Immunosuppressant agents including cyclosporine, tacrolimus, sirolimus, and mofetil mycophenolate (MMF) are the backbone of organ and bone marrow transplant success and are widely used to prevent transplant rejection. There have not been definitive reports of clinical failures with generic immunosuppressants. However, generic substitution for brand name immunosuppressants has been shown to result in variable concentrations and to have trough variability that may significantly impact the patient's therapy. Highly variable drugs, such as immunosuppressants, seldom meet FDA bioequivalence (BE) criteria and therefore require in depth understanding of the pharmacokinetic/pharmacodynamic (PK/PD) profile to allow assessment against BE criteria for generics. BE studies in transplant patients are hampered by the need for large sample sizes due to patient variability. Transplant patients take on average ten different medications simultaneously, including multiple immunosuppressants, and absorb drugs poorly due to comorbidities. Pediatric patients have been shown to require 2-4 times the dose of tacrolimus than adult patients necessitating pediatric specific studies. Application of pharmacometric modeling and simulation techniques can overcome the need for a large sample size by evaluating PK profiles and using partial AUCs as BE criteria. Population PK/PD modeling allows the full breadth and depth of the individual PK to be determined, while preserving individual variability. The objective of this project is to develop quantitative PK/PD models for cyclosporine, tacrolimus, sirolimus, and MMF to compare brand name to the generic formulations. This will be done by developing age-specific and drug specific population PK models. Clinically relevant PK/PD models for immunosuppressants require the inclusion of markers in the model that will allow prediction of achievement of desired clinical effect. There is currently limited information available on the use of partial AUCs for these drugs. Partial AUCs are currently the gold standard for MMF and cyclosporine in predicting organ rejection, and should be comparable for tacrolimus and sirolimus. We will also include in the model a novel approach to PD for predicting acute rejection using lymphocyte counts as a marker for effective drug therapy paired with graft function tests. Inclusion of these novel PD makers will expand the criteria for determining if therapy is successful. The comparison of generic drugs to brand names drugs to assure therapeutic equivalence in immunosuppressants in transplant patients is not well defined. The outcomes from this study will provide valuable information to optimize the use of these generic drugs within adult and pediatric populations receiving organ transplants and bone marrow transplants. The methods developed in this study will also provide a platform from which other drug classes could be better assessed and utilized with respect to generic evaluation.

Public Health Relevance

/PUBLIC HEALTH SIGNIFICANCE Immunosuppressants are the mainstay of post-transplant therapy and are crucial to prevent graft rejection. Clinicians have a demonstrated reluctance to substitute generic for brand name immunosuppressant drugs in high-risk patients, without knowledge, based upon solid data, that the formulations are bioequivalent (BE). Pharmacokinetic models will provide valuable tools to evaluate BE and optimize the use of substituted generic drugs within both adult and pediatric populations receiving organ and bone marrow transplants. This will provide providers, patients, and regulators with an improved method of managing these critically important drugs while providing a methodology to examine other important classes of drugs with similar reservations.

Agency
National Institute of Health (NIH)
Institute
Food and Drug Administration (FDA)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01FD005191-01
Application #
8853626
Study Section
Special Emphasis Panel (ZFD1-SRC (99))
Project Start
2014-09-10
Project End
2017-08-31
Budget Start
2014-09-10
Budget End
2015-08-31
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Utah
Department
Type
DUNS #
009095365
City
Salt Lake City
State
UT
Country
United States
Zip Code
84112