Three‐Dimensional Osteosarcoma Models for Advancing Drug Discovery and Development

Monteiro, Cátia; Custódio, Catarina A.; Mano, João F.

doi:10.1002/adtp.201800108

Cited by 26 publications

(27 citation statements)

References 162 publications

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“…In the past 50 years, clinical practice has been going through a transition fueled by technological developments. Biomed ical engineering, [1][2][3][4][5][6][7][8][9] material science, [10][11][12][13][14][15][16][17][18] and artificial intel ligence (AI) [19][20][21][22][23][24] have been transforming the entire medical landscape: diagnostics, [25][26][27][28][29][30][31][32][33] drug development, 24,[34][35][36] drug delivery, [37][38][39][40][41][42][43][44][45][46] and data analytics. [47][48][49]…”

Section: Introductionmentioning

confidence: 99%

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

2020

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The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials’ results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual’s response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team’s experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach—phenotypic personalized medicine (PPM)—finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals’ lives.

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Section: Introductionmentioning

confidence: 99%

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

2020

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“…Therefore, to mimic the bone environment, an ideal scaffold should not only be biocompatible but should also have an adequate pore size (at least 100 µm) to allow the diffusion of molecules and cells; the mechanical properties should match the ones from bone tissues (Young's modulus of cortical bone varies from 15 to 20 GPa); and since high levels of extracellular calcium, hypoxia, and low pH are normal in bone tissues, these properties should also be taken into account when designing biomimetic bone scaffolds [118]. Keeping this in mind, multiple materials have been employed to produce scaffolds to study OS, such as Matrigel®, collagen, alginate, chitosan, silk fibroin, agarose, gelatin, bacterial cellulose, and methylcellulose [119,120]. Matrigel® is one of the most used materials to perform 3D cell culture.…”

Section: Scaffold-based Modelsmentioning

confidence: 99%

“…For instance, the chemical composition, porosity, and stiffness can all be modulated independently to properly mimic the desired environment. Commonly used polymers include synthetic polymers PLA, PEG, polycaprolactone (PCL), and poly(glycolic acid) (PGA) [119,120]. These materials have mainly been applied to tissue engineering and drug delivery approaches.…”

Section: Scaffold-based Modelsmentioning

confidence: 99%

Osteosarcoma tumor microenvironment: the key for the successful development of biologically relevant 3D in vitro models

2022

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Osteosarcoma (OS) is the most common primary bone cancer in children and young adults. This type of cancer is characterized by a high mortality rate, especially for patients with resistant lung metastases. Given its low incidence, high genetic heterogeneity, the lack of effective targets, and poor availability of relevant in vitro and in vivo models to study the tumor progression and the metastatic cascade, the pathophysiology of OS is still poorly understood and the translation of novel drugs into the market has become stagnant. Due to the importance of the tumor microenvironment (TME) in the development of metastases and the growing interest in targeting TME-specific pathways for novel therapeutics in cancer, models that closely represent these interactions are crucial for a better understanding of cancer-related events. In OS research, most studies rely on oversimplified two-dimensional (2D) assays and complex animal models that do not faithfully recapitulate OS development and progression. In turn, three-dimensional (3D) models are able to mimic not only the physical 3D environment in which cancer cells grow but also involve interactions with the TME, including its extracellular matrix, and thus are promising tools for drug screening studies. In this review, the existing and innovative OS in vitro 3D models are highlighted, focusing on how the TME is crucial to develop effective platforms for OS tumor and metastasis modeling in a physiologically relevant context.

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“…Currently, less than 5% of the effective treatments against OS tested in vitro have succeeded in clinical trials [ 133 , 134 , 135 ]. This can be explained by the inability of these in vitro models to recapitulate the in vivo tumor complexity [ 133 ].…”

Section: Challenges Of Cap For Os Therapymentioning

confidence: 99%

Cold Atmospheric Plasma: A New Strategy Based Primarily on Oxidative Stress for Osteosarcoma Therapy

Mateu-Sanz

Tornín

Ginebra

et al. 2021

JCM

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Osteosarcoma is the most common primary bone tumor, and its first line of treatment presents a high failure rate. The 5-year survival for children and teenagers with osteosarcoma is 70% (if diagnosed before it has metastasized) or 20% (if spread at the time of diagnosis), stressing the need for novel therapies. Recently, cold atmospheric plasmas (ionized gases consisting of UV–Vis radiation, electromagnetic fields and a great variety of reactive species) and plasma-treated liquids have been shown to have the potential to selectively eliminate cancer cells in different tumors through an oxidative stress-dependent mechanism. In this work, we review the current state of the art in cold plasma therapy for osteosarcoma. Specifically, we emphasize the mechanisms unveiled thus far regarding the action of plasmas on osteosarcoma. Finally, we review current and potential future approaches, emphasizing the most critical challenges for the development of osteosarcoma therapies based on this emerging technique.

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Three‐Dimensional Osteosarcoma Models for Advancing Drug Discovery and Development

Cited by 26 publications

References 162 publications

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

Osteosarcoma tumor microenvironment: the key for the successful development of biologically relevant 3D in vitro models

Cold Atmospheric Plasma: A New Strategy Based Primarily on Oxidative Stress for Osteosarcoma Therapy

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