Influence of FcγRIIIA genetic polymorphism on T-lymphocyte depletion induced by rabbit...

Influence of FccRIIIA genetic polymorphism on T-lymphocytedepletion induced by rabbit antithymocyte globulins in kidneytransplant patientsDavid Ternanta,b,c, Matthias Buchlere, Gilles Thibaulta,b,d, Marc Ohresserb,Herve Watiera,b,d, Yvon Lebranchue and Gilles Paintauda,b,c

Introduction Polyclonal antithymocyte globulins (ATG)

have been used in transplantation for several decades,

but the sources of the interindividual variability of their

effect are poorly understood. An influence of the

FCGR3A-158V/F genetic polymorphism on the horse ATG

concentration–effect relationship was reported in kidney

transplant patients. The objective of the present study was

to confirm the influence of the FCGR3A polymorphism

on the extent of lymphocyte depletion in kidney transplant

patients treated with rabbit antithymocyte globulin (r-ATG).

Materials and methods Of the 194 transplant patients

treated with r-ATG between 1998 and 2002 in our

institution, 69 patients were eligible and included in

this retrospective study. Biomarkers of response were CD3

and CD4 counts. Dose–effect data were analyzed using

a population approach, and a two-compartment turnover

model with stimulation of lymphocyte ‘output’. Since r-ATG

concentrations were not available, a K-PD model was used.

The influence of FCGR3A genotype on estimated

parameters was investigated.

Results The r-ATG infusion rate leading to a 50%

stimulation of CD3 + output (EDK50), which is inversely

related to patient sensitivity to r-ATG treatment, decreased

with the number of V alleles (P = 0.0016).

Conclusion The genetic polymorphism of FCGR3A

influences r-ATG effect on CD3 count in kidney transplant

patients, those with the V allele being more sensitive

to antilymphocyte serum. These results also suggest

that r-ATG act, at least in part, by antibody-dependent

cellular cytotoxicity. Pharmacogenetics and Genomics

24:26–34 �c 2013 Wolters Kluwer Health | Lippincott

Williams & Wilkins.

Pharmacogenetics and Genomics 2014, 24:26–34

Keywords: antithymocyte globulins, dose–response relationship,immunoglobulin G receptors, kidney transplantation, pharmacogenetics

aFrancois Rabelais University of Tours, bCNRS, UMR 7292 ‘GICC’ University ofTours, cLaboratory of Pharmacology-Toxicology, University Hospital of Tours,dLaboratory of Immunology and eDepartment of Nephrology and ClinicalImmunology, Tours, France

Correspondence to David Ternant, Laboratory of Pharmacology-Toxicology,University Hospital of Tours, 2 Boulevard Tonnelle, 37044 Tours Cedex 9, FranceTel: + 33247476008; fax: + 33247476011; e-mail: [email protected]

Received 26 April 2013 Accepted 8 October 2013

IntroductionAntithymocyte globulins (ATG) are polyclonal antibodies

used in organ transplantation to prevent or treat acute

allograft rejection. They are obtained by immunizing

animals (rabbits or horses) with human lymphoid cells

(lymphoblasts, lymphocytes, or thymocytes). Although

ATG have been in use for several decades, the dose–

concentration–effect relationship and associated sources of

interindividual variability remain poorly understood [1,2].

The mode of action of ATG is probably complex but it

eventually results in target cell lysis. Several mechanisms

were observed using in-vitro models, including apoptosis,

complement-dependent cytotoxicity (CDC), and anti-

body-dependent cellular cytotoxicity (ADCC) in the

presence of cytotoxic cells expressing receptors for the Fc

portion of IgG (FcgR) [3]. In cynomolgus monkeys, the

depletion of lymphocytes after administration of rabbit

antithymocyte globulin (r-ATG) was found to take place

primarily in peripheral lymphoid tissues. Apoptosis and

CDC seem to occur at high r-ATG concentrations, whereas

ADCC may occur at low ATG concentrations [3–5].

The FCGR3A gene, which encodes FcgRIIIA (CD16A),

an FcgR expressed on macrophages and natural killer

(NK) cells, displays a single-nucleotide polymorphism at

position 559 of the cDNA (G or T, rs number 17857127),

which generates two FcgRIIIA allotypes, with either a

valine (V) or a phenylalanine (F) at position 158. In-vitro

studies have shown that human immunoglobulins G (IgG)

have a higher affinity for FcgRIIIA-158V than for the

FcgRIIIA-158F receptors [6]. The FCGR3A polymorphism

was shown to influence the therapeutic response to

therapeutic antibodies acting, at least in part, by cyto-

toxicity: homozygous VV patients presented a better

response than F carriers to rituximab [7,8], but also

to cetuximab [9], trastuzumab [10], and infliximab [11].

We have previously studied the dose–concentration–

effect relationship of horse antilymphocyte globulins

Supplemental digital content is available for this article. Direct URL citationsappear in the printed text and are provided in the HTML and PDF versions of thisarticle on the journal’s website (www.pharmacogeneticsandgenomics.com).

26 Original article

1744-6872 �c 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins DOI: 10.1097/FPC.0000000000000017

Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

mailto:[email protected]

http://www.pharmacogeneticsandgenomics.com

(h-ALG, Lymphoglobuline) in kidney transplant patients

and observed an influence of the FCGR3A polymorphism

on individual concentration–effect relationship, patients

carrying the FcgRIIIA-158 V allele were more sensitive to

h-ALG treatment than FcgRIIIA-158 F/F patients [12].

However, our study was performed on a small number of

patients, and h-ALG is now much less frequently used

than r-ATG.

The objective of the present study was to describe the

influence of the FCGR3A polymorphism on the extent of

lymphocyte depletion in kidney transplant patients

treated with r-ATG.

Materials and methodsThis was a single-center (Tours University Hospital,

France), retrospective study performed in kidney trans-

plant patients treated with r-ATG. In this study, the

influence of the FCGR3A polymorphism on dynamics of

the CD3 + marker was assessed. Indeed, CD3 + cells

are the main target of r-ATG.

Patients

Patients who were treated with r-ATG between 1998 and

2002 were examined. Patients were excluded if (i) they

did not give written informed consent for FCGR3Agenotyping, (ii) they were transplanted more than once

at inclusion, (iii) they were readministered r-ATG within

1 year, (iv) dosing history of r-ATG infusions during the

hospitalization phase was unavailable or incomplete, or

(v) lymphocyte data were insufficient. To be informative,

lymphocyte data for one patient should consist of at least

eight measurements, including at least three measure-

ments after hospital discharge. This last requirement

aimed at selecting a subpopulation of patients for whom

CD3 counts were available during the whole follow-up,

and therefore providing a reliable estimation of individual

parameters.

Treatments

r-ATG (Thymoglobuline; Genzyme Imtix-SangStat,

Lyon, France) was administered through a central venous

catheter or an arteriovenous fistula. The first infusion was

started within the first hour after the patient entered the

recovery room and lasted for 24 h. The second infusion

was started at least 12 h after the end of the first infu-

sion and, as the following ones, was given over a period of

8–12 h. The subsequent infusions were separated by a 24-

h period. The dose of the first two infusions was 1.5 mg/

kg. Subsequent doses were halved if T-cell lymphopenia,

defined as a CD3 count of less than 20 mm – 3 [13], was

achieved, with a minimum dose of 25 mg/infusion.

Concomitant immunosuppressive treatment consisted of

ciclosporine (Neoral; Novartis Pharmaceuticals, Basel,

Switzerland), tacrolimus (Prograf; Astellas Pharma,

Tokyo, Japan), or sirolimus (Rapamune; Wyeth Europa

Ltd, Madison, New Jersey, USA), associated with

mycophenolate mofetil (Cellcept; Roche, Basel, Switzer-

land) and corticosteroids. Before each r-ATG infusion,

5 mg of dexchlorpheniramine was administered.

Biological analyses

Blood samples were collected on a routine basis to monitor

r-ATG effects on lymphocyte counts, before transplantation,

every 48 h during post-transplantation hospitalization, and

at months 1, 3, 6, 9, and 12 after transplantation. Total

CD3 + , CD19 + , and CD3–CD56 + lymphocyte counts

were available during the whole follow-up. CD4 counts

were available at baseline and at months 3, 6, 9, and 12

after transplantation. Lymphocyte counts were per-

formed by flow cytometry. FCGR3A genotyping was

performed as previously described [14].

Kinetic-pharmacodynamic modeling

Biomarkers

Lymphocyte expressing the CD3 marker represents the

largest part (around 70% in adults) of lymphocytes. NK

lymphocytes (CD3–CD56 + cell count), which repre-

sent 15% of lymphocytes, are also targeted by r-ATG.

However, these count data presented profiles that

precluded their use for PK-PD analysis; indeed, large

variations were observed at distance of r-ATG treatment.

B lymphocytes (CD19 count), which represent around

15% of lymphocytes in adults, are not targeted by r-ATG.

As CD3 + cells are the main target of r-ATG, their count

was used as a biomarker of response to r-ATG treatment.

However, CD3 + lymphocytes mainly consist of CD4 +

and CD8 + cells, which display different kinetics of

recovery after r-ATG treatment: CD8 counts recover their

baseline value within 1 or 2 years, whereas CD4 do not

recover their baseline value after several years in most

patients [1,15–18]. Therefore, CD4 counts should be

described separately from global CD3 count.

Structural model

In the present study, a classical PK-PD model could not

be used because r-ATG concentrations were not available.

We tested the kinetic-pharmacodynamic (K-PD) tech-

nique, described by Jacqmin et al. [19] and used by

others [20], which allows the analysis of drug pharmaco-

dynamics in the absence of PK data. Using this approach,

pharmacokinetics is described by a simple virtual one-

compartment model and estimated parameters depend

solely on drug dosing and pharmacodynamic (biomarker)

data. The virtual compartment represents the biophase

and contains the infused amount of r-ATG acting on

lymphocyte dynamics.

We previously described the concentration–effect rela-

tionship of horse antithymocyte globulin (h-ATG) with

an indirect response model [12]. In the present study,

the application of this approach to CD3 count was

FccRIIIA influences r-ALG pharmacodynamics Ternant et al. 27


unsatisfactory (see Supplementary Fig. S1, Supplemental

digital content 1, http://links.lww.com/FPC/A666). First,

CD3 recovery after treatment displayed at least two

phases, a feature that required the use of two compart-

ments. Second, CD4 count at the end of follow-up was

less than half the baseline count, an observation that

required a modification of the model. Since CD4 count

data were scarce, they could not be described separately,

but as part of CD3 count, assuming that CD3 count =

CD4 + non-CD4 count.

K-PD model: In the K-PD model developed, response

was assessed using both CD3 + and CD4 + counts, and

response ‘production’ (lymphocyte ‘input’ in the blood)

was inhibited by a virtual infusion of r-ATG (Fig. 1). The

kinetic part of the K-PD model was.

dA=dt ¼ �KDE�A

IR ¼ KDE�A;

where A (mg) is the amount of r-ATG in the biophase

(i.e. acting on lymphocyte dynamics), t (h) is time, KDE

(h – 1) is the first-order elimination rate constant of r-ATG

from the virtual compartment, A(0) is the baseline value

of A, and IR (mg/h) is the virtual infusion rate of r-ATG.

The initial condition for A was A(0) = 0.

The pharmacodynamic model described both CD3 and

CD4 counts and was bicompartmental (Fig. 1): compart-

ment-noted R accounted for CD3 + and CD4 + cells in

the bloodstream, where their counts are observed,

whereas the compartment-noted M accounted for CD3 +

and CD4 + cells in lymphoid network other than

bloodstream (e.g. in lymph, lymphoid tissues such as

thymus, and lymph nodes). CD3 + and CD4 + lympho-

cyte input and output took place in M and R compart-

ments, respectively; r-ATG effect was described as a

stimulation of lymphocyte output. CD4 + and CD3 +

CD4 – cells were assumed to have the same output

dynamics. Lymphocyte input was described using zero-

order rates as follows: CD3 + input was considered as the

sum of CD3 + CD4 + and CD3 + CD4 – cell input and,

because CD4 recovery is never complete even several

years after r-ATG treatment [1,15–18], CD4 input was

considered as the sum of two components, one that is

‘lost’ and one that is still present after r-ATG treatment.

The pharmacodynamic model was:

dB

dt¼ �klb�Bþklb�L�kout�B�S tð Þ;

dL

dt¼ KIþkbl�B�klb�L;

S tð Þ ¼ 1þ Em�IR

EDK50þIR;

KI ¼ kin�kCD4�þklost;

where B and L (mm – 3) are CD3 or CD4 counts in the

bloodstream and lymphoid network, respectively; kbl and

klb (h – 1) are first-order constants describing CD3 + and

CD4 + cell blood-lymph and lymph-blood exchanges,

respectively; and kout (h – 1) is the first-order output rate

of B.

KI (mm – 3 h – 1) is lymphocyte production, which is

composed of three components: kin is CD3 + input

kept after r-ATG treatment, klost is CD4 + input that no

longer exists (i.e. = 0) after r-ATG treatment, and kCD4 –

is CD3 + CD4– input. r-ATG stimulate output of R,

where Em is maximum effect, and EDK50 (mg/h) is the

IR leading to 50% of maximum ATG-induced stimulation

of lymphocyte output. Since EDK50 = EC50�CL, where

EC50 is the concentration of r-ATG leading to 50% of kout

stimulation, and CL is the clearance of r-ATG, EDK50

accounts for both r-ATG potency and drug clearance, and

its value should be interpreted with caution [19].

Initial values of R and M were obtained assuming that Rand M were at steady state (i.e. their derivatives were 0).

Bð0Þ ¼ KI=kout;

Lð0Þ ¼ KI

kbl

þklb�KI

kout

:

Population model development

The structural parameters were estimated by a popula-

tion approach using the nonlinear mixed-effects modeling

software MONOLIX 4.2.2 (Lixoft, Orsay, France), which

combines the stochastic expectation-maximization

(SAEM) algorithm and a Markov Chain Monte Carlo

Fig. 1

Dose

KDE

CD3 kout

kLB

kin

klost

(kCD3 − kCD4−)

kBL

CD3

CD4

CD4

A IR = KDE x A

Model used to describe r-ATG dose–effect relationship. Parametersof this K-PD model are as follows: KDE (h – 1) is the first-orderelimination rate constant from the virtual compartment; IR (mg/h) isthe virtual infusion rate of r-ATG and represents the amount of r-ATGin biophase, that is, available for lymphocyte depletion; R (mm – 3) isthe lymphocyte (CD3 or CD4) counts; kin is CD3 + input kept afterr-ATG treatment; klost is CD4 + input that no longer exists (i.e. = 0)after r-ATG treatment; kCD4 – is CD3 + CD4–; and kout (h – 1) is thefirst-order output constant of R. r-ATG stimulates output of R, whereEm is maximum effect and EDK50 (mg/h) is the IR leading to 50% ofmaximum ATG-induced stimulation of kout. Further details are given inthe text. IR, infusion rate; K-PD, kinetic-pharmacodynamic; r-ATG, rabbitantithymocyte globulins.

28 Pharmacogenetics and Genomics 2014, Vol 24 No 1


http://links.lww.com/FPC/A666

procedure for likelihood maximization [21]. To ensure

the best possible convergence, 600 K1 and 200 K2 (where

K1 and K2 refer to the SAEM procedure of MONOLIX,

called ‘iteration kernels’) and simulated annealing were

used to impose the convergence of the SAEM algorithm

toward the global maximum of the likelihood. Fisher

information matrix was computed using stochastic

approximation. The objective function, which is the

– 2Ln likelihood (– 2LL), was computed using impor-

tance sampling. Each run was made three times to ensure

that estimated parameters and likelihood remained

stable. The random seed was changed between runs.

Data transformation

Since lymphocyte count data were heteroscedastic, a

Box–Cox (BC) transformation [22] was performed as

follows:

DVBC ¼DVl�1

lif l 6¼ 0;

DVBC ¼ LnðDV Þ if l ¼ 0;

where DV is dependent variable (CD3 or CD4 counts),

DVBC is BC-transformed DV, and l is (nonzero)

exponent. l= 0 corresponds to a Napierian logarithm

transformation whereas l= 1 corresponds to no transfor-

mation. Two values of l were determined, that is, for

CD3 and CD4 counts. Values of l ranging from – 2 to

+ 2 were used to compute the following log likelihood:

LLðlÞ ¼ �N

2ln s2

l

� �þðl�1Þ

XN

i¼1

DV i;

where N is the number of observations and s2l

� �is the

estimated variance of BC-transformed DV. The value of lwas chosen for the maximum LL(l). Then all CD3 and

CD4 counts were transformed using their respective lvalues. This analysis was made using the ‘car’ pack-

age [23] on R 2.7.1 (Vienna, Austria) [24]. Transformed

data were used for K-PD analysis. Counts of CD3 and

CD4 that were less than 1 were considered as below the

limit of quantitation and were censored as being less than

1.

Interindividual model

The interindividual variability of the parameters was

described using an exponential model: yi = yTV�exp

(Zi), where yi is the estimated individual parameter, yTV

is the typical value of the parameter in the population,

and Zi is the random effect for the ith patient. The Zs are

assumed to be normally distributed with mean 0 and

variance o2. The shrinkage was calculated for each

parameter, as (1 – SD(Zi)/o)� 100. For each parameter,

interindividual variability was removed (i.e. o2 fixed to 0)

if its removal led to an increase in objective function

value of 3.84 and/or if it led to a shrinkage of more than

40%.

Residual error model

Additive, proportional, and mixed additive-proportional

residual error models were tested. For example, the

combined additive-proportional model was implemented

as follows: YO,ij = YP,ij(1 + Eprop,ij) + Eadd,ij, where YO,ij and

YP,ij are observed and predicted jth measurements for the

ith patient, respectively, and Eadd,ij and Eprop,ij are additive

and proportional errors, with mean 0 and respective

variances sadd2 and sprop

2.

Model goodness of fit and evaluation

Goodness of fit for a given model was assessed by plots of

population-predicted (PRED) and individual-predicted

(IPRED) versus observed CD3 or CD4 counts (DV), IPRED

and DV versus time, and by evaluating the residuals by

graphical inspection of population-weighted residual dis-

tribution (PWRES) and individual-weighted residual

distributions (IWRES), and normalized prediction dis-

tribution errors (NPDE) [25]. The departure from

normality of NPDE was tested using the Kolmogorov–

Smirnov test (a= 0.05).

Influence of FCGR3A genotype

Age, weight, sex, (cyclosporine, tacrolimus, or sirolimus),

and FCGR3A genotype were tested as covariates. Con-

comitant immunosuppressive treatment was not tested,

because patients treated with sirolimus were too few to

be tested and could not be merged to another category.

The continuous covariates (COV, i.e. age and body

weight) were centered on their median as follows:

yi = y0.(COV/med(COV))bcov, where y is the structural

parameter, y0 is the value of y for a median subject, bCOV

quantifies the influence of COV on y, and med(COV) is

the median value of COV in the population. The

influence of dichotomous covariates (i.e. sex and cotreat-

ment) on typical parameters (yTV) was implemented as

ln(yTV) = ln(yCAT = 0) + bCAT = 1, where yCAT = 0 is the

value of y for an arbitrary reference category, and bCAT = 1

is the value of yTV for the other category. The genotype of

FCGR3A was tested as a dichotomous, nominal, or ordinal

covariate. As a dichotomous covariate, FCGR3A was coded

as VV versus F carriers (model 1) or FF versus V carriers

(model 2). As a nominal covariate, FCGR3A was coded as

FF versus VF and versus VV (model 3), where yFF is the

value of yTV for FF patients, and exp(Ln(yFF) + bVF) and

exp(Ln(yFF) + bVV) are values of yTV for VF and VV

patients, respectively. As an ordinal covariate (model 4),

the influence of FCGR3A on structural parameters is

exp(Ln(yFF) + nV� bV), where nV is the number of V

alleles (i.e. nV = 0, 1, or 2) and bV quantifies the influence

of one V allele.

The influence of FCGR3A on structural parameters was

determined and the comparison of coding strategies

was made using –2LL and Akaike’s information criterion

(AIC), defined as: AIC = –2LL + 2� p, where LL is log

likelihood and p is the number of model parameters to be



estimated. A statistically significant influence of FCGR3Awas defined as a decrease in the –2LL of 6.63 (a= 0.01).

Among coding strategies, the chosen one had the lowest

significant –2LL value. If two strategies were similar in

terms of –2LL, the one with the lowest AIC was chosen.

Covariate selection

The influence of individual covariates was assessed using

two steps.

(1) Univariate step: This step aimed at identifying

covariates that may influence CD3 + dynamics.

The influence of each factor on each parameter with

an interindividual variability was tested. Covariates

were separately added to the base model. Covariates

whose influence was statistically significant (a= 0.1)

were added in the model (full model).

(2) Multivariate step: This step aimed at confirming the

influence of covariates identified during the uni-

variate step. A backward stepwise elimination was

performed: the covariates of the full model were

removed one by one. Covariates whose removal

resulted in a statistically significant increase in the

– 2LL (a= 0.02) were retained in the model.

ResultsOf the 194 kidney transplant patients who were treated

with r-ATG between 1998 and 2002, 69 patients were

eligible and included in the retrospective study (Fig. 2).

This subpopulation was not significantly different from

patients who were not included (Table 1). A total of 631

CD3 and 174 CD4 counts were available. The decrease in

CD3 + and CD3–CD56 + counts after the first r-ATG

was rapid and almost total: 24 h after the beginning of

r-ATG treatment, these counts were less than 10 mm – 3

for most patients. Most patients recovered half of their

initial counts in 4 months, with a large interindividual

variability. None of them recovered their initial counts

within the 1-year follow-up period.

Base model

Estimated BC exponent (l) for CD3 + and CD4 +

counts was 0.011 and 0.17, respectively. BC-transformed

CD3 and CD4 counts over time were satisfactory by the

K-PD (Fig. 3). The best error models were additive for

both CD3 and CD4 data. Plots of residuals did not show

Fig. 2

194 treated with r-ALG

69 eligible and included

98 DNA samples or written consents forgenotyping not available

5 transplanted more than once

96

91

81

12 insufficient lymphocyte count data

10 incomplete ALG infusion information and/orreadministered with r-ALG within 1 year

Flow chart describing the inclusion of patients treated with r-ATGat Tours University Hospital (France). r-ATG, rabbit antithymocyteglobulins.

Table 1 Summary of patient characteristics

Patients Included Not included P value

N 69 125Total r-ATG dose (mg) 365 (125–725)a 340 (50–730)a 0.79Demographic characteristics

Age (years) 44 (19–70)a 42 (19–69)a 0.45Weight (kg) 66 (44–98)a 63 (55–105)a 0.18Sex (men/women) 46/23 70/55 0.15FCGR3A genotype (VV/VF/FF) 12/30/27 7/12/8b 0.55

Lymphocyte counts at baseline (mm – 3)CD3 + 1204 (412–2789)a 1170 (462–3806)a 0.30CD4 + 769 (274–1574)a 687 (191–1446)a 0.15

Concomitant immunosuppressive treatmentCorticosteroids 69 125 1.0Mycophenolate 66 111 0.08Cyclosporine 52 90 0.77Tacrolimus 13 32 0.8Sirolimus 4 3 0.41

Patients included in the subpopulation were compared with patients not included. Data are reported as number of patients.r-ATG, rabbit antithymocyte globulins.aMedian (range).bTwenty-seven patients for which ADN and written consent were available, but excluded for other reasons.



model misspecification (Supplementary Figures S2,

Supplemental digital content 2, http://links.lww.com/FPC/A667 and S3, Supplementary digital content 3,

http://links.lww.com/FPC/A668), even if a small departure

from linearity was detected for both BC-transformed CD3

(KS = 2%, P = 0.023) and CD4 (KS = 4%, P = 0.036).

A slight overprediction of CD3 and CD4 counts was

observed for counts close to the limit of detection, with a

bias in PWRES and IWRES. A large interindividual

variability was observed for EDK50 (131%, Table 2).

Interindividual variances of kCD4 – , klost, kEm, and klb were

removed. For a median subject, the elimination half-life

of r-ATG from biophase was t1/2 = ln(2)/KDE = 19.5 h.

Covariate testing and final model

During the univariate step, both kin (–DLL = 2.86) and

kout (–DLL = 3.24) were found to be influenced by age,

and EDK50 was influenced by FCGR3A genotype.

In the final step, EDK50 was influenced by the FCGR3Agenotype. Among the coding strategies for FCGR3Ainfluence, models 3 and 4 presented the largest decrease

in – 2LL compared with base model (– 10,

P = 0.0016, Table 3). However, since model 4 used one

parameter to quantify FCGR3A influence on EDK50

whereas model 3 used two parameters, the AIC of model

4 is lower than that of model 3. Therefore, model 4 was

considered as the best one. For FF, VF, and VV patients,

typical values for EDK50 were 0.69, 0.37, and 0.20 mg/h,

respectively (P = 0.0016). This is consistent with an

increase in sensitivity of patients to r-ATG treatment

with the number of V alleles (Table 2, Fig. 4).

DiscussionWe observed a rapid, profound, and durable CD3 + count

depletion following r-ATG treatment, especially for

CD4 + cells, which is consistent with previous studies

on this ATG [1,15–17]. Our study confirms, using r-ATG,

the influence of FcgRIIIA-158V/F polymorphism on

lymphocyte depletion induced by antilymphocyte globu-

lins that we have previously reported for h-ATG. Since

elimination t½ of r-ATG is about 3 weeks [26,27], r-ATG

disappearance from the serum should be complete within

1 year. Therefore, we limited the duration of the

retrospective follow-up to 1 year.

Because r-ATG concentrations were not available, we used

K-PD modeling to describe the dose–effect relationship.

This approach was shown to provide a satisfactory

description of biomarker dynamics in the absence of

pharmacokinetic data [19]. The dynamics of CD3 +

count in the absence of antilymphocyte treatment was

described using a turnover model with two compartments.

However, KDE and EDK50 are parameters influenced by

both pharmacokinetic and pharmacodynamic phenomena

and therefore they may be difficult to interpret [19].

Fig. 3

(a) (b)7

6

5

4

3

2

1

0 20 40 60 80

15

10

5

0

0 20Time (weeks)Time (weeks)

Box

–Cox

-tran

sfor

med

CD

3 co

unts

Box

–Cox

-tran

sfor

med

CD

4 co

unts

40 60 80

Visual predictive checks for CD3 (a) and CD4 (b) counts. Full circles are Box–Cox-transformed data, solid line is median prediction, and gray surfaceis 5–95% confidence interval.

Table 2 Estimated pharmacokinetic parameters

Parameters (unit) Estimate RSE (%)

KDE (h – 1) 0.042 20kin (mm – 3 h – 1) 4.55 3kin – 4 (mm – 3 h – 1) 2.71 2kin,lost (mm – 3 h – 1) 4.93 3kout (h – 1) 0.0086 5Emax 115 4EDK50 (mg/h) 0.69 23V allele on EDK50 0.62 10klb (h – 1) 0.17 18kbl (h – 1) 0.016 5oKDE (%) 139 11ok in (%) 14 17okout (%) 27 13oC50 (%) 131 15ok12

(%) 108 15sadd_CD3 (mm – 3) 0.44 3sadd_CD4 (mm – 3) 0.87 6

Parameters are described in the text.The relative standard error (RSE%) is obtained as follows: RSE = (estimate/standarderror)�100.RAP, sirolimus.






Therefore, KDE and EDK50 are not ‘pure’ pharmaco-

kinetic or pharmacodynamic parameters. For this reason,

an influence of a covariate on one of these parameters

cannot be attributed to drug pharmacokinetics or

biomarker pharmacodynamics without additional infor-

mation. BC transformation improved CD3 + distribution

and therefore improved covariate testing, while the value

of structural parameters was not modified compared with

estimates obtained in the absence of transformation.

The FcgRIIIA-158V/F polymorphism significantly influ-

enced EDK50, estimated EDK50 values were the lowest

for VV patients, higher for VF and highest for FF patients.

Since EDK50 = EC50�CL, where EC50 is the concentra-

tion of r-ATG leading to a 50% stimulation of output and

CL is the clearance of r-ATG, this finding corresponds

either to an increase in r-ATG potency with the number

of V alleles of the treated patient or a decrease in r-ATG

clearance. Because ADCC mediated by VV NK cells is

higher than that mediated by FF NK cells [28], target-

mediated clearance of thymoglobulin may be higher in VV

patients. However, an influence of the FCGR3A genotype

on the clearance of therapeutic antibodies has never been

reported.

Human IgG have a higher affinity for NK cells from

FcgRIIIA-158VV patients than for those from 158FF

patients [6]. In vitro, the quantitative influence of the

FCGR3A polymorphism on the concentration–effect

relationship of rituximab was studied using an in-vitro

ADCC model [28]. Maximal lysis (Emax) did not differ

between VV and FF donors, but the concentration leading

to 50% of maximal lysis (EC50) was about four times lower

for VV than for FF donors. This influence was observed

in vivo for several therapeutic antibodies: an influence of

the FCGR3A genotype on clinical response to cytolytic

monoclonal antibodies such as rituximab [7,8], trastuzu-

mab [10], and cetuximab [9] was reported. This influence

was also reported for infliximab, which may also act, at

least partly, by cytolysis [11]. For nonhuman antibodies,

we showed in kidney transplant patients treated by

h-ALG that V-carrier patients were more sensitive to h-ALG

than FF kidney transplant patients [12]. The present work

confirms this result for r-ATG.

Our results suggest that rabbit IgG, like human IgG, may

have a higher affinity for the human FcgRIIIA-158V than

for the FcgRIIIA-158F allotype, which was evoked in an

earlier study [29]. However, the role of initial lymphocyte

depletion due to ADCC may be minor: indeed, apoptosis

and CDC may be preponderant at high r-ATG con-

centrations, whereas ADCC may occur at low ATG

concentrations [3–5]. This may be explained by the fact

that NK cells are rapidly and almost totally suppressed

during r-ATG treatment. At distance of r-ATG treatment,

the recovery of NK cells may contribute to the extension

of the duration of r-ATG activity. Overall, our results also

suggest that r-ATG acts, at least partly, by ADCC. In

addition, the model using one parameter to quantify the

influence of the number of V alleles of FCGR3A on EDK50

led to similar results as the model using two parameters

(Table 3). This suggests a ‘gene-dose’ effect, as

previously evoked by Koene et al. [6], with an increase

in r-ATG binding to NK cells and macrophages with the

number of V alleles.

The dynamics of CD3–CD56 + count, which represent

NK cells, was not assessed using K-PD modeling for two

reasons: (i) because this count presented large intraindi-

vidual variability at distance of r-ATG treatment and

(ii) because, after r-ATG injections, NK cells are both

effectors and target; therefore K-PD parameters would

have been even more difficult to interpret.

Table 3 Influence of FCGR3A polymorphism on virtual infusionrate leading to 50% of maximum antithymocyte globulin-inducedstimulation of lymphocyte output (EDK50): comparison of differentcoding strategies for FCGR3A polymorphism

– 2LL DLL AIC

Base 2074.60 2110.60Model 1 2067.70 – 6.90 2105.70Model 2 2067.37 –7.23 2105.37Model 3 2064.58 – 10.02 2104.58Model 4 2064.59 – 10.01 2102.59

The genotype of FCGR3A was tested as a dichotomous covariate, whereFCGR3A was coded as VV vs. F carriers (model 1) or FF vs. V carriers (model 2),as a nominal covariate (model 3), and as an ordinal covariate (model 4).AIC, Akaike’s information criterion; – 2LL, – 2Ln likelihood.

Fig. 4

7

6

5

4

ED

K50

(mg/

h)

3

2

1

0

FF VF

FCGR3A genotype

VV

Influence of FCGR3A genotype on EDK50. Boxplots represent 25th,50th, and 75th percentiles, lines represent 5th and 95th percentiles,and open circles represent EDK50 individual estimates outside the 5thand 95th percentiles.



A limited number of studies on ATG pharmacokinetics

and PK-PD are available in the literature, most of them

dealing with r-ATG. These studies reported a median

elimination t½ of r-ATG ranging from 14 to 40 days, with

large interindividual variability [26,27,30–33]. These t½values are higher than in our study (t½ = 19.5 h). This

may be due to three reasons: (i) r-ATG kinetics is

described assuming a one-compartment decay, whereas

the previous study applied a two-compartment model;

(ii) only a fraction of polyclonal r-ATG has a lympho-

suppressive activity and its elimination rate is higher than

that of total r-ATG [27]; and (iii) r-ATG elimination from

the biophase is derived from lymphocyte turnover. Thus,

estimated t½ does not reflect the global r-ATG elimina-

tion half-life, but rather the elimination of active r-ATG

that is involved in CD3 + depletion.

A two-compartment turnover model was used because

lymphocyte recovery presented at least two phases. This

may be justified by the fact that lymphocytes are

produced and maturated in lymphoid tissues and are in

transit in the bloodstream. Accounting for CD4 counts

was not intended to improve the description of CD3

count data, but rather to show that differences in CD3 +

input before and after r-ATG treatment were due to a loss

in CD4 + production capacity. Indeed, several studies

showed that most patients do not recover baseline CD4

counts even several years after treatment [1,15–18].

However, the reason for this loss is still unknown.

Except for input, our model was based on the assumption

that CD4 + cell turnover is the same as that of CD3 + .

Attempts to estimate different K-PD parameters (kout,

EDK50, Emax) and the use of transit models previously

used to describe anticancer drug-induced myelosuppres-

sion [34,35] did not improve the model (i.e. they did not

lead to a significant decrease in – 2LL; data not shown).

Our model has several limitations. First, a slight departure

from normality was observed for both CD3 and CD4 data,

despite BC transformation. Second, no r-ATG elimination

could be described in lymph networks: this phenomenon

was not identifiable and assuming the same elimination

rate in both blood and lymph compartments led to

misestimations of both Em and EDK50. However, this

does not mean that lymphocytes are not cleared in lymph

networks; indeed, IgG are distributed in lymph, and

r-ATG may therefore clear lymphocytes in this compart-

ment. Third, CD4 and CD8 counts should ideally be

modeled separately, but CD4 counts could not be

described using modeling because data were too scarce.

Conclusion

We confirmed the influence of FCGR3A genotype on the

dose–effect relationship of ATG in kidney transplant

patients using lymphocyte count as a biomarker and a

K-PD approach. As for r-ATG and several other thera-

peutic antibodies, r-ATG may act, at least in part, by

ADCC, and the presence of V allotype is associated with a

higher sensitivity to treatment.

AcknowledgementsThis work was supported by the Societe Francaise de

Transplantation. The authors thank Prof. Michel Tod and

Dr Melanie Wilbaux for statistical modeling advice.

Conflicts of interest

G.P. is currently receiving grants from Novartis, Genzyme,

and Roche Pharma. Y.L. has received grants and consult-

ing fees from Astellas, Roche, Novartis, Pfizer, Alexion,

Sanofi-Genzyme, LFB and CSL Behring. For the

remaining authors there are no conflicts of interest.

References1 Muller TF, Grebe SO, Neumann MC, Heymanns J, Radsak K, Sprenger H,

et al. Persistent long-term changes in lymphocyte subsets induced bypolyclonal antibodies. Transplantation 1997; 64:1432–1437.

2 Oertel M, Sack U, Kohlhaw K, Lehmann I, Emmrich F, Berr F, et al. Inductiontherapy including antithymocyte globulin induces marked alterations in Tlymphocyte subpopulations after liver transplantation: results of along-term study. Transpl Int 2002; 15:463–471.

3 Genestier L, Fournel S, Flacher M, Assossou O, Revillard JP,Bonnefoy-Berard N. Induction of Fas (Apo-1, CD95)-mediated apoptosis ofactivated lymphocytes by polyclonal antithymocyte globulins. Blood 1998;91:2360–2368.

4 Michallet MC, Saltel F, Preville X, Flacher M, Revillard JP, Genestier L.Cathepsin-B-dependent apoptosis triggered by antithymocyte globulins: anovel mechanism of T-cell depletion. Blood 2003; 102:3719–3726.

5 Preville X, Flacher M, LeMauff B, Beauchard S, Davelu P, Tiollier J, et al.Mechanisms involved in antithymocyte globulin immunosuppressive activityin a nonhuman primate model. Transplantation 2001; 71:460–468.

6 Koene HR, Kleijer M, Algra J, Roos D, von dem Borne AE, de Haas M. FcgammaRIIIa-158V/F polymorphism influences the binding of IgG by naturalkiller cell Fc gammaRIIIa, independently of the Fc gammaRIIIa-48L/R/Hphenotype. Blood 1997; 90:1109–1114.

7 Cartron G, Dacheux L, Salles G, Solal-Celigny P, Bardos P, Colombat P,et al. Therapeutic activity of humanized anti-CD20 monoclonal antibodyand polymorphism in IgG Fc receptor FcgammaRIIIa gene. Blood 2002;99:754–758.

8 Weng WK, Levy R. Two immunoglobulin G fragment C receptorpolymorphisms independently predict response to rituximab in patients withfollicular lymphoma. J Clin Oncol 2003; 21:3940–3947.

9 Zhang W, Gordon M, Schultheis AM, Yang DY, Nagashima F, Azuma M, et al.FCGR2A and FCGR3A polymorphisms associated with clinical outcome ofepidermal growth factor receptor expressing metastatic colorectal cancerpatients treated with single-agent cetuximab. J Clin Oncol 2007; 25:3712–3718.

10 Musolino A, Naldi N, Bortesi B, Pezzuolo D, Capelletti M, Missale G, et al.Immunoglobulin G fragment C receptor polymorphisms and clinical efficacyof trastuzumab-based therapy in patients with HER-2/neu-positivemetastatic breast cancer. J Clin Oncol 2008; 26:1789–1796.

11 Louis E, El Ghoul Z, Vermeire S, Dall’Ozzo S, Rutgeerts P, Paintaud G, et al.Association between polymorphism in IgG Fc receptor IIIa coding geneand biological response to infliximab in Crohn’s disease. Aliment PharmacolTher 2004; 19:511–519.

12 Ternant D, Buchler M, Beneton M, Alvan G, Ohresser M, Touchard G, et al.Interindividual variability in the concentration-effect relationship ofantilymphocyte globulins – a possible influence of FcgammaRIIIa geneticpolymorphism. Br J Clin Pharmacol 2008; 65:60–68.

13 Buchler M, Thibault G, al Najjar A, Valentin JF, Guerraoui A, Nivet H, et al.Monitoring of ATG therapy by flow cytometry and lymphocyte counts in renaltransplantation. Transplant Proc 1996; 28:2817–2818.

14 Dall’Ozzo S, Andres C, Bardos P, Watier H, Thibault G. Rapid single-stepFCGR3A genotyping based on SYBR Green I fluorescence in real-timemultiplex allele-specific PCR. J Immunol Methods 2003; 277:185–192.

15 Brennan DC, Flavin K, Lowell JA, Howard TK, Shenoy S, Burgess S, et al.Leukocyte response to thymoglobulin or atgam for induction



immunosuppression in a randomized, double-blind clinical trial in renaltransplant recipients. Transplant Proc 1999; 31:16S–18S.

16 Esposito L, Kamar N, Tkaczuk J, Abbal M, Durand D, Rostaing L. Long-termevolution of lymphocytes subsets after induction therapy based oncontinuous versus discontinuous administration of anti-thymocyte globulinsin renal-transplant patients. Transplant Proc 2005; 37:785–787.

17 Louis S, Audrain M, Cantarovich D, Schaffrath B, Hofmann K, Janssen U,et al. Long-term cell monitoring of kidney recipients after an antilymphocyteglobulin induction with and without steroids. Transplantation 2007; 83:712–721.

18 Tang Q, Leung J, Melli K, Lay K, Chuu EL, Liu W, et al. Altered balancebetween effector T cells and FOXP3 + HELIOS + regulatory T cells afterthymoglobulin induction in kidney transplant recipients. Transpl Int 2012;25:1257–1267.

19 Jacqmin P, Snoeck E, van Schaick EA, Gieschke R, Pillai P, Steimer JL, et al.Modelling response time profiles in the absence of drug concentrations:definition and performance evaluation of the K-PD model. J PharmacokinetPharmacodyn 2007; 34:57–85.

20 Henin E, You B, VanCutsem E, Hoff PM, Cassidy J, Twelves C, et al.A dynamic model of hand-and-foot syndrome in patients receivingcapecitabine. Clin Pharmacol Ther 2009; 85:418–425.

21 SAS. Monolix 4.2.2 user’s guide. Orsay, France: Lixoft SAS 2013; Availableat: http://www.lixoft.eu/wp-content/resources/docs/UsersGuide.pdf.[Accessed 27 July 2013].

22 Handelsman DJ. Optimal power transformations for analysis of spermconcentration and other semen variables. J Androl 2002; 23:629–634.

23 Fox J, Weisberg S, Bates D, Ellison S. Package ‘car’ for R (Version 2.0-18).Available at: http://cran.r-project.org/web/packages/car/car.pdf.[Accessed 13 August 2013].

24 R Development Core Team. R 2.7.1. R: A language and environment forstatistical computing. ISBN 3-900051-07-0. Vienna, Austria: R Foundationfor Statistical Computing 2010. Available at: http://www.R-project.org.[Accessed 13 August 2013].

25 Comets E, Brendel K, Mentre F. Computing normalised predictiondistribution errors to evaluate nonlinear mixed-effect models: the npde add-on package for R. Comput Methods Programs Biomed 2008; 90:154–166.

26 Kakhniashvili I, Filicko J, Kraft WK, Flomenberg N. Heterogeneous clearanceof antithymocyte globulin after CD34 + -selected allogeneic hematopoieticprogenitor cell transplantation. Biol Blood Marrow Transplant 2005;11:609–618.

27 Waller EK, Langston AA, Lonial S, Cherry J, Somani J, Allen AJ, et al.Pharmacokinetics and pharmacodynamics of anti-thymocyte globulin inrecipients of partially HLA-matched blood hematopoietic progenitor celltransplantation. Biol Blood Marrow Transplant 2003; 9:460–471.

28 Dall’Ozzo S, Tartas S, Paintaud G, Cartron G, Colombat P, Bardos P, et al.Rituximab-dependent cytotoxicity by natural killer cells: influence ofFCGR3A polymorphism on the concentration-effect relationship. CancerRes 2004; 64:4664–4669.

29 Vance BA, Huizinga TW, Wardwell K, Guyre PM. Binding of monomerichuman IgG defines an expression polymorphism of Fc gamma RIII on largegranular lymphocyte/natural killer cells. J Immunol 1993; 151:6429–6439.

30 Bieber CP, Griepp RB, Oyer PE, Wong J, Stinson EB. Use of rabbitantithymocyte globulin in cardiac transplantation. Relationship of serumclearance rates to clinical outcome. Transplantation 1976; 22:478–488.

31 Bunn D, Lea CK, Bevan DJ, Higgins RM, Hendry BM. The pharmacokineticsof anti-thymocyte globulin (ATG) following intravenous infusion in man. ClinNephrol 1996; 45:29–32.

32 Guttmann RD, Caudrelier P, Alberici G, Touraine JL. Pharmacokinetics,foreign protein immune response, cytokine release, and lymphocyte subsetsin patients receiving thymoglobuline and immunosuppression. TransplantProc 1997; 29:24S–26S.

33 Buchler M, Longuet H, Lemoine R, Herr F, Gatault P, Thibault G, et al.Pharmacokinetic and pharmacodynamic studies of two different rabbitantithymocyte globulin dosing regimens: results of a randomized trial.Transpl Immunol 2013; 28:120–126.

34 Friberg LE, Henningsson A, Maas H, Nguyen L, Karlsson MO. Modelof chemotherapy-induced myelosuppression with parameter consistencyacross drugs. J Clin Oncol 2002; 20:4713–4721.

35 Quartino AL, Friberg LE, Karlsson MO. A simultaneous analysis of thetime-course of leukocytes and neutrophils following docetaxel administrationusing a semi-mechanistic myelosuppression model. Invest New Drugs2010; 30:833–845.



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