TY - GEN
T1 - Identification of Cancer Cell Population Dynamics Leveraging the Effect of Pre-Treatment for Drug Schedule Design
AU - Wiggert, Marius
AU - Turnidge, Megan
AU - Cohen, Zoe
AU - Langer, Ellen M.
AU - Sears, Rosalie C.
AU - Chapman, Margaret P.
AU - Tomlin, Claire J.
N1 - Funding Information:
1M.W., Z.C., and C.J.T. are with the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA. For inquiries contact: mariuswiggert@berkeley.edu 2M.T., E.M.L., and R.C.S. are with the Department of Molecular and Medical Genetics, Oregon Health and Science University, USA. 3 M.P.C. is with the Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada. The authors gratefully acknowledge the support of the NCI CSBC program and the German Academic Exchange Service (DAAD).
Publisher Copyright:
© 2021 American Automatic Control Council.
PY - 2021/5/25
Y1 - 2021/5/25
N2 - Sequences of different drugs have shown potential to improve treatment strategies for cancer. Typical switched system approaches model the population dynamics of each drug independently, not rigorously considering the effects of pretreatment or drug-drug interactions. In this paper, a general model family incorporating pre-treatment effects and biological domain knowledge is proposed, and a model from this family is identified by using a novel experimental data set of two-drug sequences. Leveraging the data, a simulator for the cell population dynamics under sequences of up to nine drugs is developed and used to empirically evaluate the performance of a set of closed-loop drug scheduling controllers. We used the controllers to identifying promising drug schedules in silico and evaluated them in vitro. The experiments validated the effectiveness of the identified schedules in reducing the number of living cells to less than 10% of the initial. While only treating with certain toxic drugs achieves similar effectiveness, the schedules use toxic drugs for significantly shorter times which likely reduces toxicity to non-cancer cells.
AB - Sequences of different drugs have shown potential to improve treatment strategies for cancer. Typical switched system approaches model the population dynamics of each drug independently, not rigorously considering the effects of pretreatment or drug-drug interactions. In this paper, a general model family incorporating pre-treatment effects and biological domain knowledge is proposed, and a model from this family is identified by using a novel experimental data set of two-drug sequences. Leveraging the data, a simulator for the cell population dynamics under sequences of up to nine drugs is developed and used to empirically evaluate the performance of a set of closed-loop drug scheduling controllers. We used the controllers to identifying promising drug schedules in silico and evaluated them in vitro. The experiments validated the effectiveness of the identified schedules in reducing the number of living cells to less than 10% of the initial. While only treating with certain toxic drugs achieves similar effectiveness, the schedules use toxic drugs for significantly shorter times which likely reduces toxicity to non-cancer cells.
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U2 - 10.23919/ACC50511.2021.9482989
DO - 10.23919/ACC50511.2021.9482989
M3 - Conference contribution
AN - SCOPUS:85111931093
T3 - Proceedings of the American Control Conference
SP - 1909
EP - 1916
BT - 2021 American Control Conference, ACC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 American Control Conference, ACC 2021
Y2 - 25 May 2021 through 28 May 2021
ER -