Fitting of <i>k</i><sub>inact</sub> and <i>K</i><sub>I</sub> Values from Endpoint Pre-incubation IC<sub>50</sub> Data
Bibliographic record
Abstract
Experiments comprising a “pre-incubation” phase, where enzyme is incubated with inhibitor prior to the addition of assay substrate, are commonly used to evaluate covalent inhibitors, often via discontinuous or “endpoint” IC 50 assays. However, due to the lack of mathematical tools to describe its biphasic time-dependent nature, this experiment has thus far been unable to provide k inact and K I values. Herein we report EPIC-Fit, a new method to determine k inact and K I values from global fitting of E ndpoint P re-incubation IC 50 data that can be implemented using Microsoft Excel. Experimental characterization of a known tissue transglutaminase inhibitor, AA9, using EPIC-Fit provided k inact and K I values with strong correlations to the values determined by other, previously established methods of evaluation. This unprecedented method serves to finally include time-dependent pre-incubation endpoint assays in the medicinal chemist’s toolbox for rigorous characterization of irreversible inhibitors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".