POSTER ABSTRACT / DETAILS:
Engineered nanoparticles (ENPs) are being extensively used in a great variety of application with a pace that is increasingly growing. The evaluation of the biological effects of ENPs is of outmost importance and for that experimental and most recently computational methods have been suggested.
In an effort to computationally explore available datasets that will lead to ready-to-use applications we have developed and validated a QNAR model for the prediction of the cellular uptake of nanoparticles in pancreatic cancer cells. In this work we have tried to address the need of robust and predictive QNAR models for the assessment of the biological profile of ENPs and on top of that the proposed model has been made available online through Enalos InSilicoNano platform.
In the proposed workflow all computational steps were incorporated in the platform and this complete line of operations was made feasible with the invaluable help of our in house made Enalos KNIME nodes, namely Enalos Mold2 node, Enalos Model Acceptability Criteria node and Enalos Domain – Similarity node. These nodes have been developed by Novamechanics Ltd and are publicly available through the KNIME Community and the company’s website.
The platform was used in a virtual screening framework to identify promising compounds within PubChem. Within this proposed strategy EnalosInSilicoNano platform emerges as a key component for evaluating novel nano-structures that have not been experimentally evaluated or even synthesized.
This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 310451 (Project NanoMILE).