Rats pulmonary effects to acute inhalation of 5 nm TiO2 showing two distinct agglomeration states
Bibliographic record
Abstract
The biodistribution and pharmacokinetics of nanomaterials are both similar to yet fundamentally different from those that describe small molecule disposition in the body.They share similar routes of absorption, tissue sites of distribution, and modes of elimination, yet nanomaterial size, shape and surface chemistry modulate how these materials are handled by the body.Nanomaterial ADME and pharmacokinetic studies are now appearing in the literature, but the lack of consistent material characterization and appropriate metrics that mirror biological disposition hinders their interpretation.One initial level of nanomaterial characterization is whether their structure is uniform or complex (e.g.polymers, ground powders, cores with shells of different materials, rigid manufactured structures, etc).Potential contaminants from their manufacture may have to be taken into account.Once in the systemic circulation, the key factors governing disposition relates to size and shape of the particle as well the interaction between a nanomaterial's surface and the biological molecules it encounters, a process that results in so-called corona formation.Association with certain proteins results in opsonization and removal by elements of the reticuoendothelial system and localization in the liver and spleen.Association with other proteins may target the particles to endothelial cells and the vasculature.Ligands may be complexed to a particle's surface to further target a specific pharmacologic endpoint.For some materials, disposition may be a function of lymphatic trafficking which makes monitoring of particles in the systemic circulation less than optimal and possibly subject to hysteresis.Nanomaterial pharmacokinetic studies often use traditional modeling techniques but may need to be interpreted differently.For example, short plasma half-life may be related to reticuloendothelial clearance rather than elimination from the body.Because of fundamentally different mechanisms of cellular uptake in tissues (e.g., endocytic pathways versus active molecular transport pumps and diffusion), physiological based pharmacokinetic (PBPK) models may offer an advantage.A framework of nanomaterial ADME properties is beginning to be defined and will be presented, as will pharmacokinetic schemes for nanomaterial pharmacokinetic models and tissue biodistribution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".