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.|