Kristin Dickschen (1), Juergen Jaeger (1), Meike Hutt (2), Nadine Pollak (2), Martin Siegemund (2), Oliver Seifert (2), Roland Kontermann (2), Klaus Pfizenmaier (2), Michael Block (1)
(1) Bayer Technology Services GmbH, Technology Development, Enabling Technologies, Computational Systems Biology, Leverkusen, Germany; (2) Institute of Cell Biology and Immunology, University of Stuttgart, Stuttgart, Germany
Objectives: One of the hallmarks of cancer that can potentially be addressed by oncological substances is resisting cell death [1]. Here, targeting the death receptors (DR) DR4 and DR5 with tumor necrosis factor (TNF)-related apoptosis inducing ligands (TRAIL) represents a promising approach [2, 3]. Physiologically-based pharmacokinetic and pharmacodynamic (PBPK/PD) modelling provides a powerful tool to give guidance at the preclinical stage for further experiments and to assess expectations and risks for clinical development. We here present a detailed approach on how such an assessment might work in reality.
Methods: A PBPK/PD model of cetuximab targeting human epidermal growth factor receptor (EGFR) was established in mice and patients. A second PBPK model for an EGFR-targeted TRAIL fusion protein in non-tumor bearing mice was built. The PBPK models were developed by use of the systems biology software suite including PK-Sim® and MoBi®. Specific processes included in the models are target expression, target binding, FcRn binding, target receptor availability, and receptor internalization [4]. In addition, a PD model was integrated in order to represent tumor growth dynamics. By using cetuximab as a benchmark a rationale for further development of a TRAIL fusion protein in tumor-bearing mice was developed and analyzed.
Results: The developed cetuximab PBPK model adequately describes observed plasma PK in mice, tumor-bearing mice, and patients. Moreover, tumor PK and impact on tumor growth are well reflected by the model. The EGFR-targeted TRAIL fusion protein model also describes observed plasma PK in non-tumor bearing mice adequately. For both PBPK models the impact of changes in PK on tumor growth dynamics is investigated by a detailed analysis of similarities and differences of the compounds, e.g. binding to EGFR, binding to DR4/5, and FcRn binding. These results will be used to inform future experiments of the TRAIL fusion protein, e.g. inform sampling in experiments with tumor-bearing mice.
Conclusions: PBPK/PD model-guided development of a compound has the potential to analyze potential and risks of a compound very early. By means of the presented example it was shown how such modelling approaches could provide first a consistent picture of the available biological knowledge of a new compound and second a rationale for the next steps in the development with guidance from simulations, e.g. by optimizing sampling times in experiments.
References:
[1] Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011. 144 (5): 646-674
[2] Schneider B et al. Potent antitumoral activity of TRAIL through generation of tumor-targeted single-chain fusion proteins. Cell Death Dis. 2010. 1:e68
[3] Siegemund M et al. Superior antitumoral activity of dimerized targeted single-chain TRAIL fusion proteins under retention of tumor selectivity. Cell Death Dis. 2012. 3 (4): e295
[4] Garg A, Balthasar JP. Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn. 2007. 34 (5): 687-709
Reference: PAGE 24 () Abstr 3508 [www.page-meeting.org/?abstract=3508]
Poster: Drug/Disease modeling - Oncology