An analysis of the attrition of drug candidates from four major pharmaceutical companies
By Michael J. Waring, J. Arrowsmith, Andrew R. Leach, Paul D. Leeson, Sam Mandrell, R. M. Owen, G. Pairaudeau, W. Pennie, S. Pickett, Jibo Wang and 2 more. 2015
1,262 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Economics and Econometrics, Public Health, Environmental and Occupational Health
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In Computational Theory and Mathematics
- Olexandr Isayev
- Mariya Popova
- Zhiyong Lu
- Brett K. Beaulieu-Jones
- Alexander Tropsha
- Deng Cai
- Yanjun Qi
- Jinbo Xu
In Economics and Econometrics
In Public Health, Environmental and Occupational Health
Citations in the corpus
- Learning Graph-Level Representation for Drug Discovery (2017, background): “When biological researches yield evidence that a particular molecule could modulate essential pathways to achieve therapeutic activity, the discovered molecule often fails as a potential drug for a number of reasons including toxicity, low activity, and low solubility(Waring et al. 2015).”
- Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Drug repositioning (or repurposing) is an attractive option for delivering new drugs to the market because of the high costs and failure rates associated with more traditional drug discovery approaches [278,279].”
- Deep reinforcement learning for de novo drug design (2017, background): “possess the desired bioactivity in vitro. Indeed, it has been acknowledged that the broad use of these rules has substantially reduced the failure rate in experimental ADME studies of drug candidates (17). The crucial step in many new drug discovery projects is the formulation of a well-motivated hy”
- Biophysical properties of the clinical-stage antibody landscape (2017, background): “In fact, an analogous trend has been consistently observed for small-molecule drugs, whereby lipophilicity and molecular weight decrease with clinical advancement (27, 28), a trend also attributed in part to affect relative drug levels on target.”
- Biology-inspired Microphysiological System Approaches to Solve the Prediction Dilemma of Substance Testing (2016, positive): “A compilation for oral small molecules from four large pharmaceutical companies revealed an attrition rate of 35% in phase II due to efficacy concerns (Waring et al., 2015).”