Modeling codelivery of CD73 inhibitor and dendritic cell-based vaccines in cancer immunotherapy

Arabameri, Abazar; Pourgholaminejad, Arash

doi:10.1016/j.compbiolchem.2021.107585

Cited by 3 publications

(7 citation statements)

References 86 publications

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“…Additionally, these models do not consider stochasticity, although there are stochastic versions known as stochastic differential equations available. ABMs, as opposed to the conventional ODE modeling technique, provide more flexibility for individual action and are able to explain some of the behaviors observed in the tumor-immune system interaction, such as cell–cell interactions involved in the immune response, increased immune system access to tumor cells with tumor growth, and the impact of tumor antigen on the movement of immune cells within the TME 23 .…”

Section: Introductionmentioning

confidence: 99%

“…Carlos et al 24 modeled the interactions between immune system and tumor using ODE models, and used the model to test different DC vaccine injection protocols to find the amounts of DCs and injection times at which tumor treatment is significantly improved. Some studies such as 23 , 25 show that DCs have nonlinear and random behavior and ABM models are more suitable than ODEs to model these behaviors. Macfarlane et al 25 proposed a discrete mathematical model of the interactions between immune system cells and monolayer tumor development.…”

Section: Introductionmentioning

confidence: 99%

“…However in this model, the immune system is not considered. Arabameri and Pourgholaminejad 23 introduced a hybrid ABM-ODE model for studying adenosine’s role in immunotherapy. Their findings highlighted how crucial adenosine inhibition is to improve the immune system.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical modeling of hypoxia and adenosine to explore tumor escape mechanisms in DC-based immunotherapy

Ghiyabi,

Arabameri,

Charmi

2024

Sci Rep

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mathematical modeling of hypoxia and adenosine to explore tumor escape mechanisms in DC-based immunotherapy

Ghiyabi,

Arabameri,

Charmi

2024

Sci Rep

Self Cite

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“…ABMs have been used to contribute understanding to tumour growth dynamics [24,[26][27][28], such as angiogenesis [31] and cell cycling [32]; the impacts of certain treatments [30,33,34]; and the likelihood of tumour recurrence [23]. ABMs have also been used to model chemotherapy treatment and movement through a tumour [35][36][37][38][39][40][41]. For example, Tang et al [38] developed a 3D computational model of tumour growth under treatment with chemotherapy, where cells are modelled as discrete agents whose proliferation is Motivation for sustained-delivery implants for treatment of PDAC.…”

Section: Introductionmentioning

confidence: 99%

“…ABMs have been used to contribute understanding to tumour growth dynamics [ 24 , 26 – 28 ], such as angiogenesis [ 31 ] and cell cycling [ 32 ]; the impacts of certain treatments [ 30 , 33 , 34 ]; and the likelihood of tumour recurrence [ 23 ]. ABMs have also been used to model chemotherapy treatment and movement through a tumour [ 35 – 41 ]. For example, Tang et al .…”

Section: Introductionmentioning

confidence: 99%

Examining the efficacy of localised gemcitabine therapy for the treatment of pancreatic cancer using a hybrid agent-based model

et al. 2023

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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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Understanding the Interplay of CAR-NK Cells and Triple-Negative Breast Cancer: Insights from Computational Modeling

Arabameri,

Arab

2024

Bull Math Biol

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Modeling codelivery of CD73 inhibitor and dendritic cell-based vaccines in cancer immunotherapy

Cited by 3 publications

References 86 publications

Mathematical modeling of hypoxia and adenosine to explore tumor escape mechanisms in DC-based immunotherapy

Mathematical modeling of hypoxia and adenosine to explore tumor escape mechanisms in DC-based immunotherapy

Examining the efficacy of localised gemcitabine therapy for the treatment of pancreatic cancer using a hybrid agent-based model

Understanding the Interplay of CAR-NK Cells and Triple-Negative Breast Cancer: Insights from Computational Modeling

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