Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer that accounts for 95% of cases of pancreatic cancer. It develops in the ducts and shows high drug resistance. In this study, we proposed a framework to predict the pharmacokinetic (PK) properties of repurposed drugs for PDAC using artificial intelligence (AI). Initially, the molecular features of repurposable drugs for PDAC were generated through three types of molecular descriptors: RDKit, MACCS, and ECFP6. Then, the corresponding absorption (Caco-2 cell permeability), distribution (volume of distribution), metabolism (CYP2C9 inhibitor), excretion (half-life), and toxicity (hERG) properties of the drugs were obtained from ADMETlab 3.0. We constructed AI models such as multilayer perceptron (MLP), random forest (RF), extreme gradient boosting (XGB), and one-dimensional convolutional neural network with different combinations of molecular descriptors as the input. The performance of the models was evaluated on an open-access data set, Therapeutics Data Commons (TDC), and using evaluation metrics. Our results show that the highest-performing molecular descriptor combination and AI models vary with respect to the PK properties. Models on the PDAC data set achieved a mean absolute error (MAE) of 0.18 (MACCS+XGB), Spearman correlation (SC) of 0.39 (MACCS+RF), area under the precision-recall curve (AUPRC) of 59.44% (MACCS+ECFP6+MLP), SC of 0.68 (RDKit+ECFP6+XGB), and SC of 0.77 (MACCS+RF) for absorption, distribution, metabolism, excretion, and toxicity, respectively. The corresponding values on the TDC data set are an MAE of 0.26 (RDKit+MACCS+MLP), an SC of 0.62 (MACCS+ECFP6+MLP), an AUPRC of 67.11% (RDKit+MACCS+ECFP6+1D-CNN), an SC of 0.39 (MACCS+RF), and an SC of 0.92 (RDKit+XGB/RDKit+MACCS+RF). These results suggest that combining molecular fingerprints with AI can effectively model PK properties. This approach supports the use of AI for accelerating drug repurposing, especially for disease conditions.
Pragya et al. (Tue,) studied this question.