Abstract
Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r2 value, r2 (CV) value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 μM to 7.04 μM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.
Highlights
Cancer is a dangerous disease in which cells grow and divide beyond their normal limits
The quantitative structure-activity relationship (QSAR) model developed was subsequently applied to predict the photodynamic therapy (PDT) activity of unknown compounds, those in the test set, and some unknown compounds used in an external test set
The values of standard error (SEE), root mean square error (RMSE) and root mean squares error prediction (RMSEP) in this model are 0.49, 3.7 and 3.6, respectively, which further adds to the statistical significance of the developed model
Summary
Cancer is a dangerous disease in which cells grow and divide beyond their normal limits. Photodynamic therapy (PDT) provides an alternative treatment for cancer with relatively low side effects [2] This treatment uses the combined effects of light and light activated toxic drugs or photosensitizers to target tumor cells. Boyle and Dolphin [11] reported the relationship between structure and properties affecting tumoricidal effects of compounds in their development of second generation photosensitizers. Henderson and co-workers [12] reported a comparative study between tumor localizing properties and hydrophilicity, as well as dimerization abilities of 28 porphyrins and pheophorbides They observed the tumoricidal activities of the compounds to be dependent upon a delicate balance between their hydrophilic and hydrophobic characters. The QSAR model developed was subsequently applied to predict the PDT activity of unknown compounds, those in the test set (i.e., data set), and some unknown compounds used in an external test set
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