The Clinical Trials Puzzle: How Network Effects Limit Drug Discovery

TL;DR

This study analyzes four decades of clinical trial data to reveal that network effects and preference for known targets limit drug discovery, and proposes a model to guide exploration toward novel proteins.

Contribution

It uncovers network-based mechanisms limiting target discovery and develops a model to enhance exploration of underexplored proteins in drug discovery.

Findings

Over 96% of trials focus on known targets

It would take 170 years to target all druggable proteins

Network-based strategies can expand exploration to new proteins

Abstract

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 of drug discovery. We demonstrate that the model is able to accurately recreate the exploration patterns observed in clinical trials. Most importantly, we show that a network-based search strategy can widen the scope of drug discovery by guiding exploration to novel proteins that are part of under explored regions in the human interactome.