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Updated: May 9, 2026

Analytical Techniques for Assaying Nitric Oxide Bioactivity
Published on: June 18, 2012
Decoding nitric oxide release rates of amine-based diazeniumdiolates
Yan-Ni Wang1, Jack Collins, Ryan J Holland
1Advanced Biomedical Computing Center, Information Systems Program, SAIC-Frederick, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States.
Researchers developed a computational model to predict nitric oxide (NO) and nitroxyl (HNO) release from amine-based diazeniumdiolates (NONOates). This model accurately correlates dissociation rates with activation energies, aiding in the design of novel NO/HNO donor prodrugs.
Area of Science:
- Chemistry
- Computational Chemistry
- Pharmacology
Background:
- Amine-based diazeniumdiolates (NONOates) are widely used as nitric oxide (NO) donors.
- Their potential for nitroxyl (HNO) release is a recent discovery, but predicting NO/HNO production rates remains challenging.
- Existing trends are limited, with secondary amines exclusively producing NO.
Purpose of the Study:
- To develop a computational procedure for modeling amine-based NONOates in aqueous solution.
- To establish a correlation between experimentally measured dissociation rates and computed NO release activation energies.
- To investigate the influence of solvent phase on NONOate behavior compared to gas phase.
Main Methods:
- Computational modeling of amine-based NONOates in water solvent.
- Inclusion of quantum mechanical water molecules to simulate solvent effects.
- Correlation analysis between experimental dissociation rates and calculated activation energies for NO release.
Main Results:
- An excellent correlation (R(2) = 0.94) was achieved between measured dissociation rates and computed NO release activation energies for seven secondary amine species.
- The study rigorously demonstrated significant differences in NONOate behavior between gas and solvent phases.
- The simplest amine-based NONOate, [H2N-N(O)═NO(-)], was computationally identified as a potential unperturbed HNO donor.
Conclusions:
- The developed computational model provides a valuable tool for predicting NO and HNO release from amine-based NONOates.
- This work advances the understanding of NONOate chemistry in solution, crucial for drug design.
- The findings pave the way for identifying tailored prodrug candidates for NO and/or HNO delivery.
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