Exploring Biginelli hybrids in the AI-driven development of ruthenium complexes: Anticancer activity, DNA/HSA binding study, impacts on apoptosis and BCL-2/BCL-XL suppression

Abstract

Ruthenium-arene complexes are promising alternatives to platinum-based anticancer drugs due to their unique chemical properties and lower toxicity. These complexes typically have a “half-sandwich” structure where an arene ligand stabilizes the ruthenium center. This study aimed to design tetrahydropyrimidines (THPM) and their ruthenium p-cymene complexes with anticancer potential using deep learning models for binding affinity prediction. Ten compounds with binding energies lower than −31.3 kJ/mol were selected for further investigation. Molecular docking studies revealed that the ruthenium complexes 5j and 5g exhibited the most pronounced activity against Caspase 3. These complexes showed significant cytotoxic activity and selectivity against primary and metastatic cancer cell lines, inducing apoptosis as the preferred mode of cell death through the modulation of Caspases expression. The K_b and K_sv values for the interaction of 5j with EB-DNA, Hoechst-DNA, HSA, HSA-Eosin Y, and HSA-Ibuprofen were higher compared to those of 5m. Binding constants in the presence of the tested BIO-ILs followed the order IL1 (ethanoate) < IL2 (butanoate) < IL3 (hexanoate), correlating with the length of the alkyl chain in the anions and the lipophilicity of the tested BIO-ILs. The best result of this study was that treatment with 5g induced apoptosis and reduced the expression of anti-apoptotic markers (BCL-2 and BCL-XL), which are associated with resistance acquisition. The research outcomes emphasize the integration of computational methods with experimental validation, underscoring the importance of collaboration between AI technologies and traditional chemistry in drug discovery.