Venkatesh Pilla Reddy (1); Linette Ruston (1); Ian Grant (2); Gary Wilkinson (1); Barry Davies (3); Leigh Williams (3); Rebecca Ellston (3); Constanze Hilgendorf (4); Lars Lindbom (5); Jacob Brogren (5); Rhys DO Jones (1); Elizabeth Pease (6)
(1) iMED Oncology, DMPK, AstraZeneca, the UK; (2) PharmDev, AstraZeneca, the UK; (3) Bioscience, AstraZeneca, the UK; (3) iMED DMPK, AstraZeneca, Sweden; (5) qPharmetra, Sweden; (6) Global Oncology Projects, AstraZeneca, US.
Objectives: Localized release of an agent from a depot-type formulation in the proximity of the diseased tissue relative to systemic exposures could result in better therapeutic index in a cancer setting. Drug concentration-tissue/tumor-depth-time profiles can be predicted by Grabnar model (GM)[1] or by using physiology-based mechanistic model (PBMM). The main objectives of this work are 1) to reduce the existing 30 component GM, with aim of using this reduced model along with a minimal in-vitro/ex-vivo diffusion coefficient data to predict the rate and depth of tissue penetration and plasma levels in vivo after bladder local delivery (BLD) and 2) to develop an alternative model (e.g. PBMM) to predict tumour concentrations, and incorporate the possibility for permeability limited distribution.
Methods: Grabnar Model: Data from Grabnar paper was digitized and the model structure was implemented in NONMEM. The predictive performance of the models was assessed by comparison of simulated and observed concentrations across the thickness of bladder. The model parameters re-estimated with observations lumped according to what will be available in future in-house experiments: urothelium (x1-y1 µm), lamina propria (x2-y2 µm), and muscular layers (x3-y3 µm). Models with the number of compartments set to appropriate values between 3 and 30. PBMM: PBMM after systemic dosing was developed first by supplementing with tumour tissue: plasma partition coefficient (Kp) value[2] to predict tumour exposure followed by PBMM for BLD by including the permeability-limited bladder and a tumour compartments and using experimentally determined or in-silico predicted Kp value.
Results: GM: The reduced 7 compartment model in NONMEM was able to reasonably predict the urothelium exposure at different tissue layers, and this reduced model gave a better fit to the observed data over original GM. PBMM: Conceptual model in rats has been established using systemic dosing data to predict the tumour concentration, and then using data from orthotopic experiment for BLD. The results showed a good agreement with observed tumour and bladder levels.
Conclusion: In conclusion, GM for drug distribution in bladder tissue has been implemented and simplified in the NONMEM software. This analysis revealed that human bladder tissue-time profiles can be predicted with minimal in-vitro/ex-vivo data. If the bladder exposure cannot be described by a passive diffusion, it may be described with a membrane limited diffusion/transport using a novel PBMM approach refined for BLD or for systemic dosing + BLD.
References:
[1] Grabnar, M. Bogataj, A. Belic, V. Logar, R. Karba and A. Mrhar, “Kinetic model of drug distribution in the urinary bladder wall following intravesical instillation,” International Journal of Pharmaceutics, no. 322, pp. 52-59, 2006.
[2] Poulin P, Hop CE, Salphati L, and Liederer BM (2013). Correlation of Tissue – Plasma Partition Coefficients between Normal Tissues and Subcutaneous Xenografts of Human Tumor Cell Lines in house as a Prediction Tool of Drug Penetration in Tumors. Journal of pharmaceutical sciences. 102(4).
Reference: PAGE 24 () Abstr 3360 [www.page-meeting.org/?abstract=3360]
Poster: Drug/Disease modeling - Oncology