Synthesis of New Thiourea-Metal Complexes with Promising Anticancer Properties.
Guillermo Canudo-BarrerasLourdes OrtegoAnabel IzagaIsabel MarzoRaquel P HerreraMaría Concepción GimenoPublished in: Molecules (Basel, Switzerland) (2021)
In this work, two thiourea ligands bearing a phosphine group in one arm and in the other a phenyl group (T2) or 3,5-di-CF3 substituted phenyl ring (T1) have been prepared and their coordination to Au and Ag has been studied. A different behavior is observed for gold complexes, a linear geometry with coordination only to the phosphorus atom or an equilibrium between the linear and three-coordinated species is present, whereas for silver complexes the coordination of the ligand as P^S chelate is found. The thiourea ligands and their complexes were explored against different cancer cell lines (HeLa, A549, and Jurkat). The thiourea ligands do not exhibit relevant cytotoxicity in the tested cell lines and the coordination of a metal triggers excellent cytotoxic values in all cases. In general, data showed that gold complexes are more cytotoxic than the silver compounds with T1, in particular the complexes [AuT1(PPh3)]OTf, the bis(thiourea) [Au(T1)2]OTf and the gold-thiolate species [Au(SR)T1]. In contrast, with T2 better results are obtained with silver species [AgT1(PPh3)]OTf and the [Ag(T1)2]OTf. The role played by the ancillary ligand bound to the metal is important since it strongly affects the cytotoxic activity, being the bis(thiourea) complex the most active species. This study demonstrates that metal complexes derived from thiourea can be biologically active and these compounds are promising leads for further development as potential anticancer agents.
Keyphrases
- gold nanoparticles
- silver nanoparticles
- sensitive detection
- cystic fibrosis
- magnetic resonance imaging
- magnetic resonance
- reduced graphene oxide
- squamous cell carcinoma
- electronic health record
- molecular dynamics
- ionic liquid
- cell proliferation
- computed tomography
- risk assessment
- genetic diversity
- molecular docking
- machine learning
- pseudomonas aeruginosa
- cell death
- climate change
- highly efficient
- neural network
- sewage sludge