Drug disposition in three dimensions: an update on stereoselectivity in pharmacokinetics
Bibliographic record
Abstract
Many marketed drugs are chiral and are administered as the racemate, a 50:50 combination of two enantiomers. Pharmacodynamic and pharmacokinetic differences between enantiomers are well documented. Because of enantioselectivity in pharmacokinetics, results of in vitro pharmacodynamic studies involving enantiomers may differ from those in vivo where pharmacokinetic processes will proceed. With respect to pharmacokinetics, disparate plasma concentration vs time curves of enantiomers may result from the pharmacokinetic processes proceeding at different rates for the two enantiomers. At their foundation, pharmacokinetic processes may be enantioselective at the levels of drug absorption, distribution, metabolism and excretion. In some circumstances, one enantiomer can be chemically or biochemically inverted to its antipode in a unidirectional or bidirectional manner. Genetic consideration such as polymorphic drug metabolism and gender, and patient factors such as age, disease state and concomitant drug intake can all play a role in determining the relative plasma concentrations of the enantiomers of a racemic drug. The use of a nonstereoselective assay method for a racemic compound can lead to difficulties in interpretation of data from, for example, bioequivalence or dose/concentration vs effect assessments. In this review data from a number of representative studies involving pharmacokinetics of chiral drugs are presented and discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".