ArticleNPJ precision oncology2023
Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition.
Article in NPJ precision oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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
19 citing papers in PubMed, 29 citations in OpenAlex.
- Quantitative Systems Pharmacology (QSP): Bridging Biology and Mechanism with Clinical Drug Development Decisions.Journal of clinical pharmacology · 2026Review
- A Mechanistic Framework Integrating Renal QSP-PK-PD and Machine Learning for Baseline-Informed Stratification of Diuretic Resistance.The AAPS journal · 2026Article
- Comparative Molecular Insights and Computational Modeling of Multiple Myeloma and Osteosarcoma.International journal of molecular sciences · 2026Review
- Article
- A review on in-silico analysis of immune cell trafficking and interactions with the tumour microenvironment.Frontiers in oncology · 2026Review
- Identification of predictive biomarkers and dose optimization for camrelizumab combined with apatinib in the treatment of advanced hepatocellular carcinoma: a quantitative systems pharmacology approach.Frontiers in immunology · 2026Article
- Predicting survival in prospective clinical trials using weakly-supervised QSP.NPJ precision oncology · 2025Article
- Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer.NPJ systems biology and applications · 2025Article
- An integrated quantitative systems pharmacology virtual population approach for calibration with oncology efficacy endpoints.CPT: pharmacometrics & systems pharmacology · 2025Article
- A mechanistic quantitative systems pharmacology model platform for translational efficacy evaluation and checkpoint combination design of bispecific immuno-modulatory antibodies.Frontiers in pharmacology · 2025Article
- Combining mechanistic quantitative systems pharmacology modeling and patient-derived organoid testing in MET-aberrant non-small cell lung cancer for high-throughput combination efficacy analysis and personalized treatment design.Frontiers in pharmacology · 2025Article
- Virtual clinical trials via a QSP immuno-oncology model to simulate the response to a conditionally activated PD-L1 targeting antibody in NSCLC.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Integration of Clinical Trial Spatial Multiomics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery.Cancer research · 2024Article
- From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling.NPJ digital medicine · 2024Review
- Quantitative systems pharmacology modeling of macrophage-targeted therapy combined with PD-L1 inhibition in advanced NSCLC.Clinical and translational science · 2024Article
- Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology.Briefings in bioinformatics · 2024Article
- Development and Evaluation of a Quantitative Systems Pharmacology Model for Mechanism Interpretation and Efficacy Prediction of Atezolizumab in Combination with Carboplatin and Nab-Paclitaxel in Patients with Non-Small-Cell Lung Cancer.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.
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What OpenQuestion holds
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.