Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, Chapel Hill, NC, 27599, USA.
Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, Chapel Hill, NC, 27599, USA.
Drug Saf. 2018 Nov;41(11):1059-1072. doi: 10.1007/s40264-018-0688-5.
Given that adverse drug effects (ADEs) have led to post-market patient harm and subsequent drug withdrawal, failure of candidate agents in the drug development process, and other negative outcomes, it is essential to attempt to forecast ADEs and other relevant drug-target-effect relationships as early as possible. Current pharmacologic data sources, providing multiple complementary perspectives on the drug-target-effect paradigm, can be integrated to facilitate the inference of relationships between these entities.
This study aims to identify both existing and unknown relationships between chemicals (C), protein targets (T), and ADEs (E) based on evidence in the literature.
Cheminformatics and data mining approaches were employed to integrate and analyze publicly available clinical pharmacology data and literature assertions interrelating drugs, targets, and ADEs. Based on these assertions, a C-T-E relationship knowledge base was developed. Known pairwise relationships between chemicals, targets, and ADEs were collected from several pharmacological and biomedical data sources. These relationships were curated and integrated according to Swanson's paradigm to form C-T-E triangles. Missing C-E edges were then inferred as C-E relationships.
Unreported associations between drugs, targets, and ADEs were inferred, and inferences were prioritized as testable hypotheses. Several C-E inferences, including testosterone → myocardial infarction, were identified using inferences based on the literature sources published prior to confirmatory case reports. Timestamping approaches confirmed the predictive ability of this inference strategy on a larger scale.
The presented workflow, based on free-access databases and an association-based inference scheme, provided novel C-E relationships that have been validated post hoc in case reports. With refinement of prioritization schemes for the generated C-E inferences, this workflow may provide an effective computational method for the early detection of potential drug candidate ADEs that can be followed by targeted experimental investigations.
由于药物不良反应 (ADE) 导致了上市后患者伤害和随后的药物撤市，候选药物在药物开发过程中的失败，以及其他负面结果，因此尽早尝试预测 ADE 和其他相关药物-靶标-效应关系至关重要。当前的药理学数据源提供了药物-靶标-效应范式的多个互补视角，可以进行整合，以促进这些实体之间关系的推断。
本研究旨在根据文献中的证据，识别化学物质 (C)、蛋白质靶标 (T) 和 ADE 之间现有的和未知的关系。
采用化学信息学和数据挖掘方法，整合和分析公开的临床药理学数据和文献中药物、靶标和 ADE 之间的关联。基于这些关联，开发了一个 C-T-E 关系知识库。从几个药理学和生物医学数据来源收集了已知的化学物质、靶标和 ADE 之间的成对关系。这些关系根据 Swanson 的范式进行了整理和整合，形成了 C-T-E 三角形。然后推断缺失的 C-E 边作为 C-E 关系。
推断出了药物、靶标和 ADE 之间未报告的关联，并将推断作为可测试的假设进行了优先级排序。使用基于发表于确认病例报告之前的文献来源的推断，识别出了几个 C-E 推断，包括睾酮→心肌梗死。时间戳方法在更大的范围内证实了这种推断策略的预测能力。
所提出的基于免费访问数据库和基于关联的推断方案的工作流程，提供了新的 C-E 关系，这些关系已在病例报告中事后验证。通过细化生成的 C-E 推断的优先级排序方案，该工作流程可能为早期检测潜在药物候选 ADE 提供一种有效的计算方法，随后可以进行有针对性的实验研究。
