The Need for Human Exposure Projection in the Interpretation of Preclinical<i>In Vitro</i>and<i>In Vivo</i>ADME Tox Data
Bibliographic record
Abstract
The field of toxicology is currently undergoing a global paradigm shift to use of in vitro approaches for assessing the risks of chemicals and drugs in a more mechanistic and high-throughput manner than current approaches relying primarily on in vivo testing. In particular, allometric and physiological modeling methods can be used to predict the in vivo exposure conditions that would produce chemical concentrations in plasma and/or the target tissue equivalent to the concentrations at which effects were observed with in vitro assays of tissue/organ toxicity. This chapter reviews the different modeling methods used for human pharmacoki-netic (PK) projection in drug discovery with an emphasis on the prediction of tissue distribution in toxicology studies. The influence of the compound selection process was examined by performing a probability analysis and examining the clearance (CL) properties of compounds that are selected using an idealized drug discovery screening process focused on PK optimization.
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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 | 0.003 | 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 | 0.002 | 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".