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Quantifying Agonist Activity at G Protein-coupled Receptors
Published on: December 26, 2011
Quantitative structure-activity relationships for organophosphates binding to acetylcholinesterase
Christopher D Ruark1, C Eric Hack, Peter J Robinson
1Henry M. Jackson Foundation for the Advancement of Military Medicine, Molecular Bioeffects Branch, Wright-Patterson AFB, Greene, OH 45433-5707, USA. christopher.ruark.ctr@wpafb.af.mil
Archives of Toxicology
|September 20, 2012
Summary
A new quantitative structure-activity relationship (QSAR) model predicts organophosphate toxicity by estimating acetylcholinesterase inhibition rates. This model aids in assessing human toxicity risks from these widespread pesticides and nerve agents.
Area of Science:
- Computational chemistry and toxicology
- Pharmacology and drug design
- Environmental science and risk assessment
Background:
- Organophosphates are widely used pesticides and nerve agents that inhibit acetylcholinesterase, crucial for neurotransmitter regulation.
- Assessing the human toxicity of diverse organophosphate structures is challenging due to data availability limitations.
- Understanding organophosphate toxicity is vital for public health and safety, necessitating efficient predictive tools.
Purpose of the Study:
- To develop a quantitative structure-activity relationship (QSAR) model for predicting human acetylcholinesterase inhibition by pentavalent organophosphate oxons.
- To establish a robust QSAR model that can accurately estimate the bimolecular rate constants for acetylcholinesterase inhibition.
- To provide a tool for rapid toxicity assessment of organophosphate compounds.
Main Methods:
- Compilation of a database of 278 three-dimensional organophosphate structures and their corresponding acetylcholinesterase bimolecular rate constants from 15 publications.
- Calculation of 675 molecular descriptors using AMPAC 8.0 and CODESSA 2.7.10 to characterize the chemical structures.
- Development of a consensus QSAR model using orthogonal projection to latent structures regression, bootstrap cross-validation, and y-randomization, with external validation.
Main Results:
- The developed QSAR model achieved a mean training R² of 0.80, a mean test set R² of 0.76, and a consensus external test set R² of 0.66.
- The HOMO-LUMO energy gap was identified as the most significant descriptor influencing binding affinity and inhibitory activity.
- Model validation included assessment of the domain of applicability and identification of potential outliers or activity cliffs.
Conclusions:
- The developed QSAR model provides a reliable method for predicting the acetylcholinesterase inhibition rates of pentavalent organophosphate oxons.
- This QSAR model can be integrated into physiologically based pharmacokinetic/pharmacodynamic models to enhance organophosphate toxicity evaluations.
- The findings contribute to a better understanding of organophosphate mechanisms and facilitate risk assessment for human exposure.
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