Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model
Background Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and is the third-leading cause of cancer-related death worldwide. Most patients with HCC are diagnosed at an advanced stage, and the median survival for patients with advanced HCC treated with modern systemic t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2022-11-01
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| Series: | Journal for ImmunoTherapy of Cancer |
| Online Access: | https://jitc.bmj.com/content/10/11/e005414.full |
| _version_ | 1797770281141403648 |
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| author | Won Jin Ho Mark Yarchoan Hanwen Wang Richard J Sové Aleksander S Popel Babita K Verma |
| author_facet | Won Jin Ho Mark Yarchoan Hanwen Wang Richard J Sové Aleksander S Popel Babita K Verma |
| author_sort | Won Jin Ho |
| collection | DOAJ |
| description | Background Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and is the third-leading cause of cancer-related death worldwide. Most patients with HCC are diagnosed at an advanced stage, and the median survival for patients with advanced HCC treated with modern systemic therapy is less than 2 years. This leaves the advanced stage patients with limited treatment options. Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1) or its ligand, are widely used in the treatment of HCC and are associated with durable responses in a subset of patients. ICIs targeting cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) also have clinical activity in HCC. Combination therapy of nivolumab (anti-PD-1) and ipilimumab (anti-CTLA-4) is the first treatment option for HCC to be approved by Food and Drug Administration that targets more than one immune checkpoints.Methods In this study, we used the framework of quantitative systems pharmacology (QSP) to perform a virtual clinical trial for nivolumab and ipilimumab in HCC patients. Our model incorporates detailed biological mechanisms of interactions of immune cells and cancer cells leading to antitumor response. To conduct virtual clinical trial, we generate virtual patient from a cohort of 5,000 proposed patients by extending recent algorithms from literature. The model was calibrated using the data of the clinical trial CheckMate 040 (ClinicalTrials.gov number, NCT01658878).Results Retrospective analyses were performed for different immune checkpoint therapies as performed in CheckMate 040. Using machine learning approach, we predict the importance of potential biomarkers for immune blockade therapies.Conclusions This is the first QSP model for HCC with ICIs and the predictions are consistent with clinically observed outcomes. This study demonstrates that using a mechanistic understanding of the underlying pathophysiology, QSP models can facilitate patient selection and design clinical trials with improved success. |
| first_indexed | 2024-03-12T21:19:57Z |
| format | Article |
| id | doaj.art-9ff1d4b7764141d3a8e190936b947a24 |
| institution | Directory Open Access Journal |
| issn | 2051-1426 |
| language | English |
| last_indexed | 2024-03-12T21:19:57Z |
| publishDate | 2022-11-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj.art-9ff1d4b7764141d3a8e190936b947a242023-07-28T21:30:07ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262022-11-01101110.1136/jitc-2022-005414Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology modelWon Jin Ho0Mark Yarchoan1Hanwen Wang2Richard J Sové3Aleksander S Popel4Babita K Verma5The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USAThe Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USADepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USADepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USADepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USADepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USABackground Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and is the third-leading cause of cancer-related death worldwide. Most patients with HCC are diagnosed at an advanced stage, and the median survival for patients with advanced HCC treated with modern systemic therapy is less than 2 years. This leaves the advanced stage patients with limited treatment options. Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1) or its ligand, are widely used in the treatment of HCC and are associated with durable responses in a subset of patients. ICIs targeting cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) also have clinical activity in HCC. Combination therapy of nivolumab (anti-PD-1) and ipilimumab (anti-CTLA-4) is the first treatment option for HCC to be approved by Food and Drug Administration that targets more than one immune checkpoints.Methods In this study, we used the framework of quantitative systems pharmacology (QSP) to perform a virtual clinical trial for nivolumab and ipilimumab in HCC patients. Our model incorporates detailed biological mechanisms of interactions of immune cells and cancer cells leading to antitumor response. To conduct virtual clinical trial, we generate virtual patient from a cohort of 5,000 proposed patients by extending recent algorithms from literature. The model was calibrated using the data of the clinical trial CheckMate 040 (ClinicalTrials.gov number, NCT01658878).Results Retrospective analyses were performed for different immune checkpoint therapies as performed in CheckMate 040. Using machine learning approach, we predict the importance of potential biomarkers for immune blockade therapies.Conclusions This is the first QSP model for HCC with ICIs and the predictions are consistent with clinically observed outcomes. This study demonstrates that using a mechanistic understanding of the underlying pathophysiology, QSP models can facilitate patient selection and design clinical trials with improved success.https://jitc.bmj.com/content/10/11/e005414.full |
| spellingShingle | Won Jin Ho Mark Yarchoan Hanwen Wang Richard J Sové Aleksander S Popel Babita K Verma Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model Journal for ImmunoTherapy of Cancer |
| title | Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model |
| title_full | Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model |
| title_fullStr | Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model |
| title_full_unstemmed | Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model |
| title_short | Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model |
| title_sort | virtual clinical trials of anti pd 1 and anti ctla 4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model |
| url | https://jitc.bmj.com/content/10/11/e005414.full |
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