TY - JOUR
T1 - The clinical trials puzzle
T2 - How network effects limit drug discovery
AU - Vasan, Kishore
AU - Gysi, Deisy Morselli
AU - Barabási, Albert László
N1 - © 2023 The Authors.
PY - 2023/12/15
Y1 - 2023/12/15
N2 - 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.
AB - 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.
KW - Bioinformatics
KW - Medicine
UR - http://www.scopus.com/inward/record.url?scp=85179100429&partnerID=8YFLogxK
U2 - 10.1016/j.isci.2023.108361
DO - 10.1016/j.isci.2023.108361
M3 - Article
C2 - 38146432
AN - SCOPUS:85179100429
SN - 2589-0042
VL - 26
SP - 108361
JO - iScience
JF - iScience
IS - 12
M1 - 108361
ER -