Prediction of Clinical Outcomes in a Heterogenous NSCLC Virtual Patient Population: a Quantitative Systems Pharmacology (QSP) Approach

Authors: Mittal A, Duncan W, Markazia A, Parish N, Kowalski RA, Brostoff N, Simpson DA, Woodhead JL, Suderman R
Conference: ASCPT
Keywords: lung, non-small cell lung cancer, Non-small cell lung cancer (NSCLC), quantitative systems pharmacology (QSP), SimPops, tumor modeling
Division: Quantitative Systems Pharmacology (QSP)

Background

  • Non-small cell lung cancer (NSCLC) is a major cause of mortality in the United States[1]. Immune checkpoint inhibitors (ICIs) show promise for advanced or metastatic NSCLC[2,3].
  • Treatment outcomes vary based on disease traits and the immune microenvironment. Identification of effective combination therapies is inefficient and time consuming[2,3].
  • We developed a QSP model to predict the efficacy of novel ICI combinations in NSCLC patients with varying PD-L1 expression, tumor histology, and immune

By Anjali Mittal, William Duncan, Ashley Markazi, Nicole Parish, Rae-Ann Kowalski, Noah Brostoff, Destiny A. Simpson, Jeff Woodhead, Ryan Suderman

ASCPT 2025 Annual Meeting, May 28- 31, 2025, Washington, D.C.