Virtual Tumour - Predictive Modelling Simulation Tool

It’s implemented in MATLAB

An industry standard platform used to develop mathematical models with over 3m users worldwide. Recognised by many regulatory authorities as a validated coding environment.

It’s been used in over 70 Projects

Virtual Tumour™ can be used to model a huge variety of targets, drugs, cell lines and human cancers. It can simulate agents from small molecules and biologics through radiation.

In addition to clinical data, can often be calibrated using public data

Physiomics team is expert at identifying, digitising and analysing public domain data that can often be used to support calibration of its models, especially for standard of care drugs.

Can simulate clinical and pre-clinical tumours

Virtual Tumour™ can simulate both tumour-bearing mice and human tumours to support translational decision making and Phase I and II study design.

Has been validated in blinded studies

We have successfully completed multiple blinded validation studies for commercial clients and have reproduced a number of these in posters and presentations at major conferences.

Is used by companies ranging from startups to global pharmaceuticals

Physiomics has completed projects for companies ranging from small biotech to large cap pharma. We also work on grant and academic collaborations.

Our Virtual Tumour™ platform is one of the industry’s most advanced predictive models focused solely on oncology, predicting optimal drug dosing, scheduling and combinations.

Improve Success Rate

Improve success rates for oncology candidates entering clinical studies by increasing efficacy

Save Time

Reduce the time taken to do proof-of-principle studies

Reduce Cost

Reduce the cost of in vivo study programmes (fewer experiments needed)

Support with Regulatory Rationale

Provide regulatory agencies with a rationale for the design of clinical study programmes.

Virtual Tumour™ works by predicting the effects of cancer treatment regimes, simulating the growth or shrinkage of tumours under the effects of any combination of anti-cancer agents.

• Use established or de novo PK models. Source tumour growth Inhibition/biomarker data from client or public domain. Characterize MOA of agent(s).
• Model key elements of cell cycle. Calibrate model using available data. Follow cancer cells over time as they die or divide.
• Track the number of cells and mathematically distribute them in the growing layer of a tumour.
• Track the tumour size / cell cycle phases / biomarker evolution over time.Predict effect of different regimens.

• Predict behaviour over a full time course, not just a single point in time
• Powerfully and naturally combine the effects of drugs with different mechanism of actions (MOAs)
• Allows complex dosing and scheduling options e.g. dosing holidays, to be simulated
• New data can be quickly incorporated as it is generated in mouse or human trials
• Can help explain the behaviour of combination regimes in terms of cellular effects (e.g. cell cycle synchronization, cell death, DNA damage)
• Model revisions and additional simulations can be delivered in real-time to enable decision-making during early clinical trials.