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Ghiyabi E, Arabameri A, Charmi M. Mathematical modeling of hypoxia and adenosine to explore tumor escape mechanisms in DC-based immunotherapy. Sci Rep 2024; 14:11387. [PMID: 38762567 PMCID: PMC11102449 DOI: 10.1038/s41598-024-62209-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 05/14/2024] [Indexed: 05/20/2024] Open
Abstract
Identifying and controlling tumor escape mechanisms is crucial for improving cancer treatment effectiveness. Experimental studies reveal tumor hypoxia and adenosine as significant contributors to such mechanisms. Hypoxia exacerbates adenosine levels in the tumor microenvironment. Combining inhibition of these factors with dendritic cell (DC)-based immunotherapy promises improved clinical outcomes. However, challenges include understanding dynamics, optimal vaccine dosages, and timing. Mathematical models, including agent-based, diffusion, and ordinary differential equations, address these challenges. Here, we employ these models for the first time to elucidate how hypoxia and adenosine facilitate tumor escape in DC-based immunotherapy. After parameter estimation using experimental data, we optimize vaccination protocols to minimize tumor growth. Sensitivity analysis highlights adenosine's significant impact on immunotherapy efficacy. Its suppressive role impedes treatment success, but inhibiting adenosine could enhance therapy, as suggested by the model. Our findings shed light on hypoxia and adenosine-mediated tumor escape mechanisms, informing future treatment strategies. Additionally, identifiability analysis confirms accurate parameter determination using experimental data.
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Affiliation(s)
- Elahe Ghiyabi
- Department of Electrical Engineering, University of Zanjan, Zanjan, Iran
| | - Abazar Arabameri
- Department of Electrical Engineering, University of Zanjan, Zanjan, Iran.
| | - Mostafa Charmi
- Department of Electrical Engineering, University of Zanjan, Zanjan, Iran
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Arabameri A, Arab S. Understanding the Interplay of CAR-NK Cells and Triple-Negative Breast Cancer: Insights from Computational Modeling. Bull Math Biol 2024; 86:20. [PMID: 38240892 DOI: 10.1007/s11538-023-01247-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Chimeric antigen receptor (CAR)-engineered natural killer (NK) cells have recently emerged as a promising and safe alternative to CAR-T cells for targeting solid tumors. In the case of triple-negative breast cancer (TNBC), traditional cancer treatments and common immunotherapies have shown limited effectiveness. However, CAR-NK cells have been successfully employed to target epidermal growth factor receptor (EGFR) on TNBC cells, thereby enhancing the efficacy of immunotherapy. The effectiveness of CAR-NK-based immunotherapy is influenced by various factors, including the vaccination dose, vaccination pattern, and tumor immunosuppressive factors in the microenvironment. To gain insights into the dynamics and effects of CAR-NK-based immunotherapy, we propose a computational model based on experimental data and immunological theories. This model integrates an individual-based model that describes the interplay between the tumor and the immune system, along with an ordinary differential equation model that captures the variation of inflammatory cytokines. Computational results obtained from the proposed model shed light on the conditions necessary for initiating an effective anti-tumor response. Furthermore, global sensitivity analysis highlights the issue of low persistence of CAR-NK cells in vivo, which poses a significant challenge for the successful clinical application of these cells. Leveraging the model, we identify the optimal vaccination time, vaccination dose, and time interval between injections for maximizing therapeutic outcomes.
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Affiliation(s)
- Abazar Arabameri
- Department of Electrical Engineering, University of Zanjan, Zanjan, Iran.
| | - Samaneh Arab
- Department of Tissue Engineering and Applied Cell Sciences, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
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Jenner AL, Kelly W, Dallaston M, Araujo R, Parfitt I, Steinitz D, Pooladvand P, Kim PS, Wade SJ, Vine KL. Examining the efficacy of localised gemcitabine therapy for the treatment of pancreatic cancer using a hybrid agent-based model. PLoS Comput Biol 2023; 19:e1010104. [PMID: 36649330 PMCID: PMC9891514 DOI: 10.1371/journal.pcbi.1010104] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 02/01/2023] [Accepted: 12/21/2022] [Indexed: 01/18/2023] Open
Abstract
The prognosis for pancreatic ductal adenocarcinoma (PDAC) patients has not significantly improved in the past 3 decades, highlighting the need for more effective treatment approaches. Poor patient outcomes and lack of response to therapy can be attributed, in part, to a lack of uptake of perfusion of systemically administered chemotherapeutic drugs into the tumour. Wet-spun alginate fibres loaded with the chemotherapeutic agent gemcitabine have been developed as a potential tool for overcoming the barriers in delivery of systemically administrated drugs to the PDAC tumour microenvironment by delivering high concentrations of drug to the tumour directly over an extended period. While exciting, the practicality, safety, and effectiveness of these devices in a clinical setting requires further investigation. Furthermore, an in-depth assessment of the drug-release rate from these devices needs to be undertaken to determine whether an optimal release profile exists. Using a hybrid computational model (agent-based model and partial differential equation system), we developed a simulation of pancreatic tumour growth and response to treatment with gemcitabine loaded alginate fibres. The model was calibrated using in vitro and in vivo data and simulated using a finite volume method discretisation. We then used the model to compare different intratumoural implantation protocols and gemcitabine-release rates. In our model, the primary driver of pancreatic tumour growth was the rate of tumour cell division. We were able to demonstrate that intratumoural placement of gemcitabine loaded fibres was more effective than peritumoural placement. Additionally, we quantified the efficacy of different release profiles from the implanted fibres that have not yet been tested experimentally. Altogether, the model developed here is a tool that can be used to investigate other drug delivery devices to improve the arsenal of treatments available for PDAC and other difficult-to-treat cancers in the future.
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Affiliation(s)
- Adrianne L. Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- * E-mail:
| | - Wayne Kelly
- School of Computer Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Michael Dallaston
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Robyn Araujo
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Isobelle Parfitt
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Dominic Steinitz
- Tweag Software Innovation Lab, London, United Kingdom
- Kingston University, Kingston, United Kingdom
| | - Pantea Pooladvand
- School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia
| | - Peter S. Kim
- School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia
| | - Samantha J. Wade
- Illawarra Health and Medical Research Institute, Wollongong, New South Wales, Australia
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, New South Wales, Australia
| | - Kara L. Vine
- Illawarra Health and Medical Research Institute, Wollongong, New South Wales, Australia
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, New South Wales, Australia
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