Eric Fernandez, Frances Brightman, David Orrell and Christophe Chassagnole
Physiomics plc
Objectives: One of the most demanding tasks in pharmaceutical drug development is the capability to predict clinical outcome for a planned study based on preclinical observations. A technique such as in silico tumour cell population modelling can help predict the effect of anti-neoplastic agents, and therefore optimise dose and administration scheduling of these agents. Here it is our aim to demonstrate how radiation therapy mechanism of action can be translated from in vitro experiments to in vivo animal studies and then to the clinic.
Methods: Experimental radiation treatment data from in vitro [1], in vivo xenograft [2,3] and clinical efficacy studies [4] have been extracted from selected literature references. These data included FACS time series analysis of irradiated cultured cells, tumour xenograft growth rate studies and head and neck clinical tumour size from patients during the course of radiation therapy. We used these data to model irradiation mechanism of action in our VT tumour population [5] and calibrate the model for the three levels of experimental work.
Results: Using a tumour cell population model such as the VT we were able to translate the efficacy of radiation on three levels: firstly, from in vitro to in vivo and then from in vivo to clinical studies, without changing the underlying mechanism of action of radiation at the cellular level. The only adjustments were key parameters of the cell population structure.
Conclusions: We explored how the model could be converted to fit, using the same mechanism of action from in vitro to in vivo to clinical. We have shown that cell population structure is key to be able to describe the effect of irradiation at the three levels. This paves the way of using our VT technology to predict clinical outcomes from pre-clinical studies.
References:
[1] Warenius, H. M. et al. Late G1 accumulation after 2 Gy of gamma-irradiation is related to endogenous Raf-1 protein expression and intrinsic radiosensitivity in human cells. Br. J. Cancer 77, 1220–1228 (1998).
[2] Matsumoto, F. et al. The Impact of Timing of EGFR and IGF-1R Inhibition for Sensitizing Head and Neck Cancer to Radiation. Anticancer Res. 32, 3029–3035 (2012).
[3] Fatema, C. N. et al. Dual tracer evaluation of dynamic changes in intratumoral hypoxic and proliferative states after radiotherapy of human head and neck cancer xenografts using radiolabeled FMISO and FLT. BMC Cancer 14, 692 (2014).
[4] Belli, M. L. et al. Characterization of volume and shape modifications of PET-positive nodes during Tomotherapy for head and neck cancer as assessed by MVCTs. Radiother. Oncol. 115, 50–55 (2015).
[5] Fernandez, E. et al. Modeling the sequence-sensitive gemcitabine-docetaxel combination using the Virtual Tumor. in AACR 102nd Annual Meeting, Orlando, FL (2011). Avalilable at http://www.physiomics-plc.com/wp-content/uploads/downloads/2011/05/Poster_Oncodesign_AACR-2011_FINAL.pdf
Reference: PAGE 25 (2016) Abstr 5886 [www.page-meeting.org/?abstract=5886]
Poster: Drug/Disease modeling - Oncology