Abstract 4649: Using pharmacokinetic/efficacy modeling to identify the optimal schedule for MLN0264, an anti- guanylyl cyclase C (GCC) antibody-drug conjugate, in a range of xenograft models
Authors: Teng Shu-Wen , Zopf Christopher , Yang Johnny , Stringer Brad , Zhang Julie , Shyu Wen Chyi , Chakravarty Arijit , Veiby Petter , Mettetal Jerome 
Published: May 2015

Abstract
MLN0264 is an investigational antibody-drug conjugate (ADC) that consists of the human anti-guanylyl cyclase C (GCC) antibody linked to a microtubule-disrupting agent (monomethyl auristatin). As ADCs have a very long clearance half-life, the potential exists for a highly infrequent dosing schedule. A quantitative understanding of the relationship between exposure and preclinical antitumor biological activity is thus applied to support dose schedule selection in the clinic.
In this study, we develop a pharmacokinetic/efficacy (PK/E) relationship in xenograft models to evaluate the predictive contributions of exposure and xenograft characteristics to MLN0264 biological activity. Single dose pharmacokinetic (PK) data were obtained for a range of time points, and a linear two-compartment PK model was built. Xenograft biological activity studies were conducted in which MLN0264 was administered at various dose levels and dosing schedules to mice bearing one of six different xenograft models. We used multiple linear regression and tumor dynamic modeling to understand the factors contributing to the biological activity.
First, the growth rate of each tumor under control and treatment conditions was established by fitting the biological activity data within the dosing period to an exponential growth function. Then, we assessed the relationship of AUC to antitumor biological activity. Within each xenograft model, AUC was strongly correlated with biological activity across a range of schedules (R2&gt;0.9, p&lt;0.03, for all models). AUC-proportional biological activity was confirmed using dynamic modeling with direct drug effect to simulate the tumor dynamics for different dosing schedules.
Although biological activity within a xenograft model was strongly correlated with exposure, when the data from different xenograft models were pooled together, the correlation was weaker (R2= 0.30, p= 0.0004). This result suggests that, although schedule is not a major determinant of biological activity, other xenograft-specific factors may be contributing to biological activity. To test the contribution of some possible factors, we used multiple linear regression to determine which covariates (such as GCC expression level and baseline growth rate of xenograft models) are predictive of biological activity. The results suggest baseline growth rate does not correlate with biological activity while GCC expression is weakly correlated. This finding formed the basis for the development of a mechanistic model of GCC expression and biological activity.
Taken together, this work demonstrates the use of a PK/E framework to identify the scheduling effect for a first-in-man protocol for an ADC. We have also demonstrated that this PK/E framework can be further leveraged to assess the contribution of other potential predictors of ADC biological activity in xenograft models.
Citation Format: Shu-Wen Teng, Christopher Zopf, Johnny Yang, Brad Stringer, Julie Zhang, Wen Chyi Shyu, Arijit Chakravarty, Petter Veiby, Jerome Mettetal. Using pharmacokinetic/efficacy modeling to identify the optimal schedule for MLN0264, an anti- guanylyl cyclase C (GCC) antibody-drug conjugate, in a range of xenograft models. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4649. doi:10.1158/1538-7445.AM2014-4649