Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer

Hanwen Wang; Chen Zhao; Cesar A Santa-Maria; Leisha A Emens; Aleksander S Popel

doi:10.1016/j.isci.2022.104702

Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer

iScience. 2022 Jun 30;25(8):104702. doi: 10.1016/j.isci.2022.104702. eCollection 2022 Aug 19.

Authors

Hanwen Wang¹, Chen Zhao^{1

2}, Cesar A Santa-Maria³, Leisha A Emens⁴, Aleksander S Popel^{1

3}

Affiliations

¹ Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
² School of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu211166, China.
³ Department of Oncology, the Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD21205, USA.
⁴ University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA, USA.

Abstract

Quantitative systems pharmacology (QSP) modeling is an emerging mechanistic computational approach that couples drug pharmacokinetics/pharmacodynamics and the course of disease progression. It has begun to play important roles in drug development for complex diseases such as cancer, including triple-negative breast cancer (TNBC). The combination of the anti-PD-L1 antibody atezolizumab and nab-paclitaxel has shown clinical activity in advanced TNBC with PD-L1-positive tumor-infiltrating immune cells. As tumor-associated macrophages (TAMs) serve as major contributors to the immuno-suppressive tumor microenvironment, we incorporated the dynamics of TAMs into our previously published QSP model to investigate their impact on cancer treatment. We show that through proper calibration, the model captures the macrophage heterogeneity in the tumor microenvironment while maintaining its predictive power of the trial results at the population level. Despite its high mechanistic complexity, the modularized QSP platform can be readily reproduced, expanded for new species of interest, and applied in clinical trial simulation.

Keywords: Biophysics; Cancer; Immunology; Pharmacology.