Computational prediction of photosensitizers’ toxicity

Abstract

The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. Today, computational toxicology can be used in every phase of drug discovery and development, from profiling large libraries early on, to predicting off-target effects in the mid-discovery phase, and to assess potential mutagenic impurities in development and degradants as part of life-cycle management. In this study, for the first time, in silico approaches were used to analyze the possible dark toxicity of photosensitive systems based on chlorin e6 and assessed possible toxicity of these compositions. By applying quantitative structure-activity relationship models (QSARs) and modeling adverse outcome pathways (AOPs), a potential toxic effect of water-soluble (chlorin e6 and chlorin e6 aminoamid) and hydrophobic (tetraphenylporphyrin) photosensitizers (PS) was predicted. Particularly, PSs’ protein binding ability, reactivity to form peptide adducts, glutathione conjugation, activity in dendritic cells, and gene expression activity in keratinocytes were explored. Using a metabolism simulator, possible PS metabolites were predicted and their potential toxicity was assessed as well. It was shown that all tested porphyrin PS and their predicted metabolites possess low activity in the mentioned processes and therefore are unable to cause significant adverse toxic effects under dark conditions.