Understanding the Hysteresis Loop Conundrum in Pharmacokinetic / Pharmacodynamic Relationships
Bibliographic record
Abstract
Hysteresis loops are phenomena that sometimes are encountered in the analysis of pharmacokinetic and pharmacodynamic relationships spanning from pre-clinical to clinical studies. When hysteresis occurs it provides insight into the complexity of drug action and disposition that can be encountered. Hysteresis loops suggest that the relationship between drug concentration and the effect being measured is not a simple direct relationship, but may have an inherent time delay and disequilibrium, which may be the result of metabolites, the consequence of changes in pharmacodynamics or the use of a non-specific assay or may involve an indirect relationship. Counter-clockwise hysteresis has been generally defined as the process in which effect can increase with time for a given drug concentration, while in the case of clockwise hysteresis the measured effect decreases with time for a given drug concentration. Hysteresis loops can occur as a consequence of a number of different pharmacokinetic and pharmacodynamic mechanisms including tolerance, distributional delay, feedback regulation, input and output rate changes, agonistic or antagonistic active metabolites, uptake into active site, slow receptor kinetics, delayed or modified activity, time-dependent protein binding and the use of racemic drugs among other factors. In this review, each of these various causes of hysteresis loops are discussed, with incorporation of relevant examples of drugs demonstrating these relationships for illustrative purposes. Furthermore, the effect that pharmaceutical formulation has on the occurrence and potential change in direction of the hysteresis loop, and the major pharmacokinetic / pharmacodynamic modeling approaches utilized to collapse and model hysteresis are detailed. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.
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.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".