Comme nous l’avons décrit précédemment, l’efficacité du RTX pourrait être en partie liée à des facteurs pharmacocinétiques mais les covariables contribuant à la variabilité pharmacocinétique inter-individuelle sont encore pour la plupart inconnues. A l’époque de nos travaux, il avait déjà été décrit une relation entre les concentrations sériques de rituximab et la masse tumorale, mais le sujet restait controversé.
Nous avons analysé la pharmacocinétique du RTX chez des patients traités pour un LNH par 4 cures de RTX à la dose de 375 mg/m2 administrée tous les 21 jours en association à une chimiothérapie CHOP. L’objectif de ce travail était de décrire la pharmacocinétique du médicament et son lien avec la réponse thérapeutique précoce, évaluée par la décroissance lymphocytaire CD19. Nous souhaitions également appréhender les facteurs à l’origine de la variabilité pharmacocinétique ; pour cela nous avons utilisé deux approches complémentaires afin de répondre aux questions suivantes : (1) la pharmacocinétique dépend-elle des caractéristiques de la maladie au moment du traitement et (2), la pharmacocinétique varie-t-elle au cours du temps, alors même que la masse tumorale diminue en réponse au traitement ?
Pharmacokinetics of rituximab as a function of tumour burden parameters
in patients with non-Hodgkin lymphoma
Hélène Blasco
1,3, Guillaume Cartron
2, Gilles Thibault
2, Nicolas Congy-Jolivet
2, Hervé
Watier
2, Etienne Chatelut
3, Chantal Le Guellec
1.
1- CHRU de Tours ; Université François Rabelais de Tours ; Laboratoire de Pharmacologie et Toxicologie
2- Université François Rabelais de Tours, UPRES EA3853, Immuno-Pharmaco-Génétique des Anticorps thérapeutiques
3- Université Paul Sabatier de Toulouse, EA3035, Groupe de pharmacologie clinique et expérimentale des médicaments anticancéreux
Corresponding author: Chantal Le Guellec
Laboratoire de Pharmacologie et Toxicologie, CHRU de Tours
2 boulevard Tonnellé
37044 TOURS cedex – France E-mail: leguellec@med.univ-tours.fr
Total word count: 2772
Abstract word count: 228 (max 250) Tables: 2
“What this paper adds”:
What is already known about this subject:
The efficacy of rituximab against B-cell NHL is related to its pharmacokinetics. Some studies have reported a link between serum rituximab concentration and pre-treatment tumour bulk or CD20 B-lymphocyte count. However, others have reported similar pharmacokinetic characteristics in patients with different degrees of tumour burden.
What this study adds:
We found no relationship between biomarkers of tumour burden measured before rituximab administration and pharmacokinetic parameters at the first infusion. Furthermore, similar pharmacokinetic parameters were observed for all courses, despite decreases in tumour burden with treatment. Tumour burden cannot account for the considerable interindividual variability of rituximab pharmacokinetics in our patients.
ABSTRACT
Aims: Some studies have shown an effect of tumour burden on rituximab pharmacokinetics.
However, others have reported pharmacokinetic parameters to be similar in patients with different tumour types or tumour burdens. An understanding of the sources of pharmacokinetic variability would make it possible to tailor rituximab doses to individual patients. We conducted a pharmacokinetic study in patients with B-cell non-Hodgkin lymphoma (B-NHL). We described rituximab pharmacokinetics and the effects of tumour burden on pharmacokinetics.
Methods: Patients received four 375 mg/m2 doses of rituximab, at 21-day intervals. Serum rituximab concentration was determined for each course and NONMEM was used for pharmacokinetic analysis. Ann-Arbor stage, total and CD19+ B-lymphocyte counts were selected as biomarkers of tumour burden. These biomarkers were studied as potential covariates, by analysing their correlation with pharmacokinetic parameters during the first infusion. We also investigated possible time-dependent effects on rituximab pharmacokinetics, over the entire duration of treatment.
Results: Ten patients were included in this pilot study. We found no relationship between
pharmacokinetic parameters for the first course and total or CD19+ lymphocyte counts or Ann-Arbor stage. Moreover, pharmacokinetic parameters were similar over the entire treatment period, despite decreases in tumour burden in response to chemotherapy.
Conclusions: We found no effect of tumour burden on rituximab pharmacokinetics in a small
group of patients with NHL. Further studies are required to investigate the source of pharmacokinetic variability for rituximab.
INTRODUCTION
Rituximab (Mabthera®, Roche) is a chimeric anti-CD20 monoclonal antibody (mAb) that contains human immunoglobulin IgG1 constant regions and mouse variable antigen-binding regions (Fv). Based on its specificity for the CD20 antigen expressed on mature B lymphocytes and pre-B lymphocytes (1, 2), rituximab is currently licensed for the treatment of non-Hodgkin's lymphoma (NHL) and some autoimmune diseases (3).
The efficacy of rituximab against recurrent or refractory low-grade and follicular B-cell NHL is related to its pharmacokinetics, serum concentrations being significantly higher in responders than in non responders (4-7). O’Brien (8) also showed that higher doses of rituximab were required to enhance the clinical response in chronic lymphocytic lymphoma (CLL). These observations suggest that it may be possible to adjust the rituximab dose schedule according to individual characteristics, thereby influencing serum concentrations and response to treatment. However, the reasons for this interindividual pharmacokinetic variability remain unclear (9).
Regazzi et al. showed, in population pharmacokinetics studies, that the pharmacokinetic characteristics of rituximab are similar for autoimmune disorders, follicular lymphoma, and for relapsed, refractory, follicular or mantle cell lymphoma (10). By contrast, very different pharmacokinetic characteristics have been reported for rituximab in patients with small lymphocytic lymphoma (SLL), these patients having lower plasma rituximab concentrations than those with other subtypes of lymphoma (4, 6). Lower levels of CD20 expression have been reported on SLL cells than on B-cell lymphoma cells (11, 12), but higher circulating B- cell counts in SLL patients may account for the greater clearance observed in these patients (6). In recurrent, refractory low-grade or follicular B-cell NHL, serum rituximab concentrations have been shown to be inversely related to pretreatment tumour bulk and circulating CD20
B-lymphocyte count in some studies (4, 6, 13). However, other studies have reported pharmacokinetic parameters to be similar in patients with different tumour burdens (14, 15).
We carried out a pilot pharmacokinetic study to describe the inter- and intra-individual variability of rituximab pharmacokinetics and to determine whether tumour burden had an effect on rituximab pharmacokinetics in B-NHL patients. We analyzed the correlation between individual pharmacokinetic parameters at the first administration of rituximab and various biomarkers of tumour burden measured before treatment. We expected tumour burden to decrease with treatment, so we also investigated the extent to which pharmacokinetic parameters changed over time, for the entire period of treatment.
PATIENTS AND METHODS
Patient selection and study design
Patients aged 18-65 years with follicular lymphoma or diffuse large B-cell CD20 lymphoma were eligible to participate in this study. Patients were excluded if they were still being treated for lymphoma, tested positive serologically for hepatitis B or C, or had renal (estimated creatinine clearance<50 ml/min) or hepatic failure (prothrombin level <50%).
All included patients gave informed written consent before the initiation of treatment. The
study was approved by the research ethics board of Tours University Hospital (Comité de
Protection des Personnes se prêtant à une Recherche Biomédicale, CCPPRB de Tours,
France) and was carried out in accordance with good clinical practice and the Helsinki
Declaration.
Rituximab was administered intravenously at a dose of 375 mg/m2 every 21 days. The infusion took about three to four hours, the duration of infusion being increased as required if infusion-related side-effects occurred.
During the first course of treatment, CHOP chemotherapy (750 mg/m2 cyclophosphamide, 50 mg/m2 adriamycin, 1.4 mg/m2 vincristine, 50 mg/m2 prednisone) was initiated 72 hours after rituximab administration, for the investigation of CD20 cell depletion associated with rituximab alone. Four courses of rituximab-CHOP chemotherapy were planned for each patient.
Study parameters
Before entering the study, patients underwent a complete evaluation, including physical examination. Demographic information — i.e. age, weight and body surface area (BSA) — was obtained for all patients. Ann-Arbor stage was evaluated according to type of extranodal disease (proximal or contiguous), involvement of extranodal organs (spleen, bone marrow), and the presence of “bulky” disease (16).
Standard clinical laboratory tests, including blood cell counts, were performed before each infusion of rituximab. Total and B-type lymphocyte counts were determined for each patient before beginning each rituximab infusion. B-lymphocyte counts were based on the CD19 marker (CD3-CD56-CD19+), as rituximab binding could potentially mask CD20. CD19+ lymphocyte levels were determined by direct immunofluorescence on whole blood, by flow cytometry (Epics XL, Beckman Coulter, Villepinte, France). For the first administration of rituximab, CD19+ lymphocyte counts were determined for each patient before the start of the infusion and 3, 24, 48 and 72 h after the beginning of the infusion.
The response to treatment was evaluated according to World Health Organisation (WHO) criteria (17), one and five months after the last course.
Sample collection for rituximab determination
For the first administration of rituximab, blood samples were obtained immediately before beginning the infusion, 3 h after the start of infusion, at the end of the infusion (end time variable between individuals), 2 h and 4 h after the end of the infusion and then 24, 48 and 72 h after the start of the infusion.
Concentrations before, at the end (end time variable between individuals) and 24 h after the start of each subsequent infusion were also obtained. Blood samples were centrifuged and the resulting sera were stored frozen at -80°C until analysis.
Determination of rituximab concentrations
Serum rituximab concentration was determined by enzyme-linked immunosorbent assay (ELISA), based on an anti-idiotypic antibody, as previously described (18). ELISA plates (Maxisorp®) were coated with rat IgG2A anti-idiotype (MB2A4, Serotec, France) antibody (1 µg/ml) diluted in 1 M carbonate-bicarbonate buffer pH 9.6. Plates were saturated by incubation with 1% BSA in PBS for 2 h at room temperature. Solutions of rituximab (from 0 to 500 µg/mL) diluted in 1% BSA/0.05% Tween in PBS were then added and the plates were incubated for 1 h at room temperature. A peroxidase-conjugated anti-human IgG was added and the plates were incubated for 1 h at room temperature. The substrate O- phenylenediamine dihydrochloride (OPD, Sigma-Aldrich, France) was added and the plates were incubated until the appropriate colour had developed. The reaction was stopped and absorbance at 450 nm was measured with an ELISA plate reader (iEMS reader MF, Labsystems, Helsinki, Finland). Diluted samples (1/100) were quantified, using the standard curve. The quantification limit of our assay was 0.125 µg/mL.
Pharmacokinetic analysis
A population pharmacokinetic analysis was performed with NONMEM (version 5.1.1; Globomax LLC, Hanover, USA) and Wings for NONMEM software (WFN; http://sourceforge.net). A two-compartment pharmacokinetic model was fitted to the data, using the FOCE method and the NONMEM subroutine ADVAN3, parameterised in terms of clearance (CL), central compartment volume (V1), inter-compartment clearance (Q) and peripheral volume (V2) (TRANS4) by the PREDPP subroutine library. Infusion duration was either fixed at its current value or was estimated using the DURATION option. Exponential and proportional model errors were used for inter-subject and residual variability, respectively. Standard errors were calculated with the COVARIANCE option of NONMEM. Graphic model diagnostics were performed with the following diagnostic plots: observed concentrations (DV) versus population predicted concentrations (PRED), weighted residuals (WRES) versus time, individual predictions (IPRED) versus DV and individual weighted residuals (IWRES) versus time. Diagnostic plots were obtained with R software (R version 2.5.1, R project, Auckland, USA).
We began by carrying out pharmacokinetic analysis with all data for the first course of treatment alone. Individual pharmacokinetic parameters for the first course were obtained using the POSTHOC function of NONMEM. These parameters were used to simulate individual concentrations for each subsequent infusion administered to the patient. The comparison between simulated and observed concentrations was used to identify possible changes in rituximab pharmacokinetics during treatment.
NONMEM analysis was then carried out using all measurements from all courses of treatment to assess the interoccasion variability (IOV) of rituximab clearance.
Relationship between individual pharmacokinetic parameters and demographic covariates or biomarkers of tumour burden
Covariate analysis was conducted for each pharmacokinetic parameter (CL, V1, V2, Q) for the first course of treatment, by investigating the relationship between each parameter and demographic characteristics (body weight, body surface area) and biomarkers of tumour burden (basal lymphocyte count (total and CD19+ lymphocytes) and Ann Arbor stage). Covariates were selected in the full model if they decreased the objective function value (OFV) by 3.84 with respect to the base model (i.e., P < 0.05, χ2, 1 df) and decreased between-subject variability.
Statistics
Differences in rituximab pharmacokinetics between courses of treatment were analyzed by calculating mean error (ME) and root mean squared error (RMSE) from simulated and observed concentrations.
RESULTS
Characteristics of the patients
We included 10 patients, with a median (range) age of 51.5 (33 to 74) years, in this study. Most of the patients were male (70%). Median (range) weight and BSA were 77 (53 to 96 ) kg and 1.99 (1.52 to 2.21) m2, respectively. Only two groups of Ann-Arbor stages were represented in our patients (stage I and IV), with most patients at stage I (80%). The characteristics of the tumours are shown in table I. Two of the patients received three courses of rituximab and eight received four courses. In total, 157 serum samples were available for pharmacokinetic analysis.
Median (range) trough rituximab concentrations increased with each infusion, from 33.7 (0.76-45.2) mg/L before the second infusion to 76.1 (29.7-115.8) mg/L just before the last infusion.
Individual CD19+ lymphocyte counts fell very rapidly, within three hours of the start of the first rituximab infusion (figure 1). CD19+ lymphocyte counts remained very low throughout treatment.
Pharmacokinetic analysis
In most patients, maximum rituximab concentration (Cmax) was reached 2 to 4 h after the end of the infusion (corresponding to the theoretical Tmax). If data were modelled with the DURATION option (i.e. substitution of the actual rate by the rate calculated from the estimated duration of the infusion), then the estimated duration of infusion was greater than the actual duration of infusion (p<0.05), except for patients 3 and 7, and fitted the data better. However, this did not result in any change in the estimated parameters.
Individual pharmacokinetic parameters for the first course of treatment predicted rituximab concentrations for subsequent courses well, as shown by the strong correlation between simulated and observed concentrations (figure 2). Statistical analysis (RMSE = 33.4 µg/mL, ME (confidence interval) = 38.1 (-4.3-80.5 µg/mL)) showed that there was no bias. We found no relationship between individual pharmacokinetic parameters for the first course of treatment and basal levels of total and CD19+ lymphocytes or Ann-Arbor stage.
The final model obtained with data from all courses of treatment was a two-compartment pharmacokinetic model, in which BSA was associated with V1 (figure 3). We also found an association between weight and V1 but not better than that with BSA (data not shown). The inclusion of the covariate BSA in the final model was associated with a decrease in objective function from 1113.72 to 1104.5 and a decrease in the inter-individual variability of V1 from
22 to 13% with respect to the basic model (Table 2). The inclusion of CD19 B-lymphocyte count as a potential covariate of CL or V1 had no significant effect. The inclusion of IOV for rituximab clearance was associated with an increase in objective function value.
Rituximab concentrations in all patients, for all courses, and the population mean are shown in figure 4. The results obtained for patient 7 were very different from those obtained for other patients, with much lower Cmax and trough concentrations.
DISCUSSION
We report here the pharmacokinetics of rituximab in 10 patients treated for B-cell lymphoma and evaluation of the potential influence of tumour burden on individual parameters. We assessed correlations between individual pharmacokinetic parameters at the time of the first course of treatment and biomarkers of tumour burden and studied time-dependent variation of pharmacokinetic parameters over treatment, whilst tumour burden decreased.
Serum rituximab concentrations were similar to published values for patients with NHL (4, 19). There was often a time lag between the end of the infusion and the time at which maximum concentrations were observed. This may account for the bias observed on the plot of individual weighed residues against observed concentrations, with positive individual weighed residues at the highest concentrations (figure 3). Replacing the actual infusion rate by the estimated duration improved individual weighted residues for the highest concentrations but had no significant effect on parameter values. Such observations are not unusual and have been reported with monoclonal antibodies (20-22). A distribution phenomenon has been proposed to account for such results. Rituximab binds to a cellular antigen present in the bloodstream, and the saturation of binding sites may lead to a non- linear increase in free rituximab levels (measured by our assay) or the secondary release of the antibody from its binding sites. As antibodies are prone to aggregation, some of the rituximab probably remains aggregated in the blood shortly after infusion, these aggregates
gradually breaking up with time. Whatever the underlying explanation, modelling of the data with the DURATION option improved the goodness of fit. However, this modelling led to no change in pharmacokinetic parameters and we did not try to take this phenomenon into account more precisely, using time-dependent distribution volumes, for example.
A two-compartment pharmacokinetic model described the data well. The only covariate included in the model was BSA (covariate of V1), which improved goodness-of-fit plots and decreased objective function by 9 points. Ng et al. (23) obtained similar results in a population pharmacokinetic analysis of rituximab in rheumatoid arthritis patients, with BSA accounting for about 19.7 % of interindividual CL variability. However, adjusting the dose as a function of BSA does not seem to improve the predictability of rituximab CL and AUC 0-∝ in patients treated for rheumatoid arthritis.
The final pharmacokinetic parameters were consistent with those reported in previous studies (19, 23) with a very long half life, at approximately 20 days, accounting for antibody accumulation over time. The inter-individual variability of rituximab pharmacokinetics was large. The pharmacokinetic behaviour of rituximab in patient 7 was very different from that in other patients. His clearance value were much higher than the mean value for the population (19.50 vs 4.87 mL/h), accounting for his low level of rituximab accumulation after four courses of treatment. His demographic characteristics were similar to those of the other patients but he presented a stage IV lymphoma. This alone cannot account for the higher level of clearance, as no such phenomenon was observed in the other patient in this series with a stage IV lymphoma (patient 6).
Several studies (4, 6, 13) have suggested that tumour burden is a factor in interindividual pharmacokinetic variability — those with greater tumour burdens having lower levels of exposure to rituximab. All 10 patients in our study were re-evaluated at the end of the fourth course of treatment and found to be partial responders. Thus, if tumour burden has an effect,
we would expect rituximab pharmacokinetics to change during treatment. We tested this hypothesis by comparing rituximab pharmacokinetics between the four courses for each individual. Using simulation with individual parameters from the first course only, we found that the simulated concentrations were highly consistent with the concentrations actually observed during subsequent courses of treatment. We therefore conclude that the decrease in tumour burden during treatment had no effect on rituximab pharmacokinetics. This result was confirmed by the lack of IOV for all parameters studied. These findings are consistent with those of Mangel et al. (14), who found the level of rituximab exposure to be similar after four infusions in patients treated for minimal disease states and patients with active disease.
We studied the effect of pre-treatment tumour burden on rituximab pharmacokinetics during the first course in an attempt to account for this inter-individual variability. Neither total and CD19+ lymphocyte counts nor Ann-Arbor stage were correlated with pharmacokinetic parameters. However, these biomarkers may not describe tumour burden well and may have limited our analysis of the effect of tumour burden on rituximab pharmacokinetics. Ann Arbor stage depends on the location of the malignant tissue and systemic symptoms due to the lymphoma. It may thus be more representative of general clinical state than of real tumour bulk. Moreover, our analysis included only 10 patients who were very similar in terms of Ann Arbor stage, tumour characteristics and clinical response. It is difficult to determine tumour burden precisely in NHL. Computed tomography (CT) is the most widely used method for staging malignant lymphoma. Possible alternatives include (18)F-fluoro-2-deoxyglucose positron emission tomography (FDG-PET), FDG-PET/CT fusion and magnetic resonance