The absorption spectrum of the asymmetric 9-amino-2,7,12,17-tetraphenylporphycene shows new, strongly red-shifted bands compared to the symmetric parental 2,7,12,17-tetraphenylporphycene and to the also asymmetric 9-acetoxy-2,7,12,17-tetraphenylporphycene. Dual emission is also observed with relative contributions that depend strongly on the excitation wavelength and temperature. The gap between the two fluorescence bands is 84 nm. Tautomerization in both the ground and excited states is shown to account for these observations, the 9-amino group being particularly able to selectively lower the energy of the first excited singlet state of just one of the trans tautomers. Introduction of amino groups in porphycenes may be a convenient way to gain a deeper insight into the tautomerization mechanisms in this macrocyclic core.

Dual fluorescence is described and characterized for several 9-substituted porphycenes. Using both spectroscopic and computational studies, the phenomenon is related to the differential stabilization of the two trans tautomers in the excited state. The electronic nature of the 9-substituents affects both the energy and the lifetime of the two tautomers as well as their interconversion. The results are consistent with a two-step model for excited-state tautomerization that involves the population of an upper excited state, tentatively assigned to a cis species, which in turn provides an efficient non-radiative pathway to the ground state.

Photodynamic therapy (PDT) is a promising modality for the treatment of tumours based on the combined action of a photosensitiser (PS), visible light and molecular oxygen, which generates a local oxidative damage that leads to cell death. The site where the primary photodynamic effect takes place depends on the subcellular localization of the PS and affects the mode of action and efficacy of PDT. It is therefore of prime interest to develop structure-subcellular localization prediction models for a PS from its molecular structure and physicochemical properties. Here we describe such a prediction method for the localization of macrocyclic PSs into cell organelles based on a wide set of physicochemical properties and processed through an artificial neural network (ANN). 128 2D-molecular descriptors related to lipophilicity/hydrophilicity, charge and structural features were calculated, then reduced to 76 by using Pearson's correlation coefficient, and finally to 5 using Guyon and Elisseeff's algorithm. The localization of 61 PSs was compiled from literature and distributed into 3 possible cell structures (mitochondria, lysosomes and "other organelles"). A non-linear ANN algorithm was used to process the information as a decision tree in order to solve PS-organelle assignment: first to identify PSs with mitochondrial and/or lysosomal localization from the rest, and to classify them in a second stage. This sequential ANN classification method has permitted to distinguish PSs located into two of the most important cell targets: lysosomes and mitochondria. The absence of false negatives in this assignation, combined with the rate of success in predicting PS localization in these organelles, permits the use of this ANN method to perform virtual screenings of drug candidates for PDT.

In this work, a comparative study between two methods to acquire relevant information about a cosmetic formulation has been carried out. A Design of Experiments (DOE) has been applied in two stages to a capillary cosmetic cream: first, a Plackett-Burman (PB) design has been used to reduce the number of variables to be studied; second, a complete factorial design has been implemented. With the experimental data collected from the DOE, a Least Mean Square (LMS) algorithm and Artificial Neural Networks (ANN) have been utilized to obtain an equation (or model) that could explain cream viscosity. Calculations have shown that ANN are the best prediction method to fit a model to experimental data, within the interval of concentrations defined by the whole set of experiments.