Complexation Chemistry of Zirconium(IV), Uranium(VI), and Iron(III) with Acetohydroxamic Acid

2010 ◽  
Vol 45 (12-13) ◽  
pp. 1733-1742 ◽  
Author(s):  
Brent S. Matteson ◽  
Peter Tkac ◽  
Alena Paulenova
2020 ◽  
Vol 16 ◽  
Author(s):  
Wei-Wei Ni ◽  
Hai-Lian Fang ◽  
Ya-Xi Ye ◽  
Wei-Yi Li ◽  
Li Liu ◽  
...  

Background: Thiourea is a classical urease inhibitor usually as a positive control, and many N,N`-disubstituted thioureas have been determined as urease inhibitors. However, due to steric hindrance, N,N`-disubstituted thiourea motif could not bind urease as thiourea. On the contrary, N-monosubstituted thioureas with a tiny thiourea motif could theoretically bind into the active pocket as thiourea. Objective: A series of N-monosubstituted aroylthioureas were designed and synthesized for evaluation as urease inhibitors. Methods: Urease inhibition was determined by the indophenol method and IC50 values were calculated using computerized linear regression analysis of quantal log dose-probit functions. The kinetic parameters were estimated viasurface plasmon resonance (SPR) and by nonlinear regression analysis based on the mixed type inhibition model derived from Michaelis-Menten kinetics. Results: Compounds b2, b11and b19 reversibly inhibited urease with a mixed mechanism, and showed excellent potency against both cell-free urease and urease in intact cell, with IC50 values being 90-to 450-fold and 5-to 50-fold lower than the positive control acetohydroxamic acid, respectively. The most potent compound b11 showed IC50 value of 0.060 ±0.004μM against cell-free urease, which bound to urea binding site with a very low KDvalue (0.420±0.003nM) and a very long residence time (6.7 min). Compound b11was also demonstrated having very low cytotoxicity to mammalian cells. Conclusion: These results revealed that N-monosubstituted aroylthioureas clearly bind the active site of urease as expected, and represent a new class of urease inhibitors for the development of potential therapeutics against infections caused by ure-ase-containing pathogens.


Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3237
Author(s):  
Artem A. Mitrofanov ◽  
Petr I. Matveev ◽  
Kristina V. Yakubova ◽  
Alexandru Korotcov ◽  
Boris Sattarov ◽  
...  

Modern structure–property models are widely used in chemistry; however, in many cases, they are still a kind of a “black box” where there is no clear path from molecule structure to target property. Here we present an example of deep learning usage not only to build a model but also to determine key structural fragments of ligands influencing metal complexation. We have a series of chemically similar lanthanide ions, and we have collected data on complexes’ stability, built models, predicting stability constants and decoded the models to obtain key fragments responsible for complexation efficiency. The results are in good correlation with the experimental ones, as well as modern theories of complexation. It was shown that the main influence on the constants had a mutual location of the binding centers.


1991 ◽  
Vol 340 (3) ◽  
pp. 169-172 ◽  
Author(s):  
C. Caro Gamez ◽  
P. Valiente Gonz�lez ◽  
M. Jim�nez Arrabal ◽  
V. L�pez-Arza Moreno ◽  
A. Sanch�z Misiego

Urolithiasis ◽  
1981 ◽  
pp. 199-208 ◽  
Author(s):  
Donald P. Griffith ◽  
Pat Moskowitz ◽  
Stuart Feldman
Keyword(s):  

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