用知识图谱整合药物安全数据，揭示激酶抑制剂的副作用规律
Exploring Drug Safety Through Knowledge Graphs: Protein Kinase Inhibitors as a Case Study
- 构建药物-疾病二分图，融合化学、临床与真实世界数据
- 400种激酶抑制剂分析中准确识别出关键靶点群和耐受性差异
- 适合药理研究者用于发现潜在风险，支持药物安全评估
不良药物反应（ADRs）是导致发病率和死亡率的主要原因。现有预测方法多依赖化学相似性、结构化数据库的机器学习或孤立靶点信息，难以有效整合异构且部分非结构化的证据。本文提出一种基于知识图谱的框架，将药物-靶点数据（ChEMBL）、临床试验文献（PubMed）、试验元数据（ClinicalTrials.gov）及上市后安全报告（FAERS）统一为一个加权二分网络，涵盖药物与医学状况。针对400种蛋白激酶抑制剂的应用显示，该网络可实现疗效（HR、PFS、OS）对比、表型与靶点相似性分析，以及通过靶点-不良事件相关性进行不良反应预测。非小细胞肺癌案例研究成功识别出已知及候选药物、靶点社区（ERbB、ALK、VEGF）和耐受性差异。该框架设计为可扩展的互补分析工具，擅长揭示复杂模式，支持假说生成并增强药物警戒。代码与数据公开于 https://github.com/davidjackson99/PKI_KG。
原文摘要 · Abstract (English)
Adverse Drug Reactions (ADRs) are a leading cause of morbidity and mortality. Existing prediction methods rely mainly on chemical similarity, machine learning on structured databases, or isolated target profiles, but often fail to integrate heterogeneous, partly unstructured evidence effectively. We present a knowledge graph-based framework that unifies diverse sources, drug-target data (ChEMBL), clinical trial literature (PubMed), trial metadata (ClinicalTrials.gov), and post-marketing safety reports (FAERS) into a single evidence-weighted bipartite network of drugs and medical conditions. Applied to 400 protein kinase inhibitors, the resulting network enables contextual comparison of efficacy (HR, PFS, OS), phenotypic and target similarity, and ADR prediction via target-to-adverse-event correlations. A non-small cell lung cancer case study correctly highlights established and candidate drugs, target communities (ERbB, ALK, VEGF), and tolerability differences. Designed as an orthogonal, extensible analysis and search tool rather than a replacement for current models, the framework excels at revealing complex patterns, supporting hypothesis generation, and enhancing pharmacovigilance. Code and data are publicly available at https://github.com/davidjackson99/PKI_KG.
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