CHARACTERIZATION OF THE TIME-VARYING CLEARANCE OF RITUXIMAB IN NON-HODGKIN’S LYMPHOMA PATIENTS USING A POPULATION PHARMACOKINETIC ANALYSIS

METHODS

INTRODUCTION

OBJECTIVES

*Micha Levi1, *Jing Li2, Nicolas Frey3, Thian Kheoh4, Song Ren2, Michael Woo2, Amita Joshi2, Nancy Valente2, Nelson ‘Shasha’ Jumbe2, Jean-Eric Charoin3

*contributed equally to this work

Hoffman-La Roche Inc., Nutley, NJ ,2Genentech, Inc., South San Francisco, CA , 3Roche Pharma, F. Hoffmann-La Roche Ltd, Basel, Switzerland 4Biogen Idec, San Diego, CA

•to develop a population pharmacokinetic (POP PK) model using a large NHL patient population

•to investigate possible mechanisms that may explain the observed increase in half-life with time such as a B-cell/tumor burden mediated clearance

•to identify covariates as potential predictors of PK variability

Rituximab is a monoclonal antibody directed against the CD20 antigen found on the surface of normal and malignant B lymphocytes. The elimination half-life of rituximab was originally determined on data from 14 Non-Hodgkin’s Lymphoma (NHL) patients treated with a dose of 375 mg/m2 weekly x 4, and was described to increase with time from 3.2 days following the first infusion to 8.6 days following the fourth infusion. The half-life increase with time was hypothesized to be due to a decrease of rituximab clearance coinciding with the decrease in B-cell (CD19+) count and/or tumor burden.

Study Population

A total of 3739 serum rituximab concentrations from 298 patients in 6 clinical studies were used in this POP PK analysis.

Tested clinically relevant covariates are listed in Table 2

Data Analysis

A POP PK Model was simultaneously fitted to the pooled data from the 6 clinical studies using the FOCE INTER method of NONMEM V.

The interindividual variability in the PK parameters was modeled generically:

where j is to identify individuals, Pjk is the parameter value (e.g.: k=CL, V) for the jth subject; is the (population) expected value of the parameter; and ηjk is an individual random effect parameter. The random individual vectors ηj=(ηjCL, ηjV) are assumed independent, with a normal distribution; a mean of zero and a variance ω2.

The residual variability was described by a combined additive and proportional error model:

where Cij is the jth measured observation in individual i, is the jth model-predicted value in individual i, and εpij and εaij are proportional and additive residual random errors, respectively, for individual i and measurement j and are each assumed to be independently and identically distributed:

The effects of continuous covariates and the categorical covariates were described:

Where continuous covariates (centered around their median (med(Xl)) values) were modeled using the “multiplicative power” model, thus allowing θl to represent the P estimate for the typical patient with median continuous covariates. Categorical covariates were coded as 0 or 1. θm represents the fractional change in when Y=1.

A non-parametric bootstrap was used to estimate the precision of model parameters.

A visual predictive check (VPC) was used to assess the model performance.

RESULTS

aijpijij ijCC 1ˆ

kP

)exp(*ˆjkkjk PP

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Figure 1. Concentration-Time Profiles of Rituximab from Six Studies Included in the POP PK Analysis

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1000 Study D (N=161)

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1000 Study E (N=36)

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Figure 1. Concentration-Time Profiles of Rituximab from Six Studies Included in the POP PK Analysis

Time (Days)

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1000 Study A (N=9)

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1000 Study B (N=46)

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1000 Study C (N=38)

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1000 Study D (N=161)

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1000 Study E (N=36)

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1000 Study F (N=8)

Time (Days)

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1000 Study A (N=9)

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1000 Study B (N=46)

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1000 Study C (N=38)

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1000 Study D (N=161)

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1000 Study E (N=36)

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1000 Study F (N=8)

Analysis Population and Data Characteristics

POPPK Model Development

• A two compartment model with time-varying clearance (Figure 2) was markedly better than of the two-compartment linear model as determined by the individual-fit plots (Figure 3)

• Goodness-of-fit plots (Figure 4) and by VPC (Figure 5) indicate that the model describes the data reasonably well

• The good precision of POP PK model parameter estimates was demonstrated by a non-parametric bootstrap listed in Table 5

• BSA explained 27.3% of the inter-individual variability in V1 based on the final covariate model using pooled data

• SPD and CD19 at baseline were the most significant covariates affecting CL2 at time zero

• The baseline SPD was the most important covariate on Kdes

• The large inter-individual variability in CL2 and Kdes remained unexplained despite the inclusion of baseline CD19 and SPD covariates in the PK model

• No covariate significantly influencing CL1 was found

Covariates Effect

Time Varying Clearance of Rituximab in NHL Patients

• The total clearance (CLtotal) after the first infusion of rituximab is much higher than at later times, where CLtotal is determined by the non-specific clearance (CL1) only

• The higher specific clearance (CL2) correlated with higher CD19 and SPD at time zero

• Figure 6 illustrates the gradual decrease in CLtotal with diminishing CD19 counts and SPD after rituximab treatment

CONCLUSIONS

•A two compartment model with time-varying clearance described rituximab PK data pooled from six clinical studies.

•This model offered for the first time a quantitative estimation of the decrease in rituximab clearance by using an empirical first order time-dependent decline in rituximab clearance.

•The median of individual estimates of rituximab terminal half-life was approximately 22.4 days (range, 6.14 to 51.9 days), which is typical for immunoglobulin isotype IgG in humans and is longer than that reported for humanized anti-CD20 clinical candidates, IMMU106 and ofatumumab of 12.0 and 14.3 days, respectively.

•Covariates associated with tumor burden (SPD and CD19+) appeared to affect parameter estimates of specific clearance(CL2) and rate of specific clearance decay (Kdes), thus, offering support to the hypothesis that rituximab PK in NHL patients was affected by the disease.

Figure 3. Representative individual-fir of the POPPK model, comparing 2-compartments linear model to 2-compartments with time varaying clearance

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Figure 4. Goodness-of-fits Plots

Table 1. Summary of Studies Included in the POP PK Analysis

CHOP cyclophosphamide, doxorubicin, vincristine, and prednisone; DLBCL diffuse, large B-cell lymphoma; NHL non-Hodgkin’s lymphoma. aNumber of rituximab-treated patients included in the POP PK analysis.

6 or 8 infusions of 375 mg/m2 on Day 1 of each 21-day cycleRituximabCHOPPreviously untreated intermediate/high-grade NHL (DLBCL)8IIIF

Multiple-dose infusion of 375 mg/m2; once weekly 4 weeksRituximabaloneRelapsed low-grade or follicular B-cell lymphoma161IIIE

Multiple-dose infusion of 375 mg/m2; once weekly 8 weeksRituximabaloneRelapsed low-grade or follicular B-cell lymphoma36IID

Total of 6 infusions of rituximab (375 mg/m2)RituximabCHOPPreviously treated or recurrent low-grade B-cell lymphoma38IIC

Multiple-dose infusion: 125, 250, and 375 mg/m2; once weekly 4 weeksRituximabaloneRecurrent B-cell lymphoma46I/IIB

Single-dose infusion: 100, 250, and 500 mg/m2RituximabaloneRecurrent B-cell lymphoma9I/IIA

RituximabDoseTreatmentPopulationn aPhaseStudy

Table 1. Summary of Studies Included in the POP PK Analysis

CHOP cyclophosphamide, doxorubicin, vincristine, and prednisone; DLBCL diffuse, large B-cell lymphoma; NHL non-Hodgkin’s lymphoma. aNumber of rituximab-treated patients included in the POP PK analysis.

6 or 8 infusions of 375 mg/m2 on Day 1 of each 21-day cycleRituximabCHOPPreviously untreated intermediate/high-grade NHL (DLBCL)8IIIF

Multiple-dose infusion of 375 mg/m2; once weekly 4 weeksRituximabaloneRelapsed low-grade or follicular B-cell lymphoma161IIIE

Multiple-dose infusion of 375 mg/m2; once weekly 8 weeksRituximabaloneRelapsed low-grade or follicular B-cell lymphoma36IID

Total of 6 infusions of rituximab (375 mg/m2)RituximabCHOPPreviously treated or recurrent low-grade B-cell lymphoma38IIC

Multiple-dose infusion: 125, 250, and 375 mg/m2; once weekly 4 weeksRituximabaloneRecurrent B-cell lymphoma46I/IIB

Single-dose infusion: 100, 250, and 500 mg/m2RituximabaloneRecurrent B-cell lymphoma9I/IIA

RituximabDoseTreatmentPopulationn aPhaseStudy

Baseline CD19 counts (CD19); Baseline sum of the product of perpendicular diameters for the measurable tumor lesions (SPD)Laboratory

Combination with cyclophosphamide, doxorubicin, vincristine, and prednisone therapy (CHOP)Concurrent Medication

Table 2. Covariates Tested in the Model

Age (AGE); Body Surface Area (BSA); Gender (SEX); Race (RACE); WHO performance Status (WHO)Demographic

Variable Names in the AnalysisCovariates

Baseline CD19 counts (CD19); Baseline sum of the product of perpendicular diameters for the measurable tumor lesions (SPD)Laboratory

Combination with cyclophosphamide, doxorubicin, vincristine, and prednisone therapy (CHOP)Concurrent Medication

Table 2. Covariates Tested in the Model

Age (AGE); Body Surface Area (BSA); Gender (SEX); Race (RACE); WHO performance Status (WHO)Demographic

Variable Names in the AnalysisCovariates

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ID: 214.00 ID: 215.00 ID: 216.00

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Figure 6. Representative Clearance vs. Time profiles

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Figure 6. Representative Clearance vs. Time profiles

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