Coupling cellular drug-target engagement to downstream pharmacology with CeTEAM
Bibliographic record
Abstract
Abstract Cellular target engagement technologies are reforming drug discovery by enabling quantification of intracellular drug binding; however, simultaneous assessment of drug-associated phenotypes has proven challenging. CeTEAM ( ce llular target e ngagement by a ccumulation of m utant) is a platform that can concomitantly evaluate drug-target interactions and phenotypic responses for holistic assessment of drug pharmacology using conditionally-stabilized drug biosensors. We observe that drug-responsive proteotypes are prevalent among reported mutants of known drug targets. CeTEAM-compatible mutants follow structural and biophysical logic that permits intra-protein and paralogous expansion of the biosensor pool, as exemplified by alanine scanning of leucines within the PARP1 helical domain and transfer of PARP1 destabilization to the analogous PARP2 residue. We then apply CeTEAM to uncouple target engagement from divergent cellular activities of MTH1 inhibitors, dissect NUDT15-associated thiopurine metabolism with the R139C pharmacogenetic variant, and profile the live-cell dynamics of PARP1/2 binding and DNA trapping by PARP inhibitors. Further, PARP1-derived biosensors facilitated high-throughput screening of drug-like libraries for PARP1 binders, as well as multimodal ex vivo analysis and non-invasive tracking of PARPi binding in live animals. Our data suggests that CeTEAM can facilitate real-time, comprehensive characterization of target engagement by bridging drug binding events and their biological consequences.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".