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open access journal The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses.

The immunological safety of drugs, nanomaterials and contaminants is a central point in the regulatory evaluation and safety monitoring of working and public places and of the environment. In fact, anomalies in immune responses may cause diseases and hamper the physical and functional integrity of living organisms, from plants to human beings. In the case of nanomaterials, many experimental models are used for assessing their immunosafety, some of which have been adopted by regulatory bodies. All of them, however, suffer from shortcomings and approximations, and may be inaccurate in representing real-life responses, thereby leading to incomplete, incorrect or even misleading predictions. Here, we review the advantages and disadvantages of current nanoimmunosafety models, comparing in vivo vs. in vitro models and examining the use of animal vs. human cells, primary vs. transformed cells, complex multicellular and 3D models, organoids and organs-on-chip, in view of implementing a reliable and personalized nanoimmunosafety testing. The general conclusion is that the choice of testing models is key for obtaining reliable predictive information, and therefore special attention should be devoted to selecting the most relevant and realistic suite of models in order to generate relevant information that can allow for safer-by-design nanotechnological developments.