Can the Area Under the Curve/Trough Level Ratio Be Used to Optimize Tacrolimus Individual Dose Adjustment?

Pharmacokinetic Boosting of Calcineurin Inhibitors in Transplantation: Pros, Cons, and Perspectives

ABSTRACT

The concept of pharmacokinetic (PK) boosting of calcineurin inhibitors (CNI) emerged after the FDA approval of cyclosporine-A. Several studies followed, and the proof of concept was well established by the late 1990s. This also continued for the next blockbuster immunosuppressant, tacrolimus. The driver for such research was an endeavor to save costs, as both drugs were expensive due to patent protection. Two CYP inhibitors, ketoconazole and diltiazem, have been extensively studied in this context and continue to be prescribed off-label along with the CNI. It has been observed that using ketoconazole reduces the dose requirement of tacrolimus by about 50% and 30% with diltiazem, which is in conformity with their pharmacological actions. Off-label co-prescription of these drugs with CNI is often encountered in low and middle-income countries. The foremost reason cited is economic. This article collates the evidence from the clinical studies that evaluate the PK-boosting effects of CNI and also reviews the gaps in the current evidence base. The current knowledge prevents the transplant community from making meaningful inferences about the risks and benefits of such strategies. Although the PK-boosting strategy can lead to serious adverse events, emerging evidence suggests that it may be advantageous for individuals with high CNI dose requirements. Hence, PK boosting may be an unmet need in the therapeutics of CNI. Nevertheless, there are several unanswered questions surrounding such use, and therefore, this merits testing in well-designed clinical studies. Moreover, drugs with better safer profiles and a history of successful PK boosting may be considered for evaluation with CNI.

Getting Tacrolimus Dosing Right

ABSTRACT

Tacrolimus (TAC) dosing is typically guided by the trough concentration (C0). Yet, significant relationships between TAC C0 and clinical outcomes have seldom been reported or only with adverse events. Large retrospective studies found a moderate correlation between TAC C0 and the area under the curve (AUC), where, for any given C0 value, the AUC varied 3- to 4-fold between patients (and vice versa). However, no randomized controlled trial evaluating the dose adjustment based on TAC AUC has been conducted yet. A few observational studies have shown that the AUC is associated with efficacy and, to a lesser extent, adverse effects. Other studies showed the feasibility of reaching predefined target ranges and reducing underexposure and overexposure. TAC AUC 0-12 h is now most often assessed using Bayesian estimation, but machine learning is a promising approach. Microsampling devices are well accepted by patients and represent a valuable alternative to venous blood sample collection during hospital visits, especially when a limited sampling strategy is required. As AUC monitoring cannot be proposed very frequently, C0 monitoring has to be used in the interim, which has led to fluctuating doses in patients with an AUC/C0 ratio far from the population mean, because of different dose recommendations between the 2 biomarkers. We proposed estimating the individual AUC/C0 ratio and derived individual C0 targets to be used in between or as a replacement for AUC monitoring. Existing technology and evidence are now sufficient to propose AUC monitoring interspersed with individualized-C0 monitoring for all patients with kidney transplants while collecting real-world data to strengthen the evidence.

Model-informed precision dosing: State of the art and future perspectives

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine.

Pharmacokinetics of Envarsus in pediatric kidney transplant recipients - phase 1 pilot conversion study

INTRODUCTION

Tacrolimus is the standard immunosuppressant for pediatric kidney transplants and is routinely administered twice daily (BD-tac). Envarsus (LCP-tac), an extended-release formulation, is approved for adults but not for pediatric patients.

METHODS

We conducted a pilot open-label phase 1 study in stable pediatric kidney transplant recipients (age < 18 at the time of study). Our primary objective was to compare the pharmacokinetics (Pk) of LCP-tac versus BD-tac. We conducted two 24-h Pk studies: pre-conversion (BD-tac) and 4 weeks post-conversion to LCP-tac. Patients were followed for 6 months, with the option to continue LCP-tac.

RESULTS

Five patients completed the study, with no returns to BD-tac. Median age was 15 years (range 11-17). LCP-tac exhibited an extended-release profile versus the bimodal profile of BD-tac. Time to maximum concentration was delayed (5 h vs. 1 h), and maximum concentration was lower (9.9 ng/mL vs. 14.4 ng/mL). Tacrolimus area under the curve (24 h) was comparable (141 ± 46.5 ng/mL vs. 164 ± 27.8 ng/mL). No new safety concerns arose. There were no rejection and no difference in eGFR at the study's end (1.5 mL/min/1.73 m2 , range - 1.7 to 2.3 mL/min/1.73 m2 ). Concentration/dose ratio was higher in LCP-tac (1.8 ± 0.64 vs. 0.8 ± 0.39). The final conversion ratio was 0.6 (BD-tac: LCP-tac).

CONCLUSION

Our pilot study confirms the extended-release Pk profile and improved absorption of LCP-tac compared to BD-tac. A larger study is needed to further evaluate the population Pk characteristics in children.

Application of machine learning to predict tacrolimus exposure in liver and kidney transplant patients given the MeltDose formulation

PURPOSE

Machine Learning (ML) algorithms represent an interesting alternative to maximum a posteriori Bayesian estimators (MAP-BE) for tacrolimus AUC estimation, but it is not known if training an ML model using a lower number of full pharmacokinetic (PK) profiles (= "true" reference AUC) provides better performances than using a larger dataset of less accurate AUC estimates. The objectives of this study were: to develop and benchmark ML algorithms trained using full PK profiles to estimate MeltDose^®-tacrolimus individual AUCs using 2 or 3 blood concentrations; and to compare their performance to MAP-BE.

METHODS

Data from liver (n = 113) and kidney (n = 97) transplant recipients involved in MeltDose-tacrolimus PK studies were used for the training and evaluation of ML algorithms. "True" AUC0-24 h was calculated for each patient using the trapezoidal rule on the full PK profile. ML algorithms were trained to estimate tacrolimus true AUC using 2 or 3 blood concentrations. Performances were evaluated in 2 external sets of 16 (renal) and 48 (liver) transplant patients.

RESULTS

Best estimation performances were obtained with the MARS algorithm and the following limited sampling strategies (LSS): predose (0), 8, and 12 h post-dose (rMPE = - 1.28%, rRMSE = 7.57%), or 0 and 12 h (rMPE = - 1.9%, rRMSE = 10.06%). In the external dataset, the performances of the final ML algorithms based on two samples in kidney (rMPE = - 3.1%, rRMSE = 11.1%) or liver transplant recipients (rMPE = - 3.4%, rRMSE = 9.86%) were as good as or better than those of MAP-BEs based on three time points.

CONCLUSION

The MARS ML models developed using "true" MeltDose^®-tacrolimus AUCs yielded accurate individual estimations using only two blood concentrations.