Simulation of differential drug pharmacokinetics under heat and exercise stress using a physiologically based pharmacokinetic modeling approach
Bibliographic record
Abstract
Under extreme conditions of heat exposure and exercise stress, the human body undergoes major physiological changes. Perturbations in organ blood flows, gastrointestinal properties, and vascular physiology may impact the body's ability to absorb, distribute, and eliminate drugs. Clinical studies on the effect of these stressors on drug pharmacokinetics demonstrate that the likelihood of pharmacokinetic alteration is dependent on drug properties and the intensity of the stressor. The objectives of this study were to use literature data to quantify the correlation between exercise and heat exposure intensity to changing physiological parameters and further, to use this information for the parameterization of a whole-body, physiologically based pharmacokinetic model for the purposes of determining those drug properties most likely to demonstrate altered drug pharmacokinetics under stress. Cardiac output and most organ blood flows were correlated with heart rate using regression analysis. Other altered parameters included hematocrit and intravascular albumin concentration. Pharmacokinetic simulations of intravenous and oral administration of hypothetical drugs with either a low or high value of lipophilicity, unbound fraction in plasma, and unbound intrinsic hepatic clearance demonstrated that the area under the curve of those drugs with a high unbound intrinsic clearance was most affected (up to a 130% increase) following intravenous administration, whereas following oral administration, pharmacokinetic changes were smaller (<40% increase in area under the curve) for all hypothetical compounds. A midazolam physiologically based pharmacokinetic model was also used to demonstrate that simulated changes in pharmacokinetic parameters under exercise and heat stress were generally consistent with those reported in the literature.
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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) | 0.001 | 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) | 0.001 | 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".