Frances Brightman(1), Hitesh Mistry(1), Eric Fernandez(1), David Orrell(1), William L. Dahut(2), William D. Figg, Sr.(2), Wilfried D. Stein(2), Christophe Chassagnole(1).
(1)Physiomics plc, Oxford, United Kingdom; (2)National Cancer Institute - NIH, Bethesda, MD
Objectives: A major cause of drug failure is that preclinical studies do not predict with sufficient certainty what will happen in clinical practice. Accurate translation of information from animal studies to the clinic would have a major impact on attrition rate1.
Methods: We have developed a mathematical model of a tumour cell population called the Virtual Tumour, which has been used extensively to predict the efficacy of single drug or drug combination treatment in preclinical studies2–5. We have now extended and adapted our preclinical model to predict efficacy in the clinic, thereby creating the ‘Virtual Tumour Clinical’.
Results: Here we show a comparative study of the preclinical Virtual Tumour calibrated for prostate tumour xenografts in mice, with a Virtual Tumour Clinical version calibrated with a clinical data set comprising 53 prostate cancer patients treated with thalidomide, 25 treated with docetaxel and 50 treated with a docetaxel and thalidomide combination6,7. PSA measurements were used as proxy for tumour size. We analysed the consistency, the capability and the limitations of the models in translating the effect of the drug combination from the preclinical situation to the clinic.
Conclusions: Virtual Tumour Clinical was used to make successful predictions from preclinical data of docetaxel and docetaxel/thalidomide efficacy in the clinical setting.
References:
[1] Van der Worp, H. B. et al. Can animal models of disease reliably inform human studies? PLoS Med 7, e1000245 (2010).
[2] Fernandez, E. et al. Modeling the sequence-sensitive gemcitabine-docetaxel combination using the Virtual Tumor. AACR 102nd Annual Meeting, Orlando FL (2011).
[3] Fernandez, E. et al. Modeling ionizing radiation exposure in vitro and in vivo using the Virtual Tumor. AACR 104th Annual Meeting, Washington DC (2013).
[4] Orrell, D. & Fernandez, E. Using predictive mathematical models to optimise the scheduling of anti-cancer drugs. Innov. Pharm. Technol. 33, 58–62 (2010).
[5] Orrell, D. et al. Predicting the effect of combination schedules on xenograft tumor using the Virtual Tumor. Proc. Am. Assoc. Cancer Res. (2011).
[6] Dahut, W. L. et al. Randomized phase II trial of docetaxel plus thalidomide in androgen-independent prostate cancer. J. Clin. Oncol. 22, 2532–2539 (2004).
[7] Figg, W. D. et al. A randomized phase II trial of thalidomide, an angiogenesis inhibitor, in patients with androgen-independent prostate cancer. Clin. Cancer Res. 7, 1888–1893 (2001).
Reference: PAGE 23 () Abstr 3118 [www.page-meeting.org/?abstract=3118]
Poster: Drug/Disease modeling - Oncology