Recent advances in precision medicine for individualized immunosuppression

Smart transplants: emerging role of nanotechnology and big data in kidney and islet transplantation, a frontier in precision medicine

Kidney and islet transplantation has revolutionized the management of renal failure and diabetes. Transplantation is considered as excellent therapeutic intervention for most suitable patients. While advancements in the surgical aspects, immunosuppression and outcomes have potentially plateaued, new technologies have developed which could enhance transplantation with benefits to patients and clinical teams alike. The science of nanotechnology and big data advancements are two such technologies, collectively paving the way for smarter transplantation solutions. Nanotechnology offers novel strategies to overcome critical challenges, including organ preservation, ischemia-reperfusion injury and immune modulation. Innovations such as nanoparticle-based drug delivery systems, biocompatible encapsulation technologies for islet transplants, and implantable artificial kidneys are redefining the standards of care. Meanwhile, big data analytics harness vast datasets to optimize donor-recipient matching, refine predictive models for post-transplant outcomes, and personalize therapeutic regimens. Integrating these technologies forms a synergistic framework where nanotechnology enhances therapeutic precision and big data provides actionable insights, enabling clinicians to adopt proactive, patient-specific strategies. By addressing unmet needs and leveraging the combined potential of nanotechnology and big data, this transformative approach promises to improve graft survival, functionality, and overall patient outcomes, marking a paradigm shift in transplantation medicine. These developments will also be accelerated with integration of the rapidly advancing science of artificial intelligence.

The impact of artificial intelligence and machine learning in organ retrieval and transplantation: A comprehensive review

This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks. Predictive analytics further enable personalized treatment plans by forecasting organ rejection, infection risks, and patient recovery trajectories, thereby supporting early intervention strategies and long-term patient management. AI also optimizes operational efficiency within transplant centers by predicting organ demand, scheduling surgeries efficiently, and managing inventory to minimize wastage, thus streamlining workflows and enhancing resource allocation. Despite these advancements, several challenges hinder the widespread adoption of AI and ML in organ transplantation. These include data privacy concerns, regulatory compliance issues, interoperability across healthcare systems, and the need for rigorous clinical validation of AI models. Addressing these challenges is essential to ensuring the reliable, safe, and ethical use of AI in clinical settings. Future directions for AI and ML in transplantation medicine include integrating genomic data for precision immunosuppression, advancing robotic surgery for minimally invasive procedures, and developing AI-driven remote monitoring systems for continuous post-transplantation care. Collaborative efforts among clinicians, researchers, and policymakers are crucial to harnessing the full potential of AI and ML, ultimately transforming transplantation medicine and improving patient outcomes while enhancing healthcare delivery efficiency.

Identification of urinary metabolites correlated with tacrolimus levels through high-precision liquid chromatography-mass spectrometry and machine learning algorithms in kidney transplant patients

Background and aim

Tacrolimus, a widely used immunosuppressive drug in kidney transplant recipients, exhibits a narrow therapeutic window necessitating careful monitoring of its concentration to balance efficacy and minimize dose-related toxic effects. Although essential, this approach is not optimal, and tacrolinemia, even in the therapeutic interval, might be associated with toxicity and rejection within range. This study aimed to identify specific urinary metabolites associated with tacrolimus levels in kidney transplant patients using a combination of serum high-precision liquid chromatography-mass spectrometry (HPLC-MS) and machine learning algorithms.

Methods

A cohort of 42 kidney transplant patients, comprising 19 individuals with high tacrolimus levels (>8 ng/mL) and 23 individuals with low tacrolimus levels (<5 ng/mL), were included in the analysis. Urinary samples were subjected to HPLC-MS analysis, enabling comprehensive metabolite profiling across the study cohort. Additionally, tacrolimus concentrations were quantified using established clinical assays.

Results

Through an extensive analysis of the HPLC-MS data, a panel of five metabolites were identified that exhibited a significant correlation with tacrolimus levels (Valeryl carnitine, Glycyl-tyrosine, Adrenosterone, LPC 18:3 and 6-methylprednisolone). Machine learning algorithms were then employed to develop a predictive model utilizing the identified metabolites as features. The logistic regression model achieved an area under the curve of 0.810, indicating good discriminatory power and classification accuracy of 0.690.

Conclusions

This study demonstrates the potential of integrating HPLC-MS metabolomics with machine learning algorithms to identify urinary metabolites associated with tacrolimus levels. The identified metabolites are promising biomarkers for monitoring tacrolimus therapy, aiding in dose optimization and personalized treatment approaches.

A World Update of Progress in Lower Extremity Transplantation: What's Hot and What's Not

ABSTRACT

The field of vascularized composite allotransplantation (VCA) is the new frontier of solid organ transplantation (SOT). VCA spans life-enhancing/life-changing procedures such as upper extremity, craniofacial (including eye), laryngeal, tracheal, abdominal wall, penis, and lower extremity transplants. VCAs such as uterus transplants are life giving unlike any other SOT. Of all VCAs that have shown successful intermediate- to long-term graft survival with functional and immunologic outcomes, lower extremity VCAs have remained largely underexplored. Lower extremity transplantation (LET) can offer patients with improved function compared to the use of conventional prostheses, reducing concerns of phantom limb pain and stump complications, and offer an option for eligible amputees that either fail prosthetic rehabilitation or do not adapt to prosthetics. Nevertheless, these benefits must be carefully weighed against the risks of VCA, which are not trivial, including the adverse effects of lifelong immunosuppression, extremely challenging perioperative care, and delayed nerve regeneration. There have been 5 lower extremity transplants to date, ranging from unilateral or bilateral to quadrimembral, progressively increasing in risk that resulted in fatalities in 3 of the 5 cases, emphasizing the inherent risks. The advantages of LET over prosthetics must be carefully weighed, demanding rigorous candidate selection for optimal outcomes.

Cell/Bacteria-Based Bioactive Materials for Cancer Immune Modulation and Precision Therapy

Numerous clinical trials for cancer precision medicine research are limited due to the drug resistance, side effects, and low efficacy. Unsatisfactory outcomes are often caused by complex physiologic barriers and abnormal immune events in tumors, such as tumor target alterations and immunosuppression. Cell/bacteria-derived materials with unique bioactive properties have emerged as attractive tools for personalized therapy in cancer. Naturally derived bioactive materials, such as cell and bacterial therapeutic agents with native tropism or good biocompatibility, can precisely target tumors and effectively modulate immune microenvironments to inhibit tumors. Here, the recent advances in the development of cell/bacteria-based bioactive materials for immune modulation and precision therapy in cancer are summarized. Cell/bacterial constituents, including cell membranes, bacterial vesicles, and other active substances have inherited their unique targeting properties and antitumor capabilities. Strategies for engineering living cell/bacteria to overcome complex biological barriers and immunosuppression to promote antitumor efficacy are also summarized. Moreover, past and ongoing trials involving personalized bioactive materials and promising agents such as cell/bacteria-based micro/nano-biorobotics are further discussed, which may become another powerful tool for treatment in the near future.

Machine Learning Applications in Solid Organ Transplantation and Related Complications

The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.

Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant

Advances in systems immunology, such as new biomarkers, offer the potential for highly personalized immunosuppression regimens that could improve patient outcomes. In the future, integrating all of this information with other patient history data will likely have to rely on artificial intelligence (AI). AI agents can help augment transplant decision making by discovering patterns and making predictions for specific patients that are not covered in the literature or in ways that are impossible for humans to anticipate by integrating vast amounts of data (e.g. trending across numerous biomarkers). Similar to other clinical decision support systems, AI may help overcome human biases or judgment errors. However, AI is not widely utilized in transplant to date. In this rapid review, we survey the methods employed in recent research in transplant-related AI applications and identify concerns related to implementing these tools. We identify three key challenges (bias/accuracy, clinical decision process/AI explainability, AI acceptability criteria) holding back AI in transplant. We also identify steps that can be taken in the near term to help advance meaningful use of AI in transplant (forming a Transplant AI Team at each center, establishing clinical and ethical acceptability criteria, and incorporating AI into the Shared Decision Making Model).

Donor-specific cell-free DNA as a biomarker in liver transplantation: A review

Due to advances in modern medicine, liver transplantation has revolutionised the prognosis of many previously incurable liver diseases. This progress has largely been due to advances in immunosuppressant therapy. However, despite the judicious use of immunosuppression, many liver transplant recipients still experience complications such as rejection, which necessitates diagnosis via invasive liver biopsy. There is a clear need for novel, minimally-invasive tests to optimise immunosuppression and improve patient outcomes. An emerging biomarker in this ''precision medicine'' liver transplantation field is that of donor-specific cell free DNA. In this review, we detail the background and methods of detecting this biomarker, examine its utility in liver transplantation and discuss future research directions that may be most impactful.