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.
Description
The Clinical Trials Puzzle: How Network Effects Limit Drug Discovery
%0 Generic
%1 vasan2023clinical
%A Vasan, Kishore
%A Gysi, Deisy
%A Barabasi, Albert-Laszlo
%D 2023
%K clinical_trials innovation medicine network_data_analysis network_dynamics science_as_a_social_process
%T The Clinical Trials Puzzle: How Network Effects Limit Drug Discovery
%U http://arxiv.org/abs/2301.10709
%X 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.
@misc{vasan2023clinical,
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.},
added-at = {2023-09-06T15:10:07.000+0200},
author = {Vasan, Kishore and Gysi, Deisy and Barabasi, Albert-Laszlo},
biburl = {https://www.bibsonomy.org/bibtex/2f9422d710df27f055b9877b88c114822/tabularii},
description = {The Clinical Trials Puzzle: How Network Effects Limit Drug Discovery},
interhash = {43893c0579836e58f9e978a9e3052169},
intrahash = {f9422d710df27f055b9877b88c114822},
keywords = {clinical_trials innovation medicine network_data_analysis network_dynamics science_as_a_social_process},
note = {cite arxiv:2301.10709Comment: manuscript + SI},
timestamp = {2023-09-06T15:10:07.000+0200},
title = {The Clinical Trials Puzzle: How Network Effects Limit Drug Discovery},
url = {http://arxiv.org/abs/2301.10709},
year = 2023
}