Prediction of Ocular Toxicity of Prostaglandin F2α Analogs Based on Local Computational Models: Superiority over Global Models and Experimental Validation
Abstract
Drug-induced ocular toxicity is difficult to predict and evaluate, particularly for prostaglandin F2α (PGF2α) analogs used in the management of glaucoma. Traditional global predictive models, which are built with large and various datasets (n = 6187), provide systematic false-negative results for groups of structurally uniform compounds. To address this limitation, we developed special local classification models for PGF2α analogs. A structurally consistent local training dataset (n = 350) was assembled using Murcko scaffold filtering and Tanimoto similarity selection (≥0.5). Binary classification models were built using three different molecular fingerprints in combination with machine learning and two deep learning methods. Although two-dimensional molecular representations do not encode stereochemistry, computational predictions still apply to the shared two-dimensional scaffold of these compounds. These improved models were used to predict the ocular toxicity of latanoprost and its related impurities. The local models (n = 350) provided accurate toxicity predictions for latanoprost, latanoprost acid, 15(S)-latanoprost, and the trans-5,6-latanoprost isomer, whereas all 16 global computational models provided false-negative results. The experimental assessment performed with primary rabbit corneal epithelial cells (pRCECs) and a human corneal epithelial cell line (HCE-T) confirmed the cytotoxic effects, which were in agreement with the predictions made by the models. Among…
Read the paper
Related studies
- The Prediction of the In Vitro Release Curves for PLGA-Based Drug Delivery Systems with Neural Networks
- Promising Ursolic Acid as a Novel Antituberculosis Agent: Current Progress and Challenges
- Proof of principle concept for the analysis and functional prediction of rare genetic variants in the CYP2C19 and CYP2D6 genes
- Fabrication, characterization, and docking studies of furosemide-loaded nanosponges using the emulsion solvent diffusion method
- The Effect of Concomitant Administration of Proton Pump Inhibitors on the Pharmacokinetics of CDK4/6 Inhibitors in Rats: Implications for the Evaluation of Hepatic and Transporter-Mediated Drug–Drug Interactions
- Mining the Metabolic Capacity of Clostridium sporogenes Aided by Machine Learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.