Intra-molecular triplet energy transfer is a general approach to improve organic fluorophore photostability.

Bright, long-lasting and non-phototoxic organic fluorophores are essential to the continued advancement of biological imaging. Traditional approaches towards achieving photostability, such as the removal of molecular oxygen and the use of small-molecule additives in solution, suffer from potentially toxic side effects, particularly in the context of living cells. The direct conjugation of small-molecule triplet state quenchers, such as cyclooctatetraene (COT), to organic fluorophores has the potential to bypass these issues by restoring reactive fluorophore triplet states to the ground state through intra-molecular triplet energy transfer. Such methods have enabled marked improvement in cyanine fluorophore photostability spanning the visible spectrum. However, the generality of this strategy to chemically and structurally diverse fluorophore species has yet to be examined. Here, we show that the proximal linkage of COT increases the photon yield of a diverse range of organic fluorophores widely used in biological imaging applications, demonstrating that the intra-molecular triplet energy transfer mechanism is a potentially general approach for improving organic fluorophore performance and photostability.

Quenching enhancement of the singlet excited state of pheophorbide-a by DNA in the presence of the quinone carboquone.

Changes in the emission fluorescence intensity of pheophorbide-a (PHEO) in the presence of carboquone (CARBOQ) were used to obtain the association constant, the number of CARBOQ molecules interacting with PHEO, and the fluorescence quantum yield of the complex. Excitation spectra of mixtures of PHEO and CARBOQ in ethanol (EtOH) show an unresolved doublet in the red-most excitation band of PHEO, indicating the formation of a loose ground-state complex. The 1:1 CARBOQ-PHEO complex shows a higher fluorescence quantum yield in EtOH (0.41 ± 0.02) than in buffer solution (0.089 ± 0.002), which is also higher than that of the PHEO monomer (0.28). Quenching of the PHEO fluorescence by DNA nucleosides and double-stranded oligonucleotides was also observed and the bimolecular quenching rate constants were determined. The quenching rate constant increase as the oxidation potential of the DNA nucleoside increases. Larger quenching constants were obtained in the presence of CARBOQ suggesting that CARBOQ enhances DNA photo-oxidation, presumably by inhibiting the back-electron-transfer reaction from the photoreduced PHEO to the oxidized base. Thus, the enhanced DNA-base photosensitized oxidation by PHEO in the presence of CARBOQ may be related to the large extent by which this quinone covalently binds to DNA, as previously reported.

Comparison of quantitative structure-activity relationship model performances on carboquinone derivatives.

Quantitative structure-activity relationship (qSAR) models are used to understand how the structure and activity of chemical compounds relate. In the present study, 37 carboquinone derivatives were evaluated and two different qSAR models were developed using members of the Molecular Descriptors Family (MDF) and the Molecular Descriptors Family on Vertices (MDFV). The usual parameters of regression models and the following estimators were defined and calculated in order to analyze the validity and to compare the models: Akaike's information criteria (three parameters), Schwarz (or Bayesian) information criterion, Amemiya prediction criterion, Hannan-Quinn criterion, Kubinyi function, Steiger's Z test, and Akaike's weights. The MDF and MDFV models proved to have the same estimation ability of the goodness-of-fit according to Steiger's Z test. The MDFV model proved to be the best model for the considered carboquinone derivatives according to the defined information and prediction criteria, Kubinyi function, and Akaike's weights.

Application of a self-organizing map to quantitative structure-activity relationship analysis of carboquinone and benzodiazepine.

Self-organizing map (SOM) of Kohonen seems to be a promising approach beyond the standard one to regression for some classification problems encountered in the field of pharmacy. We apply them, therefore, to the quantitative structure-activity relationship (QSAR) in carboquinones and benzodiazepines, and show their usefulness. Most QSAR analysis using neural networks has been made by adopting neural networks with supervised learning. On the contrary, SOM obeys unsupervised learning and originally does not involve the use of desired target data. If we note that an appreciable fraction of data may be missing without making the similarity comparison impossible in SOM if the number of attributes considered is appreciable, QSAR analysis using SOM is found to be possible as if supervised learning. Similar to target data in supervised learning, we can take into account target data (=observed activity) as one of attributes in addition to other attributes (=structural descriptors). Choice of optimal descriptors as input parameters was found to be essential to generate valuable SOM.

Mechanisms of action of quinone-containing alkylating agents: DNA alkylation by aziridinylquinones.

Aziridinyl quinones can be activated by cellular reductases eg. DT-diaphorase and cytochrome P450 reductase to form highly reactive DNA alkylating agents. The mechanisms by which this activation and alkylation take place are many and varied. Using clinically relevant and experimental agents this review will describe many of these mechanisms. The agents discussed are Mitomycin C, EO9 and analogues, diaziridinylbenzoquinones and the pyrrolo[1, 2-alpha]benzimidazolequinones.

Modeling antileukemic activity of carboquinones with electrotopological state and chi indices.

The antileukemic activity (medium effective dose, MED) of a set of 37 carboquinones was modeled using a combination of the electrotopological state (E-state) and molecular connectivity indices with multiple linear regression. A four-variable model gave good statistics: r2 = 0.90, s = 0.21. Using the leave-one-out method, the cross-validation statistics indicate a model useful for prediction: r2press = 0.85, spress = 0.26. The same variables were used to model the optimum effective dose (OD): r2 = 0.88, s = 0.19. The cross-validation statistics indicate a model useful for prediction: r2press = 0.83, spress = 0.23. The descriptor variables are interpreted in terms of the molecular structure.

How to see characteristics of structural parameters in QSAR analysis: descriptor mapping using neural networks.

In addition to its outstanding abilities in both classification and fitting, the neural network can also accurately predict the values of the untrained region. To rationalize this ability of prediction, the authors mathematically discussed the valid region of prediction. Based on such a background, the authors proposed "descriptor mapping" in the QSAR analysis, which visualizes the nonlinear dependencies between structural parameters. A variable of the linear multiple regression analysis in the QSAR study is supposed to be linear to the biological intensity and is independent of other variables. Analysis by the descriptor mapping method discloses the reality.