In Silico Investigations of Multi-Drug Adaptive Therapy Protocols

Daniel S Thomas; Luis H Cisneros; Alexander R A Anderson; Carlo C Maley

doi:10.3390/cancers14112699

In Silico Investigations of Multi-Drug Adaptive Therapy Protocols

Cancers (Basel). 2022 May 30;14(11):2699. doi: 10.3390/cancers14112699.

Authors

Daniel S Thomas^{1

2

3}, Luis H Cisneros^{1

2

3}, Alexander R A Anderson⁴, Carlo C Maley^{1

2

3

5

6}

Affiliations

¹ Arizona Cancer Evolution Center, Arizona State University, Tempe, AZ 85287, USA.
² School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA.
³ Biodesign Center for Biocomputing, Security and Society, Arizona State University, Tempe, AZ 85287, USA.
⁴ Integrated Mathematical Oncology Department, Moffitt Cancer Center, Tampa, FL 33647, USA.
⁵ Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85287, USA.
⁶ Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85287, USA.

Abstract

The standard of care for cancer patients aims to eradicate the tumor by killing the maximum number of cancer cells using the maximum tolerated dose (MTD) of a drug. MTD causes significant toxicity and selects for resistant cells, eventually making the tumor refractory to treatment. Adaptive therapy aims to maximize time to progression (TTP), by maintaining sensitive cells to compete with resistant cells. We explored both dose modulation (DM) protocols and fixed dose (FD) interspersed with drug holiday protocols. In contrast to previous single drug protocols, we explored the determinants of success of two-drug adaptive therapy protocols, using an agent-based model. In almost all cases, DM protocols (but not FD protocols) increased TTP relative to MTD. DM protocols worked well when there was more competition, with a higher cost of resistance, greater cell turnover, and when crowded proliferating cells could replace their neighbors. The amount that the drug dose was changed, mattered less. The more sensitive the protocol was to tumor burden changes, the better. In general, protocols that used as little drug as possible, worked best. Preclinical experiments should test these predictions, especially dose modulation protocols, with the goal of generating successful clinical trials for greater cancer control.

Keywords: adaptive therapy; agent-based model; cancer; dose modulation; drug resistance; evolution.

Grants and funding

This work was supported in part by NIH grants U54 CA217376, U01CA23238, U2C CA233254, P01 CA91955, and R01 CA140657, in addition to the CDMRP Breast Cancer Research Program Award BC132057, the Arizona Biomedical Research Commission grant ADHS18-198847 and the Moffitt Cancer Center of Excellence for Evolutionary Therapy. The findings, opinions and recommendations expressed here are those of the authors and not necessarily those of the universities where the research was performed or the National Institutes of Health.