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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".