Utilization of artificial intelligence for evaluation of targeted cancer therapy via drug nanoparticles to estimate delivery efficiency to various sites

• Published in: Chemometrics and intelligent laboratory systems 2025-02, Vol.257, p.105309, Article 105309
• Main Authors: Mahdi, Wael A., Alhowyan, Adel, Obaidullah, Ahmad J.
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Delivery efficiency; Machine learning; Optimization; Targeted cancer therapy
• ISSN: 0169-7439
• DOI: 10.1016/j.chemolab.2024.105309

Poor delivery efficiency of drug nanoparticles to tumor sites in targeted cancer therapy is a major issue towards developing this technique. The type of drug nanocarrier, its shape, size, materials. and physicochemical properties play important roles on the delivery efficiency which should be well understood. This study presents a machine learning approach to predict the delivery efficiency of nanoparticles across various organs for targeted cancer therapy via nanoparticles. The focus was made on three advanced regression models: Gaussian Process Regression (GPR), Extra Trees (ET) regression, and Local Polynomial Regression (LPR). The integration of these models into the analysis of a complex biomedical dataset—comprising 534 records of nanoparticle properties and their distribution across organs such as the tumor, heart, liver, spleen, lung, and kidney—demonstrates their potential in enhancing predictive accuracy in chemical and biological processes. GPR, a non-parametric probabilistic model, was selected for its robustness in handling small, intricate datasets with complex nonlinear relationships, offering precise uncertainty quantification. ET regression, an ensemble learning method, was chosen for its resilience against overfitting in high-dimensional data, thanks to its unique approach of constructing multiple unpruned decision trees with randomized splits. LPR was included for its ability to capture local trends in data, providing nuanced predictions without assuming a global parametric form. The dataset underwent rigorous preprocessing, including missing data imputation using the Multivariate Imputation by Chained Equations (MICE) method, outlier detection through Subspace Outlier Detection (SOD), and feature selection using Conditional Mutual Information (CMI). Z-score normalization was applied to standardize the features, aligning them with the Gaussian assumptions of GPR and improving the overall performance of the models. The models were optimized using the Whale Optimization Algorithm (WOA) to maximize predictive accuracy, with GPR and ET models showing significant improvements over baseline models in predicting the biodistribution outcomes. •Analysis of delivery efficiency of nanoparticles in targeted cancer treatment.•Machine learning evaluation of drug delivery efficiency to various organs.•Whale Optimization Algorithm (WOA) for hyper-parameter tuning of models.