A practical consideration for the substrate concentration when determining IC <sub>50</sub> values for enzyme inhibition
Bibliographic record
Abstract
Determination of IC 50 values at a fixed substrate concentration ([S]) is frequently used to rank the potency of enzyme inhibitors and estimate inhibitor concentrations ([I]) to use in full inhibition analyses, particularly for structure-activity studies wherein the mode of inhibition is often known. Assays at an [S] yielding the greatest difference between the initial rates observed in the absence ( v o ) and in the presence ( v i ) of an inhibitor (i.e., v o – v i ) will increase the sensitivity for the detection of enzyme inhibition. For noncompetitive and uncompetitive inhibitors of single-substrate enzymes, v o – v i increases with increasing [S]; however, for competitive and linear mixed-type (LMT) inhibitors, v o – v i obtains a maximum at a specific “optimal” substrate concentration ([S] opt ). Equations are derived describing the dependence of [S] opt on [I], the dissociation constant for the inhibitor ( K i ), and the Michaelis constant for the substrate ( K m ). For example, for competitive inhibition, [S] opt = K m [Formula: see text]. For [I]/ K i values typically employed for inhibition studies (e.g., 0.5 ≤ [I]/ K i ≤ 4), [S] ≈ 2 K m or 3 K m will generally maximize the v o – v i difference for competitive or LMT (α ≥ 7) inhibitors, respectively. For competitive inhibition of bireactant enzymes, the “optimal” substrate concentrations depend on the Michaelis constants for both substrates, [I]/ K i , and the concentration of the second substrate.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".