@article {West263327, author = {Jeffrey West and Paul Newton}, title = {Optimizing chemo-scheduling based on tumor growth rates}, elocation-id = {263327}, year = {2018}, doi = {10.1101/263327}, publisher = {Cold Spring Harbor Laboratory}, abstract = {We review the classic tumor growth and regression laws of Skipper and Schable based on fixed exponential growth assumptions, and Norton and Simon{\textquoteright}s law based on a Gompertzian growth assumption. We then discuss ways to optimize chemotherapeutic scheduling using a Moran process evolutionary game-theory model of tumor growth that incorporates more general dynamical and evolutionary features of tumor cell kinetics. Using this model, and employing the quantitative notion of Shannon entropy which assigns high values to low-dose metronomic (LDM) therapies, and low values to maximum tolerated dose (MTD) therapies, we show that low-dose metronomic strategies can outperform maximum tolerated dose strategies, particularly for faster growing tumors. The general concept of designing different chemotherapeutic strategies for tumors with different growth characteristics is discussed.}, URL = {https://www.biorxiv.org/content/early/2018/02/10/263327}, eprint = {https://www.biorxiv.org/content/early/2018/02/10/263327.full.pdf}, journal = {bioRxiv} }