Machine Learning Predictions Of Concentration-Specific Aggregate Hazard Scores Of Inorganic Nanomaterials In Embryonic Zebrafish

The possibility of employing computational approaches like nano-QSAR or nano-read-across to predict nanomaterial hazard is attractive from both a financial, and most importantly, where in vivo tests are required, ethical perspective. In the present work, we have employed advanced Machine Learning techniques, including stacked model ensembles, to create nano-QSAR tools for modeling the toxicity of metallic and metal oxide nanomaterials, both coated and uncoated and with a variety of different core compositions, tested at different dosage concentrations on embryonic zebrafish. Using both computed and experimental descriptors, we have identified a set of properties most relevant for the assessment of nanomaterial toxicity and successfully correlated these properties with the associated biological responses observed in zebrafish. Our findings suggest that for the group of metal and metal oxide nanomaterials, the core chemical composition, concentration and properties dependent upon nanomaterial surface and medium composition (such as zeta potential and agglomerate size) are significant factors influencing toxicity, albeit the ranking of different variables is sensitive to the exact analysis method and data modeled. Our generalized nano-QSAR ensemble models provide a promising framework for anticipating the toxicity potential of new nanomaterials and may contribute to the transition out of the animal testing paradigm. However, future experimental studies are required to generate comparable, similarly high quality data, using consistent protocols, for well characterized nanomaterials, as per the dataset modeled herein. This would enable the predictive power of our promising ensemble modeling approaches to be robustly assessed on large, diverse and truly external datasets.