Fluorenyl-phthalimide hybrids as potent aldose reductase inhibitors with selective anticancer activity: Rational design, synthesis, and molecular insights

Metabolic reprogramming in Cancer cells creates actionable vulnerabilities for precision oncology. Aldose Reductase (ALR2, AKR1B1; EC 1.1.1.21), a cytosolic NADPH-dependent oxidoreductase, has emerged as a multifunctional player in tumor progression, drug resistance, and oxidative stress modulation, yet remains underexploited as a Cancer target. In this study, we report the rational design, synthesis, and biological evaluation of a focused series of fluorenyl-phthalimide carboxylic-acid hybrids (5a-5l) as dual-acting agents with selective Anticancer potential and potent ALR2 inhibition. Among them, compound 5a exhibited the strongest ALR2 inhibition with a K_I of 8.71 ± 0.81 nM, far surpassing the clinical comparator epalrestat (K_I = 232.10 ± 14.42 nM). In MCF-7 and A549 carcinoma cells, 5a demonstrated moderate antiproliferative activity (IC₅₀ = 52.04 μM and 192.91 μM, respectively) while sparing normal bronchial epithelial cells, indicating tumor selectivity. Molecular docking and MM-GBSA calculations (ΔGbind = -44.02 kcal/mol) revealed a consistent bidentate hydrogen bonding network supporting high-affinity binding involving Tyr48, His110, and Trp111, further stabilized by crystallographic water molecules. To explore structure-activity relationships, a Random Forest regression model was developed using 24 descriptors derived from QikProp. The model achieved strong predictive performance (Q²CV = 0.71), and SHAP analysis identified water/gas partition coefficient, skin permeability, and human oral absorption as the most influential features shaping inhibitory potency. In silico ADME/Tox profiling supported the drug-like nature of compound 5a, with no major CYP or hERG liabilities predicted. Collectively, these findings position 5a as a first-in-class ALR2-targeted Anticancer lead, introducing a chemically novel and mechanistically validated chemotype for further development within the framework of metabolically targeted precision therapies.

Aldose reductase inhibitors; Fluorenyl-phthalimide hybrids; Machine learning; Precision oncology; Rational drug design; Selective cytotoxicity; Targeted therapy.