Dynamics of Acquired Resistance to Nivolumab Therapies Varies From Administration Strategies

Jiatong Ji; Hong Wu; Xiaobing Feng; Xiaoquan Liu; Chenrong Huang; Shuyun Zheng; Jianjun Zou; Jun Liao

doi:10.1016/j.clinthera.2021.10.004

Dynamics of Acquired Resistance to Nivolumab Therapies Varies From Administration Strategies

Clin Ther. 2021 Dec;43(12):2088-2103. doi: 10.1016/j.clinthera.2021.10.004. Epub 2021 Nov 13.

Authors

Jiatong Ji¹, Hong Wu², Xiaobing Feng¹, Xiaoquan Liu³, Chenrong Huang⁴, Shuyun Zheng⁵, Jianjun Zou⁶, Jun Liao⁷

Affiliations

¹ School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
² School of Science, China Pharmaceutical University, Nanjing, China.
³ Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, China.
⁴ Department of Clinical Pharmacology, First Affiliated Hospital of Soochow University, Suzhou, China.
⁵ Department of Critical Care Medicine, Jiangsu Cancer Hospital, Nanjing, China.
⁶ Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing, China.
⁷ High Performance Computing Center, School of Science, China Pharmaceutical University, Nanjing, China. Electronic address: liaojun@cpu.edu.cn.

PMID: 34782163
DOI: 10.1016/j.clinthera.2021.10.004

Abstract

Purpose: The identification of optimal drug administration schedules to overcome the emergence of resistance that causes treatment failure is a major challenge in cancer research. We report the outcomes of a computational strategy to assess the dynamics of tumor progression as a function of time under different treatment regimens.

Methods: We developed an evolutionary game theory model that combined Lotka-Volterra equations and pharmacokinetic properties with 2 competing cancer species: nivolumab-response cells and Janus kinase (JAK1/2) mutation cells. We selected 3 therapeutic schemes that have been tested in the clinical trials: 3 mg/kg Q2w, 10 mg/kg Q2w, and 480 mg Q4w. The simulation was performed under the intervals of 75, 125, and 175 days, respectively, for each regimen. The data sources of the pharmacokinetic parameters used in this study were collected from previous published clinical trials. Other parameters in the evolutionary model come from the existing references.

Findings: Predictions under various dose schedules indicated a strong selection for nivolumab-independent cells. Under the 3 mg/kg dose strategy, the reproduction rate of JAK mutation cells was highest, with strongest tumor elimination ability at a 75-day interval between treatments. Prolonged drug intervals to 125 or 175 days delayed tumor evolution but accelerated tumor recurrence. Although 10 mg/kg Q2w had an obvious clinical effect in a short time, it further promotes the progress of resistant population compared with the 3 mg/kg dose. Our model suggests that 480 mg Q4w would be more valuable in terms of clinical efficacy, but complete resistant occurs earlier regardless the interval.

Implications: The results of this study emphasize that increasing the dose or shortening the interval between doses accelerates the evolution of heterogeneous populations, although the short-term effect is significant. In practice, the therapeutic regimen should be balanced according to the evolutionary principle.

Keywords: dosing schedule; drug resistance; drug sensitivity; nivolumab; tumor.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computer Simulation
Drug Administration Schedule
Humans
Neoplasms* / drug therapy
Nivolumab* / therapeutic use
Treatment Outcome

Substances

Nivolumab