High-Throughput Agonist Shift Assay Development for the Analysis of M1-Positive Allosteric Modulators
Why this work is in the frame
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Bibliographic record
Abstract
Agonist shift assays feature cross-titrations of allosteric modulators and orthosteric ligands. Information generated in agonist shift assays can include a modulator’s effect on the orthosteric agonist’s potency (alpha) and efficacy (beta), as well as direct agonist activity of the allosteric ligand (tauB) and the intrinsic binding affinity of the modulator to the unoccupied receptor (KB). Because of the heavy resource demand and complex data handling, these allosteric parameters are determined infrequently during the course of a drug discovery program and on a relatively small subset of compounds. Automation of agonist shift assays enables this data-rich analysis to evaluate a larger number of compounds, offering the potential to differentiate compound classes earlier and prospectively prioritize based on desired molecular pharmacology. A high-throughput calcium-imaging agonist shift assay was pursued to determine the allosteric parameters of over 1000 positive allosteric modulator (PAM) molecules for the human muscarinic acetylcholine receptor 1 (M1). Control compounds were run repeatedly to demonstrate internal consistency. Comparisons between potency measurements and the allosteric parameter results demonstrate that these different types of measurements do not necessarily correlate, highlighting the importance of fully characterizing and understanding the allosteric properties of leads.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 it