Caution when using network partners for target identification in drug discovery
Summary: Identifying novel, high-yield drug targets is challenging and often results in a high failure rate. However, recent data indicate that leveraging human genetic evidence to identify and validate these targets significantly increases the likelihood of success in drug development. Two recent p...
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| Language: | English |
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Elsevier
2025-04-01
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| Series: | HGG Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666247725000120 |
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| author | Dandan Tan Yiheng Chen Yann Ilboudo Kevin Y.H. Liang Guillaume Butler-Laporte J. Brent Richards |
| author_facet | Dandan Tan Yiheng Chen Yann Ilboudo Kevin Y.H. Liang Guillaume Butler-Laporte J. Brent Richards |
| author_sort | Dandan Tan |
| collection | DOAJ |
| description | Summary: Identifying novel, high-yield drug targets is challenging and often results in a high failure rate. However, recent data indicate that leveraging human genetic evidence to identify and validate these targets significantly increases the likelihood of success in drug development. Two recent papers from Open Targets claimed that around half of US Food and Drug Administration-approved drugs had targets with direct human genetic evidence. By expanding target identification to include protein network partners—molecules in physical contact—the proportion of drug targets with genetic evidence support increased to two-thirds. However, the efficacy of using these network partners for target identification was not formally tested. To address this, we tested the approach on a list of robust positive control genes. We used the IntAct database to find physically interacting proteins of genes identified by exome-wide association studies (ExWASs), genome-wide association studies (GWASs) combined with a locus-to-gene mapping algorithm called the Effector Index, and Genetic Priority Score (GPS), which integrated eight genetic features with drug indications from the Open Targets and SIDER databases. We assessed how accurately including interacting genes with the ExWAS-, Effector Index-, and GPS-selected genes identified positive controls, focusing on precision, sensitivity, and specificity. Our results indicated that although molecular interactions led to higher sensitivity in identifying positive control genes, their practical application is limited by low precision. Expanding genetically identified targets to include network partners using IntAct did not increase the likelihood of identifying drug targets across the 412 tested traits, suggesting that such results should be interpreted with caution. |
| format | Article |
| id | doaj-art-d30535328f1940479246d45a7623c538 |
| institution | Kabale University |
| issn | 2666-2477 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | HGG Advances |
| spelling | doaj-art-d30535328f1940479246d45a7623c5382025-02-09T05:01:26ZengElsevierHGG Advances2666-24772025-04-0162100409Caution when using network partners for target identification in drug discoveryDandan Tan0Yiheng Chen1Yann Ilboudo2Kevin Y.H. Liang3Guillaume Butler-Laporte4J. Brent Richards5Quantitative Life Sciences Program, McGill University, Montréal, QC, Canada; Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, CanadaLady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada; Department of Human Genetics, McGill University, Montréal, QC, Canada; 5Prime Sciences, Montréal, QC, CanadaLady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, CanadaQuantitative Life Sciences Program, McGill University, Montréal, QC, Canada; Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, CanadaLady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada; Division of Infectious Diseases, McGill University, Montréal, QC, CanadaLady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada; Department of Human Genetics, McGill University, Montréal, QC, Canada; 5Prime Sciences, Montréal, QC, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada; Department of Medicine, McGill University, Montréal, QC, Canada; Department of Twin Research, King’s College London, London, UK; Corresponding authorSummary: Identifying novel, high-yield drug targets is challenging and often results in a high failure rate. However, recent data indicate that leveraging human genetic evidence to identify and validate these targets significantly increases the likelihood of success in drug development. Two recent papers from Open Targets claimed that around half of US Food and Drug Administration-approved drugs had targets with direct human genetic evidence. By expanding target identification to include protein network partners—molecules in physical contact—the proportion of drug targets with genetic evidence support increased to two-thirds. However, the efficacy of using these network partners for target identification was not formally tested. To address this, we tested the approach on a list of robust positive control genes. We used the IntAct database to find physically interacting proteins of genes identified by exome-wide association studies (ExWASs), genome-wide association studies (GWASs) combined with a locus-to-gene mapping algorithm called the Effector Index, and Genetic Priority Score (GPS), which integrated eight genetic features with drug indications from the Open Targets and SIDER databases. We assessed how accurately including interacting genes with the ExWAS-, Effector Index-, and GPS-selected genes identified positive controls, focusing on precision, sensitivity, and specificity. Our results indicated that although molecular interactions led to higher sensitivity in identifying positive control genes, their practical application is limited by low precision. Expanding genetically identified targets to include network partners using IntAct did not increase the likelihood of identifying drug targets across the 412 tested traits, suggesting that such results should be interpreted with caution.http://www.sciencedirect.com/science/article/pii/S2666247725000120protein interactionnetwork partnersdrug discoveryIntActStringdb |
| spellingShingle | Dandan Tan Yiheng Chen Yann Ilboudo Kevin Y.H. Liang Guillaume Butler-Laporte J. Brent Richards Caution when using network partners for target identification in drug discovery HGG Advances protein interaction network partners drug discovery IntAct Stringdb |
| title | Caution when using network partners for target identification in drug discovery |
| title_full | Caution when using network partners for target identification in drug discovery |
| title_fullStr | Caution when using network partners for target identification in drug discovery |
| title_full_unstemmed | Caution when using network partners for target identification in drug discovery |
| title_short | Caution when using network partners for target identification in drug discovery |
| title_sort | caution when using network partners for target identification in drug discovery |
| topic | protein interaction network partners drug discovery IntAct Stringdb |
| url | http://www.sciencedirect.com/science/article/pii/S2666247725000120 |
| work_keys_str_mv | AT dandantan cautionwhenusingnetworkpartnersfortargetidentificationindrugdiscovery AT yihengchen cautionwhenusingnetworkpartnersfortargetidentificationindrugdiscovery AT yannilboudo cautionwhenusingnetworkpartnersfortargetidentificationindrugdiscovery AT kevinyhliang cautionwhenusingnetworkpartnersfortargetidentificationindrugdiscovery AT guillaumebutlerlaporte cautionwhenusingnetworkpartnersfortargetidentificationindrugdiscovery AT jbrentrichards cautionwhenusingnetworkpartnersfortargetidentificationindrugdiscovery |