Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate

TL;DR

This paper applies dimensionality reduction and ensemble regression techniques to accurately predict PLGA micro- and nanoparticle dissolution rates, addressing high feature redundancy and improving prediction accuracy in pharmaceutical applications.

Contribution

It introduces an evolutionary weighted ensemble method that significantly enhances prediction accuracy over individual algorithms and other ensemble approaches.

Findings

Ensemble method outperforms individual algorithms.

Dimensionality reduction improves prediction accuracy.

The proposed method reduces prediction error substantially.

Abstract

Prediction of poly(lactic co glycolic acid) (PLGA) micro- and nanoparticles' dissolution rates plays a significant role in pharmaceutical and medical industries. The prediction of PLGA dissolution rate is crucial for drug manufacturing. Therefore, a model that predicts the PLGA dissolution rate could be beneficial. PLGA dissolution is influenced by numerous factors (features), and counting the known features leads to a dataset with 300 features. This large number of features and high redundancy within the dataset makes the prediction task very difficult and inaccurate. In this study, dimensionality reduction techniques were applied in order to simplify the task and eliminate irrelevant and redundant features. A heterogeneous pool of several regression algorithms were independently tested and evaluated. In addition, several ensemble methods were tested in order to improve the accuracy of prediction. The empirical results revealed that the proposed evolutionary weighted ensemble method offered the lowest margin of error and significantly outperformed the individual algorithms and the other ensemble techniques.