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

Distribution of photoactive β-carboline alkaloids across Passiflora caerulea floral organs

This study reports valuable information regarding the presence and concentration of a series of photoactive β-carboline (βCs) alkaloids (norharmane, harmane, harmine, harmol, harmaline, and harmalol) and their distribution across the floral age and organs of Passiflora caerulea. UHPLC-MS/MS data reported herein reveal that the βCs' content ranged from 1 to 110 μg kg-1 , depending on the floral organ and age. In certain physiologically relevant organs, such as anthers, βCs' content was one order of magnitude higher than in other organs, suggesting a special role for βCs in this specific organ. βCs' content also varied in a structure-dependent manner. Alkaloids bearing a hydroxyl group at position C(7) of the main βC ring were present at concentrations one order of magnitude higher than other βC derivatives investigated. UV-visible and fluorescence spectroscopy of the flower extracts provided complementary information regarding other biologically relevant groups of chromophores (phenolic/indolic derivatives, flavonoids/carotenes, and chlorophylls). Since flowers are constantly exposed to solar radiation, the presence of photoactive βCs in floral organs may have several (photo)biological implications that are further discussed.

Insights into Molecular Structure of Pterins Suitable for Biomedical Applications

Pterins are an inseparable part of living organisms. Pterins participate in metabolic reactions mostly as tetrahydropterins. Dihydropterins are usually intermediates of these reactions, whereas oxidized pterins can be biomarkers of diseases. In this review, we analyze the available data on the quantum chemistry of unconjugated pterins as well as their photonics. This gives a comprehensive overview about the electronic structure of pterins and offers some benefits for biomedicine applications: (1) one can affect the enzymatic reactions of aromatic amino acid hydroxylases, NO synthases, and alkylglycerol monooxygenase through UV irradiation of H₄pterins since UV provokes electron donor reactions of H₄pterins; (2) the emission properties of H₂pterins and oxidized pterins can be used in fluorescence diagnostics; (3) two-photon absorption (TPA) should be used in such pterin-related infrared therapy because single-photon absorption in the UV range is inefficient and scatters in vivo; (4) one can affect pathogen organisms through TPA excitation of H₄pterin cofactors, such as the molybdenum cofactor, leading to its detachment from proteins and subsequent oxidation; (5) metal nanostructures can be used for the UV-vis, fluorescence, and Raman spectroscopy detection of pterin biomarkers. Therefore, we investigated both the biochemistry and physical chemistry of pterins and suggested some potential prospects for pterin-related biomedicine.

Quantitative structure-activity relationship models for predicting singlet oxygen reaction rate constants of dissociating organic compounds

As singlet oxygen (¹O₂) is ubiquitous in the environment, ¹O₂-involved oxidation may play an important role in the transformation and fate of organic pollutants. Accordingly, the reaction rate constants (k_1O2) of organic compounds with ¹O₂ are important to determine the environmental fate and persistence assessment of organic pollutants. However, currently there are limited k_1O2 data available, especially for organic chemicals with different charged (deprotonated/protonated) forms. Herein three quantitative structure-activity relationship (QSAR) models (one comprehensive model and two models for neutral and deprotonated molecules) were created for predicting aqueous k_1O2 values for diversely dissociating molecules. The models include larger datasets (180 chemicals) and have wider applicability domain than previous ones. Molecular structural characteristics (only half-wave potential is present in both models) determining the ¹O₂ reaction rate of neutral and deprotonated molecules vary greatly. The comparison results of predicting k_1O2 values of organic compounds at certain pH conditions show that the combination of the QSAR models for neutral and deprotonated molecules has advantages over the comprehensive QSAR model. This work is the first study to predict k_1O2 for a wide variety of neutral and deprotonated molecules and provides an important tool for assessing the fate of organic pollutants in aquatic environments.

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

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 (¹O₂), an arguably underappreciated atmospheric ROS. Specifically, we quantified ¹O₂, OH radical, and H₂O₂ 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 ¹O₂ 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 PM₁₀ 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, organo-nitrogen compounds, and phenolic compounds have shortened lifetimes by more than a factor of 2 when ¹O₂ is considered as an additional sink. Overall, our findings highlight the importance of SOA as a source of ¹O₂ and its potential as a competitive ROS species in photooxidation processes.

Chemical and photochemical properties of chloroharmine derivatives in aqueous solutions

In water, chloroharmines follow very distinctive thermal and photochemical pH- and O₂-dependent-reaction pathways.

In silico model for predicting soil organic carbon normalized sorption coefficient (K(OC)) of organic chemicals

As a kind of in silico method, the methodology of quantitative structure-activity relationship (QSAR) has been shown to be an efficient way to predict soil organic carbon normalized sorption coefficients (KOC) values. In the present study, a total of 824 logKOC values were used to develop and validate a QSAR model for predicting KOC values. The model statistics parameters, adjusted determination coefficient (R(2)adj) of 0.854, the root mean square error (RMSE) of 0.472, the leave-one-out cross-validation squared correlation coefficient (Q(2)LOO) of 0.850, the external validation coefficient Q(2)ext of 0.761 and the RMSEext of 0.558 were obtained, which indicate satisfactory goodness of fit, robustness and predictive ability. The squared Moriguchi octanol-water partition coefficient (MLOGP2) explained 66.5% of the logKOC variance. The applicability domain of the current model has been extended to emerging pollutants like polybrominated diphenyl ethers, perfluorochemicals and heterocyclic toxins. The developed model can be used to predict the compounds with various functional groups including C=C, -C≡C-, -OH, -O-, -CHO, C=O, -C=O(O), -COOH, -C6H5, -NO2, -NH2, -NH-, N-, -N-N-, -NH-C(O)-NH-, -O-C(O)-NH2, -C(O)-NH2, -X(F, Cl, Br, I), -S-, -SH, -S(O)2-, -OS(O)2-, -NH-S(O)2-, (SR)2PH(OR)2 and Si.

Improvement of the Prediction Power of the CoMFA and CoMSIA Models on Histamine H3 Antagonists by Different Variable Selection Methods

The aim of this study is to enhance the predictivity power of CoMFA and CoMSIA models by means of different variable selection algorithms. The genetic algorithm (GA), successive projection algorithm (SPA), stepwise multiple linear regression (SW-MLR), and the enhanced replacement method (ERM) were used and tested as variable selection algorithms. Then, the selected variables were used to generate a simple and predictive model by the multilinear regression algorithm. A set of 74 histamine H₃ antagonists were split into 40 compounds as a training set, and 17 compounds as a test set, by the Kennard-Stone algorithm. Before splitting the data, 17 compounds were randomly selected from the pool of the whole data set as an evaluation set without any supervision, pretreatment, or visual inspection. Among applied variable selection algorithms, ERM had noticeable improvement on the statistical parameters. The r² values of training, test, and evaluation sets for the ERM-MLR model using CoMFA fields were 0.9560, 0.8630, and 0.8460 and using the CoMSIA fields were 0.9800, 0.8521, and 0.9080, respectively. In this study, the principles of organization for economic cooperation and development (OECD) for regulatory acceptability of QSARs are considered.

Computational modeling of in vitro biological responses on polymethacrylate surfaces

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.