The clinical trials puzzle: How network effects limit drug discovery

Kishore Vasan; Deisy Morselli Gysi; Albert-László Barabási

doi:10.1016/j.isci.2023.108361

The clinical trials puzzle: How network effects limit drug discovery

iScience. 2023 Oct 30;26(12):108361. doi: 10.1016/j.isci.2023.108361. eCollection 2023 Dec 15.

Authors

Kishore Vasan¹, Deisy Morselli Gysi^{1

2

3

4}, Albert-László Barabási^{1

3

5}

Affiliations

¹ Network Science Institute, Northeastern University, Boston, MA, USA.
² Department of Statistics, Federal University of Parana, Curtiba, Brazil.
³ Department of Veteran Affairs, Boston, MA, USA.
⁴ Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁵ Department of Data and Network Science, Central European University, Budapest, Hungary.

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 to enhance drug discovery in clinical trials.

Keywords: Bioinformatics; Medicine.