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CHARACTERIZATION OF THE TIME-VARYING CLEARANCE OF RITUXIMAB IN NON-HODGKIN’S LYMPHOMA PATIENTS USING A POPULATION PHARMACOKINETIC ANALYSIS *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 RESULTS •to identify covariates as potential predictors of PK variability Conc. (ug/mL) 1 10 1000 •to investigate possible mechanisms that may explain the observed increase in half-life with time such as a B-cell/tumor burden mediated clearance 50 100 150 200 Time (Days) 250 300 Study B (N=46) 0 50 100 150 200 Time (Days) 250 300 0 50 100 150 200 Time (Days) 250 0 50 100 150 200 Time (Days) 250 300 IWRES vs. TIME WRES vs. TIME 0 50 100 150 200 Time (Days) 250 300 250 300 600 1000 0 200 600 1000 4 200 400 600 800 5 0 -10 -2 0 -4 -4 -10 -2 200 0 200 0 0 5 2 4 2 0 600 WRES vs. PRED 10 15 20 IWRES vs. IPRED 10 15 20 DV vs. IPRED 1000 1000 Study E (N=36) 0 200 300 Study D (N=161) 600 Conc. (ug/mL) 1 10 1000 Study C (N=38) Conc. (ug/mL) 1 10 1000 Study A (N=9) 0 •to develop a population pharmacokinetic (POP PK) model using a large NHL patient population Conc. (ug/mL) 1 10 1000 Figure 1. Concentration-Time Profiles of Rituximab from Six Studies Included in the POP PK Analysis Conc. (ug/mL) 1 10 1000 OBJECTIVES DV vs. PRED Analysis Population and Data Characteristics Conc. (ug/mL) 1 10 1000 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. Figure 4. Goodness-of-fits Plots 0 INTRODUCTION 0 200 400 600 0 100 200 300 400 0 100 200 300 400 Study F (N=8) 0 50 100 150 200 Time (Days) METHODS Study Population A total of 3739 serum rituximab concentrations from 298 patients in 6 clinical studies were used in this POP PK analysis. Table 1. Summary of Studies Included in the POP PK Analysis Study Phase na A I/II 9 Recurrent B-cell lymphoma Rituximab alone Single-dose infusion: 100, 250, and 500 mg/m2 B I/II 46 Recurrent B-cell lymphoma Rituximab alone Multiple-dose infusion: 125, 250, and 375 mg/m2; once weekly 4 weeks C II 38 Previously treated or recurrent low-grade B-cell lymphoma Rituximab CHOP Total of 6 infusions of rituximab (375 mg/m2) D II 36 Relapsed low-grade or follicular B-cell lymphoma Rituximab alone Multiple-dose infusion of 375 mg/m2; once weekly 8 weeks E III 161 Relapsed low-grade or follicular B-cell lymphoma Rituximab alone Multiple-dose infusion of 375 mg/m2; once weekly 4 weeks F III 8 Previously untreated intermediate/high-grade NHL (DLBCL) Rituximab CHOP 6 or 8 infusions of 375 mg/m2 on Day 1 of each 21-day cycle Population Treatment Rituximab Dose Covariates Effect • 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 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. • The baseline SPD was the most important covariate on Kdes Tested clinically relevant covariates are listed in Table 2 • The large inter-individual variability in CL2 and Kdes remained unexplained despite the inclusion of baseline CD19 and SPD covariates in the PK model Table 2. Covariates Tested in the Model Covariates Demographic Laboratory Concurrent Medication Variable Names in the Analysis Age (AGE); Body Surface Area (BSA); Gender (SEX); Race (RACE); WHO performance Status (WHO) • No covariate significantly influencing CL1 was found Baseline CD19 counts (CD19); Baseline sum of the product of perpendicular diameters for the measurable tumor lesions (SPD) Combination with cyclophosphamide, doxorubicin, vincristine, and prednisone therapy (CHOP) 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: Pjk Pˆk * exp( jk ) where j is to identify individuals, Pjk is the parameter value (e.g.: k=CL, V) for the jth subject; Pˆk 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: Cij Cˆ 1 pij aij 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 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 ij i,Cˆ ij is where Cij is the jth measured observation in individual 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 ~ N 0, 2 to be independently and identically distributed: Figure 3. Representative individual-fir of the POPPK model, comparing 2-compartments linear model to 2-compartments with time varaying clearance 2-CMT linear model •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 effects of continuous covariates and the categorical covariates were described: xlj l m Ymj Pˆ l m edxl m 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 Pˆ 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. CONCLUSIONS 2 CLs empirical model with time varying CL •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.