Transplant medications have a very narrow therapeutic index (margin between efficacy and toxicity), so that under-treatment may allow rejection while over-treatment causes toxicity. Our research on quantitative pharmacokinetic methods monitoring ADME characteristics (absorption, distribution, metabolism and elimination) to guide the use of the principal immunosuppressive drugs calcineurin inhibitors (CNI), purine synthesis inhibitors (PSI), corticosteroids and more rarely, mTOR inhibitors (mTORI) has helped to inform the clinical use of these agents, and underpins the therapeutic strategies now in general clinical use. We have shown that PG and PD data are also informative in explaining individual PK performance, but these have not yet been incorporated into clinical practice due to the marked biological heterogeneity that confounds predictive utility in this patient population. Transplantation lags behind other areas, particularly oncology, where systems pharmacology methods have been extensively used to integrate multidisciplinary data and to model new therapies through rigorous Phase II trial strategies.
Transplant medications have a very narrow therapeutic index (margin between efficacy and toxicity), so that under-treatment may allow rejection while over-treatment causes toxicity. Our research on quantitative pharmacokinetic methods monitoring ADME characteristics (absorption, distribution, metabolism and elimination) to guide the use of the principal immunosuppressive drugs calcineurin inhibitors (CNI), purine synthesis inhibitors (PSI), corticosteroids and more rarely, mTOR inhibitors (mTORI) has helped to inform the clinical use of these agents, and underpins the therapeutic strategies now in general clinical use. We have shown that PG and PD data are also informative in explaining individual PK performance, but these have not yet been incorporated into clinical practice due to the marked biological heterogeneity that confounds predictive utility in this patient population. Transplantation lags behind other areas, particularly oncology, where systems pharmacology methods have been extensively used to integrate multidisciplinary data and to model new therapies through rigorous Phase II trial strategies.
Activity 3A
To Develop a Systems Pharmacology Model for Personalized Immune Suppression
Transplant medications have a very narrow therapeutic index, where under-treatment may lead to rejection while over-treatment may result in toxicity. Current best clinical practices for the use of immunosuppressive drugs are informed by quantitative pharmacokinetic methods that monitor ADME characteristics (absorption, distribution, metabolism and elimination) of these therapeutic agents. We have shown that pharmacogenetics and pharmacodynamics data are also informative in explaining individual PK performance. However, these measures have not yet been incorporated into clinical practice due to the marked genetic diversity that confounds predictive utility in this patient population.
Unlike transplantation, drug treatments in oncology have extensively used systems pharmacology methodologies to model new therapies through rigorous Phase II trial strategies. We aim to use these oncology clinical practices in transplantation. Using large clinical trial data, we have developed retrospective models for risk stratification for rejection, toxicity and infection after kidney transplantation. We will develop a robust and comprehensive systems pharmacology model build by incorporating objective measures of patient risk and clinical outcomes with 1) the degree of epitope mismatch, 2) recipient immune response, 3) dynamic measures drug exposure, and biological effect and 4) non-invasive graft injury. Ultimately, the integration of the multiple data sets for a systems pharmacology model will enable personalized therapy and optimize outcomes.
Activity 3B
To Implement Risk-Based Stratification for Therapy and Clinical Trials
The last 2 decades evolving clinical practices have yielded significant reductions in early rejection but have not been successful in reducing later-stage complications including viral infection or chronic AM. The systems pharmacology models developed in activity 3a will be incorporated with the patient-specific information data generated in Activities 1 and 2 to more precisely classify the immune risk for the transplant patients. In addition, the data and models generated would help to select subjects for clinical trial with the greatest probability of success and the lowest probability of harm. Clinical trials will not be conducted as part of this project but rather by the Canadian Transplant Study Group, which includes all transplant centres across Canada with a combined transplant rate of over 1,500 patients per year. These studies will employ standardized protocols for immune suppression and viral prophylaxis generated by the systems pharmacology model (Activity 3) and will incorporate immunological monitoring based on the assays developed from this award (Activity 2).
