Michael Hartung1*, Elisa Anastasi2, Zeinab M. Mamdouh3,4, Cristian Nogales3, Harald HHW Schmidt3, Jan Baumbach1,5, Olga Zolotareva1,6, and Markus List6
1Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
2School of Computing, Newcastle University, Newcastle upon Tyne, UK
3Department of Pharmacology and Personalised Medicine, Maastricht University, Maastricht, Netherlands
4Department of Pharmacology and Toxicology, Faculty of Pharmacy, Zagazig University, Zagazig, Egypt
5Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
6Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
michael.hartung [at] uni-hamburg.de
Abstract
Drug repurposing is the use of previously developed and tested pharmaceutical agents in new application cases and lately often used as a solution to the increasing drug development costs. Cancers are extremely heterogeneous disorders demonstrating a wide variability of drug responses due to diverse subtypes, quickly evolving and acquiring drug resistance. Therefore, the identification of compounds that can effectively combat a specific tumor type is crucial. Drug candidates that are potentially effective against a specific tumor can be chosen based on the set of driver mutations acquired by this tumor. For optimal treatment, it is important to consider targeted anti-cancer therapies and drugs initially developed to treat non-cancerous diseases.
To overcome this hurdle, we present CADDIE (Cancer Driver Drug Interaction Explorer), a web platform to identify oncological drug repurposing candidates. CADDIE’s biomedical knowledge base integrates a multitude of gene-gene and drug-gene interaction datasets, detailed anticancer drug information and cancer biology data such as cancer driver genes, mutation frequencies and gene expressions. For the purpose of locating drug targets and candidates for drug repurposing, CADDIE makes network medicine algorithms available to the researchers. It guides the users from the choice of seed genes through the discovery of therapeutic targets or drug candidates. Network medicine also provides indirect strategies that take into account other functionally relevant targets in the gene interaction network since potential cancer driver genes may be inaccessible for direct targeting. We demonstrate the application of CADDIE in different cancer subtypes such as sarcoma and ovarian cancer with a detailed analysis of the found drug targets and chemical compounds. CADDIE is available online at https://exbio.wzw.tum.de/caddie/ and as a python package at https://pypi.org/project/caddiepy/.
Keywords: Drug repurposing, Drug prioritization, Network medicine, Cancer, Network Analysis, Network Medicine,
Acknowledgement: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777111. This project reflects only the authors’ view and the European Commission is not responsible for any use that may be made of the information it contains. This work was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the *e:Med* research and funding concept (*grants 01ZX1910D and 01ZX2210D*). JB was partially funded by his VILLUM Young Investigator Grant nr.13154.; Z.M. is funded by a full scholarship [40463/2019] from the Ministry of Higher Education of the Arab Republic of Egypt.
