Three-dimensional (3D) liver cell models - a tool for bridging the gap between animal studies and clinical trials when screening liver accumulation and toxicity of nanobiomaterials
Bibliographic record
Abstract
Despite the exciting properties and wide-reaching applications of nanobiomaterials (NBMs) in human health and medicine, their translation from bench to bedside is slow, with a predominant issue being liver accumulation and toxicity following systemic administration. In vitro 2D cell-based assays and in vivo testing are the most popular and widely used methods for assessing liver toxicity at pre-clinical stages; however, these fall short in predicting toxicity for NBMs. Focusing on in vitro and in vivo assessment, the accurate prediction of human-specific hepatotoxicity is still a significant challenge to researchers. This review describes the relationship between NBMs and the liver, and the methods for assessing toxicity, focusing on the limitations they bring in the assessment of NBM hepatotoxicity as one of the reasons defining the poor translation for NBMs. We will then present some of the most recent advances towards the development of more biologically relevant in vitro liver methods based on tissue-mimetic 3D cell models and how these could facilitate the translation of NBMs going forward. Finally, we also discuss the low public acceptance and limited uptake of tissue-mimetic 3D models in pre-clinical assessment, despite the demonstrated technical and ethical advantages associated with them. 3D culture models for use as in vitro alternatives to traditional methods and conventional in vivo animal testing for testing liver accumulation and toxicity of nanobiomaterials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".