PAGE. Abstracts of the Annual Meeting of the Population Approach Group in Europe.
PAGE 25 (2016) Abstr 5810 [www.page-meeting.org/?abstract=5810]
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Poster: Methodology - New Modelling Approaches
Leonid Gibiansky (2), Matts Kågedal (1), Jian Xu (1), Nina Wang (1), Charles Chen (1), Sandhya Girish (1), Jin Jin (1), Chunze Li (1)
(1) Genentech, Inc., South San Francisco, CA; (2) QuantPharm LLC, North Potomac, MD
Objectives: ADCs developed using Vc-MMAE platform share the same antibody technology, linker, and toxin (MMAE). The similarity of ADC structures resulted in similar PK properties. The goal of the analysis was to develop a mega model that simultaneously described antibody-conjugated MMAE (acMMAE) data from multiple ADCs, and assess differences and similarities of model parameters and predictions among different compounds.
Methods: Clinical data of 8 ADCs were obtained from 8 studies with ADC doses ranging from 0.1 to 3.2 mg/kg every 3 weeks (Q3W). Initially, data were treated as coming from the same ADC. Models with time-dependent clearance (CL) and parallel linear and Michaelis-Menten (MM) elimination were explored. Effects of weight, sex, and dose were evaluated by inclusion in the model. After the unified covariate model was developed, differences in model parameters between ADCs were investigated. A series of mega-models, from the model with all common parameters to the model with all compound-specific parameters were developed. Alternatively, the inter-compound variability was described explicitly using the third random effect level implemented using LEVEL option of Nonmem 7.3. Visual predictive checks (VPC) were used to assess ability of the models to predict PK for each compound.
Results: A two-compartment model with time dependent CL; CL and central volume (VC) increasing with weight; VC higher for males; and CL mildly decreasing with the dose described acMMAE PK of 8 ADCs. MM elimination had only minor effect on PK and was not included in the model. Time-dependence of CL had no effect beyond the first dosing cycle. The model with all parameters shared by all compounds provided reasonable acMMAE predictions and VPC plots for all compounds. For the model with all compound-specific parameters, CL and VC were similar among ADCs, with the inter-compound variability of 17% and 7%. Similar results (15% and 5%) were obtained when the inter-compound variability was described using LEVEL option. Differences among ADCs were minor relative to the inter-subject variability.
Conclusions: The population mega-model successfully described acMMAE PK of 8 ADCs. PK of acMMAE are largely comparable across different vc-MMAE ADCs. The model can be applied to predict properties of ADCs under development, estimate individual exposure for the subsequent PK-PD analysis, and propose optimal dosing regimens. PK of acMMAE is similar among ADCs of the same platform.