Open AccessPredicting High‐Throughput Screening Results With Scalable Literature‐Based Discovery Methods
Author(s) -
Cohen T,
Widdows D,
Stephan C,
Zinner R,
Kim J,
Rindflesch T,
Davies P
Publication year - 2014
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1038/psp.2014.37
Subject(s) - computer science , repurposing , drug discovery , identification (biology) , scalability , drug repositioning , in silico , high throughput screening , a priori and a posteriori , computational biology , machine learning , data mining , artificial intelligence , bioinformatics , drug , biology , pharmacology , database , ecology , biochemistry , philosophy , botany , epistemology , gene
The identification of new therapeutic uses for existing agents has been proposed as a means to mitigate the escalating cost of drug development. A common approach to such repurposing involves screening libraries of agents for activities against cell lines. In silico methods using knowledge from the biomedical literature have been proposed to constrain the costs of screening by identifying agents that are likely to be effective a priori . However, results obtained with these methods are seldom evaluated empirically. Conversely, screening experiments have been criticized for their inability to reveal the biological basis of their results. In this paper, we evaluate the ability of a scalable literature‐based approach, discovery‐by‐analogy, to identify a small number of active agents within a large library screened for activity against prostate cancer cells. The methods used permit retrieval of the knowledge used to infer their predictions, providing a plausible biological basis for predicted activity. CPT Pharmacometrics Syst. Pharmacol . (2014) 3, e140; doi: 10.1038/psp.2014.37 ; published online 08 October 2014