Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks
IntroductionMost drugs fail during development and there is a clear and unmet need for approaches to better understand mechanistically how drugs exert both their intended and adverse effects. Gaining traction in this field is the use of disease data linking genes with pathological phenotypes and com...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1470931/full |
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| author | Elena Díaz-Santiago Aurelio A. Moya-García Jesús Pérez-García Raquel Yahyaoui Raquel Yahyaoui Christine Orengo Florencio Pazos James R. Perkins James R. Perkins James R. Perkins Juan A. G. Ranea Juan A. G. Ranea Juan A. G. Ranea Juan A. G. Ranea |
| author_facet | Elena Díaz-Santiago Aurelio A. Moya-García Jesús Pérez-García Raquel Yahyaoui Raquel Yahyaoui Christine Orengo Florencio Pazos James R. Perkins James R. Perkins James R. Perkins Juan A. G. Ranea Juan A. G. Ranea Juan A. G. Ranea Juan A. G. Ranea |
| author_sort | Elena Díaz-Santiago |
| collection | DOAJ |
| description | IntroductionMost drugs fail during development and there is a clear and unmet need for approaches to better understand mechanistically how drugs exert both their intended and adverse effects. Gaining traction in this field is the use of disease data linking genes with pathological phenotypes and combining this with drugtarget interaction data.MethodsWe introduce methodology to associate drugs with effects, both intended and adverse, using a tripartite network approach that combines drug-target and target-phenotype data, in which targets can be represented as proteins and protein domains.ResultsWe were able to detect associations for over 140,000 ChEMBL drugs and 3,800 phenotypes, represented as Human Phenotype Ontology (HPO) terms. The overlap of these results with the SIDER databases of known drug side effects was up to 10 times higher than random, depending on the target type, disease database and score threshold used. In terms of overlap with drug-phenotype pairs extracted from the literature, the performance of our methodology was up to 17.47 times greater than random. The top results include phenotype-drug associations that represent intended effects, particularly for cancers such as chronic myelogenous leukemia, which was linked with nilotinib. They also include adverse side effects, such as blurred vision being linked with tetracaine.DiscussionThis work represents an important advance in our understanding of how drugs cause intended and adverse side effects through their action on disease causing genes and has potential applications for drug development and repositioning. |
| format | Article |
| id | doaj-art-71e915d508504057b71cfb67a4083833 |
| institution | Kabale University |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-71e915d508504057b71cfb67a40838332025-01-22T07:11:00ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-01-011510.3389/fphar.2024.14709311470931Better understanding the phenotypic effects of drugs through shared targets in genetic disease networksElena Díaz-Santiago0Aurelio A. Moya-García1Jesús Pérez-García2Raquel Yahyaoui3Raquel Yahyaoui4Christine Orengo5Florencio Pazos6James R. Perkins7James R. Perkins8James R. Perkins9Juan A. G. Ranea10Juan A. G. Ranea11Juan A. G. Ranea12Juan A. G. Ranea13Department of Molecular Biology and Biochemistry, University of Malaga, Malaga, SpainDepartment of Molecular Biology and Biochemistry, University of Malaga, Malaga, SpainDepartment of Molecular Biology and Biochemistry, University of Malaga, Malaga, SpainLaboratory of Inherited Metabolic Diseases and Newborn Screening, Malaga Regional University Hospital, Malaga, SpainInstituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Malaga, SpainDepartment of Structural and Molecular Biology, University College London, London, United KingdomComputational Systems Biology Group, Systems Biology Department, National Centre for Biotechnology (CNB-CSIC), Madrid, SpainDepartment of Molecular Biology and Biochemistry, University of Malaga, Malaga, SpainInstituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Malaga, SpainCIBER de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, SpainDepartment of Molecular Biology and Biochemistry, University of Malaga, Malaga, SpainInstituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Malaga, SpainCIBER de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, SpainSpanish National Bioinformatics Institute (INB/ELIXIR-ES), Instituto de Salud Carlos III (ISCIII), Madrid, SpainIntroductionMost drugs fail during development and there is a clear and unmet need for approaches to better understand mechanistically how drugs exert both their intended and adverse effects. Gaining traction in this field is the use of disease data linking genes with pathological phenotypes and combining this with drugtarget interaction data.MethodsWe introduce methodology to associate drugs with effects, both intended and adverse, using a tripartite network approach that combines drug-target and target-phenotype data, in which targets can be represented as proteins and protein domains.ResultsWe were able to detect associations for over 140,000 ChEMBL drugs and 3,800 phenotypes, represented as Human Phenotype Ontology (HPO) terms. The overlap of these results with the SIDER databases of known drug side effects was up to 10 times higher than random, depending on the target type, disease database and score threshold used. In terms of overlap with drug-phenotype pairs extracted from the literature, the performance of our methodology was up to 17.47 times greater than random. The top results include phenotype-drug associations that represent intended effects, particularly for cancers such as chronic myelogenous leukemia, which was linked with nilotinib. They also include adverse side effects, such as blurred vision being linked with tetracaine.DiscussionThis work represents an important advance in our understanding of how drugs cause intended and adverse side effects through their action on disease causing genes and has potential applications for drug development and repositioning.https://www.frontiersin.org/articles/10.3389/fphar.2024.1470931/fulldrug effectsside effectsadverse effectsintended effectsnetworksdiseases |
| spellingShingle | Elena Díaz-Santiago Aurelio A. Moya-García Jesús Pérez-García Raquel Yahyaoui Raquel Yahyaoui Christine Orengo Florencio Pazos James R. Perkins James R. Perkins James R. Perkins Juan A. G. Ranea Juan A. G. Ranea Juan A. G. Ranea Juan A. G. Ranea Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks Frontiers in Pharmacology drug effects side effects adverse effects intended effects networks diseases |
| title | Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks |
| title_full | Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks |
| title_fullStr | Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks |
| title_full_unstemmed | Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks |
| title_short | Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks |
| title_sort | better understanding the phenotypic effects of drugs through shared targets in genetic disease networks |
| topic | drug effects side effects adverse effects intended effects networks diseases |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2024.1470931/full |
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