The relationship between a biomarker, the clinical endpoint it is proposed to predict, and the indication in which it has been validated is one of the most complex and commercially important knowledge structures in clinical development. A biomarker's status — exploratory, reasonably likely surrogate, validated surrogate — determines the feasibility of accelerated approval, the design of biomarker-stratified trials, and the label claims that can be made. Managing this knowledge without a formal semantic layer means managing it in slide decks and spreadsheets — a fragile, inconsistently maintained, and analytically limited approach.
The Biomarker Knowledge Graph
A biomarker knowledge graph represents biomarkers as nodes connected to the molecular targets they measure, the pathways those targets are part of, the disease stages in which they are informative, and the clinical outcomes they predict — along with the evidence quality, study design, and patient population in which each relationship was established. This representation enables queries that are not possible with document-based knowledge management: "identify all validated surrogate endpoints for overall survival in B-cell malignancies", "which of our ongoing trials include a biomarker for primary endpoints that has regulatory precedent for accelerated approval", "what is the evidentiary basis for the progression-free survival endpoint in our next indication filing?"
Programme Strategy Applications
The biomarker knowledge graph supports programme strategy in several ways. Indication sequencing decisions — which indication to pursue first, which to develop in parallel, which to defer — depend on understanding where the biomarker evidence is strongest, where the competitive landscape is most favourable, and where regulatory pathways are most clearly defined. These analyses, which currently require days of manual literature and competitive intelligence review, can be reduced to structured graph queries when the underlying evidence is captured in a semantic layer.
Trial Design and Regulatory Engagement
For clinical trial design, the biomarker knowledge graph informs enrichment strategies, eligibility criterion design, and endpoint selection. For regulatory engagement, it provides the evidence base for discussions about endpoint qualification, accelerated approval pathways, and post-marketing study commitments. In both contexts, the ability to present structured, traceable evidence about biomarker validation status — rather than narrative literature summaries — substantially improves the quality and efficiency of scientific discussions.
