Conducting a Virtual Clinical Trial in HER2-Negative Breast Cancer Using a Quantitative Systems Pharmacology Model With an Epigenetic Modulator and Immune Checkpoint Inhibitors

Frontiers in Bioengineering and Biotechnology

Mohammad Jafarnejad¹  Hanwen Wang¹  Richard J. Sové¹  Sondra Rahmeh¹  Evanthia T. Roussos Torres²  Aleksander S. Popel²  Vered Stearns²  Roisin M. Connolly³  Elizabeth M. Jaffee³ 

[1] Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States;Department of Oncology, The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States;Viragh Center for Pancreatic Clinical Research and Care, Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, United States;

关键词: immuno-oncology;    immune checkpoint inhibitor;    computational model;    systems biology;    epigenetic modulator;    quantitative systems pharmacology;   

DOI  :  10.3389/fbioe.2020.00141

来源: DOAJ

【 摘 要 】

The survival rate of patients with breast cancer has been improved by immune checkpoint blockade therapies, and the efficacy of their combinations with epigenetic modulators has shown promising results in preclinical studies. In this prospective study, we propose an ordinary differential equation (ODE)-based quantitative systems pharmacology (QSP) model to conduct an in silico virtual clinical trial and analyze potential predictive biomarkers to improve the anti-tumor response in HER2-negative breast cancer. The model is comprised of four compartments: central, peripheral, tumor, and tumor-draining lymph node, and describes immune activation, suppression, T cell trafficking, and pharmacokinetics and pharmacodynamics (PK/PD) of the therapeutic agents. We implement theoretical mechanisms of action for checkpoint inhibitors and the epigenetic modulator based on preclinical studies to investigate their effects on anti-tumor response. According to model-based simulations, we confirm the synergistic effect of the epigenetic modulator and that pre-treatment tumor mutational burden, tumor-infiltrating effector T cell (Teff) density, and Teff to regulatory T cell (Treg) ratio are significantly higher in responders, which can be potential biomarkers to be considered in clinical trials. Overall, we present a readily reproducible modular model to conduct in silico virtual clinical trials on patient cohorts of interest, which is a step toward personalized medicine in cancer immunotherapy.

【 授权许可】

Unknown