1598P - Translational PKPD of DNIB0600A, an anti-Napi2b-vc-MMAE ADC in ovarian and NSCLC cancers

Date 28 September 2014
Event ESMO 2014
Session Poster Display session
Topics Drug Development
Ovarian Cancer
Lung and other Thoracic Tumours
Translational Research
Presenter Kedan Lin
Citation Annals of Oncology (2014) 25 (suppl_4): iv546-iv563. 10.1093/annonc/mdu358
Authors K. Lin1, S. Sukumaran1, J. Xu1, C. Zhang1, Y. Choi2, S. Yu2, P. Polakis2, D. Maslyar2
  • 1Pkpd, Genentech, 94080 - South San Francisco/US
  • 2Gred, Genentech, 94080 - South San Francisco/US

Abstract

Aim

Antibody-drug conjugates (ADCs) represent an expanding class of therapeutic molecules in preclinical and clinical development for oncologic indications. Understanding the relationship between pre-clinical to clinical studies with strategic application of pharmacokinetic/pharmacodynamics modeling may allow for optimized dosing strategies for ADCs. NaPi2b is a multi-transmembrane, sodium-dependent phosphate transporter that is expressed in human lung, ovarian, and thyroid cancers. DNIB0600A, which consists of an anti-NaPi2b monoclonal antibody conjugated to the cytotoxic drug MMAE through a cleavable VC linker, is currently in Phase I/II clinical trials. The purpose of this study is to develop a PKPD model based on preclinical data and to utilize the preclinical exposure response relationships to predict clinical outcomes.

Methods

DNIB0600A PK was evaluated in normal SCID mice. Dose ranging in-vivo efficacy studies were performed in NaPi2b-expressing xenograft mouse models of ovarian and lung cancers. A semi-mechanistic PKPD model was developed to describe exposure-efficacy relationships in both types of the tumor models. Human PK and PD data were collected from ongoing Phase I/II trials.

Results

DNIB0600A demonstrated differential anti-tumor activities in the models, with the ovarian model being more responsive when compared with lung tumor model to NaPi2b ADC treatment. Human efficacious doses for treating ovarian cancer and NSCLC were predicted based on the pre-clinical PKPD relationships. Observed efficacy data from preliminary analysis of Phase I/II trials were in general agreements with model predictions.

Conclusions

We built an integrated PKPD model to predict clinical outcome. This approach can be extended to other vc-MMAE based ADCs, and can help in preclinical model validation and ADC optimization.

Disclosure

All authors have declared no conflicts of interest.