Camila de Almeida (1), Klas Petersson (3), Lars Lindbom (3), Peter Ballard (1), Phillip MacFaul (1), Hazel M. Weir (2), Jon Curwen (2), Zena Wilson (2), Michael Hulse (2), Steve Powell (2), Owen Jones (1)
(1) Oncology iMed DMPK, AstraZeneca, Alderley Park, UK; (2) Oncology iMed Bioscience, AstraZeneca, Alderley Park, UK; (3) qPharmetra, Stockholm, Sweden
Objectives: To develop a preclinical PK-PD-Efficacy model for AZD9496, a novel oral selective estrogen receptor downregulator [1]. Using a parent-metabolite PK model for AZD9496, to quantify the relative contributions of both to the pharmacodynamics effects in MCF7 xenografts. To use the predicted biomarker modulation to drive tumour growth inhibition (TGI) in xenografts, integrating diverse endpoints into a single model using nonlinear mixed effect analyis [2, 3].
Methods: Discrete Parent-metabolite PK data pooled with PK info from PD and efficacy studies were analysed in a population model with proportional error using NONMEM.
Progesterone receptor A (PRA) in tumours of male MCF7 xenografts were measured in time course PD studies after oral administration of AZD9496 or its metabolite with doses ranging from 0.2 to 5 mg/kg for three days. Tumour growth inhibition (TGI) data has been generated from MCF7 xenograft studies conducted at a range of doses from 0.02 to 50 mg/kg for 21 days.
A ”PRA driven” TGI model was proposed and the total of data was fitted using an integrated population PK, indirect PD and biomarker driven efficacy model in NONMEM.
Results: A two comparment model for parent and one compartment model for metabolite with proportional error adequately fitted the PK data. The terminal half-lives of both parent and metabolite are around 5 hours.
An indirect response model for inhibition of PRA synthesis adquately described the observed delayed between PK and PD for both parent and active metabolite, predicts PRA degradation half-life to be 24 hours and a maximum of 40% contribution of the metabolite to the observed PD effects in xenografts.
A biomarker-driven efficacy model, with tumour growth proportional to the levels of PRA, adequately descrived the efficacy data. This model was used to predict likely human doses based on PBPK predicted human PK and target PRA inhibition.
Conclusions: Complete integration of preclinical PK-PD and efficacy data into a single model was obtained used nonlinear mixed effect analysis in NONMEM, taking into account the inter-animal variability observed in PKstudies. This is a novel approach that focuses on the mechanism of action of the drug and helps bridge the gap between preclinical and clinical studies, deviating from the traditional PK-efficacy models, for a better estimation of doses in humans [4].
References:
[1] Wijayaratne, A.L. and D.P. McDonnell, The human estrogen receptor-alpha is a ubiquitinated protein whose stability is affected differentially by agonists, antagonists, and selective estrogen receptor modulators. J Biol Chem, 2001. 276(38): p. 35684-92. 2.
[2] Mould, D.R., et al., Developing Exposure/Response Models for Anticancer Drug Treatment: Special Considerations. CPT: Pharmacometrics & Systems Pharmacology, 2015. 4(1): p. 1-16.
[3] Ribba, B., et al., A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis. CPT: pharmacometrics & systems pharmacology, 2014. 3: p. e113.
[4] Wong, H., et al., Bridging the gap between preclinical and clinical studies using pharmacokinetic-pharmacodynamic modeling: an analysis of GDC-0973, a MEK inhibitor. Clin Cancer Res, 2012. 18(11): p. 3090-9.
Reference: PAGE 24 () Abstr 3515 [www.page-meeting.org/?abstract=3515]
Poster: Drug/Disease modeling - Oncology