Exploring Drug Safety Through Knowledge Graphs: Protein Kinase Inhibitors as a Case Study

TL;DR

A knowledge graph-based framework that unifies diverse sources, drug-target data, clinical trial literature, trial metadata, and post-marketing safety reports into a single evidence-weighted bipartite network of drugs and medical conditions that excels at revealing complex patterns, supporting hypothesis generation, and enhancing pharmacovigilance.

Abstract

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.

Figures (5)

• Figure 1: Overview of the $N_{Giant}$ component properties and research coverage of PKIs by clinical trial phase.
• Figure 2: Chord graph linking PKI pairings by the number of common related conditions (left) and chord graph linking PKI pairings by the number of common targets (right). A cutoff was introduced (left: > 10 conditions, right: > 1 targets and weight > 0.4) for clarity.
• Figure 3: Scatter plot displaying the HR scores collected from all papers for eight exemplary PKIs. Dots represent single papers and lines represent median HR values. Blue dots indicate a favorable effect and red dots represent a negative effect.
• Figure 4: Directed graph linking NSCLC drugs to the genes they bind to. Node colors are divided into drugs, VEGFR, ALK, ERbB, and other genes.
• Figure 5: Directed graph linking drugs to the genes they bind to, having two types of drugs and targets, respectively. Green nodes and large red nodes represent the drugs/targets from the original set in Figure \ref{['fig:target_graph1']}. Purple nodes are the drugs with an overlapping target profile towards the drugs from the original set and small red nodes are the gene targets only hit by 'purple' drugs. Sorafenib was excluded here for clarity as it shares its targets with many other drugs.