QAL333's antitumor activity and predictive modeling: integrated transcriptomic-bioinformatic analysis reveals selective cytotoxicity and sensitivity determinants.

Despite significant advances in chemotherapy, tumor heterogeneity and resistance mechanisms continue to limit clinical efficacy, underscoring the need for novel compounds with mechanistic biomarkers that enable patient stratification. QAL333 is a newly synthesized isoindolin-1-one derivative identified as a promising anticancer candidate. We evaluated its activity across 15 human cancer and three non-malignant cell lines, as well as in zebrafish xenograft models. QAL333 exhibited potent and selective cytotoxicity in colorectal cancer, with IC₅₀ values in the low micromolar range (7.8 µM in SW620), while showing limited efficacy in triple-negative breast cancer (IC₅₀ > 90 µM). In vivo, QAL333 (20 µM) significantly suppressed tumor growth in SW620 xenografts, whereas MDA-MB-231 xenografts showed minimal response. Transcriptomic profiling revealed that QAL333 downregulated cell cycle regulators (CDK1, CDC25A, CCND2, CCNE2, and PCNA) and suppressed PI3K–Akt and p53 signaling in sensitive cells, while resistant cells activated NF-κB–driven stress pathways. By integrating drug-induced transcriptomic data with machine learning, we developed a composite pathway score derived from three KEGG pathways: Metabolic pathways, Proteoglycans in cancer, and Cytoskeleton in muscle cells. This score correlated strongly with IC₅₀ values across 16 lines (Pearson correlation coefficient = 0.737, p = 0.0011), providing a mechanistically interpretable biomarker for predicting sensitivity. These findings support QAL333 as a selective anticancer agent with translational biomarker potential. Secondary observations also indicated that QAL333 interferes with quorum sensing pathways, broadening its pharmacological relevance beyond the central focus of this study.