(699c) Modeling Drug Target Screening and Drug Treatments Using a Stochastic Cell Cycle Model

Clinical data from drug tests are often probabilistic in nature. For instance, test with a given drug may indicate different probabilities of killing cancer cells versus healthy cells. Describing such data or predicting the effect of drugs from model studies therefore requires models that are stochastic or statistical in nature. We have used a stochastic model of the eukaryotic cell cycle to carry out in-silico screenings of potential drug targets, of different drug treatment strategies and of drug synergisms. The model is based on a stochastic kinetic model of the cell cycle oscillator and is solved numerically using Monte Carlo simulations. Drugs are assumed to inhibit specified reactions in the model and different cell types can be simulated using different sets of model parameters. Different drug removal scenarios, such as excretion through kidneys or metabolic breakdown of the drug are easily included in the model. Simulating the effect of different drug targets and of different drug treatment protocols allows one to optimize in-silico treatments and propose clinical drug tests based on these results.