Minovski_2018_Ecotoxicol.Environ.Saf_170_548

The release of active pharmaceutical ingredients (APIs) into the environment is of great concern for aquatic ecosystem as many of these chemicals are designed to exert biological activity. Hence, their impact on non-target organisms like fish would not be surprising. In this respect, we revisited fish toxicity data of pharmaceuticals to generate linear and non-linear quantitative structure-toxicity relationships (QSTRs). We predicted fish lethality data from the validated QSTR models for 120 APIs with no experimental fish toxicity data. Toxicity of APIs on aquatic organisms is not fully characterized. Therefore, to provide a mechanistic insight for the assessment of API's toxicity to fish, the outcome of the derived QSTR models was integrated with structure-based toxicophore and molecular docking studies, utilizing the biomarker enzyme acetylcholinesterase originating from fish Torpedo californica (TcAChE). Toxicophore virtual screening of 60 chemicals with pT>0 identified 23 hits as potential TcAChE binders with binding free energies ranging from -6.5 to -12.9kcal/mol. The TcAChE-ligand interaction analysis revealed a good nesting of all 23 hits within TcAChE binding site through establishing strong lipophilic and hydrogen bonding interactions with the surrounding key amino acid residues. Among the chemicals passing the criteria of our integrated approach, majority of APIs belong noticeably to the Central Nervous System class. The screened chemicals displayed not only comprehensive toxicophore coverage, but also strong binding affinities according to the docking calculations, mainly due to interactions with TcAChE's key amino acid residues Tyr121, Tyr130, Tyr334, Trp84, Phe290, Phe330, Phe331, Ser122, and Ser200. Moreover, we propose here that binding of pharmaceuticals to AChE might have a potential in triggering molecular initiating events for adverse outcome pathways (AOPs), which in turn can play an important role for future screening of APIs lacking fish lethality data.