Pharmacogenomic Analysis of CYP3A5*3 and Tacrolimus Trough Concentrations in Vietnamese Renal Transplant Outcomes

Figures & data

Table 1 Characteristics of Our External Dataset

Characteristics	Results (N=54)
Sex, n (%)
Male	42 (77.8%)
Female	12 (22.2%)
Age^a	36.0 ± 8.2
Weight (kg)^b	58 (53.0–63.0)
Hematocrit (%)^a	43.5 ± 3.8
Postoperative period (days)^a	1426.6 ± 74.2
Total daily tacrolimus dose (mg)^b	6.0 (4.0–7.0)
Tacrolimus trough concentration (ng/mL)^a	5.6 ± 1.5
CYP3A5 genotype, n (%)
*1*1/*1*3	23 (42.6%)
*3*3	31 (57.4%)

Notes: ^aMean ± SD, ^bMedian (quartile).

Figure 1 Tacrolimus concentration over time of the observed patients.

Figure 2 PRISMA diagram of screening Tacrolimus PopPK studies.

Table 2 Summary of Demographic Characteristics of the Included Published Studies

Study	Han et al 2013.^Citation11	Zhang et al 2017.^Citation12	Woillard et al 2011^Citation13	Zhu et al 2018.^Citation14	Ling et al 2020.^Citation15
Mean age ± SD (range) (years)	38 (19–65)	39 (19–64)	55 (18–69)	38.26 ± 9.95	39 (18–68)
Mean weight ± SD (range) (kg)	59.0 ± 10.2	60.2 (44.0–107.0)	65 (46–97)	57.02 ± 9.92	60 (34–95)
Mean Hematocrit ± SD (range) (%)	35 ± 7.8	39.1 (18.4–60.7)	32.3 (20.9–46.6)	33.77 ± 7.83	30.9 (18.8–49.6)
CYP3A5 *1*1/ *1*3	32	39	1	57	213
CYP3A5 *3*3	48	44	31	84	121
No. of patients	80	83	32	141	234
No. of samples	2788	2109	145	1232	936
No. of PK dots/ patients	34.85	25.41	4.53	8.74	4
Bioassay	MEIA	EMIT	LCMS	CMIA	EMIT

Abbreviations: CMIA, chemiluminescence immunoassay; EMIT, enzyme-multiplied immunoassay; MEIA, microparticle enzyme-linked immunoassay; LCMS, Liquid chromatography–mass spectrometry.

Table 3 Summary of Structural, Statistical, and Covariate Models of the Included Published Studies

Study	Structural Model	Pharmacokinetic Parameters and Covariate Relationships	Inter-Subject Variability	Residual Variability	Model Evaluation
Han et al 2013.^Citation11	1-CMT First-order absorption and elimination	Ka = 4.5 h^−1	49.8 48.7	Prop: 40.0%	RMSE, Bootstrap
Zhang et al 2017.^Citation12	1-CMT First-order absorption and elimination	Ka = 4.5 h^−1CYP3A5 = 0 if patients did not express CYP3A5, others CYP3A5 = 1	18.8 43.7	Add: 2.33 ng/mL	GOF, VPC, Bootstrap
Woillard et al 2011.^Citation13	2-CMT Erlang’s delay and absorption time First-order elimination	Ktr = 5.11 h^−1 Mtt = 0.78 h Ka = 7.31 h^−1V1/F = 140.94 (L) Q/F (L/h) = 79 V2/F (L) = 271 CYP3A5 = 0 if patients did not express CYP3A5, others CYP3A5 = 1	24 32.6 28 31 54 60	Prop: 11.3% Add: 0.71 ng/mL	VPC
Zhu et al 2018.^Citation14	1-CMT First-order absorption and elimination	Ka=3.089 h^−1CYP3A5 = 0.753 if patients did not express CYP3A5, others CYP3A5 =1 V/F=240 L (constant)	28.8	Prop: 18.8% Add: 2.5 ng/mL	Bootstrap
Ling et al 2020.^Citation15	1-CMT First-order absorption and elimination	Ka=4.5 h^−1CYP3A5 = 0 if patients did not express CYP3A5, others CYP3A5 =1 V/F (L)=240	21.9	Prop: 24.4% Add: 1.40 ng/mL	GOF, Bootstrap

Abbreviations: Add, additive; CL/F, the clearance; CMT, compartment; CYP, cytochrome P450; CYP3A5*1*3 and CYP3A5*3*3, alleles of CYP3A5; 1GOF, goodness of fit; HCT, hematocrit; Ka, absorption rate constant; Ktr, the absorption rate; Mtt, mean transit time; POD, post-operative day; Prop, proportional; Q/F, apparent intercompartmental oral clearance; RMSE, Root mean square error; V/F, apparent volume of distribution; V1/F, apparent volume of distribution of the central; V2/F, volume of distribution of peripheral compartment; VPC, visual predictive check; WTKG, body weight (kg).

Figure 3 The MPE differences between Bayesian approach and priori approach.^11–15

Abbreviation: MPE, mean prediction error.

Figure 4 The MAPE differences between Bayesian approach and priori approach.^11–15

Abbreviation: MAPE, mean absolute prediction error.

Figure 5 Individual-predicted concentrations against observed concentration using Goodness-of-fit plot.^11–15

Notes: Horizontal axis: Individual predictions of tacrolimus concentration (ng/mL), vertical axis: Observed tacrolimus concentration (ng/mL).

Figure 6 The difference on predicted concentrations between two groups of expressers versus non-expresser CYP3A5.

Han N, Yun HY, Hong JY, et al. Prediction of the tacrolimus population pharmacokinetic parameters according to CYP3A5 genotype and clinical factors using NONMEM in adult kidney transplant recipients. Eur J Clin Pharmacol. 2013;69(1):53–63. doi:10.1007/s00228-012-1296-4

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Woillard JB, de Winter BC, Kamar N, Marquet P, Rostaing L, Rousseau A. Population pharmacokinetic model and Bayesian estimator for two tacrolimus formulations--twice daily Prograf and once daily Advagraf. Br J Clin Pharmacol. 2011;71(3):391–402. doi:10.1111/j.1365-2125.2010.03837.x

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