The clinical trials puzzle: How network effects limit drug discovery

Summary

The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model to enhance drug discovery in clinical trials.

Graphical abstract

Highlights

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  The rate of novel drugs has decreased since 2001, entering a drug discovery winter

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  Target selection is by two processes: preferential attachment and local network effects

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  A quantitative model helps unveil the mechanisms capable of boosting drug innovation

Abstract

Medicine; Bioinformatics