MitoTarget Modeling Using ANN-QSTR Approach Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors.

Recently, it has been suggested that the mitochondrial oligomycin A-sensitive F0-ATPase subunit is an uncoupling channel linked to apoptotic cell death and as such, the toxicological inhibition of mitochondrial F0-ATP hydrolase can be an interesting mitotoxicity-based therapy under pathological conditions. In addition, carbon nanotubes (CNTs) have shown to offer higher selectivity like mitotoxic-targeting nanoparticles. In this work, linear and non-linear nano-quantitative structure-toxicity relationship-based artificial neural network (ANN-QSTR) models were setup using the fractal dimensions calculated from CNTs as source of structural complex-geometrical information to predict the potential ability of CNT-family members to induce mitochondrial toxicity-based inhibition of the mitochondrial H+-F0F1-ATPase from in vitro assays. The attained experimental data suggest that CNTs have high ability to inhibit the F0-ATPase active-binding site following the order: oxidizedCNT (CNTCOOH > CNTOH) > pristineCNT and mimicking the oligomycin A mitotoxicity behavior. Meanwhile the performance of the ANN-QSTR models was found to be improved by including different non-linear combinations of the calculated fractal Scanning Electron Microscopy (SEM) nano-descriptors, leading to models with excellent internal accuracy and predictivity on external data. Finally, the present study can contribute towards the rational-design of carbon nanomaterials and opens new opportunities towards mitochondrial nanotoxicology-based in silico models.