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



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