Novel approach to clinical data analysis: application to kidney transplantation

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The research aims to develop a novel mathematical approach to build a model of antibody pathogenicity in antibody incompatible kidney transplantation (AIT). Currently, renal replacement is expensive and cost-effective provision of kidney transplantation constitutes a major global health priority. The number of patients receiving renal replacement therapy exceeds 1.4 million worldwide and is growing by 8 percent annually, in excess of the growth rate of the general population. Solutions for the prevention or reversal of renal disease have so far failed to significantly change the development of global patient numbers. An economically viable alternative to allograft transplantation is not foreseen in the mid-term future; hence renal transplant is the only solution. The successful outcome of a transplantation depends on how well the donor and recipient are matched for tissue proteins called HLA. Since only about 25 percent of transplants can be fully matched many needing a kidney have developed antibodies against HLA and these can cause transplant rejection. Patients with preformed HLA antibodies wait longer or cannot receive a kidney. AIT has been pioneered making it possible to reduce levels of antibody before surgery and transplant 'mismatched' patients. More than 40 percent of kidneys are however still rejected. This is because complete elimination of antibodies is not possible. Types of harmful antibodies and levels to which they must be reduced are also not known. Traditional clinical studies utilise standard statistical analysis that requires very large number of participants and have until now failed to predict kidney rejection. The project will therefore employ an alternative approach that combines statistical analysis with the development of novel methods of dynamic patterns analysis. The human immunological reaction to kidney transplantation will be modelled in the framework of non-linear stochastic systems approaches followed by their translation into clinical context via following objectives: (1) to develop an appropriate methodology and subsequently analyse dynamic and static properties of antibody evolution in AIT (2) to provide the most informative data processing tool for antibody risk assessment (3) to create a rigorous foundation for a comprehensive data source based on the available research data. The research aims to address the following clinical questions: (a) what types of preformed HLA antibodies are most harmful and associated with significant risk of kidney rejection; (b) what are critical levels of preformed antibodies at the time of surgery i.e. how much of the antibodies can be tolerated to ensure safe acceptance of the donor kidney? This engineering and primarily non-medical strategy has not been attempted before. The strength of this project lies in its translational aspect from areas of maths/engineering to tackling medical challenges thus strengthening the cross-disciplinary Biomedical Engineering interface. This is concomitant with the unique clinical data set available for the project. The key aspect of the data concerns the patients being sampled daily in the critical first 3-4 weeks following transplantation when antibody levels change rapidly thus capturing key events in the behaviour of antibodies. This is the only programme anywhere in the world to have carried out with such level of antibody monitoring.For the long term perspective, the project outputs will bring significant clinical benefits through an in-depth understanding of the humoral immune response in AIT. Also, this will improve risk management of transplants and the provision of access to transplantation for many untransplantable patients. The database created will consolidate research activities in this emerging field and advance outputs of the research of national and international significance.