NSAID-Based Coordination Compounds for Biomedical Applications: Recent Advances and Developments

Bridging the Coordination Chemistry of Small Compounds and Metalloproteins Using Machine Learning

Metalloproteins require metal ions as cofactors to catalyze specific reactions with remarkable efficiency and specificity. In various electron transfer reactions, metals in the active sites change their oxidation states to facilitate the biochemical reactions. Cryogenic electron microscopy, X-ray, and X-ray free electron laser (XFEL) crystallography are used to image metalloproteins to understand the reaction mechanisms. However, radiation damage in cryoEM and X-ray crystallography, and the challenge of generating homogeneous crystals and keeping the appropriate experimental conditions for all the crystals in XFEL crystallography, may alter the oxidation states. Here, we build machine learning models trained on a large data set from the Cambridge Crystallographic Data Center to evaluate the metal oxidation states. The models yield high accuracy scores (from 82% to 94%) for all metals in the small molecules. Then, they were used to predict the oxidation states of more than 30 000 metal clusters in metalloproteins with Fe, Mn, Co, and Cu in their active sites. We found that most of the metals exist in the lower oxidation states (Fe2+ 77%, Mn2+ 85%, Co2+ 65%, and Cu+ 64%), and these populations correlate with the standard reduction potentials of the metal ions. Furthermore, we found no clear correlation between these populations and the resolution of the structures, which suggests no significant dependence of these predictions on the resolution. Our models represent a valuable tool for evaluating the oxidation states of the metals in metalloproteins imaged with different techniques. The data files and the machine learning code are available in a public GitHub repository: https://github.com/mamin03/OxitationStatesMetalloprotein.git.

Dithiocarbazate ligands and their Ni(II) complexes with potential biological activity: Structural, antitumor and molecular docking study

In the search for new metal complexes with antitumor potential, two dithiocarbazate ligands derived from 1,1,1-trifluoro-2,4-pentanedione (H2L1) and (H2L2) and four Ni(II) complexes, [Ni(L1)PPh3] (1), [Ni(L1)Py] (2), [Ni(L2)PPh3] (3), and [Ni(L2)Py] (4), were successfully synthesized and investigated by physical-chemistry and spectroscopic methods. The crystal structure of the H2L1 and the Ni(II) complexes has been elucidated by single-crystal X-ray diffraction. The obtained structure from H2L1 confirms the cyclization reaction and formation of the pyrazoline derivative. The results showed square planar geometry to the metal centers, in which dithiocarbazates coordinated by the ONS donor system and a triphenylphosphine or pyridine molecule complete the coordination sphere. Hirshfeld surface analysis by dnorm function was investigated and showed π–π stacking interactions upon the molecular packing of H2L1 and non-classical hydrogen bonds for all compounds. Fingerprint plots showed the main interactions attributed to H⋅H C⋅H, O⋅H, Br⋅H, and F⋅H, with contacts contributing between 1.9% and 38.2%. The mass spectrometry data indicated the presence of molecular ions [M + H]+ and characteristic fragmentations of the compounds, which indicated the same behavior of the compounds in solution and solid state. Molecular docking simulations were studied to evaluate the properties and interactions of the free dithiocarbazates and their Ni(II) complexes with selected proteins and DNA. These results were supported by in vitro cytotoxicity assays against four cancer cell lines, showing that the synthesized metal complexes display promising biological activity.

Comparison of bromazepam and ibuprofen influence on tooth pulp-evoked potentials in humans

Introduction/Objective Somatosensory evoked potentials are a neurophysiological tool for testing the effects of drugs in humans and animals. The aim of this study was to estimate the way that bromazepam and ibuprofen had on tooth pulp-evoked potentials (TPEPs) after non-painful stimuli, as well as to detect possible differences in this activity. Methods Sixty young healthy subjects were included in the study. They were arranged into three groups: ibuprofen, bromazepam and placebo. To record TPEPs response, dental pulp was electrically stimulated through intact enamel with non-painful stimuli. For stimulation and registration, we used Xltek Protektor 32 system, software EPWorks, version 5.0. The experiment consisted of two testing sessions. Five recordings were performed in each session. The first test session was before, and the second was 45 minutes after administration of a single dose of the ibuprofen (400 mg), bromazepam (1.5 mg) or placebo. Results The results of the present study exhibit that both ibuprofen and bromazepam significantly increased all the latencies; ibuprofen decreased amplitudes of all the waves except the first one (p < 0.05), and bromazepam decreased amplitudes of all the waves except the first one (p < 0.05); placebo did not modified TPEPs waves (p > 0.05). Additionally, there were no significant differences in influence on TPEPs between bromazepam and ibuprofen (p > 0.05). Conclusion Our study showed that both bromazepam and ibuprofen had the same influence on TPEPs after non-painful stimuli. That indicates that anxiolytic dose of bromazepam affects neurotransmission in the same manner as non-opioid analgesics ibuprofen.