QSAR for Cholinesterase Inhibition by Organophosphorus Esters and CNDO/2 Calculations for Organophosphorus Ester Hydrolysis

Abstract

Quantitative structure‐activity relationships were derived for acetyl‐ and butyrylcholinesterase inhibition by various organophosphorus esters. Bimolecular inhibition rate constants correlate well with hydrophobic substituent constants, and with the presence or absence of cationic groups on the inhibitor, but not with steric substituent constants. CNDO/2 calculations were performed on a separate set of organophosphorus esters, RR′P(O)X, where R and R′ are alkyl and/or alkoxy groups and X is fluorine, chlorine or a phenoxy group. For each subset with the same X, the CNDO‐derived net atomic charge at the central phosphorus atom in the ester correlates well with the alkaline hydrolysis rate constant. For the whole set of esters with different X, two equations were derived that relate either charge and leaving group steric bulk, or orbital energy and bond order to the hydrolysis rate constant.

Screening for Improved Nerve Agent Simulants and Insights into Organophosphate Hydrolysis Reactions from DFT and QSAR Modeling

“…The bond order of the P=O bond (BO) was considered important, and we anticipated that the bond strength between P and the leaving group atom should weaken as the P=O bond order increases, resulting in a lower barrier. The conjugate acid p K a of the leaving group is relevant because the overall hydrolysis rate should be dependent on the relative basicity of the leaving group compared to the nucleophile, and p K a was already shown in the previous section to have an effect on the degradation mechanism followed. The molecular volume ( V ) was selected to investigate if the relative size has any effect on the approach of the nucleophile, and thus the reaction barrier.…”

“…Aside from providing valuable insight into the chemistry governing certain reaction classes or binding events, QSAR models can also drastically reduce the expense and time required to discover and test novel candidate molecules. To the best of our knowledge, there has only been one previous QSAR model on organophosphate hydrolysis using molecular orbital descriptors, but it only studied a small number of compounds and used outdated computational methods. Due to the threat of nerve agent attacks in today's world, along with the significant increase in research aimed at neutralizing these agents in recent years, we believe there is a crucial need for a more thorough understanding of the basic chemistry underlying their detoxification mechanisms.…”

Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates

“…Although QSAR models to predict K i (Johnson et al 1985;Plyamovatyi et al 1997;Ruark et al 2013;Lee and Barron 2016) and median lethal dose (LD 50 ) values (Devillers 2004;Bermúdez-Saldaña and Cronin 2006;García-Domenech et al 2007;Zhao and Yu 2013;Camacho-Mendoza et al 2018;Ding et al 2018) have been reported, these models suffer from small datasets, insufficient structural structure diversity, oversimplified modeling algorithms, and the lack of both strict validation and applicability domain (AD) evaluations; thus, their practical use is limited. For example, based on semi-empirical quantum chemical (QC) descriptors, Johnson et al (1985) constructed linear QSAR models of 19 OPs with their hydrolysis rate constants using a stepwise linear regression analysis method. They found that the hydrolysis rates of OPs are closely related to the charge of the phosphorus atom and the steric effects of the leaving group.…”

Quantitative structure–activity relationships for the toxicity of organophosphorus and carbamate pesticides to the Rainbow trout Onchorhyncus mykiss

“…QSARs have been developed to predict the toxicity and/or AChE inhibition (i.e. the bimolecular rate constant) of mostly OP, but also carbamate, pesticides 7, 19–22. Johnson et al 19 showed that the bimolecular inhibition rate constants for bovine AChE were well correlated with hydrophobic substituent constants and with the presence or absence of cationic groups on the inhibitor, but not with steric substituent constants.…”

“…the bimolecular rate constant) of mostly OP, but also carbamate, pesticides 7, 19–22. Johnson et al 19 showed that the bimolecular inhibition rate constants for bovine AChE were well correlated with hydrophobic substituent constants and with the presence or absence of cationic groups on the inhibitor, but not with steric substituent constants. Hermens et al 20 built a model, albeit with limited predictive applicability, for the toxicity to fish of a small dataset of structurally related OP pesticides based on hydrophobic and electronic parameters.…”