Toxicokinetics and Physiologically Based Toxicokinetics in Toxicology and Risk Assessment
Bibliographic record
Abstract
Toxicokinetics is the study of kinetics of absorption, distribution, metabolism, and excretion of a xenobiotic under the conditions of toxicity evaluation. Conventional toxicokinetics uses the hypothetical compartments, and the model is composed of rate equations that describe the time course of drug and chemical disposition. The utility of toxicokinetics in toxicity evaluation and interpretation of animal toxicology data is emerging as an important tool in product discovery and development. With implementation of the International Conference on Harmonization (ICH) guidelines on systemic exposure and dose selection, toxicokinetics have been integrated in routine toxicity evaluations. Although traditional compartmental/noncompartmental models are generally adequate for assessing systemic exposure, they are unable to the predict time course of drug disposition in target tissues and often fail to relate systemic drug levels to a biological response. Physiologically based toxicokinetic (PB-TK) models address this deficiency of traditional compartmental models. PB-TK models are the kinetic models of the uptake and disposition of chemicals based on rates of biochemical reactions, physiological and anatomical characteristics. These models, when developed appropriately, can predict target organ drug distribution in different species under variety of conditions. This minireview discusses the basic principles, and applications of traditional compartmental toxicokinetic and physiologically based toxicokinetics (PB-TK) models in drug development and risk assessment. Special emphasis will be placed on discussion related to interpretation of the ICH guidelines related to toxicokinetics and the utility of toxicokinetics data in dose selection for toxicity and carcinogenicity studies. The utility of PB-TK models in risk assessment of methylene chloride, vinyl chloride, retinoic acid, dioxin, and inhaled organic esters is discussed.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".