Biomarker and surrogate development in vascularised composite allograft transplantation: Current progress and future challenges

What is needed to ensure long-term sustainability for the field of vascularized composite allotransplantation?

The field of vascularized composite allotransplantation (VCA) has demonstrated remarkable advances since its inception with some excellent long-term results in a variety of graft types. However, unlike solid organ transplantation, it has yet to become mainstream. We therefore discuss strategies on ensuring long-term sustainability by addressing continued clinical developments of VCA to improve the risk-to-benefit balance, importance of public support, improved policy and financial support, and need for a bridge to the future of transplant surgery. There has been headway on all fronts and collaboration among the VCA centers for centralization of data and incorporation of patient voices will be essential for continued progress.

Methods of ex vivo analysis of tissue status in vascularized composite allografts

Vascularized composite allotransplantation can improve quality of life and restore functionality. However, the complex tissue composition of vascularized composite allografts (VCAs) presents unique clinical challenges that increase the likelihood of transplant rejection. Under prolonged static cold storage, highly damage-susceptible tissues such as muscle and nerve undergo irreversible degradation that may render allografts non-functional. Skin-containing VCA elicits an immunogenic response that increases the risk of recipient allograft rejection. The development of quantitative metrics to evaluate VCAs prior to and following transplantation are key to mitigating allograft rejection. Correspondingly, a broad range of bioanalytical methods have emerged to assess the progression of VCA rejection and characterize transplantation outcomes. To consolidate the current range of relevant technologies and expand on potential for development, methods to evaluate ex vivo VCA status are herein reviewed and comparatively assessed. The use of implantable physiological status monitoring biochips, non-invasive bioimpedance monitoring to assess edema, and deep learning algorithms to fuse disparate inputs to stratify VCAs are identified.

Real-Time Monitoring Using Multiplexed Multi-Electrode Bioelectrical Impedance Spectroscopy for the Stratification of Vascularized Composite Allografts: A Perspective on Predictive Analytics

Vascularized composite allotransplantation addresses injuries to complex anatomical structures such as the face, hand, and abdominal wall. Prolonged static cold storage of vascularized composite allografts (VCA) incurs damage and imposes transportation limits to their viability and availability. Tissue ischemia, the major clinical indication, is strongly correlated with negative transplantation outcomes. Machine perfusion and normothermia can extend preservation times. This perspective introduces multiplexed multi-electrode bioimpedance spectroscopy (MMBIS), an established bioanalytical method to quantify the interaction of the electrical current with tissue components, capable of measuring tissue edema, as a quantitative, noninvasive, real-time, continuous monitoring technique to provide crucially needed assessment of graft preservation efficacy and viability. MMBIS must be developed, and appropriate models explored to address the highly complex multi-tissue structures and time-temperature changes of VCA. Combined with artificial intelligence (AI), MMBIS can serve to stratify allografts for improvement in transplantation outcomes.

Clinical and preclinical tolerance protocols for vascularized composite allograft transplantation

The field of vascularized composite allografts (VCAs) has undergone significant advancement in recent decades, and VCAs are increasingly common and accepted in the clinical setting, bringing hope of functional recovery to patients with debilitating injuries. A major obstacle facing the widespread application of VCAs is the side effect profile associated with the current immunosuppressive regimen, which can cause a wide array of complications such as infection, malignancy, and even death. Significant concerns remain regarding whether the treatment outweighs the risk. The potential solution to this dilemma would be achieving VCA tolerance, which would allow recipients to receive allografts without significant immunosuppression and its sequelae. Promising tolerance protocols are being studied in kidney transplantation; four major trials have attempted to withdraw immunosuppressive treatment with various successes. The common theme in all four trials is the use of radiation treatment and donor cell transplantation. The knowledge gained from these trials can provide valuable insight into the development of a VCA tolerance protocol. Despite similarities, VCAs present additional barriers compared to kidney allografts regarding tolerance induction. VCA donors are likely to be deceased, which limits the time for significant pre-conditioning. VCA donors are also more likely to be human leukocyte antigen–mismatched, which means that tolerance must be induced across major immunological barriers. This review also explores adjunct therapies studied in large animal models that could be the missing element in establishing a safe and stable tolerance induction method.

Software-based Detection of Acute Rejection Changes in Face Transplant

BACKGROUND

 An objective, non-invasive method for redness detection during acute allograft rejection in face transplantation (FT) is lacking.

METHODS

 A retrospective cohort study was performed with 688 images of 7 patients with face transplant (range, 1 to 108 months post-transplant). Healthy controls were matched to donor age, sex, and had no prior facial procedures. Rejection state was confirmed via tissue biopsy. An image-analysis software developed alongside VicarVision (Amsterdam, Netherlands) was used to produce R, a measure of differences between detectable color and absolute red. R is inversely proportional to redness, where lower R values correspond to increased redness. Linear mixed models were used to study fixed effect of rejection state on R values. Estimated marginal means of fitted models were calculated for pairwise comparisons.

RESULTS

 Of 688 images, 175, 170, 202, and 141 images were attributable to Banff Grade 0,1,2, and 3, respectively. Estimated change in R value of facial allografts decreased with increasing Banff Grade (p = 0.0001). The mean R value of clinical rejection (Banff Grade ⅔) (16.67, 95% Confidence Interval [CI] 14.79-18.58) was lower (p = 0.005) than non-rejection (Banff Grade 0/1) (19.38, 95%CI 17.43-21.33). Both clinical and non-rejection mean R values were lower (p = 0.0001) than healthy controls (24.12, 95%CI 20.96-27.28).

CONCLUSION

 This proof-of-concept study demonstrates that software-based analysis can detect and monitor acute rejection changes in FT. Future studies should expand on this tool's potential application in telehealth and as a screening tool for allograft rejection.