The dynamic field of renal transplantation is characterised by limited access to data, the high cost of equipment and underlying complexity of immunological responses to transplants. Traditional clinical studies often fail to predict the postoperative outcome as they utilize standard statistical analysis requiring large number of participants. An alternative approach, combining traditional statistical tools with the development and application of machine learning algorithms, can open new perspectives for preoperative risk assessment and provide access to safe transplantation for many currently untranslatable patients. Our study demonstrates a novel data-driven approach based on classification decision trees (DTs) for prediction of acute antibody mediated rejection in the early post-transplant period after HLA-antibody incompatible transplantation. The available clinical dataset featured 15 potential predictor variables including pre-treatment DSA IgG subclass levels across 80 observation samples, out of which 46 belonged to rejector group and 34 represented non-rejector controls.

Donor HLA specific antibodies (DSA) can cause acute rejection and graft lost after renal transplantation. However neither the damaging antibody types, nor their acceptable levels are currently known. The aim of this research was to investigate the role of four DSA IgG subclasses in the immune response in order to identify any potentially damaging antibodies and their influence on short and long-term postoperative outcomes. Levels of pan-IgG and IgG subclasses of the DSAs were determined pre-treatment, at the day of peak pan-IgG level and at 30 day post-transplantation by single antigen microbead assay in eighty HLA-antibody incompatible kidney transplant recipients. Multivariate regression models accounting for the pan-IgG and IgG subclass levels of the DSAs and other potentially confounding patient baseline characteristics were developed to assess the risk of early rejection and graft failure. We have found that IgG4 was predictive of acute antibody mediated rejection (p=0.003) in the early post-transplant period. The multiple binary regression model and the likelihood significance analysis have shown that the occurrence of the early rejection was due to three factors: total pre-treatment IgG4 MFI levels, the highest pre-treatment MFI DSA levels and the total number of HLA mismatches. The long term graft survival times were also affected by the presence of the IgG4 in pre-treatment samples (p=0.004). Cox proportional hazard method identified 2 significant (p<0.05) factors reducing graft survival times: MFI of the highest IgG and presence of IgG4 in pre-treatment samples with the hazard ratios of 161.218 and 5.945, respectively. Thus, pre-treatment IgG4 DSA is a useful biomarker to predict and risk stratify cases with higher levels of pan-IgG DSA in HLA-antibody incompatible renal transplantation.

The 3rd International Transplant Conference took place on 31st October and 1st November 2014 at the University of Warwick, Coventry, UK. Key focal points of the meeting were the exploration of the molecular basis of antibody-antigen interactions and their relation to clinical practice and to share experiences and knowledge regarding strategies to transplant the high-risk' patient. In addition, lively debate sessions were hosted where controversial clinical and immunological themes were discussed by leading experts in the field.

Binding affinities are useful measures of target interaction and have an important role in understanding biochemical reactions that involve binding mechanisms. Surface plasmon resonance (SPR) provides convenient real-time measurement of the reaction that enables subsequent estimation of the reaction constants necessary to determine binding affinity. Three models are considered for application to SPR experiments—the well-mixed Langmuir model and two models that represent the binding reaction in the presence of transport effects. One of these models, the effective rate constant approximation, can be derived from the other by applying a quasi-steady state assumption. Uniqueness of the reaction constants with respect to SPR measurements is considered via a structural identifiability analysis. It is shown that the models are structurally unidentifiable unless the sample concentration is known. The models are also considered for analytes with heterogeneity in the binding kinetics. This heterogeneity further confounds the identifiability of key parameters necessary for reliable estimation of the binding affinity.

Blood group (ABO) incompatible transplants carry an increased risk of rejection. This risk could be dramatically reduced by the removal and suppression of the antibody types that would attack the donor organ. A prerequisite for this removal is an experimental procedure that can estimate the binding affinities of multiple antibodies from patient blood samples. This paper presents the usage of surface plasmon resonance (SPR) experiments with a pre existing mathematical model and a recently created expanded version of it that can estimate multiple antibody binding affinities from parallel experiments. SPR experiments were conducted on purified patient antibody samples of the IgG and IgA isotype, as well as mixed antibody from the other isotypes. The result of these experiments was analyzed with both mathematical models, and the expanded model was demonstrated to give a vastly improved fit. Estimates of antibody binding affinity were compared between samples and the non IgG/IgA protein sample was seen to have the highest binding affinity.

Introduction: The response of the kidney to antibody that may potentially bind and cause damage is critical. However, there is clearly a qualitative as well as a quantitative element to ABO specific and donor HLA specific antibodies that requires investigation before we can define the maximum acceptable or optimal level of donor-specific antibody present at the time of surgery in antibody incompatible transplantation. Surface plasmon resonance (SPR) is a powerful biophysical technique which will enable us to understand complex biochemical and kinetic parameters of the anti-HLA response. Method: Individual biotinylated HLA proteins derived from mammalian cell lines were coupled to separate channels of a Bio-rad XPR36 sensor chip. Monoclonal HLA-specific antibody and HLA-specific antibody derived from renal transplant patients were run over the chip and binding curves were obtained and analysed. Results: Binding of HLA-specific antibody to immobilised HLA proteins was observed in accordance with known patterns of HLA reactivity as defined by luminex microbead analysis and epitope theory. The conditions by which the chip can be regenerated and re-used have been optimised. We have previously shown with the binding of ABO antibodies to ABO bound onto SPR chips that estimates of binding affinity obtained from proprietary software were inaccurate and optimised fits were obtained using effective rate constant modelling based on heterogenous Langmuir with transplant modelling. Binding kinetics of HLA antibodies are currently being mathematically modelled to determine the net affinity derived from a complex polyclonal antibody population. Discussion: Surface plasmon resonance technology provides a rapid analysis and unique insight into the kinetics of the anti-HLA response. This will allow studies to define the affinity characteristics of the anti-HLA response in conventional and HLA-incompatible transplants.

Krishnan, N1,2; Higgins, R1; Fleetwood, P2; Zehnder, D1,3; Mitchell, D3; Hamer, R1,3; Briggs, D2 Author Information