Review about Structure and Evaluation of Reactivators of Acetylcholinesterase Inhibited with Neurotoxic Organophosphorus Compounds

Abstract

Background: Neurotoxic chemical warfare agents can be classified as some of the most dangerous chemicals for humanity. The most effective of those agents are the organophosphates (OPs) capable of restricting the enzyme acetylcholinesterase (AChE), which in turn controls the nerve impulse transmission. When AChE is inhibited by OPs, its reactivation can be usually performed through cationic oximes. However, until today it has not been developed one universal defense agent, with complete effective reactivation activity for AChE inhibited by any of the many types of existing neurotoxic OPs. For this reason, before treating people intoxicated by an OP, it is necessary to determine the neurotoxic compound that was used for contamination, in order to select the most effective oxime. Unfortunately, this task usually requires a relative long time, raising the possibility of death. Cationic oximes also display a limited capacity of permeating the blood-brain barrier (BBB). This fact compromises their capacity of reactivating AChE inside the nervous system. Methods: We performed a comprehensive search on the data about OPs available on the scientific literature today in order to cover all the main drawbacks still faced in the research for the development of effective antidotes against those compounds. Results: Therefore, this review about neurotoxic OPs and the reactivation of AChE, provides insights for the new agents’ development. The most expected defense agent is a molecule without toxicity and effective to reactivate AChE inhibited by all neurotoxic OPs. Conclusion: To develop these new agents it is necessary the application of diverse scientific areas of research, especially theoretical procedures as computational science (computer simulation, docking and dynamics); organic synthesis; spectroscopic methodologies; biology, biochemical and biophysical information; medicinal chemistry, pharmacology and toxicology.

Copper-decorated covalent organic framework as a heterogeneous photocatalyst for phosphorylation of terminal alkynes

“…1 Organophosphorus moieties form the building blocks of many drugs and biomolecules and play a key role in their activities. 2 They have a wide range of applications in organic synthesis, agriculture, medicinal chemistry, materials, catalysis and coordination chemistry. 3 Among them, alkynyl phosphonate compounds, bearing reactive triple bonds and phosphoryl groups, are an important class of valuable chemicals containing carbon–phosphorus bonds, which are widely used as bioactive molecules in medicinal chemistry and as additives or flame retardants in polymer science.…”

Halide-bridged tetranuclear organocopper(i) clusters supported by indolyl-based NCN pincer ligands and their catalytic activities towards the hydrophosphination of alkenes

“…Phosphorus-containing compounds play a crucial role in the chemical, biological, materials and pharmaceutical fields. [35][36][37] In particular, organophosphorus compounds are of paramount importance in organic synthesis and as ligands for transition-metal complexes in organometallic catalysis reactions. 38 The traditional synthesis methods for organophosphorus compounds included the reactions of organometallic reagents with phosphorus halides or phosphanes bearing a good leaving group, reactions of metal phosphides with an organic electrophile, and reduction of phosphorus halides or oxides or elemental phosphorus.…”

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

“…One possible reason for this failure in predicting binding affinity using classical scoring functions is the methodology applied in the creation of these computational models. We may highlight that most of the classical scoring functions use energy terms for van der Waals, electrostatic energy, hydrogen bonding, and solvation effects and then determine the relative weight of each energy term based on the regression method [1,[119][120][121][122][123][124]. Such an approach creates a model bias against the structures not employed in the training set so that these computational models to predict binding affinity are prone to work for proteins present in the training set used to determine the relative weights of each energy term in the empirical scoring function.…”