Fig. 3

Application of CRC Model Cells in Drug Discovery Research and Machine Learning Methods for Analysis. a Dose–response curves showing the cytotoxic effects of standard-of-care drugs on healthy cells (WT), engineered CRC models (A, AK, AKT, AKTS), and patient-derived cultures (P1–P5). Error bars represent standard deviation, n = 3. b High-throughput screening (HTS) workflow of 4,255 compounds on AKTS cells, n = 3. Screening was performed at a fixed concentration of 1 μM. c Scatter plot showing hits from the primary screening on AKTS cells, with compounds achieving ≥ 70% inhibition selected for further validation. d Scatter plot comparing IC50 values of compound families between WT and AKTS cells. Selective responses in engineered CRC models highlight key drug target families, including mTOR, AKT and EZH2, n = 3. e Heatmap of differential compound responses (Δ AUC) across CRC models and healthy cells, revealing enrichment of inhibitors targeting key pathways, including mTOR, AKT, EZH2, and ALK. f Machine learning pipeline enhancing HTS capabilities: a U-Net-based neural network generates FITC segmentation masks from DAPI-stained images, replacing EdU assays for cell proliferation analysis. g Representative images demonstrating the machine learning model’s input (DAPI), predicted segmentation (FITC), and actual FITC results.