The clinical trials puzzle: How network effects limit drug discovery
Summary: 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 mechanisti...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
|
| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223024380 |
| _version_ | 1797388760198938624 |
|---|---|
| author | Kishore Vasan Deisy Morselli Gysi Albert-László Barabási |
| author_facet | Kishore Vasan Deisy Morselli Gysi Albert-László Barabási |
| author_sort | Kishore Vasan |
| collection | DOAJ |
| description | Summary: 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. |
| first_indexed | 2024-03-08T22:45:29Z |
| format | Article |
| id | doaj.art-a9883be4113243f1811436051459faec |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-08T22:45:29Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-a9883be4113243f1811436051459faec2023-12-17T06:40:29ZengElsevieriScience2589-00422023-12-012612108361The clinical trials puzzle: How network effects limit drug discoveryKishore Vasan0Deisy Morselli Gysi1Albert-László Barabási2Network Science Institute, Northeastern University, Boston, MA, USANetwork Science Institute, Northeastern University, Boston, MA, USA; Department of Statistics, Federal University of Parana, Curtiba, Brazil; Department of Veteran Affairs, Boston, MA, USA; Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USANetwork Science Institute, Northeastern University, Boston, MA, USA; Department of Veteran Affairs, Boston, MA, USA; Department of Data and Network Science, Central European University, Budapest, Hungary; Corresponding authorSummary: 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.http://www.sciencedirect.com/science/article/pii/S2589004223024380MedicineBioinformatics |
| spellingShingle | Kishore Vasan Deisy Morselli Gysi Albert-László Barabási The clinical trials puzzle: How network effects limit drug discovery iScience Medicine Bioinformatics |
| title | The clinical trials puzzle: How network effects limit drug discovery |
| title_full | The clinical trials puzzle: How network effects limit drug discovery |
| title_fullStr | The clinical trials puzzle: How network effects limit drug discovery |
| title_full_unstemmed | The clinical trials puzzle: How network effects limit drug discovery |
| title_short | The clinical trials puzzle: How network effects limit drug discovery |
| title_sort | clinical trials puzzle how network effects limit drug discovery |
| topic | Medicine Bioinformatics |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223024380 |
| work_keys_str_mv | AT kishorevasan theclinicaltrialspuzzlehownetworkeffectslimitdrugdiscovery AT deisymorselligysi theclinicaltrialspuzzlehownetworkeffectslimitdrugdiscovery AT albertlaszlobarabasi theclinicaltrialspuzzlehownetworkeffectslimitdrugdiscovery AT kishorevasan clinicaltrialspuzzlehownetworkeffectslimitdrugdiscovery AT deisymorselligysi clinicaltrialspuzzlehownetworkeffectslimitdrugdiscovery AT albertlaszlobarabasi clinicaltrialspuzzlehownetworkeffectslimitdrugdiscovery |