Integrative omics in translational pharmacology: A target‐credibility framework for biomarker‐guided therapy

Background Drug discovery remains costly and failure‐prone because molecular associations do not necessarily translate into therapeutically actionable hypotheses. Integrative omics can connect disease biology with target nomination, functional validation, pharmacological assessment, patient stratification and biomarker‐guided therapy. Methods This narrative review was informed by iterative, topic‐guided searches of PubMed, Scopus, Web of Science, ScienceDirect and Google Scholar, updated through September 2026. Searches focused on multi‐omics, target validation, pharmacological assessment, biomarker development and clinical translation. Primary experimental and clinical studies, methodological publications, regulatory guidance and relevant reviews were selected according to their contribution to these topics. No formal risk‐of‐bias assessment or quantitative meta‐analysis was performed. Results The synthesis indicates that genomic and pharmacogenomic evidence supports causal inference and therapeutic directionality; transcriptomic and epigenomic data define dynamic disease states; proteomic and chemoproteomic approaches assess protein function, target engagement and pathway modulation; and metabolomic, lipidomic, single‐cell and spatial approaches provide biochemical, cellular and tissue‐resolved evidence. We propose a multidomain, gate‐based target‐credibility framework that distinguishes causal relevance, biological context, functional validation, tractability, target engagement, pharmacodynamic response, safety, biomarker readiness and clinical implementation. The framework links convergent, unresolved and contradictory evidence to go, refine or stop decisions. Conclusion Integrative omics is most useful when guided by explicit therapeutic questions, intervention‐oriented study designs, independent validation, analytically robust biomarkers and regulatory readiness. The framework is a proposed qualitative decision aid and has not been clinically validated to predict clinical success.