RT Journal Article
SR Electronic
T1 Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 095653
DO 10.1101/095653
A1 Artem V. Artemov
A1 Evgeny Putin
A1 Quentin Vanhaelen
A1 Alexander Aliper
A1 Ivan V. Ozerov
A1 Alex Zhavoronkov
YR 2016
UL http://biorxiv.org/content/early/2016/12/20/095653.abstract
AB Despite many recent advances in system biology and a marked increase in the availability of high-throughput biological data, the productivity of research and development in the pharmaceutical industry is on the decline. This is primarily due to clinical trial failure rates reaching up to 95% in oncology and other disease areas. We have developed a comprehensive analytical and computational pipeline utilizing deep learning techniques and novel systems biology analytical tools to predict the outcomes of phase I/II clinical trials. The pipeline predicts the side effects of a drug using deep neural networks and estimates drug-induced pathway activation. It then uses the predicted side effect probabilities and pathway activation scores as an input to train a classifier which predicts clinical trial outcomes. This classifier was trained on 577 transcriptomic datasets and has achieved a cross-validated accuracy of 0.83. When compared to a direct gene-based classifier, our multi-stage approach dramatically improves the accuracy of the predictions. The classifier was applied to a set of compounds currently present in the pipelines of several major pharmaceutical companies to highlight potential risks in their portfolios and estimate the fraction of clinical trials that were likely to fail in phase I and II.