QSAR Modeling for Predicting IC₅₀ and GI₅₀ Values for Human Cell Lines Used in Toxicological Studies

Assessing cytotoxicity towards human cells is a critical step in preclinical drug development. In preclinical toxicology, human cell lines allow for the analysis of both general and organ-specific toxicity, thus, helping reduce development time and costs. Predicting cytotoxic IC₅₀ and GI₅₀ values facilitates the early evaluation of new pharmaceutical agents by assessing the possible therapeutic window. Ten non-tumor and 10 tumor cell lines commonly used in toxicology were selected to develop QSAR models using GUSAR software and ChEMBL data. GUSAR employs atom-centric electrotopological QNA and substructural MNA descriptors to encode molecular structure and utilizes the RBF-SCR algorithm to train QSAR models. The best-performing models (R² > 0.5, RMSE < 0.8; mean R² = 0.691, mean RMSE = 0.584) were selected using 5-fold cross-validation. These models were implemented in the freely available web application CLC-Pred 2.0 (Cell Line Cytotoxicity Predictor), initially developed for qualitative prediction of cytotoxicity in human cell lines.