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System for predicting efficacy of a target-directed drug to treat a disease

A technology for the treatment of diseases and diseases, applied in the direction of drug combinations, drugs or prescriptions, applications, etc., can solve problems such as the inability to accurately predict the results of clinical trials

Active Publication Date: 2018-12-21
F HOFFMANN LA ROCHE & CO AG
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, current tools and techniques cannot accurately predict clinical trial outcomes

Method used

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  • System for predicting efficacy of a target-directed drug to treat a disease
  • System for predicting efficacy of a target-directed drug to treat a disease
  • System for predicting efficacy of a target-directed drug to treat a disease

Examples

Experimental program
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Embodiment Construction

[0156] figure 1 is a line graph 100 depicting the increasing number of publications in the scientific literature for target-disease pairs in the field of targeted cancer therapy. The x-axis represents a time scale covering 20 years, and the y-axis indicates the number of publications per year, including identifiers for the target and the disease for a given target-disease pair. The first appearance of biomedical documentation (eg scientific articles describing target molecules in the context of and in conjunction with a specific disease (eg, a specific cancer type)) was followed by a stream of "continuous research" on the topic. In addition, the drug development process begins, which may include the following stages: target identification / validation (TI / V), to identify targets whose activity modification can treat the disease; identification of lead compounds (IL) (identification of particularly suitable or effective The process of modifying the activity of a target drug or v...

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Abstract

The invention relates to a system for predicting the efficacy of a drug directed at a target to treat a disease, the system comprising a processor configured for: - receiving (602) biomedical documents (214) comprising an identifier of the target and / or of the disease; - specifying (604) an offset time (d) the offset time indicating a time interval ahead of the performing of the prediction; - specifying (606) a time window (706) ending at the begin of the offset time; - extracting (608) a plurality of features (222) selectively from the ones of the received documents published during said timewindow; - providing (610) a classifier (226.3) having been trained on training features (220) extracted from biomedical training documents published within a training time window ending at the beginof the offset time ahead of a moment (OC) the outcome of one or more training studies on training target-disease-pairs was disclosed; - executing (612) the classifier, thereby providing the extractedfeatures as input; - outputting (614) a classification result indicating whether the drug directed at the target can be used to treat the disease.

Description

technical field [0001] The present invention relates to the field of machine learning, and more particularly to the field of predicting the effects of drugs to treat disease. Background technique [0002] Drug development is time-consuming and expensive. Failure in clinical trials, especially late-stage clinical trials, is a major cost driver for pharmaceutical companies. Therefore, a method that provides some insight into the chances of success of a new potential drug could be of great help in deciding whether more resources should be spent on the development and clinical testing of a particular drug. [0003] For example, previous work has been performed on the use of text-mining methods for detecting new “game-changing” technical domains (Reardon, S. 2014: “Text-mining offers clues to success”, Nature 509, 1). In addition, numerous publications have been reported that may indicate the drug's success in clinical trials (Joshi, V. and Milletti, F., 2014, "Quantifying the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G16H50/20
CPCG16H50/20A61P43/00G16H20/10G06N20/00A61B5/4848A61B5/7264
Inventor M·邦德舒斯F·海涅曼C·迈泽尔T·胡贝尔U·莱泽
Owner F HOFFMANN LA ROCHE & CO AG
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