%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 }