Improved Tacrolimus Target Concentration Achievement Using Computerized Dosing in Renal Transplant Recipients--A Prospective, Randomized Study

What Does it Take to Make Model-Informed Precision Dosing Common Practice? Report from the 1st Asian Symposium on Precision Dosing

Model-informed precision dosing (MIPD) is modeling and simulation in healthcare to predict the drug dose for a given patient based on their individual characteristics that is most likely to improve efficacy and/or lower toxicity in comparison to traditional dosing. This paper describes the background and status of MIPD and the activities at the 1st Asian Symposium of Precision Dosing. The theme of the meeting was the question, "What does it take to make MIPD common practice?" Formal presentations highlighted the distinction between genetic and non-genetic sources of variability in drug exposure and response, the use of modeling and simulation as decision support tools, and the facilitators to MIPD implementation. A panel discussion addressed the types of models used for MIPD, how the pharmaceutical industry views MIPD, ways to upscale MIPD beyond academic hospital centers, and the essential role of healthcare professional education as a way to progress. The meeting concluded with an ongoing commitment to use MIPD to improve patient care.

Challenges and Solutions for Future Pharmacy Practice in the Era of Precision Medicine

As precision medicine research and its clinical applications continue to advance, it is critical for pharmacists to be involved in these developments to deliver optimal, tailored drug therapies for patients. To ensure pharmacists remain leaders in the field, the annual Pharmaceutical Sciences Conference convened by the University of North Carolina at Chapel Hill Eshelman School of Pharmacy focused on the role of pharmacy within precision medicine. This is a summary of the conference, highlighting the major challenges and solutions that will help advance individualized pharmacological methods within practice and research.

A New CYP3A5*3 and CYP3A4*22 Cluster Influencing Tacrolimus Target Concentrations: A Population Approach

BACKGROUND

Single nucleotide polymorphisms (SNPs) in the CYP3A5 and CYP3A4 genes have been reported to be an important cause of variability in the pharmacokinetics of tacrolimus in renal transplant patients. The aim of this study was to merge all of the new genetic information available with tacrolimus pharmacokinetics to generate a more robust population model with data from renal transplant recipients.

METHODS

Tacrolimus exposure data from 304 renal transplant recipients were collected throughout the first year after transplantation and were simultaneously analyzed with a population pharmacokinetic approach using NONMEM^® version 7.2.

RESULTS

The tacrolimus whole-blood concentration versus time data were best described by a two-open-compartment model with inter-occasion variability assigned to plasma clearance. The following factors led to the final model, which significantly decreased the minimum objective function value (p < 0.001): a new genotype cluster variable combining the CYP3A5*3 and CYP3A4*22 SNPs defined as extensive, intermediate, and poor metabolizers; the standardization of tacrolimus whole blood concentrations to a hematocrit value of 45%; and age included as patients <63 years versus patients ≥63 years. External validation confirmed the prediction ability of the model with median bias and precision values of 1.17 ng/mL (95% confidence interval [CI] -3.68 to 4.50) and 1.64 ng/mL (95% CI 0.11-5.50), respectively. Simulations showed that, for a given age and hematocrit at the same fixed dose, extensive metabolizers required the highest doses followed by intermediate metabolizers and then poor metabolizers.

CONCLUSIONS

Tacrolimus disposition in renal transplant recipients was described using a new population pharmacokinetic model that included the CYP3A5*3 and CYP3A4*22 genotype, age, and hematocrit.

Tacrolimus Trough Concentration Variability and Disparities in African American Kidney Transplantation

BACKGROUND

Low tacrolimus concentrations have been associated with higher risk of acute rejection, particularly within African American (AA) kidney transplant recipients; little is known about intrapatient tacrolimus variabilities impact on racial disparities.

METHODS

Ten year, single-center, longitudinal cohort study of kidney recipients. Intrapatient tacrolimus variability was assessed using the coefficient of variation (CV) measured between 1 month posttransplant and the clinical event, with a comparable period assessed in those without events. Pediatrics, nontacrolimus/mycophenolate regimens, and nonrenal transplants were excluded. Multivariable Cox regression models were used to analyze data.

RESULTS

One thousand four hundred eleven recipients were included (54.4% AA) with 39 521 concentrations used to assess intrapatient tacrolimus CV. Overall, intrapatient tacrolimus CV was higher in AAs versus non-AAs (39.9 ± 19.8 % vs 34.8 ± 15.8% P < 0.001). Tacrolimus variability was a significant risk factor for deleterious clinical outcomes. A 10% increase in tacrolimus CV augmented the risk of acute rejection by 20% (adjusted hazard ratio, 1.20, 1.13-1.28; P < 0.001) and the risk of graft loss by 30% (adjusted hazard ratio, 1.30, 1.23-1.37; P < 0.001), with significant effect modification by race for acute rejection, but not graft loss. High tacrolimus variability (CV >40%) was a significant explanatory variable for disparities in AAs; the crude relative risk of acute rejection in AAs was reduced by 46% when including tacrolimus variability in modeling and reduced by 40% for graft loss.

CONCLUSIONS

These data demonstrate that intrapatient tacrolimus variability is strongly associated with acute rejection in AAs and graft loss in all patients. Tacrolimus variability is a significant explanatory variable for disparities in AA recipients.

Recovery of CYP3A Phenotype after Kidney Transplantation

End-stage renal disease impairs drug metabolism via cytochrome P450 CYP3A; however, it is unclear whether CYP3A activity recovers after kidney transplantation. Therefore, the aim of this study was to evaluate the change in CYP3A activity measured as 4β-hydroxycholesterol (4βOHC) concentration after kidney transplantation. In total, data from 58 renal transplant recipients with 550 prospective 4βOHC measurements were included in the study. One sample per patient was collected before transplantation, and 2-12 samples per patient were collected 1-82 days after transplantation. The measured pretransplant 4βOHC concentrations ranged by >7-fold, with a median value of 22.8 ng/ml. Linear mixed-model analysis identified a 0.16-ng/ml increase in 4βOHC concentration per day after transplantation (P < 0.001), indicating a regain in CYP3A activity. Increasing estimated glomerular filtration rate after transplantation was associated with increasing 4βOHC concentration (P < 0.001), supporting that CYP3A activity increases with recovering uremia. In conclusion, this study indicates that CYP3A activity is regained subsequent to kidney transplantation.

Pretransplant 4β-hydroxycholesterol does not predict tacrolimus exposure or dose requirements during the first days after kidney transplantation

AIMS

The CYP3A metric 4β-hydroxycholesterol (4βOHC) has been shown to correlate with tacrolimus steady-state apparent oral clearance (CL/F). Recently, pretransplant 4βOHC was shown not to predict tacrolimus CL/F after transplantation in a cohort of renal recipients (n = 79). The goal of the current study was determine whether these findings could be validated in a substantially larger cohort.

METHODS

In a retrospective analysis of 279 renal recipients, tacrolimus trough concentrations (C0), daily dose, haematocrit and other relevant covariates were registered every day for the first 14 days after transplantation. 4βOHC and cholesterol were quantified on plasma collected immediately pretransplant using liquid chromatography tandem-mass spectrometry. Patients were genotyped for CYP3A5*1 and CYP3A4*22.

RESULTS

A total of 3551 tacrolimus C0 concentrations were registered. In a linear mixed model for the 14-day period, determinants of tacrolimus C0 were CYP3A5 genotype, haematocrit, age and weight (overall R²  = 0.179). Determinants of daily dose were CYP3A5 genotype, age, methylprednisolone dose, tacrolimus formulation, ALT and estimated glomerular filtration rate (overall R²  = 0.242). Considering each of the first 5 days separately, 4βOHC had a limited effect on tacrolimus C0 on day 3 only (-1.00 ng ml^-1 per ln, P = 0.035) but not on any other day, and no effect on dose or C0/dose. During the first 5 days, haematocrit and age, which were previously established as determinants of tacrolimus disposition under steady-state conditions, never explained more than 17.7% of between-subject variability in tacrolimus C0/dose.

CONCLUSIONS

The CYP3A metric 4βOHC cannot be used to predict tacrolimus dose requirements in the first days after transplantation.

Tacrolimus Updated Guidelines through popPK Modeling: How to Benefit More from CYP3A Pre-emptive Genotyping Prior to Kidney Transplantation

Tacrolimus (Tac) is a profoundly effective immunosuppressant that reduces the risk of rejection after solid organ transplantation. However, its use is hampered by its narrow therapeutic window along with its highly variable pharmacological (pharmacokinetic [PK] and pharmacodynamic [PD]) profile. Part of this variability is explained by genetic polymorphisms affecting the metabolic pathway. The integration of CYP3A4 and CY3A5 genotype in tacrolimus population-based PK (PopPK) modeling approaches has been proven to accurately predict the dose requirement to reach the therapeutic window. The objective of the present study was to develop an accurate PopPK model in a cohort of 59 kidney transplant patients to deliver this information to clinicians in a clear and actionable manner. We conducted a non-parametric non-linear effects PopPK modeling analysis in Pmetrics^®. Patients were genotyped for the CYP3A4^∗22 and CYP3A5^∗3 alleles and were classified into 3 different categories [poor-metabolizers (PM), Intermediate-metabolizers (IM) or extensive-metabolizers (EM)]. A one-compartment model with double gamma absorption route described very accurately the tacrolimus PK. In covariate analysis, only CYP3A genotype was retained in the final model (Δ-2LL = -73). Our model estimated that tacrolimus concentrations were 33% IC_95%[20–26%], 41% IC_95%[36–45%] lower in CYP3A IM and EM when compared to PM, respectively. Virtually, we proved that defining different starting doses for PM, IM and EM would be beneficial by ensuring better probability of target concentrations attainment allowing us to define new dosage recommendations according to patient CYP3A genetic profile.

The CYP3A biomarker 4β-hydroxycholesterol does not improve tacrolimus dose predictions early after kidney transplantation

AIMS

Tacrolimus is a cornerstone in modern immunosuppressive therapy after kidney transplantation. Tacrolimus dosing is challenged by considerable pharmacokinetic variability, both between patients and over time after transplantation, partly due to variability in cytochrome P450 3A (CYP3A) activity. The aim of this study was to assess the value of the endogenous CYP3A marker 4β-hydroxycholesterol (4βOHC) for tacrolimus dose individualization early after kidney transplantation.

METHODS

Data were obtained from 79 adult kidney transplant recipients who contributed a total of 625 4βOHC measurements and 1999 tacrolimus whole blood concentrations during the first 2 months after transplantation. The relationships between 4βOHC levels and individual estimates of tacrolimus apparent plasma clearance (CL/F_plasma ) at different time points after transplantation were investigated using scatterplots and population pharmacokinetic modelling.

RESULTS

There was no significant correlation between pre-transplant 4βOHC levels and tacrolimus CL/F_plasma the first week (r = 0.19 [95% CI -0.03-0.40]) or between 4βOHC and tacrolimus CL/F_plasma 1 week (r = 0.20 [-0.11-0.47]), 4 weeks (r = 0.21 [-0.07-0.46]) or 2 months (r = 0.24 [-0.03-0.48]) after transplantation (P ≥ 0.06). In the population analysis, time-varying 4βOHC was not a statistically significant covariate on tacrolimus CL/F_plasma , neither in terms of absolute values (P = 0.11) nor in terms of changes from baseline (P = 0.17). 4βOHC values increased between 1 week and 2 months after transplantation (median change +57% [IQR +22-83%], P < 0.001), indicating increasing CYP3A activity. Contradictorily, tacrolimus CL/F_plasma decreased over the same period (median change -13% [IQR -3 to -26%], P < 0.001).

CONCLUSIONS

4βOHC does not appear to have a clinical potential to improve individualization of tacrolimus doses early after kidney transplantation.

Dosage Adjustments Related to Young or Old Age and Organ Impairment

Differences in physiology related to young or old age and/or organ system impairment alter the absorption, distribution, metabolism, and excretion of many medications and consequently their effectiveness and toxicity. This module discusses common alterations in medication use and dosage that are required in the pediatric age group, in the elderly, and in patients with renal or hepatic disease.

Long-Term Clinical Impact of Adaptation of Initial Tacrolimus Dosing to CYP3A5 Genotype

Pretransplantation adaptation of the daily dose of tacrolimus to CYP3A5 genotype is associated with improved achievement of target trough concentration (C0 ), but whether this improvement affects clinical outcomes is unknown. In the present study, we have evaluated the long-term clinical impact of the adaptation of initial tacrolimus dosing according to CYP3A5 genotype: The transplantation outcomes of the 236 kidney transplant recipients included in the Tactique study were retrospectively investigated over a period of more than 5 years. In the Tactique study, patients were randomly assigned to receive tacrolimus at either a fixed dosage or a dosage determined by their genotype, and the primary efficacy end point was the proportion of patients for whom tacrolimus C0 was within target range (10-15 ng/mL) at day 10. Our results indicate that the incidence of biopsy-proven acute rejection and graft survival were similar between the control and the adapted tacrolimus dose groups, as well as between the patients who achieve the tacrolimus C0 target ranges earlier. Patients' death, cancer, cardiovascular events, and infections were also similar, and renal function did not change. We conclude that optimization of initial tacrolimus dose using pharmacogenetic testing does not improve clinical outcomes.

Pharmacogenetic aspects of the use of tacrolimus in renal transplantation: recent developments and ethnic considerations

INTRODUCTION

Tacrolimus (Tac) is effective in preventing acute rejection but has considerable toxicity and inter-individual variability in pharmacokinetics and pharmacodynamics. Part of this is explained by polymorphisms in genes encoding Tac-metabolizing enzymes and transporters. A better understanding of Tac pharmacokinetics and pharmacodynamics may help to minimize different outcomes amongst transplant recipients by personalizing immunosuppression.

AREAS COVERED

The pharmacogenetic contribution of Tac metabolism will be examined, with a focus on recent discoveries, new developments and ethnic considerations.

EXPERT OPINION

The strongest and most consistent association in pharmacogenetics is between the CYP3A5 genotype and Tac dose requirement, with CYP3A5 expressers having a ~ 40-50% higher dose requirement compared to non-expressers. Two recent randomized-controlled clinical trials using CYP3A5 genotype, however, did not show a decrease in acute rejections nor reduced toxicity. CYP3A4*22, CYP3A4*26, and POR*28 are also associated with Tac dose requirements and may be included to provide the expected improvement of Tac therapy. Studies focusing on the intracellular drug concentrations and on calcineurin inhibitor-induced nephrotoxicity also seem promising. For all studies, however, the ethnic prevalence of genotypes should be taken into account, as this may significantly impact the effect of pre-emptive genotyping.

Prediction of Fat-Free Mass in Kidney Transplant Recipients

BACKGROUND

Individualization of drug doses is essential in kidney transplant recipients. For many drugs, the individual dose is better predicted when using fat-free mass (FFM) as a scaling factor. Multiple equations have been developed to predict FFM based on healthy subjects. These equations have not been evaluated in kidney transplant recipients. The objectives of this study were to develop a kidney transplant specific equation for FFM prediction and to evaluate its predictive performance compared with previously published equations.

METHODS

Ten weeks after transplantation, FFM was measured by dual-energy X-ray absorptiometry. Data from a consecutive cohort of 369 kidney transplant recipients were randomly assigned to an equation development data set (n = 245) or an evaluation data set (n = 124). Prediction equations were developed using linear and nonlinear regression analysis. The predictive performance of the developed equation and previously published equations in the evaluation data set was assessed.

RESULTS

The following equation was developed: FFM (kg) = {FFMmax × body weight (kg)/[81.3 + body weight (kg)]} × [1 + height (cm) × 0.052] × [1-age (years) × 0.0007], where FFMmax was estimated to be 11.4 in males and 10.2 in females. This equation provided an unbiased, precise prediction of FFM in the evaluation data set: mean error (ME) (95% CI), -0.71 kg (-1.60 to 0.19 kg) in males and -0.36 kg (-1.52 to 0.80 kg) in females, root mean squared error 4.21 kg (1.65-6.77 kg) in males and 3.49 kg (1.15-5.84 kg) in females. Using previously published equations, FFM was systematically overpredicted in kidney-transplanted males [ME +1.33 kg (0.40-2.25 kg) to +5.01 kg (4.06-5.95 kg)], but not in females [ME -2.99 kg (-4.07 to -1.90 kg) to +3.45 kg (2.29-4.61) kg].

CONCLUSIONS

A new equation for FFM prediction in kidney transplant recipients has been developed. The equation may be used for population pharmacokinetic modeling and clinical dose selection in kidney transplant recipients.

Tacrolimus Induces Insulin Resistance and Increases the Glucose Absorption in the Jejunum: A Potential Mechanism of the Diabetogenic Effects

Background

The use of the immunosuppressive drug tacrolimus (TAC) is related to new onset diabetes after transplantation. Herein, we examined the effect of intraperitoneal administered TAC on intestinal glucose absorption in mice.

Methods

Animals received low, medium, or high dose TAC (0.5, 1, or 5 mg/kg/d, respectively), or 0.9% saline solution (control) for 14 days. Oral glucose tolerance test (OGTT), insulin concentration test, and serum TAC concentration measurements was performed after 14 days of TAC exposure. Plasma insulin was assessed and electrogenic glucose absorption were measured by the sodium-dependent increase of the short-circuit current. Expression levels of the glucose transporters sodium glucose co-transporter (SGLT) 1, glucose transporter (GLUT) 2, and GLUT5 were also determined.

Results

Oral glucose absorption assessed by OGTT was significantly enhanced in the low, medium, and high groups. Serum insulin was elevated in the medium and high group compared with the control. Moreover, glucose-induced Isc was significantly higher in TAC administrated groups, which indicates that SGLT1 activity increased. Transcription levels and protein abundance of SGLT1 in the experimental groups also increased compared with the control.

Conclusions

TAC induced insulin resistance and strengthened intestinal glucose absorption by increasing the activity and expression of the glucose transporter, SGLT1.