Chemometric and computational modeling of polysaccharide coated drugs for colonic drug delivery.

A methodology based on Principal Component Analysis (PCA) and machine learning (ML) regression was developed in this study for predicting 5-aminosalicylic acid drug release from polysaccharide-coated formulation. The Raman method was used for collection of spectral data which were then used as inputs to the ML models for estimation of drug release. For ML modeling, we examined the predictive accuracy of three machine learning models-Elastic Net (EN), Group Ridge Regression (GRR), and Multilayer Perceptron (MLP)-for forecasting the release behavior of samples. The dataset, consisting of 155 data points with over 1500 spectral features, underwent preprocessing involving normalization, Principal Component Analysis (PCA) for dimensionality reduction, and outlier detection using Cook's Distance. Model hyperparameters were tuned using the Slime Mould Algorithm (SMA), and each model's performance was evaluated through k-fold cross-validation (k = 3). Assessment metrics, such as the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE), emphasize the MLP model's exceptional performance. On the test set, MLP achieved an R² of 0.9989, notably higher than EN's R² of 0.9760 and GRR's R² of 0.7137. Additionally, MLP exhibited remarkably low test RMSE and MAE values at 0.0084 and 0.0067, respectively, in comparison to EN's RMSE of 0.0342 and MAE of 0.0267, as well as GRR's RMSE of 0.0907 and MAE of 0.0744. Parity plots and learning curves further validate MLP's predictive reliability, demonstrating close alignment between actual and predicted values and efficient learning with minimal overfitting. Consequently, the MLP model emerges as the most effective approach for this predictive task, offering a robust tool for accurately modeling complex spectral data. These findings underscore the robustness of the MLP model, providing a reliable and efficient approach for predicting drug release in polysaccharide-coated formulations, with implications for advancing colonic drug delivery systems.