1. CHARACTERIZATION OF THE TIME-VARYING CLEARANCE OF RITUXIMAB IN NON-HODGKIN’S LYMPHOMA PATIENTS USING A POPULATION PHARMACOKINETIC ANALYSIS METHODS INTRODUCTION OBJECTIVES *Micha Levi 1, *Jing Li 2, Nicolas Frey 3, Thian Kheoh 4, Song Ren 2, Michael Woo 2, Amita Joshi 2, Nancy Valente 2, Nelson ‘Shasha’ Jumbe 2, Jean-Eric Charoin 3 *contributed equally to this work Hoffman-La Roche Inc., Nutley, NJ, 2 Genentech, Inc., South San Francisco, CA, 3 Roche Pharma, F. Hoffmann-La Roche Ltd, Basel, Switzerland 4 Biogen 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 2Data AnalysisA POP PK Model was simultaneously fitted to the pooled data from the 6 clinical studies using the FOCE INTER method ofNONMEM 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 j th subject; is the (population) expected value of the parameter; and η jk 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 j th measured observation in individual i, is the j th model-predicted value in individual i, and ε pij and ε aij 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 “multiplicativepower” 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 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 V 1 based on the final covariate model using pooled data SPD and CD19 at baseline were the most significant covariates affecting CL 2 at time zero The baseline SPD was the most important covariate on K des The large inter-individual variability in CL 2 and K des remained unexplained despite the inclusion of baseline CD19 and SPD covariates in the PK model No covariate significantly influencing CL 1 was found Covariates Effect Time Varying Clearance of Rituximab in NHL Patients The total clearance (CL total ) after the first infusion of rituximab is much higher than at later times, where CL total is determined by the non-specific clearance (CL 1 ) only The higher specific clearance (CL 2 ) correlated with higher CD19 and SPD at time zero Figure 6 illustrates the gradual decrease in CL total 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(CL 2 ) and rate of specific clearance decay (K des ), 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 2-CMT linear model 2 CLs empirical model with time varying CL Figure 4. Goodness-of-fits Plots