Abstract 372: Choosing the right schedule for progression free survival: A systems pharmacology approach
Authors: Patel Mayankbhai , Mettetal Jerome , Cohen Matthew , Palani Santhosh , Kuida Keisuke , Hixon Mark , Bolen Joseph , Shyu Wen Chyi , Noe Dennis , Chakravarty Arijit 
Published: May 2015

Drug resistance is a common feature of many cancer therapies and often limits their long-term effectiveness. Most anticancer agents are developed with an initial focus on Objective Response (short-term response), despite clinical evidence that, for most cancers, this does not correlate with long term therapeutic benefit (Progression-Free Survival), due to the emergence of resistance.

Viewing cancer as a disease of somatic Darwinian evolution reframes resistance as a consequence of competing populations of tumor cells within a polyclonal tumor, each responding differentially to selection pressure. In this context, the amplification of pre-existing resistance to therapy has been implicated as a common cause of treatment failure.

Here we ask how drug scheduling can impact long-term response to therapy, using an evolutionary model of tumor kinetics. First, we apply a dynamic model of tumor kinetics with competing subclones of tumor cells to a published dataset of prostate cancer progression (Treatment thalidomide+docetaxel or ketoconazole+ hydrocortisone+alendronate), and show that it is capable of closely predicting Progression Free Survival. Tumor cells are modeled as being either sensitive or resistant, cell growth is modeled using a simple exponential growth model, and drug effect is incorporated in the model as overall inhibition on the growth-rate. Competition between tumor sub-populations is modeled as frequency-dependent fitness, using a logistic tumor growth model.

Next, we use this dynamic model derived from the prostate cancer dataset to understand the effect of schedule on resistance, simulating tumor growth upon drug treatment for different doses and schedules. Beyond a literal simulation of resistance for a given drug schedule, we assess the contributions of resistance made by dose amplitude and dose frequency. Our model predicts that for a graded dose-response curve (Hill slope of 1), more frequent dosing schedules minimize the emergence of resistance. On the other hand, for a steep dose-response curve (Hill slope of 5), for constant dose density, we find that a high infrequent schedule is better than a low frequent dosing schedule for low doses. As the dose density increases, the low frequency schedule again emerges as optimal for minimizing resistance. As a final test, a competition term between the subpopulations is also included in our model, with essentially identical results.

We then apply our model to published datasets of clinical response to treatment, and find close agreement with published results. Our generalized approach to this problem provides a framework for dissecting the underlying drivers of the emergence of resistance, and provides a recommendation for the minimization of resistance in a practical setting.

Citation Format: Mayankbhai Patel, Jerome Mettetal, Matthew Cohen, Santhosh Palani, Keisuke Kuida, Mark Hixon, Joseph Bolen, Wen Chyi Shyu, Dennis Noe, Arijit Chakravarty. Choosing the right schedule for progression free survival: A systems pharmacology approach. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 372. doi:10.1158/1538-7445.AM2014-372