Predictive modeling of the total deactivation rate constant of singlet oxygen by heterocyclic compounds

Mercader, Andrew G.; Duchowicz, Pablo R.; Fernández, Francisco M.; Castro, Eduardo A.; Cabrerizo, Franco M.; Thomas, Andrés H.

doi:10.1016/j.jmgm.2009.03.002

Cited by 20 publications

(20 citation statements)

References 31 publications

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“…A recent work presents an extensive and reliable comparison of the ERM and RM against the GA. The results for 10 different experimental databases suggested that the ERM is clearly preferable to GA, supporting earlier results based on comparisons made one case at a time [26][27][28]. In addition to the quality of the results, it was reflected the fact that the ERM is much simpler than GA.…”

Section: Linear Qsar Methodology Erm Rm and Gasupporting

confidence: 79%

Partial‐Order Ranking and Linear Modeling: Their Use in Predictive QSAR/QSPR Studies

Mercader

Castro

2012

Statistical Modelling of Molecular Descriptors in QSAR/QSPR

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Section: Linear Qsar Methodology Erm Rm and Gasupporting

confidence: 79%

Partial‐Order Ranking and Linear Modeling: Their Use in Predictive QSAR/QSPR Studies

Mercader

Castro

2012

Statistical Modelling of Molecular Descriptors in QSAR/QSPR

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“…The goal is to highlight the potential of 1 O 2 as a relatively important oxidant in organic aerosol processing. For 1 O 2 and OH radical steady-state concentrations, we used the average values from our SOA measurements of 3 × 10 –14 M and 4 × 10 –17 M, respectively, and we used literature reaction rate constants for the following organic compounds: benzimidazole, 4-nitrophenol, vanillin, imidazole, indole, syringol, histidine, resorcinol, niclosamide, tryptophan, hydroquinone, methionine, tyrosine, and cysteine. − Some of these compounds are potentially found in atmospheric aerosols, such as benzimidazole, cysteine, nitrophenols, , tyrosine, syringol, and vanillin. , We opted not to consider H 2 O 2 as part of this box model because of its low concentrations and low reactivity with organics compared to the other two ROS.…”

Section: Resultsmentioning

confidence: 99%

Reactive Oxygen Species Production from Secondary Organic Aerosols: The Importance of Singlet Oxygen

Manfrin

Nizkorodov

Malecha

et al. 2019

Environ. Sci. Technol.

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Organic aerosols are subjected to atmospheric processes driven by sunlight, including the production of reactive oxygen species (ROS) capable of transforming their physicochemical properties. In this study, secondary organic aerosols (SOA) generated from aromatic precursors were found to sensitize singlet oxygen ( 1 O 2 ), an arguably underappreciated atmospheric ROS. Specifically, we quantified 1 O 2 , OH radical, and H 2 O 2 quantum yields within photoirradiated solutions of laboratory-generated SOA from toluene, biphenyl, naphthalene, and 1,8-dimethylnaphthalene. At 5 mg C L −1 of SOA extracts, the average steady-state concentrations of 1 O 2 and of OH radicals in irradiated solutions were 3 ± 1 × 10 −14 M and 3.6 ± 0.9 × 10 −17 M, respectively. Furthermore, ROS quantum yields of irradiated ambient PM 10 extracts were comparable to those from laboratory-generated SOA, suggesting a similarity in ROS production from both types of samples. Finally, by using our measured ROS concentrations, we predict that certain organic compounds found in aerosols, such as amino acids, organonitrogen compounds, and phenolic compounds have shortened lifetimes by more than a factor of 2 when 1 O 2 is considered as an additional sink. Overall, our findings highlight the importance of SOA as a source of 1 O 2 and its potential as a competitive ROS species in photooxidation processes.

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“…SIC3 (structural information content – neighborhood symmetry of third order) gives information about the third order neighborhoods of the vertices of chemical graph by applying information theory on chemical graphs [35]. GATS8m (Geary autocorrelation – lag 8 / weighted by atomic masses) is also based on molecular graph theory, describing the distribution of atomic masses along the paths connecting atom pairs of length 8 and characterizes the importance of atomic mass distribution [36]. Radial distribution function descriptors, on the other hand, are based on the distance distribution in the molecule [21].…”

Section: Resultsmentioning

confidence: 99%

Computational modeling of in vitro biological responses on polymethacrylate surfaces

Ghosh

Lewitus

Chandra

et al. 2011

Polymer

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The objective of this research was to examine the capabilities of QSPR (Quantitative Structure Property Relationship) modeling to predict specific biological responses (fibrinogen adsorption, cell attachment and cell proliferation index) on thin films of different polymethacrylates. Using 33 commercially available monomers it is theoretically possible to construct a library of over 40,000 distinct polymer compositions. A subset of these polymers were synthesized and solvent cast surfaces were prepared in 96 well plates for the measurement of fibrinogen adsorption. NIH 3T3 cell attachment and proliferation index were measured on spin coated thin films of these polymers. Based on the experimental results of these polymers, separate models were built for homo-, co-, and terpolymers in the library with good correlation between experiment and predicted values. The ability to predict biological responses by simple QSPR models for large numbers of polymers has important implications in designing biomaterials for specific biological or medical applications.

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Predictive modeling of the total deactivation rate constant of singlet oxygen by heterocyclic compounds

Cited by 20 publications

References 31 publications

Partial‐Order Ranking and Linear Modeling: Their Use in Predictive QSAR/QSPR Studies

Partial‐Order Ranking and Linear Modeling: Their Use in Predictive QSAR/QSPR Studies

Reactive Oxygen Species Production from Secondary Organic Aerosols: The Importance of Singlet Oxygen

Computational modeling of in vitro biological responses on polymethacrylate surfaces

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