Prediction of tumoricidal activity and accumulation of photosensitizers in photodynamic therapy using multiple linear regression and artificial neural networks.

The biological activities of a congeneric series of pyropheophorbides used as sensitizers in photodynamic therapy have been predicted on the basis of their molecular structures, using multiple linear regression and artificial neural network (ANN) computations. Theoretical descriptors (a total of 81) were calculated by the 3DNET program based on the three-dimensional structure (3D) of the geometry-optimized molecules. These input descriptors were tested as independent variables and used for model building. Systematic descriptor selections yielded models with one, two or three descriptors with good cross-validation results. The predictive abilities of the best fitting models were checked by shuffling and cross-validation procedures. ANN was suitable for building models for both linear and nonlinear relationships. Lipophilicity was sufficient to predict the accumulation of the sensitizers in the target tissue. Weighted holistic invariant molecular descriptors weighted by atomic mass, Van der Waals volume or electronegativity were also needed to predict photodynamic activity properly. Our models were able to predict the biological activities of 13 pyropheophorbide derivatives solely on the basis of their 3D molecular structures. Moreover, linear and nonlinear variable selection methods were compared in models built linearly and nonlinearly. It is expedient to use the same method (linear or nonlinear) for variable selection as for parameter estimation.