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Record W1973165085 · doi:10.1080/15376520490429382

Incorporation of Pharmacokinetic and Pharmacodynamic Data into Risk Assessments

2004· article· en· W1973165085 on OpenAlex
John C. Lipscomb, M.E. Meek, Kannan Krishnan, Gregory L. Kedderis, Harvey J. Clewell, Lynne T. Haber

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueToxicology Mechanisms and Methods · 2004
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsHealth Canada
Fundersnot available
KeywordsPhysiologically based pharmacokinetic modellingIn silicoPharmacokineticsPharmacologyPharmacodynamicsRisk assessmentComputational biologyMedicineChemistryBiologyComputer scienceBiochemistry

Abstract

fetched live from OpenAlex

Risk assessment methodologies are being updated to allow the inclusion of numerical values for variance in pharmacokinetic (PK) measures and pharmacodynamic (PD) processes related to toxicity. The key PK measures and PD processes are identified from the results of carefully conducted and adequately reported studies. In some instances, studies with humans are not possible, and so the development of data useful for human PK evaluations and on PD processes in vitro or in silico represent an alternative. These results can be integrated under physiologic, anatomic, and biochemical constraints of the intact body through physiologically based pharmacokinetic (PBPK) modeling. This manuscript presents the rational for and key considerations related to the inclusion of quantitative PK and PD data in assessing chemical risks.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.147
GPT teacher head0.541
Teacher spread0.393 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it