Individualizing Immunosuppressive Therapy for Transplant Patients.
Pharmacokinetic Models and Software
A recent report by Zarrinpar and colleagues (1) used a "parabolic personalized dosing" (PPD) platform to manage dosing changes of tacrolimus in liver transplant patients. The PPD platform is not a pharmacokinetic predictive modeling approach, but instead uses phenotypic outputs such as clinical efficacy and/or safety to plot the parabolic surface. It is represented by a second-order algebraic equation with experimentally determined coefficients of the equation being unique to each patient. PPD uses clinical data, including blood concentrations of tacrolimus to calibrate these coefficients and pinpoint the optimal dose that results in the desired patient-specific response (1). Each patient is characterized by a parabola, and barring any changes to the treatment protocol, this parabola is a robust map that identifies drug doses. This approach to TDM is anticipated to ensure that a patient will stay in a target tacrolimus therapeutic range. However, introducing new drugs or procedures into the regimen, like antibiotics or hemodialysis, which may change the patient's trough concentrations substantially would necessitate establishing of a new parabola, and determination of a new tacrolimus dose to bring the trough values back into the target range. In this study, PPD was more effective in managing patients by keeping their tacrolimus blood trough concentrations within the target ranges with significantly less variability. However, this limited study only compared 4 transplant patients prospectively treated with tacrolimus using PPD to 4 control patients treated according to the clinical standard of care, for which tacrolimus dosages were adjusted on the basis of daily trough concentrations. Further studies looking at clinical outcomes (e.g., transplant rejection) and using larger numbers of patients involving different types of transplants are needed, but this provides a preliminary look at a phenotypic medicine approach that could be successfully applied to this and other clinical areas/classes of medications.
Other complex pharmacokinetic methods also exist that require dedicated software and use either non-Bayesian least-squares (where the population model is not well-known) or Bayesian least-squares methods (where the population model is reasonably well-known) (2). For example, there are several studies looking at the use of Maximum A Posteriori (MAP) Bayesian estimation to predict tacrolimus exposure and subsequent drug dosage requirements in solid organ transplant recipients (3). However, no study to date has examined how closely MAP Bayesian dosage predictions of tacrolimus actually achieve target area under the curve (AUC) by comparing dosage prediction from one occasion with a future measured AUC. Another group developed a Bayesian limited sampling model for monitoring the immunosuppressive drug mycophenolate mofetil (MMF), which metabolizes to the active drug, mycophenolic acid, after liver transplantation (4). The authors concluded that the flexible Bayesian limited sampling model for MMF improved TDM-based dosage guiding in liver transplant patients. Pharmacokinetic modeling has also been applied to other immunosuppressant drugs such as cyclosporine A (5). In fact, there is a free on-line tool to perform Bayesian estimations (the "Immunosuppressant Bayesian Dose Adjustment" (ISBA/ABIS) website [https://pharmaco. chu-limoges.fr/ (accessed June 2016)] for routine dose adjustment in transplant patients. However, all of these approaches require further research involving larger prospective studies with more diverse transplant groups and key clinical outcomes.
Clinical Challenges for Bayesian Forecasting and Parabolic Personalized Dosing
Right now, one of the biggest hurdles for implementing routine predictive modeling is the laboratory information system (LIS) and/or the electronic medical record (EMR). The TDM results used for these models/platforms typically get entered in the LIS by the clinical laboratory. Most LIS and EMRs have limited ability to perform many of these complex calculations and curves, necessitating separate software or web-based applications. In addition, this process has not been fully automated and requires additional time and resources from the laboratorians, clinicians, and/or pharmacists to input the data into these systems, manually transfer the results back into the LIS or EMR, and review and approve the final dosing orders. However, all of these barriers can be overcome because PPD is embedded with upper and lower dosing limits to prevent over- and underdosing.
In the end, PPD and/or other Bayesian forecasting has the potential to achieve stable drug concentrations more quickly, maximize patient care, reduce treatment complications and expenses, shorten the duration of postoperative hospitalization, and improve transplantation success rates. Transplant drug regimens constantly have to be adjusted to account for each patient's unique response to the medications, drug-drug interactions, infections, rejection, and other organ (e.g., kidney) function. As a result, clinicians would welcome any clinical approach that could easily be applied and aid in the management or individualization of immunosuppressive therapy for transplant patients in whom combination therapies with multiple medications are routinely used. However, transplantation is not the only clinical area with complex pharmacology and combination therapies. Therefore, the exciting aspect of this application is that these approaches to individualize therapeutic drug management are independent of disease mechanism, and could be applied beyond transplant medicine to dosing for cancer, infectious diseases, and cardiovascular medicine, where patient response is variable and requires careful adjustments through optimized inputs.
Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.
Authors' Disclosures or Potential Conflicts of Interest: No authors declared any potential conflicts of interest.
(1.) Zarrinpar A, Lee DK, Silva A, Datta N, Kee T, Eriksen C, et al. Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform. Sci Transl Med 2016; 8:333ra49.
(2.) Tatarinova T, Neely M, Bartroff J, van Guilder M, Yamada W, Bayard D, et al.Two general methods for population pharmacokinetic modeling: non-parametric adaptive grid and non-parametric Bayesian. J Pharmacokinet Pharmacodyn 2013; 40:189-99.
(3.) Brooks E, Tett SE, Isbel NM, Staatz CE. Population pharmacokinetic modelling and Bayesian estimation of tacrolimus exposure: is this clinically useful for dosage prediction yet? Clin Pharmacokinet [Epub ahead of print 2016 May 4].
(4.) Langers P, Press RR, Inderson A, Cremers SC, den Hartigh J, Baranski AG, van Hoek B. Limited sampling model for advanced mycophenolic acid therapeutic drug monitoring after liver transplantation. Ther Drug Monit 2014; 36:141-7.
(5.) Fruit D, Rousseau A, Amrein C, Rolle F, Kamar N, Sebbag L, et al. Ciclosporin population pharmacokinetics and Bayesian estimation in thoracic transplant recipients. Clin Pharmacokinet 2013; 52:277-88.
Loralie J. Langman  * and Paul J. Jannetto 
 Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN.
* Address correspondence to this author at: Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St. SW, Rochester, MN 55905. Fax 507-284-9758; e-mail firstname.lastname@example.org.
Received July 5, 2016; accepted July 8, 2016.
Previously published online at DOI: 10.1373/clinchem.2016.260380
 Nonstandard abbreviations: TDM, therapeutic drug monitoring; PPD, parabolic personalized dosing; MAP, Maximum A Posteriori; AUC, area under the curve; MMF, mycophenolate mofetil; LIS, laboratory information system; EMR, electronic medical record.
|Printer friendly Cite/link Email Feedback|
|Author:||Langman, Loralie J.; Jannetto, Paul J.|
|Article Type:||Drug overview|
|Date:||Oct 1, 2016|
|Previous Article:||Continuous Improvement in Continuous Quality Control.|
|Next Article:||CRISPR-Cas9 System: Opportunities and Concerns.|