Patient-Specific Organ-on-Chip Models for Surgery: Transforming Preclinical Evaluation and Personalized Surgical Strategies

Abstract

Patient-specific organ-on-chip (OoC) models represent a significant leap forward in personalized medicine, particularly in the context of surgical planning and evaluation. These microfluidic platforms, populated with patient-derived cells, recapitulate organ-level physiology and disease processes with high fidelity, enabling precise modeling of surgical interventions and responses. This review synthesizes the latest scientific findings on the utility of patient-specific OoC systems in surgery, detailing their development, clinical implications, and future directions. Emphasis is placed on the mechanisms by which these platforms enhance understanding of surgical outcomes, optimize preoperative planning, and predict patient-specific risks and benefits, thereby contributing to safer, more effective, and tailored surgical care.

Introduction

Surgical outcomes remain highly variable due to individual differences in anatomy, physiology, and disease state. Traditional preclinical models often lack the granularity to predict patient-specific responses, particularly in complex or rare cases. Organ-on-chip (OoC) technology, leveraging patient-derived tissues and advanced microengineering, offers a transformative approach for simulating human organ function and surgical interventions. By enabling real-time, physiologically relevant modeling of tissue responses, patient-specific OoC models provide a powerful platform for preclinical evaluation, risk stratification, and personalized surgical strategy development. This review addresses the scientific underpinnings, current clinical relevance, and translational potential of patient-specific OoC systems in contemporary surgical practice.

Epidemiology / Disease Burden

The global burden of surgically treatable diseases remains substantial, with millions of patients facing complex procedures each year. Variability in surgical outcomes is influenced by patient heterogeneity, comorbidities, and underlying pathophysiology. Traditional models, including animal studies and static cell cultures, often fail to capture the nuanced interplay of patient-specific factors, contributing to suboptimal prediction of surgical risks and benefits. This unmet need underscores the importance of advanced preclinical models that can more accurately reflect individual disease states and responses to interventions. The integration of OoC technology into surgical research addresses this gap and holds promise for reducing postoperative complications, optimizing resource utilization, and improving overall patient outcomes on a global scale.

Pathophysiology

OoC platforms are engineered to mimic the dynamic microenvironments of human organs, incorporating key elements such as vascular flow, tissue-tissue interfaces, and biomechanical forces. By seeding these devices with patient-derived cells—including induced pluripotent stem cells (iPSCs) or primary tissue samples—investigators can recapitulate both healthy and diseased states at a level unattainable with conventional models. These systems facilitate mechanistic explorations of disease progression, tissue regeneration, and surgical trauma, capturing inter-individual variability in cellular signaling, immune responses, and repair mechanisms. Importantly, patient-specific OoC models allow for the assessment of pathophysiological responses to surgical manipulations, such as ischemia-reperfusion injury, tissue resection, and anastomosis, providing insights critical for personalizing surgical techniques.

Risk Factors

Risk assessment for surgical interventions is traditionally based on population-level data and generalized scoring systems, which may not account for unique patient susceptibilities. Patient-specific OoC models enable the direct evaluation of individual risk factors, including genetic predispositions, comorbidities, and pharmacologic sensitivities. For instance, OoC platforms seeded with cells from patients with diabetes, chronic kidney disease, or cardiovascular comorbidities can reveal distinct patterns of tissue healing, inflammation, and susceptibility to infection following simulated surgical procedures. This mechanistic insight supports more nuanced risk stratification and individualized perioperative management.

Clinical Features

Clinical features relevant to surgical planning—such as tissue architecture, vascular integrity, and local immune milieu—can be faithfully recapitulated in patient-specific OoC systems. These models facilitate the investigation of intraoperative variables (e.g., resection margins, tissue viability) and postoperative outcomes (e.g., wound healing, fibrosis, infection) in a controlled and patient-relevant context. Furthermore, OoC platforms can be engineered to model specific disease phenotypes, such as cirrhotic liver, fibrotic lung, or neoplastic tissues, thereby informing surgical decision-making in complex cases where standard preclinical models offer limited insight.

Diagnosis

In the diagnostic workflow, OoC models offer complementary information to imaging, histopathology, and genetic testing. By recreating patient-specific disease microenvironments, these platforms facilitate functional assays—such as drug response profiling and tissue viability assessments—that can inform both diagnosis and surgical planning. For example, in oncology, tumor-on-chip models derived from individual patients enable preoperative testing of chemotherapeutic sensitivity, which can guide neoadjuvant treatment choices and surgical margins. In transplant surgery, organ-specific OoC systems can predict graft viability and recipient responses, supporting more precise donor-recipient matching and perioperative risk assessment.

Treatment & Management

Patient-specific OoC models have direct implications for optimizing treatment strategies before, during, and after surgery. These systems allow for the evaluation of surgical techniques (e.g., energy-based resection, anastomotic methods), biomaterial compatibility (e.g., grafts, meshes), and perioperative pharmacotherapies (e.g., antibiotics, immunosuppressants) in a patient-tailored fashion. By simulating surgical trauma and subsequent healing responses, OoC platforms help identify the most effective interventions for individual patients, minimizing complications and enhancing recovery. Moreover, these models facilitate the iterative refinement of surgical protocols in response to unique patient biology, advancing the paradigm of precision surgery.

Recent Advances / Emerging Therapies

Recent advances in microfabrication, biosensor integration, and high-content imaging have markedly enhanced the capability of patient-specific OoC models. Multi-organ chips now enable the study of inter-organ crosstalk relevant to complex surgical scenarios, such as multi-visceral resections or organ transplantation. Integration with artificial intelligence and machine learning platforms has facilitated the analysis of large datasets generated from OoC experiments, supporting predictive modeling of surgical outcomes and complications. Emerging therapies—such as gene editing, regenerative biomaterials, and cell-based therapies—can be evaluated in patient-specific OoC models to assess safety and efficacy in a clinically relevant context prior to first-in-human trials.

Guideline Recommendations

Although formal guideline integration remains in its infancy, leading surgical and translational research organizations increasingly recognize the value of OoC technology for preclinical and translational studies. The FDA and EMA have endorsed the use of OoC models in drug development, and similar acceptance is growing in the surgical domain. Best practices recommend the incorporation of patient-derived OoC data in multidisciplinary case conferences, especially for high-risk or novel interventions. Ongoing clinical trials are evaluating the prospective impact of OoC-guided surgical planning on postoperative outcomes, with early results suggesting improved risk prediction and individualized care pathways.

Conclusion

Patient-specific organ-on-chip models are poised to revolutionize surgical planning, risk assessment, and perioperative management by enabling high-fidelity, individualized simulation of human tissue responses. These platforms bridge the translational gap between bench and bedside, offering mechanistic insights and predictive data that inform safer, more effective, and personalized surgical care. Continued integration of OoC technology into clinical research, guideline development, and routine practice holds significant promise for improving surgical outcomes and advancing the field of precision surgery.