Pre-clinical 2D and 3D toxicity response to a panel of nanomaterials; comparative assessment of NBM-induced liver toxicity
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Notice bibliographique
Résumé
Abstract Nanobiomaterials, or NBMs, have been used in medicine and bioimaging for decades, with wide-reaching applications ranging from their uses as carriers of genes and drugs, to acting as sensors and probes. When developing nanomedicine products, it is vitally important to evaluate their safety, ensuring that both biocompatibility and efficacy are achieved so their applications in these areas can be safe and effective. When discussing the safety of nanomedicine in general terms, it is foolish to make generalised statements due to the vast array of different manufactured nanomaterials, formulated from a multitude of different materials, in many shapes and sizes; therefore, NBM pre-clinical screening can be a significant challenge. Outside of their distribution in the various tissues, organs and cells in the body, a key area of interest is the impact of NBMs on the liver. A considerable issue for researchers today is accurately predicting human-specific liver toxicity prior to clinical trials, with hepatotoxicity not only the most cited reasons for withdrawal of approved drugs, but also a primary cause of attrition in pre-launched drug candidates. To date, no simple solution to adequately predict these adverse effects exists prior to entering human experimentation. The limitations of the current pre-clinical toolkit are believed to be one of the main reasons for this, with questions being raised on the relevance of animal models in pre-clinical assessment, and over the ability of conventional, simplified in vitro cell–based assays to adequately assess new drug candidates or NBMs. Common 2D cell cultures are unable to adequately represent the functions of 3D tissues and their complex cell–cell and cell–matrix interactions, as well as differences found in diffusion and transport conditions. Therefore, testing NBM toxicity in conventional 2D models may not be an accurate reflection of the actual toxicity these materials impart on the body. One such method of overcoming these issues is the use of 3D cultures, such as cell spheroids, to more accurately assess NBM-tissue interaction. In this study, we introduce a 3D hepatocellular carcinoma model cultured from HepG2 cells to assess both the cytotoxicity and viability observed following treatment with a variety of NBMs, namely a nanostructured lipid carrier (in the specific technical name = LipImage ™ 815), a gold nanoparticle (AuNP) and a panel of polymeric (in the specific technical name = PACA) NBMs. This model is also in compliance with the 3Rs policy of reduction, refinement and replacement in animal experimentation [1], and meets the critical need for more advanced in vitro models for pre-clinical nanotoxicity assessment. Graphical abstract Pipeline for the pre-clinical assessment of NBMs in liver spheroid model
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle