ArticleClinical and translational science2024
Quantitative systems pharmacology modeling of macrophage-targeted therapy combined with PD-L1 inhibition in advanced NSCLC.
Article in Clinical and translational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Quantitative Systems Pharmacology Model for TROP-2 Targeted Antibody-Drug Conjugate in Triple-Negative Breast Cancer.bioRxiv : the preprint server for biology · 2026Article
- Model-Based Patient Selection and Dosing Strategies for HRAS and PIK3CA Dysregulated HNSCC: A QSP Model for Alpelisib and Tipifarnib Combination.Clinical pharmacology and therapeutics · 2026Article
- Application of nanobody‑based CAR‑T in tumor immunotherapy (Review).International journal of molecular medicine · 2025Review
- Article
- Review
- Analysis of Efficacy and Survival Prognosis for Astragalus Polysaccharide Injection as Adjuvant Therapy in Non - Small Cell Lung Cancer: A Propensity Score Matching - Based Study.Cancer management and research · 2025Article
- Evaluating the combined efficacy of Telisotuzumab Vedotin and artificial intelligence in the treatment of non-squamous non-small cell lung cancer: a narrative review focusing on pharmaceutical and technical insights.Frontiers in oncology · 2025Review
- Quantitative systems pharmacology modeling of macrophage-targeted therapy combined with PD-L1 inhibition in advanced NSCLC.Clinical and translational science · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Immune checkpoint inhibitors remained the standard-of-care treatment for advanced non-small cell lung cancer (NSCLC) for the past decade. In unselected patients, anti-PD-(L)1 monotherapy achieved an overall response rate of about 20%. In this analysis, we developed a pharmacokinetic and pharmacodynamic module for our previously calibrated quantitative systems pharmacology model (QSP) to simulate the effectiveness of macrophage-targeted therapies in combination with PD-L1 inhibition in advanced NSCLC. By conducting in silico clinical trials, the model confirmed that anti-CD47 treatment is not an optimal option of second- and later-line treatment for advanced NSCLC resistant to PD-(L)1 blockade. Furthermore, the model predicted that inhibition of macrophage recruitment, such as using CCR2 inhibitors, can potentially improve tumor size reduction when combined with anti-PD-(L)1 therapy, especially in patients who are likely to respond to anti-PD-(L)1 monotherapy and those with a high level of tumor-associated macrophages. Here, we demonstrate the application of the QSP platform on predicting the effectiveness of novel drug combinations involving immune checkpoint inhibitors based on preclinical or early-stage clinical trial data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.