Limitations of conventional inhibitor classifications
Bibliographic record
Abstract
Enzyme inhibitors are usually classified as competitive, non-competitive or mixed non-competitive. Each of these designations has a serious limitation in that it only describes an extreme of inhibitory behaviour. The non-competitive inhibition equation only considers an approach to complete inhibition of the catalytic turnover rate, while the competitive inhibition equation predicts an infinite increase in the Michaelis-Menten constant (decrease in enzyme affinity for substrate), resulting from increased inhibitor concentration. Both of these models exclude the possibility of a finite inhibitor-induced change in the kinetic parameters of the enzyme they are affecting. They also exclude the possibility of an inhibitor affecting both the substrate affinity and the catalytic turnover at the same time. Mixed non-competitive inhibition describes a hybrid form of inhibition displaying some characteristics of both competitive and non-competitive inhibition. It also suffers from an inability to describe finite changes in activity and to describe concomitant changes in substrate affinity and catalytic turnover. Two inhibitor binding constants are invoked in this equation, suggesting that such inhibitors interact with the enzyme in two completely independent manners. From these considerations, it is suggested here that conventional equations do not adequately describe observed kinetic data due to a lack of distinction between the mass action binding term describing inhibitor-enzyme association and the terms representing the actual effect of the inhibitor on the enzyme. Herein we describe an alternate approach for representing enzyme activity modulation based on a re-examination of conventional inhibition equations. The arguments presented are illustrated using the known competitive inhibition of Kallikrein with benzamidine.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".