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Combining Data to Find New Indications, Develop New Targets & Predict Adverse Events

Posted on August 21st, 2017 by in Pharma R&D

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High-quality compound bioactivity data and information about biological pathways and potential drug targets is more readily available than ever—and in enormous amounts that are often daunting to researchers working in drug discovery. Access is great, but how do you find the time to search and analyze all of that data? If anything, it can almost make the discovery process seem more difficult.

However, the industry has risen to the challenge, developing analytical methods that make it easier to cope with the wealth of information now available. In silico screening of compounds to determine their structure-activity relationships relative to various targets is one such method, and modeling pathways to see how different compounds could affect them is another. These techniques make it possible for researchers to glean needed insights from the data they find.

All of this information, along with the tools to analyze it, now empowers researchers to combine data in revelatory ways. In particular, combining pathway and bioactivity data to create predictive models of compound behavior can generate answers that support decision-making during the early stages of discovery. This technique for assessing a compound’s potential is proving to be effective in a variety of applications, including 1) finding new indications for existing drugs, 2) developing new target ideas and 3) predicting the adverse event potential for a compound.

In the new white paper “Combining Pathway and Bioactivity Data for New Insights,” all three of these applications are explained, along with a breakdown of each method and some interesting case study examples. It is easy to see how powerful this data combination can be, and, fortunately, modern text mining tools make this type of insightful analysis possible.

Read the white paper to find out more.


All opinions shared in this post are the author’s own.

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