Synthesis and structural and DNA binding studies of mono‐ and dinuclear copper(II) complexes constructed with ─O and ─N donor ligands: Potential anti‐skin cancer drugs

Mononuclear and dinuclear copper(II) complexes with thiophenecarboxylic acid, [Cu(3‐TCA)₂(2,2′‐bpy)] ( 1 ), [Cu(3‐Me‐2‐TCA)₂(H₂O)(2,2′‐bpy)] ( 2 ), [Cu(5‐Me‐2‐TCA)₂(H₂O)(2,2′‐bpy)] ( 3 ) and [Cu₂(2,5‐TDCA)(DMF)₂(H₂O)₂(2,2′‐bpy)₂](ClO₄)₂ ( 4 ) (where 3‐TCA = 3‐thiophenecarboxylic acid; 3‐Me‐2‐TCA = 3‐methyl‐2‐thiophenecarboxylic acid; 5‐Me‐2‐TCA = 5‐methyl‐2‐thiophenecarboxylic acid; 2,5‐TDCA = thiophene‐2,5‐dicarboxylic acid; 2,2′‐bpy = 2,2′‐bipyridyl; DMF = N,N‐dimethylformamide), were synthesized. Compounds 1 – 4 were extensively characterized using both analytical and spectroscopic methods. Additionally, the solid‐state structures of 1 and 4 were unambiguously established from single‐crystal X‐ray diffraction studies. The hexacoordinated Cu(II) centre in 1 (CuO₄N₂) is a distorted octahedral geometry whereas the pentacoodinated 4 (CuO₃N₂) has distorted square pyramidal geometry. Compounds 1 and 4 exhibit intermolecular hydrogen bonding which leads to the formation of two‐ and three‐dimensional supramolecular architectures, respectively. Spectrophotometric and computational investigations suggest that these compounds bind with DNA in minor groove binding such that K_b = 4.9 × 10⁵ M^?1 and K_sv = 3.4 × 10⁵ M^?1, and binding score of ?5.26 kcal mol^?1. The binding affinity of these complexes to calf thymus DNA is in the order 2 > 3 > 4 > 1 . Methyl‐substituted thiophene ring increases the DNA binding affinity whereas unsubstituted thiophene ring DNA binding rate is reduced. The methyl group on the thiophene ring would sterically hinder π–π stacking of the ring with DNA base pairs, and subsequently they are involved in hydrophobic interaction with the DNA surface rather than partial intercalative interaction. Compounds 1 – 4 show pronounced activity against B16 mouse melanoma skin cancer cell lines as measured by MTT assay yielding IC₅₀ values in the micromolar concentration range. The compounds could prove to be efficient anti‐cancer agents, since at a concentration as low as 2.1 μg ml^?1 they exerted a significant cytotoxic effect in cancer cells whereas cell viability was not affected in normal cells.     

Nonlinear System Identification of Systems with Periodic Limit-Cycle Response

A nonlinear system identification methodology based on the principle of harmonic balance and bifurcation theory techniques like center manifold analysis and normal form reduction, is presented for multi-degree-of-freedom systems. The methodology, called Bifurcation Theory System IDentification, (BiTSID), is a general procedure for any nonlinear system that exhibits periodic limit cycle response and can be used to capture the bifurcation behavior of the nonlinear systems. The BiTSID methodology is demonstrated on an experimental system single-degree-of-freedom system that deals with self-excited motions of a fluid-structure system with a sub-critical Hopf bifurcation. It is shown that BiTSID performs excellently in capturing the stable and unstable limit cycles within the experimental regime. Its performance outside the experimental regime is also studied. The application of BiTSID to experimental multi-degree-of-freedom systems has also been very successful. However in this study only the results of the single-degree-of-freedom system are presented.     

Nonlinear System Identification of Multi-Degree-of-Freedom Systems

A nonlinear system identification methodology based on theprinciple of harmonic balance is extended tomulti-degree-of-freedom systems. The methodology, called HarmonicBalance Nonlinearity IDentification (HBNID), is then used toidentify two theoretical two-degree-of-freedom models and anexperimental single-degree-of freedom system. The three modelsand experiments deal with self-excited motions of afluid-structure system with a subcritical Hopf bifurcation. Theperformance of HBNID in capturing the stable and unstable limitcycles in the global bifurcation behavior of these systems is alsostudied. It is found that if the model structure is well known,HBNID performs well in capturing the unknown parameters. If themodel structure is not well known, however, HBNID captures thestable limit cycle but not the unstable limit cycle.