The Evolution of Therapeutic Drug Monitoring for Tacrolimus
Therapeutic drug monitoring is essential with tacrolimus, which is one of the key immunosuppressive agents used after solid organ transplant.
Solid organ transplantation of the lungs, heart, liver, intestines, or kidney offers life-saving treatment for patients living with end-organ dysfunction. During transplantation, various immunosuppressive medications are used at different times to prevent the patient’s body from rejecting the transplanted organ.
Administering these medications requires a delicate balance. The drugs must suppress the patient’s immune system enough to prevent rejection of the transplanted organ, but not so much that the patient develops opportunistic infections.
Tacrolimus, which is frequently administered in both the inpatient and outpatient setting, is one of the key immunosuppressive agents used after solid organ transplant. Therapeutic drug monitoring (TDM) is essential, and the patient’s serum level must be closely monitored throughout the course of treatment.
In the past decade, researchers have intensified their focus on ways to minimize the adverse effects of tacrolimus and maximize its efficacy.
In 2019, the International Association of Therapeutic Drug Monitoring and Clinical Toxicity issued an updated consensus report to modernize drug-exposure and drug-monitoring recommendations for tacrolimus treatment.1 For years, clinicians had been following guidelines published in 2009, which were heavily reliant on monitoring the patient’s serum trough concentrations as a surrogate marker for drug exposure.
Considering that undertreatment with tacrolimus may result in a loss of the transplanted organ, clinicians typically erred on the side of over-suppressing the immune system, which risked nephrotoxicity and infections.2
Since 2009, there have been many changes in available medications, laboratory techniques (such as liquid chromatography with tandem mass spectrometry), and analytics technology that have impacted our understanding of tacrolimus TDM best practices. Target concentrations for tacrolimus are significantly lower than they were a decade ago, and therapeutic ranges have been refined based on post-transplant time, immunological risk, and the advent of mammalian target of rapamycin (mTOR) inhibitor drugs, which has led to new combination regimens.
In the past several years, the rise of user-friendly model-informed precision dosing (MIPD) software to optimize dosage regimens has opened a new world of possibilities for clinicians. These tools rely on Bayesian forecasting—a method of statistical inference that uses data from existing patients, expressed in a PK and/or pharmacodynamic (PD) model, combined with the evidence provided by an individual sample—to predict a patient’s likely response to a specific dosing regimen.
Bayesian analytics leads to improved target achievement
In 2020, a new consensus guideline for the therapeutic monitoring of vancomycin firmly recommended Bayesian forecasting software as the preferred method of dosage calculation, citing its clinical benefit and widespread availability.3 A MAP-Bayesian analysis uses the maximum a posteriori estimate, or the most probable model parameters, to interpret the patient’s levels in the context of the PK model.
As Bayesian forecasting accommodates the variability of measured levels in the body, it produces the most accurate patient-specific drug exposure predictions available.
Although the 2019 tacrolimus guideline stops just short of recommending Bayesian analysis as the preferred methodology, it does promote population PK model-based Bayesian estimators as improving target achievement compared with standard TDM. Although most transplant centers rely on trough concentrations drawn immediately before the dose, as these have historically been easier to obtain than area under the curve (AUC) values, the consensus notes that evidence shows a poor correlation between tacrolimus trough concentrations and outcome.4
The authors conclude that trough concentrations can only be considered the correct proxy of the overall exposure if the blood sampling is perfectly timed. In the absence of being able to monitor AUC directly, they suggest obtaining a trough AUC correlation early in therapy to guide patient-specific trough targets.5
The 2019 tacrolimus consensus frames Bayesian estimation software as the clear path for the future, stating that the use of population PK model-based Bayesian estimators provide AUC predictions with minimal bias (<5%) and imprecision (<20%).5 The authors conclude that Bayesian estimation, rather than standard trough concentration-based TDM, seems to be a better way to improve future tacrolimus TDM.
Incorporating pharmacogenetic data into PK/PD models
In another nod to the future of personalized medicine, the 2019 tacrolimus TDM guideline also recommends the integration of pharmacogenetic information into population PK models, primarily in order to optimize initial dosing. As the consistent association between CYP3A4/5 genotypes and tacrolimus dose requirements has been observed among kidney, liver, heart, and lung transplant recipients, the authors call for reevaluating the incorporation of CYP3A4/5 genotypes and potentially other genetic markers in population PK models.
These recommendations are a further crystallization of guidelines released by the Clinical Pharmacogenetics Implementation Consortium in 2015, which outline dosing schema for tacrolimus patients in the context of various CYP3A5 genotypes.5
The integration of pharmacogenetic and pharmacogenomic data into existing PK/PD models is an exciting new frontier for pharmacology, one that is fully dependent upon our technology advances of the past decade. The process of making a patient’s pharmacogenetic and pharmacogenomic data clinically applicable at the point of care is the future of precision medicine. Importing pharmacogenetic data into Bayesian MIPD software tools adds another layer of individualization to the patient-specific PK/PD predictions such tools deliver.
As clinicians, we do not have the capacity to balance all the covariates in play in one equation. With the aid of technology, however, practitioners can import all the information that impacts how tacrolimus acts upon and within the body to create a mathematical model that will accurately predict how the drug will impact this particular patient’s specific body.
As precision dosing becomes more mainstream, we expect to see Bayesian analytics and MIPD tools incorporated into a wider variety of drugs, from immunology to oncology, anticoagulation, psychiatry and more. With drug dosing becoming as individualized as the person receiving it, treatment failures and toxicity could become a relic of a bygone age.
About the Authors
Jon Faldasz, PharmD BCPS, is the Senior Director of Product and Customer Experience for InsightRX, a health tech company that provides precision dosing intelligence from clinical development to the point of care. John Pilla, PharmD, is the Director of Health System Partnerships at InsightRX.
References
1. Brunet M, van Gelder T, Asberg A, et al. Therapeutic drug monitoring of tacrolimus-personalized therapy: Second consensus report. Ther Drug Monit. 2019;41(3):261-307 doi:10.1097/FTD.0000000000000640
2. Andrews L M, Li Y, De Winter B C M, et al. Pharmacokinetic considerations related to therapeutic drug monitoring of tacrolimus in kidney transplant patients. Taylor Francis Forensic Sci Ser. 2017; 13(12):1225-1236. doi:10.1080/17425255.2017.1395413
3. Rybak M, Le J, Lodise T, et al. Therapeutic monitoring of vancomycin for serious methicillin-resistant Staphylococcus aureus infections: A revised consensus guideline and review by the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, the Pediatric Infectious Diseases Society, and the Society of Infectious Diseases Pharmacists. Am J Health Syst Pharm. 2020;77(11):835–864. doi:10.1093/ajhp/zxaa036
4. Brunet M, van Gelder T, Asberg A, et al. Therapeutic drug monitoring of tacrolimus-personalized therapy: Second consensus report. Ther Drug Monit. 2019;41(3):261-307 doi:10.1097/FTD.0000000000000640
5. Birdwell K A, Decker B, Barbarino J M, et al. Clinical pharmacogenetics implementation consortium (CPIC) guidelines forCYP3A5 genotype and tacrolimus dosing. Clin Pharmacol Ther. 2015; 98(1):19-24. doi:10.1002/cpt.113.
