Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

Frieboes, Hermann B.; Raghavan, Shreya; Godin, Biana

doi:10.3389/fbioe.2020.01011

Cited by 9 publications

(5 citation statements)

References 254 publications

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“…The studies performed to determine the optimum pH of nanoparticle synthesis demonstrated that nanoparticle synthesis accelerated with an increase in pH. This observation complies with the work of Singh et al 18 . Also, the nanoparticles' synthesis was investigated until pH 8.5 only since microcystin molecules degrade at pH above 8.5 3 .…”

Section: Discussionsupporting

confidence: 89%

Untitled

2022

IJPSR

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Hepatocellular carcinoma (HCC) is liver cancer characterized by high prevalence and low survival rate. Hence, there is an urgent need to screen resources to develop therapeutic drugs to treat patients suffering from HCC. In this context, the current study involves developing a microcystin conjugated nano-drug from cyan bacteria Anabaena cylindrica to target the tumor liver cells. The microcystin molecules in the aqueous algal extract were quantified by High-performance liquid chromatography (HPLC) and used as reducing and capping agents at a concentration below the toxic limit to synthesize silver nanoparticles. These nanoparticles were characterized by Nanoparticle Tracking analysis (NTA), Fourier Transform Infra-red (FTIR), Transmission emission microscopy (TEM), and their anti-tumor activity was evaluated on HepG2 cell line by MTT assay. The highest nanoparticle synthesis was obtained at pH 8.5 after 120 min of incubation. NTA and TEM analysis showed mean particle size of 30nm. FTIR spectrum confirmed the presence of functional groups related to proteins conjugated to silver nanoparticles. The nanoparticle formulation exhibited enhanced to toxic activity in HepG2 cell line compared to the extract and a standard drug. The present investigation is significant as it demonstrates a green method of synthesis of nano-drug from a biological source with potent therapeutic activity against HCC.INTRODUCTION: Cyanobacteria, also known as blue-green algae, are photoautotrophic bacteria commonly found in fresh and brackish waters. They are a prolific source of biologically active metabolites, including compounds that exhibit toxic and enzyme-inhibiting activities. Cyanobacteria also known as, blue-green algae are photoautotrophic bacteria commonly found in fresh and brackish waters.

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Section: Discussionsupporting

confidence: 89%

Untitled

2022

IJPSR

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“…Mathematical models can also be used to better understand the treatment options for liver tumors. For example, drug-based cancer nanotherapy (Frieboes et al, 2020 ) or radioembolization dosimetry (Taebi et al, 2020 ) can be examined for their effectiveness. Although there are already many developed models for the simulation of hepatic processes, some liver diseases are not yet represented by mathematical or numerical models.…”

Section: Discussionmentioning

confidence: 99%

Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review

et al. 2021

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MRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginning of an engineering-level approach to how the liver disease affects material properties and liver function. Aimed at researchers in the field of MRI-based liver simulation, research articles pertinent to MRI-based liver modeling were identified, reviewed, and summarized systematically. Various MRI applications for liver biomechanics are highlighted, and the limitations of different viscoelastic models used in magnetic resonance elastography are addressed. The clinical application of the simulations and the diseases studied are also discussed. Based on the developed questionnaire, the papers' quality was assessed, and of the 46 reviewed papers, 32 papers were determined to be of high-quality. Due to the lack of the suitable material models for different liver diseases studied by magnetic resonance elastography, researchers may consider the effect of liver diseases on constitutive models. In the future, research groups may incorporate various aspects of machine learning (ML) into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification.

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“…Current strategies for tumor treatment include targeting malignant and metastatic components of the tumor microenvironment [ 12 ], as well as targeting over-activated metabolic signaling pathways characteristic of cancerous tissue [ 46 ]. Mathematical modeling supports the connection of entities of different scales (from blood vessels to surface interaction with biological molecules), which leads to an improved interpretation of specific tumor characteristics [ 47 ]. The modeling of such systems actually requires the use of multiscale approaches that can store information transferred between different scales [ 48 ].…”

Section: Importance Of Mathematical Modeling In Nanomedicinementioning

confidence: 99%

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Kutumova

Akberdin

Kiselev

et al. 2022

IJMS

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Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.

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Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

Cited by 9 publications

References 254 publications

Untitled

Untitled

Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

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