Synthesis, Antitumor Evaluation, Molecular Modeling and Quantitative Structure-Activity Relationship (QSAR) of Novel 2-[(4-Amino-6-N-substituted-1,3,5-triazin-2-yl)methylthio]-4-chloro-5-methyl-N-(1H-benzo[d]imidazol-2(3H)-ylidene)Benzenesulfonamides

A series of novel 2-[(4-amino-6-R2-1,3,5-triazin-2-yl)methylthio]-4-chloro-5-methyl-N-(5-R1-1H-benzo[d]imidazol-2(3H)-ylidene)benzenesulfonamides 6-49 was synthesized by the reaction of 5-substituted ethyl 2-{5-R1-2-[N-(5-chloro-1H-benzo[d]imidazol-2(3H)-ylidene)sulfamoyl]-4-methylphenylthio}acetate with appropriate biguanide hydrochlorides. The most active compounds, 22 and 46, showed significant cytotoxic activity and selectivity against colon (HCT-116), breast (MCF-7) and cervical cancer (HeLa) cell lines (IC50: 7-11 µM; 15-24 µM and 11-18 µM), respectively. Further QSAR (Quantitative Structure-Activity Relationships) studies on the cytotoxic activity of investigated compounds toward HCT-116, MCF-7 and HeLa were performed by using different topological (2D) and conformational (3D) molecular descriptors based on the stepwise multiple linear regression technique (MLR). The QSAR studies allowed us to make three statistically significant and predictive models for them. Moreover, the molecular docking studies were carried out to evaluate the possible binding mode of the most active compounds, 22 and 46, within the active site of the MDM2 protein.

Synthesis, Anticancer Evaluation and Structure-Activity Analysis of Novel (E)- 5-(2-Arylvinyl)-1,3,4-oxadiazol-2-yl)benzenesulfonamides

To learn more about the structure-activity relationships of (E)-3-(5-styryl-1,3,4-oxadiazol-2-yl)benzenesulfonamide derivatives, which in our previous research displayed promising in vitro anticancer activity, we have synthesized a group of novel (E)-5-[(5-(2-arylvinyl)-1,3,4-oxadiazol-2-yl)]-4-chloro-2-R1-benzenesulfonamides 7-36 as well as (E)-4-[5-styryl1,3,4-oxadiazol-2-yl]benzenesulfonamides 47-50 and (E)-2-(2,4-dichlorophenyl)-5-(2-arylvinyl)-1,3,4-oxadiazols 51-55. All target derivatives were evaluated for their anticancer activity on HeLa, HCT-116, and MCF-7 human tumor cell lines. The obtained results were analyzed in order to explain the influence of a structure of the 2-aryl-vinyl substituent and benzenesulfonamide scaffold on the anti-tumor activity. Compound 31, bearing 5-nitrothiophene moiety, exhibited the most potent anticancer activity against the HCT-116, MCF-7, and HeLa cell lines, with IC50 values of 0.5, 4, and 4.5 µM, respectively. Analysis of structure-activity relationship showed significant differences in activity depending on the substituent in position 3 of the benzenesulfonamide ring and indicated as the optimal meta position of the sulfonamide moiety relative to the oxadizole ring. In the next stage, chemometric analysis was performed basing on a set of computed molecular descriptors. Hierarchical cluster analysis was used to examine the internal structure of the obtained data and the quantitative structure-activity relationship (QSAR) analysis with multiple linear regression (MLR) method allowed for finding statistically significant models for predicting activity towards all three cancer cell lines.

Machine learning-driven insights into retention mechanism in IAM chromatography of anticancer sulfonamides: Implications for biological efficacy

Machine learning (ML) tools offer new opportunities in drug discovery, especially for enhancing our understanding of molecular interactions with biological systems. This study develops a comprehensive quantitative structure-retention relationship (QSRR) model to elucidate sulfonamides' binding mechanisms to phospholipids via immobilized artificial membrane (IAM) chromatography. Using a dataset of over 500 sulfonamide derivatives, we combined experimental IAM-HPLC data with computational molecular descriptors and ML techniques, achieving robust predictive models. The descriptor-based LASSO regression model effectively predicts retention behavior (R² = 0.71, Q² = 0.77), providing insights into molecular interactions. Critical descriptors influencing these interactions include aqueous solubility, nitrogen-to-oxygen ratio, atomic and mass descriptors such as atom and ring count, as well as logP, indicative of molecular lipophilicity. Furthermore, the fingerprint-based predictive support vector machine model demonstrated superior performance (R² = 0.899 Q² = 0.810) highlighting structural features such as benzene rings and nitrogen-attached fragments as crucial factors in determining phospholipid affinity. Furthermore, predictive models for anticancer activities across three cell lines-HCT-116, HeLa, and MCF-7-were constructed, highlighting CHIIAM value as a critical determinant of bioactivity. The findings underscore the utility of integrated ML and chromatographic approaches in streamlining the drug development pipeline, improving predictions of biological efficacy while reducing experimental burden.