Frontiers in pharmacology · 2025
Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Díaz-Santiago Elena, Moya-García Aurelio A, Pérez-García Jesús, Yahyaoui Raquel, Orengo Christine, Pazos Florencio, Perkins James R, Ranea Juan A G
Аннотация
INTRODUCTION: Most 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.
METHODS: We 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.
RESULTS: We 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.
DISCUSSION: This 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.
METHODS: We 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.
RESULTS: We 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.
DISCUSSION: This 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.