title: Reflection of successful anticancer drug development processes in the literature.
abstract: The development of cancer drugs is time-consuming and expensive. In particular, failures in late-stage clinical trials are a major cost driver for pharmaceutical companies. This puts a high demand on methods that provide insights into the success chances of new potential medicines. In this study, we systematically analyze publication patterns emerging along the drug discovery process of targeted cancer therapies, starting from basic research to drug approval - or failure. We find clear differences in the patterns of approved drugs compared with those that failed in Phase II/III. Feeding these features into a machine learning classifier allows us to predict the approval or failure of a targeted cancer drug significantly better than educated guessing. We believe that these findings could lead to novel measures for supporting decision making in drug development.
authors.affiliation: Roche Diagnostics, Scientific Information Services, Nonnenwald 2, 82377 Penzberg, Germany. Electronic address: fabian.heinemann@roche.com.
authors.forename: Fabian
authors.initials: F
authors.lastname: Heinemann
authors.affiliation: Humboldt-Universität zu Berlin, Wissensmanagement in der Bioinformatik, Rudower Chaussee 25, 12489 Berlin, Germany.
authors.forename: Torsten
authors.initials: T
authors.lastname: Huber
authors.affiliation: Roche Innovation Center Munich, Roche Pharma Research and Early Development, Roche Diagnostics, Nonnenwald 2, 82377 Penzberg, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Meisel
authors.affiliation: Roche Diagnostics, Scientific Information Services, Nonnenwald 2, 82377 Penzberg, Germany.
authors.forename: Markus
authors.initials: M
authors.lastname: Bundschus
authors.affiliation: Humboldt-Universität zu Berlin, Wissensmanagement in der Bioinformatik, Rudower Chaussee 25, 12489 Berlin, Germany.
authors.forename: Ulf
authors.initials: U
authors.lastname: Leser
mesh_terms: D000970:Antineoplastic Agents; D035843:Biomedical Research; D017277:Drug Approval / Q000706:statistics & numerical data*; D055808:Drug Discovery; D000069550:Machine Learning; D011643:Publishing / Q000706:statistics & numerical data*
chemical_list: D000970:Antineoplastic Agents