The joint Aalto University, Karolinska Institute and Institute for Molecular Medicine Finland (FIMM) study included over 1,300 known pharmaceutical molecules, on which there is a wealth of measurement data available.
'The study uses systematic data-driven analysis to combine toxicity measurements taken on cell lines with gene expression responses describing gene activation. Toxicity includes growth inhibitory and cell killing effects. The method developed in the study makes it possible to more accurately predict the toxicity of new molecules because it makes use of advanced statistical methods and much bigger datasets than before,' explains Juuso Parkkinen, who completed his doctoral dissertation at Aalto University.
At present, toxicity is primarily measured by means of animal testing. Thanks to this new method, animal testing can be partly replaced in the future by a combination of cell line testing and statistical modelling. This would also result in considerable cost savings for pharmaceutical development.
Advances in statistical machine learning and artificial intelligence methods have risen to play a crucial role in many application areas in addition to medical research.
'Juuso Parkkinen is an excellent example of the usefulness of Aalto University's artificial intelligence research and doctoral studies: He wrote his dissertation on medicinal applications in my research group and then transferred to Reaktor to apply data science to a wide range of business needs,' praises Parkkinen's dissertation adviser, Professor Samuel Kaski.
More information: Pekka Kohonen et al. A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury, Nature Communications (2017). DOI: 10.1038/ncomms15932
Journal information: Nature Communications
Provided by Aalto University