Physiological Modeling of Age-Specific Changes in the Pharmacokinetics of Organic Chemicals in Children
Bibliographic record
Abstract
Age-specific changes in the pharmacokinetics of chemicals are primarily due to differences in physiological and biochemical factors. For integrating the available information on the age-dependent changes in the physiological and biochemical factors, and for evaluating their combined influence on the pharmacokinetics of chemicals, physiologically based pharmacokinetic (PBPK) models are potentially useful. The objectives of this study were, therefore, (1) to assemble information on age-specific differences in physiological parameters such as alveolar ventilation rate, cardiac output, tissue volumes, tissue blood flow rates, and tissue composition for children of various age groups, and (2) to incorporate these data within a PBPK model for simulating the inhalation pharmacokinetics of a highly metabolized, volatile organic chemical (furan) in children of specific age groups (6, 10, and 14 yr old). The age-specific data on various physiological parameters were assembled following a review of the relevant literature and the hepatic metabolism rate of furan was set equal to the liver blood flow rate in adults and children. The blood:air and tissue:blood partition coefficients were calculated using molecular structure information along with the data on the blood and tissue composition (lipid and water contents) in children and adults. The PBPK model was used to simulate the pharmacokinetics of furan in adults and children (6, 10, and 14 yr old) exposed continuously for 30 h to 1 microgram/L of this chemical in inhaled air. The model simulations suggest that, for the same exposure conditions, the blood concentration of furan is likely to be greater in children by a factor of 1.5 (at steady state) than in adults, and the maximal factor of adult-children differences in liver concentration of furan metabolite is about 1.25. The PBPK model framework developed in this study should be useful for predicting the adult-children differences in internal dose of chemicals for risk assessment applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".