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  • Integrating Digital Twin Technology in Precision Oncology: A Nursing Paradigm Shift in Pharmacogenomic Care

  • Sharareh Zeighami Mohammadi,1,*
    1. Assistant Professor, Clinical Cares and Health Promotion Research Center, Karaj Branch, Islamic Azad University, Karaj, Iran . E-mail: Zeighami20@yahoo.com Tel: +989125680679


  • Introduction: Precision oncology is rapidly evolving, yet the translation of genomic data into bedside nursing practice remains a critical gap. Pharmacogenomics (PGx) offers insights into inter-individual variability in drug response and toxicity. However, static clinical decision support systems often fail to capture the dynamic physiological changes occurring during chemotherapy. Digital Twin (DT) technology a virtual replica of a patient’s physiological and genetic profile presents a transformative solution. This review explores the integration of DTs into nursing practice to predict chemotherapy-induced toxicities based on individual pharmacogenomic signatures, aiming to optimize patient safety and treatment efficacy.
  • Methods: A systematic narrative review was conducted using databases including PubMed, Scopus, and Web of Science (2020–2026). Search terms included “Digital Twin,” “Pharmacogenomics,” “Precision Nursing,” and “Oncology.” Articles focusing on AI-driven simulation, real-time data integration (IoMT), and nursing-led intervention protocols were synthesized to develop a framework for DT-enabled nursing care.
  • Results: The synthesis highlights that DTs, fueled by multi-omic data (genomics, transcriptomics) and real-time monitoring, significantly enhance the prediction of adverse drug reactions (ADRs). Current models suggest that DTs can simulate drug-gene interactions before administration, allowing nurses to anticipate specific metabolic phenotypes (e.g., CYP450 polymorphisms). The role of the nurse is shifting from task-based care to “System-Decision Nursing,” where the clinician interprets complex DT-generated risk scores to proactively adjust supportive care, hydration protocols, and symptom management strategies. Data indicates that predictive modeling reduces chemotherapy-related toxicity and healthcare costs by enabling preemptive, rather than reactive, interventions.
  • Conclusion: Digital Twins represent the next frontier in nursing oncology. By bridging the gap between molecular diagnostics and bedside care, DTs empower nurses to become precision care coordinators. Future research must prioritize the development of user-friendly DT dashboards tailored for clinical nursing workflows and address the ethical considerations of data privacy. This technological integration is essential to move from “standardized chemotherapy protocols” to truly personalized, patient-centric cancer care.
  • Keywords: Digital Twin; Pharmacogenomics; Precision Oncology; Nursing Informatics; Cancer Care.

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