ServicesVirtual Tumour Clinical

Key Facts

 

Physiomics Virtual Tumour Clinical is an extension of the Virtual Tumour platform to the clinic. It provides these benefits:

Arrow Potential to improve response and survival rate in the clinic through enhanced drug potency

Arrow Transform clinical decision making in terms of dose and schedule

Arrow Model the dynamics of tumour size and circulating biomarkers available from individual patients

Arrow Understand how changes in schedules and doses of drug combinations affect the dynamics of tumour burden

 

Physiomics Virtual Tumour Clinical

The current Virtual Tumour platform is a mathematical model of a growing tumour that accurately predicts the outcome of xenograft experiments. The model can also be used as a start point to design first-time-in-human clinical regimens. Physiomics is now developing Virtual Tumour Clinical, based on the same principles as the existing platform but using human patient data to calibrate the model.

This technology has the potential to transform clinical decision making in terms of dose and schedule. The impact of optimising regimens in patients could be significant improvements in overall survival rates. Our customers would benefit from improving the success rate of candidates progressing through clinical trials.

 

Brief Overview of PKPD-Survival Models in Oncology

The simplest models that are used to link clinical pharmacokinetics (PK) to changes in tumour dynamics (pharmacodynamics, PD) involve the classical direct and indirect response models (Mager et al., 2003). These models have evolved over time to better represent the biology of tumour growth (Ribba et al., 2012), but their complexity has always been limited by the amount of data collected in clinical studies. In addition to building models that relate PK to PD there are disease models that relate PD to survival, which have shown to add value in clinical development in a number of disease areas (Claret et al., 2013; Neal et al., 2013; Stein et al., 2009, 2011; Wang et al., 2009). Although models exist that relate PK to changes in tumour dynamics, and then changes in tumour dynamics to survival, none have shown how altering schedules/doses of combination treatment is likely to impact survival. This is the ultimate goal of Virtual Tumour Clinical.

 

Virtual Tumour Clinical Development

The success of Virtual Tumour with regard to its predictivity in the preclinical space provides us with confidence that our approach is likely to be successful in the clinical arena. The initial development of Virtual Tumour Clinical has focussed on the following differences between xenografts and human tumours:

  • Scale:
  • the size of the primary tumour and of distant metastases.
  • Homogeneity (human tumours are highly variable): 
  • cell-cycle heterogeneity has been shown to be an important factor when using targeted therapies in combination (Davies et al., 2006, 2009);
  • genetic heterogeneity within a patient's tumour and how the drug dosing/schedules will affect the different sub-populations (Chmielecki et al., 2011). 
  • Physiology:
  • the physical structure of the tumour and its growth dynamics, e.g. tumour volume doubling times.

The resulting model can successfully reproduce the dynamics of tumour size and circulating biomarkers available from individual patients in the literature (Stein et al., 2008a, 2008b, 2012). This now allows Virtual Tumour Clinical to link changes in tumour burden to survival; a key requisite for any successful clinical model. The final and crucial stage of development will be to initiate case studies with our partners regarding how changes in schedules and doses of drug combinations affect the dynamics of tumour burden.

Virtual Tumour Clinical

Fig 1. Physiomics Virtual Tumour Clinical platform.

 

Potential partners who are interested in testing Virtual Tumour Clinical should contact Dr Jim Millen at jmillen@physiomics-plc.com.

 

References

Chmielecki, J., Foo, J., Oxnard, G.R., Hutchinson, K., Ohashi, K., Somwar, R., Wang, L., Amato, K.R., Arcila, M., Sos, M.L., et al. (2011). Optimization of Dosing for EGFR-Mutant Non-Small Cell Lung Cancer with Evolutionary Cancer Modeling. Sci Transl Med 3, 90ra59.

Claret, L., Gupta, M., Han, K., Joshi, A., Sarapa, N., He, J., Powell, B., and Bruno, R. (2013). Evaluation of Tumor-Size Response Metrics to Predict Overall Survival in Western and Chinese Patients With First-Line Metastatic Colorectal Cancer. J. Clin. Oncol.

Davies, A.M., Ho, C., Lara, P.N., Jr, Mack, P., Gumerlock, P.H., and Gandara, D.R. (2006). Pharmacodynamic separation of epidermal growth factor receptor tyrosine kinase inhibitors and chemotherapy in non-small-cell lung cancer. Clin Lung Cancer 7, 385–388.

Davies, A.M., Ho, C., Beckett, L., Lau, D., Scudder, S.A., Lara, P.N., Perkins, N., and Gandara, D.R. (2009). Intermittent erlotinib in combination with pemetrexed: phase I schedules designed to achieve pharmacodynamic separation. J Thorac Oncol 4, 862–868.

Mager, D.E., Wyska, E., and Jusko, W.J. (2003). Diversity of Mechanism-Based Pharmacodynamic Models. Drug Metab Dispos 31, 510–518.

Neal, M.L., Trister, A.D., Ahn, S., Baldock, A., Bridge, C.A., Guyman, L., Lange, J., Sodt, R., Cloke, T., Lai, A., et al. (2013). Response classification based on a minimal model of glioblastoma growth is prognostic for clinical outcomes and distinguishes progression from pseudoprogression. Cancer Res. 73, 2976–2986.

Ribba, B., Kaloshi, G., Peyre, M., Ricard, D., Calvez, V., Tod, M., Cajavec-Bernard, B., Idbaih, A., Psimaras, D., Dainese, L., et al. (2012). A tumor growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy. Clin. Cancer Res. 18, 5071–5080.

Stein, W.D., Yang, J., Bates, S.E., and Fojo, T. (2008a). Bevacizumab Reduces the Growth Rate Constants of Renal Carcinomas: A Novel Algorithm Suggests Early Discontinuation of Bevacizumab Resulted in a Lack of Survival Advantage. The Oncologist 13, 1055–1062.

Stein, W.D., Figg, W.D., Dahut, W., Stein, A.D., Hoshen, M.B., Price, D., Bates, S.E., and Fojo, T. (2008b). Tumor Growth Rates Derived from Data for Patients in a Clinical Trial Correlate Strongly with Patient Survival: A Novel Strategy for Evaluation of Clinical Trial Data. The Oncologist 13, 1046–1054.

Stein, W.D., Huang, H., Menefee, M., Edgerly, M., Kotz, H., Dwyer, A., Yang, J., and Bates, S.E. (2009). Other paradigms: growth rate constants and tumor burden determined using computed tomography data correlate strongly with the overall survival of patients with renal cell carcinoma. Cancer J 15, 441–447.

Stein, W.D., Gulley, J.L., Schlom, J., Madan, R.A., Dahut, W., Figg, W.D., Ning, Y., Arlen, P.M., Price, D., Bates, S.E., et al. (2011). Tumor Regression and Growth Rates Determined in Five Intramural NCI Prostate Cancer Trials: The Growth Rate Constant as an Indicator of Therapeutic Efficacy. Clin Cancer Res 17, 907–917.

Stein, W.D., Wilkerson, J., Kim, S.T., Huang, X., Motzer, R.J., Fojo, A.T., and Bates, S.E. (2012). Analyzing the Pivotal Trial That Compared Sunitinib and IFN-α in Renal Cell Carcinoma, Using a Method That Assesses Tumor Regression and Growth. Clin Cancer Res 18, 2374–2381.

Wang, Y., Sung, C., Dartois, C., Ramchandani, R., Booth, B.P., Rock, E., and Gobburu, J. (2009). Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development. Clin. Pharmacol. Ther. 86, 167–174.