Population pharmacokinetics and dosing regimen optimisation of tacrolimus in Chinese pediatric hematopoietic stem cell transplantation patients Abstract 1. Several tacrolimus population pharmacokinetic (PPK) models in hematopoietic stem cell transplantation (HSCT) patients have been set up to recommend an optimal dosage schedule. However, the PPK model of Chinese pediatric HSCT patients has not been reported. The study is to investigate whether published PPK models of HSCT patients can be used to simulate Chinese pediatric HSCT patients and establish the tacrolimus PPK model of 1 1 2 3 4 5 6 7 8 9 10 11 12 13 1 2
Transcript
Population pharmacokinetics and dosing regimen optimisation of
tacrolimus in Chinese pediatric hematopoietic stem cell
transplantation patients
Abstract
1. Several tacrolimus population pharmacokinetic (PPK) models in hematopoietic
stem cell transplantation (HSCT) patients have been set up to recommend an
optimal dosage schedule. However, the PPK model of Chinese pediatric HSCT
patients has not been reported. The study is to investigate whether published PPK
models of HSCT patients can be used to simulate Chinese pediatric HSCT patients
and establish the tacrolimus PPK model of Chinese pediatric HSCT patients.
2. Published PPK models were collected from the literature and assessed using
Chinese pediatric HSCT patients via the individual prediction error method. The
establishment of tacrolimus PPK model in Chinese pediatric HSCT patients were
characterized with nonlinear mixed-effects modeling (NONMEM).
3. Three published HSCT PPK models were identified, two of which could be applied
to our external dataset. However, these models were dissatisfactory in terms of
individual prediction error and hence, inadequate for extrapolation. Finally, a new
tacrolimus PPK model in Chinese pediatric HSCT patients was established. Based
on the simulation results of our model, new initial dosage suggestions were
recommendated. In conclusion, the tacrolimus PPK model in Chinese pediatric
HSCT patients was presented and the model could be used to predict individualized
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dosing regimens in children with HSCT.
Keywords: population pharmacokinetics, tacrolimus, Chinese pediatric hematopoietic
stem cell transplantation, real world study, ursodeoxycholic acid
Introduction
Hematopoietic stem cell transplantation (HSCT) is increasingly used to treat many
malignant and nonmalignant diseases (Jacobson et al., 2001). However, post-
transplant immunologic complications, for example, acute and chronic graft-versus-
host disease (GVHD), are barriers to successful transplantation. Thus, it is critical to
reduce the risk of GVHD. Fortunately, tacrolimus has been used as an drug therapy
for the prevention of GVHD following HSCT (Fay et al., 1995; Nash et al., 1995; Fay
et al., 1996; Nash et al., 1996; Przepiorka et al., 1996; Uberti et al., 1997; Nash et al.,
2000).
However, tacrolimus has a narrow therapeutic range (Venkataramanan et al.,
1995), high concentrations seem to be associated with toxicity and lower
concentrations are connected with an increased risk of acute rejection episodes (Staatz
and Tett, 2004; Passey et al., 2011). Additionally, tacrolimus has considerable inter-
and intra-individual variabilities in pharmacokinetics. Tacrolimus, given orally is
absorbed incompletely with a lag time of average 0.4 h (range 0-2 h) and absorption
rates spanning from very fast to slow (Jusko et al., 1995a). In spite of such variation,
the trough concentrations of tacrolimus correlate well with the area under the
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concentration-time curve (AUC) (Grevel and Kahan, 1991; Jusko et al., 1995a; Jusko
et al., 1995b; Venkataramanan et al., 1995).
Population pharmacokinetics (PPK) can collect pharmacokinetic information
from sparse data in patients (Marsot et al., 2017). What is more, PPK analysis could
distinguish inter- and intra-individual variabilities (Vadcharavivad et al., 2016; Wang
et al., 2018). Hence, PPK has greater statistical power to ascertain the effects of
multiple factors on pharmacokinetics of tacrolimus compared to traditional
pharmacokinetic method (Vadcharavivad et al., 2016; Wang et al., 2018), and makes it
possible to design an optimal dosage regimen.
Several tacrolimus population pharmacokinetic (PPK) models in HSCT have
been established to design an optimal dose schedule (Wallin et al., 2009; Xue et al.,
2009). However, the PPK model of Chinese pediatric HSCT patients has not been
reported. Therefore, it is vital to investigate whether published PPK models of HSCT
patients can be used to simulate Chinese pediatric HSCT patients and to establish
tacrolimus PPK model of Chinese pediatric HSCT patients and formulate an ideal
dose regimen for personalized medicine.
Materials and method
Review of published PPK models
Literatures were collected with the PubMed, Web of Sci, CNKI and Wanfang
databases up to 9 January 2019. The included criteria for publications were as
follows: (I) studies involving HSCT patients with tacrolimus treatment and (II)
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studies involving PPK modeling.
Patients and data collection
Chinese pediatric HSCT patients between June 2015 and June 2018 from Children’s
Hospital of Fudan University were retrospectively analyzed. Drug concentration data
and relevant clinical information were collected from therapeutic drug monitoring
(TDM) records and medical records, respectively. The study was approved by the
Research Ethics Committee of Children’s Hospital of Fudan University.
Information extracted from the medical records included gender, age, weight,
post transplant day (POD), albumin (ALB), globulin (GLB), alanine transaminase
(ALT), aspartate transaminase (AST), creatinine (Cr), Urea (Ur), total protein (TP),
total bile acid (TBA), direct bilirubin (DBIL), total bilibrubin (TBIL), hematocrit
(HCT), hemoglobin (HGB), mean corpuscular hemoglobin (MCH), mean corpuscular
hemoglobin concentration (MCHC) and concomitant drugs.
Dosing and sampling schedule
Tacrolimus was orally administered and the starting dosage was 0.12 mg/kg/day
splited into two doses. The drug concentration of tacrolimus was measured twice
weekly or more frequently if required (e.g., in case of suspicion of intolerance or
adverse events) by therapeutic drug monitoring (TDM). Tacrolimus dose was later
adjusted according to the clinical efficacy and adverse effect as well as its trough
concentration in TDM. All of the blood concentrations were collected before next
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administration and the tacrolimus concentrations used in the current research were
trough concentrations.
Generally, the target trough concentrations range cited was 10-20 ng/ml. The
upper limit of 20 ng/ml comes from the analysis correlating toxicity with higher blood
levels (Wingard et al., 1998; Przepiorka et al., 1999b). The lower limit of 10 ng/ml
was due to the fact that there is less published experience using lower target levels
(Przepiorka et al., 1999a). A number of centers have used an upper level of 15 ng/ml,
which allowed for an interval of 5 ng/ml before getting into the range of increased
risk of nephrotox-icity (Przepiorka et al., 1999a). Therefore, in the present study, the
target therapeutic range is 10-15ng/ml.
Analytical method
Blood concentrations of tacrolimus were measured by Emit® 2000 Tacrolimus Assay
(Siemens Healthcare Diagnostics Inc, Newark, US). Several different measurement
techniques were used in the included studies and there were system deviations among
the different analysis methods (Agrawal et al., 2014). For adjusting the differences,
the tacrolimus concentrations of the external data were converted to their
corresponding equivalents according to previously published bioassay methodology
with the following formulae (Hesse et al., 2002), Equation 1:
MEIA=(EMIT−0.05)/0.96 (1)
where EMIT was the concentration of the external data measured via the enzyme
multiplied immunoassay technique and MEIA was the after-conversion equivalent
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analyzed using the microparticle enzyme immunoassay.
Evaluation of external predictiveness
The external evaluation was carried out by a non-linear mixed-effects modeling
(NONMEM) software (edition 7, ICON Development Solutions, Ellicott City, MD,
USA). The results were performed with the R package (version 3.4.2, http://www.r-
project.org). PPK models from published papers were rebuilt using parameters set
based on the published values. Given the sparse PK sampling, maximum a posterior
(MAP) Bayesian was used to assess the influence of observations on model
predictability (Brooks et al., 2016; Zhao et al., 2016). The predictive performances of
these PPK models with the external data were evaluated using the individual
prediction error method, which compared differences between observations with
individual predictions, Equation 2:
IPE = (IP−OB)/OB×100% (2)
where IPE was individual prediction error, IP represented the individual predicted
concentration and OB was the observation.
Establishment of a new model
Data were analyzed with NONMEM. The first-order conditional estimation method
with interaction (FOCE-I) option was used to estimate pharmacokinetic (PK)
parameters and their variability. One-compartment model with first-order elimination
was used to describe the absorption phase because all the tacrolimus concentrations in
Three studies were included, finally (Figure 1 and Table 1). One study (Wallin et al.,
2009) focused on children and the other two studies (Jacobson et al., 2001; Xue et al.,
2009) had a wide range of ages from children to adult. Because the model from
Jacobson et al (Jacobson et al., 2001) is incomplete (lack of information on apparent
volume of distribution), only two models could be applied to our external dataset.
Evaluation of external predictiveness
The data obtained from 17 Chinese pediatric HSCT patients were available. Patient
characteristics and drug combination were summarized in Table 2 and Table 3,
respectively. As shown in Figure 2, the individual prediction errors obtained from the
two models were not satisfactory and it was required to produce a new model to apply
to Chinese pediatric HSCT patients.
Production of the new model and model evaluation
A one-compartment model with first absorption and elimination best fitted the data.
The PK parameters of tacrolimus, CL/F and V/F, were estimated by NONMEM. The
final covariate models were as follows:
CL/F = 15.4 × (WT/70)0.75 × (1+URSO×0.964) (8)
V/F = 6250 × (WT/70) (9)
where CL/F was apparent oral clearance, V/F was apparent volume of distribution,
WT and URSO were weight and ursodeoxycholic acid, respectively. Figure 3 showed
the covariate relationships.
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Validation of new model
In Figure S1, visual inspection of routine diagnostic plots were shown. In Table 4,
parameter estimates of final model and bootstrap validation were shown. The median
values of the parameter estimate from bootstraps were close to the respective values
from the final population model, and the absolute value of all bias were < 15%,
showing that the estimates for the PK parameters in the final population model were
accurate, and the model was reliable. In Figure S2, the distribution of weighted
residuals for the final model was shown. In Figure S3, the prediction-corrected visual
predictive check plot of the final model was shown. Most of the observed
concentrations are within the 95% prediction intervals from the simulation data,
revealing that the prediction-corrected concentrations were well predicted by the final
model.
Simulation of weight effect at different dose
The predicted median (2.5-97.5%) and probability to achieve the target concentration
with respect to body weight for different dosing regimens were were shown in Table
5. Based on the simulation results of our model, new initial dosage suggestions were
recommendated. The 1.0mg/0.5mg q24h regimen is appropriate for children with a
weight of 5kg; the 1.0mg/1.0mg q24h regimen is suitable for children with a weight
of 7.5kg; the 1.5mg/1.0mg q24h regimen is fit for children with a weight of 10kg; the
1.5mg/1.5mg q24h regimen is appropriate for children with a weight of 12.5kg; the
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2.0mg/1.5mg q24h regimen is appropriate for children with a weight of 15kg; the
2.5mg/2.0mg q24h regimen is appropriate for children with a weight of 17.5kg; the
2.5mg/2.5mg q24h regimen is appropriate for children with a weight of 20kg.
Discussion
Currently, different tacrolimus PPK models have been built in many populations
including renal transplant patients (Zhao et al., 2009; Benkali et al., 2010; Zuo et al.,
2013; Bergmann et al., 2014; Han et al., 2014; Andreu et al., 2015), liver transplant
patients (Wallin et al., 2011; Zhang et al., 2012; Musuamba et al., 2014; Lu et al.,
2015; Yang et al., 2015; Zhu et al., 2015), and lung transplant patients (Monchaud et
al., 2012). However, only several tacrolimus PPK models in HSCT have been
established (Jacobson et al., 2001; Wallin et al., 2009; Xue et al., 2009) and the PPK
model of Chinese pediatric HSCT patients has not been reported. The study is to
investigate whether published PPK models of HSCT patients can be used to simulate
Chinese pediatric HSCT patients and establish the tacrolimus PPK model in Chinese
pediatric HSCT patients.
In our study, three published HSCT PPK models were identified, two of which
could be applied to our external dataset. However, these models were dissatisfactory
in terms of individual prediction error and hence, inadequate for extrapolation. In
Wallin et al study (Wallin et al., 2009), all patients aged from birth to 18 years who
underwent HSCT between 2002 and 2007 at Queen Silvia Children’s Hospital in
Gothenburg, Sweden, and received tacrolimus as initial prophylaxis against GVHD
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were eligible for inclusion in study. Pharmacokinetic, demographic, and other clinical
data were collected retrospectively from patient electronic medical records. However,
the patients were limited to Swede and the effect of concomitant medications is
unknown (Wallin et al., 2009). In Xue et al study (Xue et al., 2009), the age range
was 12-60 years, which was not fitted to our age group.
To explore the influence of demographic features, biological characteristics, and
concomitant medications on tacrolimus CL/F in Chinese pediatric HSCT patients, our
model was built. Our present study is the first report, to our knowledge, of a
population pharmacokinetics model of tacrolimus in Chinese pediatric HSCT patients
based on real world study. The typical values of CL/F and V/F in final tacrolimus
PPK model were 15.4L/h and 6250L. However, in Xue et al study (Xue et al., 2009).
The population typical values of tacrolimus CL and V were 12.1L/h and 686L,
respectively. In Wallin et al study (Wallin et al., 2009), Typical clearance was 106
mL/h/kg-0.75, typical distribution volume was 3.71 L/kg. This may partly explain the
possible mechanism for why our new PPK model differs from other models.
Furthermore, the database used to build models varied from study to study. It was
mainly referring to potential differences in areas of: patient eligibility based on
inclusion/exclusion criteria, patient's baseline disease status, use of concurrent-
medications, and potential differences in their treatment dose regimen and
demographic information. All of these may attribute to the inability of extrapolating
these models to describe the our dataset.
We also examined various covariates on different parameters and the following
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covariates were determined to be meaningful: weight and ursodeoxycholic acid were
included as significant covariates for CL/F, weight was for V/F. Many studies have
demonstrated a non-linear relationship between drug clearance and body weight in
pediatric patients, and it may be well described with allometric scaling using a
coefficient of 0.75 for clearance and 1 for volume (Holford, 1996; Anderson and
Holford, 2008; 2011). Body weight is the most important predictor of clearance and
volume in children with maturation of elimination processes (Anderson and Holford,
2011) and is also considered to be the primary factor determining clearance and
volume because of theory explaining the link between mass, function and structure
and the extensive support for this theory across many orders of magnitude of body
weight (Savage et al., 2008). Therefore, the factor for body weight for clearance may
be expected to scale to weight with a power 0.75 and a coefficient of 1 for volume
(Holford, 1996; Anderson and Holford, 2008; 2011). For ease of comparison with
other results, the body weight is usually standardized to a value of 70 kg (Anderson
and Holford, 2011). It is especially valuable to use a standard when reporting the
results of studies in children and neonates (Anderson and Holford, 2011). The major
differences between age groups of differing body weights require standardization to a
common size to make meaningful comparisons (Anderson and Holford, 2011). Even
if the age groups appear to be comparable, different studies may have substantial
differences in typical weights (Anderson and Holford, 2011). Interpretation of
parameter estimates is more convenient when comparing standard values (Anderson
and Holford, 2011). In addition, biliary elimination is the major excretion pathway for
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tacrolimus (Moller et al., 1999) and ursodeoxycholic acid can promote bile excretion,
increasing tacrolimus clearance.
Although the TDM was not originally designed to research the pharmacokinetics
of tacrolimus, the PPK method provides a powerful tool to extract useful information
from sparse drug concentrations (Thomson and Whiting, 1992) from traditional TDM.
Thus, PPK can promote to optimize the use for tacrolimus to realize satisfactory
therapeutic concentrations. Additionally, it is ethical suitable in studying pediatric
patients prohibited excessive blood sampling compared with traditional
pharmacokinetic studies (Kauffman and Kearns, 1992). Consequently, the tacrolimus
PPK model has clinical value in predicting pharmacokinetic process in individual
pediatric patients who has HSCT condition. In terms of model application, 1000
virtual patients were simulated in each of the seven weight groups and for nine dosing
regimens and new initial dosage suggestions were recommendated. Current starting
dosing recommendations for tacrolimus is based on weight and is fixed at 0.12
mg/kg/day, which is lacking of individualized treatment options. In our study, we
simulated more accurate individualized drug treatment for different body weights.
The 1.0mg/0.5mg q24h regimen is appropriate for children with a weight of 5kg; the
1.0mg/1.0mg q24h regimen is suitable for children with a weight of 7.5kg; the
1.5mg/1.0mg q24h regimen is fit for children with a weight of 10kg; the 1.5mg/1.5mg
q24h regimen is appropriate for children with a weight of 12.5kg; the 2.0mg/1.5mg
q24h regimen is appropriate for children with a weight of 15kg; the 2.5mg/2.0mg
q24h regimen is appropriate for children with a weight of 17.5kg; the 2.5mg/2.5mg
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q24h regimen is appropriate for children with a weight of 20kg.
The same as three published articles, there were limitations in our study. As for
HSCT patients pharmacogenomic consideration hasn’t been used in clinical, whether
the inclusion of genotyping in our model would better explain the variabilities of
tacrolimus in Chinese pediatric HSCT patients should be studied in future. In
addition, small group of patients, use of trough concentrations only instead of
intensive sampling were also limitations in our study.
In a word, the tacrolimus PPK model in Chinese pediatric HSCT patients was
established and the model could be used to predict individualized dosing regimens in
children with HSCT. A large external evaluation of our model will be conducted in
future studies.
Funding
This work was supported by the Clinical Pharmacy Key Specialty Construction
Project of Shanghai under Grant number YZ2017/5; the Young Medical Talents of
Wuxi under Grant number QNRC020; the Young Project of Wuxi Health and Family
Planning Research under Grant number Q201706; the Wuxi science and technology
development guidance plan (medical and health care) under Grant number
CSZON1744; the AOSAIKANG pharmaceutical foundation under Grant number
A201826.
Declaration of interest
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The authors report no declarations of interest. The authors alone are responsible for
the content and writing of this article.
References
Agrawal, Y.P., Cid, M., Westgard, S., Parker, T.S., Jaikaran, R., and Levine, D.M. (2014). Transplant patient classification and tacrolimus assays: more evidence of the need for assay standardization. Ther Drug Monit 36(6), 706-709. doi: 10.1097/ftd.0000000000000094.
Anderson, B.J., and Holford, N.H. (2008). Mechanism-based concepts of size and maturity in pharmacokinetics. Annu Rev Pharmacol Toxicol 48, 303-332. doi: 10.1146/annurev.pharmtox.48.113006.094708.
17
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365
366
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368
369
370
371
372
373
374
375376377378379380
3334
Anderson, B.J., and Holford, N.H. (2011). Tips and traps analyzing pediatric PK data. Paediatr Anaesth 21(3), 222-237. doi: 10.1111/j.1460-9592.2011.03536.x.
Andreu, F., Colom, H., Grinyo, J.M., Torras, J., Cruzado, J.M., and Lloberas, N. (2015). Development of a population PK model of tacrolimus for adaptive dosage control in stable kidney transplant patients. Ther Drug Monit 37(2), 246-255. doi: 10.1097/ftd.0000000000000134.
Benkali, K., Rostaing, L., Premaud, A., Woillard, J.B., Saint-Marcoux, F., Urien, S., et al. (2010). Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in renal transplant recipients on a new once-daily formulation. Clin Pharmacokinet 49(10), 683-692. doi: 10.2165/11535950-000000000-00000.
Bergmann, T.K., Hennig, S., Barraclough, K.A., Isbel, N.M., and Staatz, C.E. (2014). Population pharmacokinetics of tacrolimus in adult kidney transplant patients: impact of CYP3A5 genotype on starting dose. Ther Drug Monit 36(1), 62-70. doi: 10.1097/FTD.0b013e31829f1ab8.
Brendel, K., Dartois, C., Comets, E., Lemenuel-Diot, A., Laveille, C., Tranchand, B., et al. (2007). Are population pharmacokinetic and/or pharmacodynamic models adequately evaluated? A survey of the literature from 2002 to 2004. Clin Pharmacokinet 46(3), 221-234. doi: 10.2165/00003088-200746030-00003.
Brooks, E., Tett, S.E., Isbel, N.M., and Staatz, C.E. (2016). Population Pharmacokinetic Modelling and Bayesian Estimation of Tacrolimus Exposure: Is this Clinically Useful for Dosage Prediction Yet? Clin Pharmacokinet 55(11), 1295-1335. doi: 10.1007/s40262-016-0396-1.
Fay, J.W., Nash, R.A., Wingard, J.R., Przepiorka, D., Collins, R.H., Anasetti, C., et al. (1995). FK 506-based immunosuppression for prevention of graft versus host disease after unrelated donor marrow transplantation. Transplant Proc 27(1), 1374.
Fay, J.W., Wingard, J.R., Antin, J.H., Collins, R.H., Pineiro, L.A., Blazar, B.R., et al. (1996). FK506 (Tacrolimus) monotherapy for prevention of graft-versus-host disease after histocompatible sibling allogenic bone marrow transplantation. Blood 87(8), 3514-3519.
Grevel, J., and Kahan, B.D. (1991). Area under the curve monitoring of cyclosporine therapy: the early posttransplant period. Ther Drug Monit 13(2), 89-95.
Han, N., Ha, S., Yun, H.Y., Kim, M.G., Min, S.I., Ha, J., et al. (2014). Population pharmacokinetic-pharmacogenetic model of tacrolimus in the early period after kidney transplantation. Basic Clin Pharmacol Toxicol 114(5), 400-406. doi: 10.1111/bcpt.12176.
Hesse, C.J., Baan, C.C., Balk, A.H., Metselaar, H.J., Weimar, W., and van Gelder, T. (2002). Evaluation of the new EMIT enzyme immunoassay for the determination of whole-blood tacrolimus concentrations in kidney, heart, and liver transplant recipients. Transplant Proc 34(7), 2988-2990.
Holford, N.H. (1996). A size standard for pharmacokinetics. Clin Pharmacokinet 30(5), 329-332. doi: 10.2165/00003088-199630050-00001.
Jacobson, P., Ng, J., Ratanatharathorn, V., Uberti, J., and Brundage, R.C. (2001). Factors affecting the pharmacokinetics of tacrolimus (FK506) in hematopoietic cell transplant (HCT) patients. Bone Marrow Transplant 28(8), 753-758. doi: 10.1038/sj.bmt.1703224.
Jolling, K., Perez Ruixo, J.J., Hemeryck, A., Vermeulen, A., and Greway, T. (2005). Mixed-effects modelling of the interspecies pharmacokinetic scaling of pegylated human erythropoietin. Eur J Pharm Sci 24(5), 465-475. doi: 10.1016/j.ejps.2005.01.002.
Jusko, W.J., Piekoszewski, W., Klintmalm, G.B., Shaefer, M.S., Hebert, M.F., Piergies, A.A., et al.
(1995a). Pharmacokinetics of tacrolimus in liver transplant patients. Clin Pharmacol Ther 57(3), 281-290. doi: 10.1016/0009-9236(95)90153-1.
Jusko, W.J., Thomson, A.W., Fung, J., McMaster, P., Wong, S.H., Zylber-Katz, E., et al. (1995b). Consensus document: therapeutic monitoring of tacrolimus (FK-506). Ther Drug Monit 17(6), 606-614.
Kauffman, R.E., and Kearns, G.L. (1992). Pharmacokinetic studies in paediatric patients. Clinical and ethical considerations. Clin Pharmacokinet 23(1), 10-29. doi: 10.2165/00003088-199223010-00002.
Lu, Y.X., Su, Q.H., Wu, K.H., Ren, Y.P., Li, L., Zhou, T.Y., et al. (2015). A population pharmacokinetic study of tacrolimus in healthy Chinese volunteers and liver transplant patients. Acta Pharmacol Sin 36(2), 281-288. doi: 10.1038/aps.2014.110.
Marsot, A., Michel, F., Chasseloup, E., Paut, O., Guilhaumou, R., and Blin, O. (2017). Phenobarbital in intensive care unit pediatric population: predictive performances of population pharmacokinetic model. Fundam Clin Pharmacol 31(5), 558-566. doi: 10.1111/fcp.12291.
Moller, A., Iwasaki, K., Kawamura, A., Teramura, Y., Shiraga, T., Hata, T., et al. (1999). The disposition of 14C-labeled tacrolimus after intravenous and oral administration in healthy human subjects. Drug Metab Dispos 27(6), 633-636.
Monchaud, C., de Winter, B.C., Knoop, C., Estenne, M., Reynaud-Gaubert, M., Pison, C., et al. (2012). Population pharmacokinetic modelling and design of a Bayesian estimator for therapeutic drug monitoring of tacrolimus in lung transplantation. Clin Pharmacokinet 51(3), 175-186. doi: 10.2165/11594760-000000000-00000.
Musuamba, F.T., Guy-Viterbo, V., Reding, R., Verbeeck, R.K., and Wallemacq, P. (2014). Population pharmacokinetic analysis of tacrolimus early after pediatric liver transplantation. Ther Drug Monit 36(1), 54-61. doi: 10.1097/FTD.0b013e31829dcbcd.
Nash, R.A., Antin, J.H., Karanes, C., Fay, J.W., Avalos, B.R., Yeager, A.M., et al. (2000). Phase 3 study comparing methotrexate and tacrolimus with methotrexate and cyclosporine for prophylaxis of acute graft-versus-host disease after marrow transplantation from unrelated donors. Blood 96(6), 2062-2068.
Nash, R.A., Etzioni, R., Storb, R., Furlong, T., Gooley, T., Anasetti, C., et al. (1995). Tacrolimus (FK506) alone or in combination with methotrexate or methylprednisolone for the prevention of acute graft-versus-host disease after marrow transplantation from HLA-matched siblings: a single-center study. Blood 85(12), 3746-3753.
Nash, R.A., Pineiro, L.A., Storb, R., Deeg, H.J., Fitzsimmons, W.E., Furlong, T., et al. (1996). FK506 in combination with methotrexate for the prevention of graft-versus-host disease after marrow transplantation from matched unrelated donors. Blood 88(9), 3634-3641.
Passey, C., Birnbaum, A.K., Brundage, R.C., Oetting, W.S., Israni, A.K., and Jacobson, P.A. (2011). Dosing equation for tacrolimus using genetic variants and clinical factors. Br J Clin Pharmacol 72(6), 948-957. doi: 10.1111/j.1365-2125.2011.04039.x.
Przepiorka, D., Devine, S., Fay, J., Uberti, J., and Wingard, J. (1999a). Practical considerations in the use of tacrolimus for allogeneic marrow transplantation. Bone Marrow Transplant 24(10), 1053-1056. doi: 10.1038/sj.bmt.1702032.
Przepiorka, D., Ippoliti, C., Khouri, I., Woo, M., Mehra, R., Le Bherz, D., et al. (1996). Tacrolimus and minidose methotrexate for prevention of acute graft-versus-host disease after matched unrelated donor marrow transplantation. Blood 88(11), 4383-4389.
Przepiorka, D., Nash, R.A., Wingard, J.R., Zhu, J., Maher, R.M., Fitzsimmons, W.E., et al. (1999b). Relationship of tacrolimus whole blood levels to efficacy and safety outcomes after unrelated donor marrow transplantation. Biol Blood Marrow Transplant 5(2), 94-97.
Savage, V.M., Deeds, E.J., and Fontana, W. (2008). Sizing up allometric scaling theory. PLoS Comput Biol 4(9), e1000171. doi: 10.1371/journal.pcbi.1000171.
Staatz, C.E., and Tett, S.E. (2004). Clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplantation. Clin Pharmacokinet 43(10), 623-653.
Thomson, A.H., and Whiting, B. (1992). Bayesian parameter estimation and population pharmacokinetics. Clin Pharmacokinet 22(6), 447-467. doi: 10.2165/00003088-199222060-00004.
Uberti, J.P., Silver, S.M., Adams, P.T., Jacobson, P., Scalzo, A., and Ratanatharathorn, V. (1997). Tacrolimus and methotrexate for the prophylaxis of acute graft-versus-host disease in allogeneic bone marrow transplantation in patients with hematologic malignancies. Bone Marrow Transplant 19(12), 1233-1238. doi: 10.1038/sj.bmt.1700813.
Vadcharavivad, S., Praisuwan, S., Techawathanawanna, N., Treyaprasert, W., and Avihingsanon, Y. (2016). Population pharmacokinetics of tacrolimus in Thai kidney transplant patients: comparison with similar data from other populations. J Clin Pharm Ther 41(3), 310-328. doi: 10.1111/jcpt.12396.
Venkataramanan, R., Swaminathan, A., Prasad, T., Jain, A., Zuckerman, S., Warty, V., et al. (1995). Clinical pharmacokinetics of tacrolimus. Clin Pharmacokinet 29(6), 404-430. doi: 10.2165/00003088-199529060-00003.
Wallin, J.E., Bergstrand, M., Wilczek, H.E., Nydert, P.S., Karlsson, M.O., and Staatz, C.E. (2011). Population pharmacokinetics of tacrolimus in pediatric liver transplantation: early posttransplantation clearance. Ther Drug Monit 33(6), 663-672. doi: 10.1097/FTD.0b013e31823415cc.
Wallin, J.E., Friberg, L.E., Fasth, A., and Staatz, C.E. (2009). Population pharmacokinetics of tacrolimus in pediatric hematopoietic stem cell transplant recipients: new initial dosage suggestions and a model-based dosage adjustment tool. Ther Drug Monit 31(4), 457-466. doi: 10.1097/FTD.0b013e3181aab02b.
Wang, D.D., Lu, J.M., Li, Q., and Li, Z.P. (2018). Population pharmacokinetics of tacrolimus in paediatric systemic lupus erythematosus based on real-world study. J Clin Pharm Ther 43(4), 476-483. doi: 10.1111/jcpt.12707.
Wingard, J.R., Nash, R.A., Przepiorka, D., Klein, J.L., Weisdorf, D.J., Fay, J.W., et al. (1998). Relationship of tacrolimus (FK506) whole blood concentrations and efficacy and safety after HLA-identical sibling bone marrow transplantation. Biol Blood Marrow Transplant 4(3), 157-163. doi: 10.1053/bbmt.1998.v4.pm9923414.
Xue, L., Rui, J.Z., Zhang, Y., and Miao, L.Y. (2009). Population pharmacokinetic study of tacrolimus in patients with hematopoietic stem cell transplant [in Chinese]. Acta Pharmaceutica Sinica 44(10), 1145-1151.
Yang, J.W., Liao, S.S., Zhu, L.Q., Zhao, Y., Zhang, Y., Sun, X.Y., et al. (2015). Population pharmacokinetic analysis of tacrolimus early after Chinese pediatric liver transplantation. Int J Clin Pharmacol Ther 53(1), 75-83. doi: 10.5414/cp202189.
Zhang, X.Q., Wang, Z.W., Fan, J.W., Li, Y.P., Jiao, Z., Gao, J.W., et al. (2012). The impact of sulfonylureas on tacrolimus apparent clearance revealed by a population pharmacokinetics
analysis in Chinese adult liver-transplant patients. Ther Drug Monit 34(2), 126-133. doi: 10.1097/FTD.0b013e31824a67eb.
Zhao, C.Y., Jiao, Z., Mao, J.J., and Qiu, X.Y. (2016). External evaluation of published population pharmacokinetic models of tacrolimus in adult renal transplant recipients. Br J Clin Pharmacol 81(5), 891-907. doi: 10.1111/bcp.12830.
Zhao, W., Elie, V., Roussey, G., Brochard, K., Niaudet, P., Leroy, V., et al. (2009). Population pharmacokinetics and pharmacogenetics of tacrolimus in de novo pediatric kidney transplant recipients. Clin Pharmacol Ther 86(6), 609-618. doi: 10.1038/clpt.2009.210.
Zhu, L., Yang, J., Zhang, Y., Jing, Y., Zhang, Y., and Li, G. (2015). Effects of CYP3A5 genotypes, ABCB1 C3435T and G2677T/A polymorphism on pharmacokinetics of Tacrolimus in Chinese adult liver transplant patients. Xenobiotica 45(9), 840-846. doi: 10.3109/00498254.2015.1021733.
Zuo, X.C., Ng, C.M., Barrett, J.S., Luo, A.J., Zhang, B.K., Deng, C.H., et al. (2013). Effects of CYP3A4 and CYP3A5 polymorphisms on tacrolimus pharmacokinetics in Chinese adult renal transplant recipients: a population pharmacokinetic analysis. Pharmacogenet Genomics 23(5), 251-261. doi: 10.1097/FPC.0b013e32835fcbb6.
Figure legend
Figure 1. Overview of the strategy used in the literature search.
Figure 2. Box plots of individual prediction error.
