Medicinal Chemistry and NMR Driven Discovery of Novel UDP-glucuronosyltransferase 1A Inhibitors That Overcome Therapeutic Resistance in Cells
Bibliographic record
Abstract
The UDP glucuronosyltransferases (UGT) deactivate many therapeutics via glucuronidation while being required for clearance of normal metabolites and xenobiotics. There are 19 UGT enzymes categorized into UGT1A and UGT2B families based on sequence conservation. This presents a challenge in terms of targeting specific UGTs to overcome drug resistance without eliciting overt toxicity. Here, we identified for the first time that UGT1A4 is highly elevated in acute myeloid leukemia (AML) patients and its reduction corresponded to objective clinical responses. To develop inhibitors to UGT1A4, we leveraged previous NMR-based fragment screening data against the C-terminal domain of UGT1A (UGT1A-C). NMR and medicinal chemistry strategies identified novel chemical matter based on fragment compounds with the capacity to bind ∼ 20 fold more tightly to UGT1A-C (Kd ∼600 μ M vs ∼30 μM). Some compounds differentially inhibited UGT1A4 versus UGT1A1 enzyme activity and restored drug sensitivity in resistant human cancer cells. NMR-based NOE experiments revealed these novel compounds recognised a region distal to the catalytic site suggestive of allosteric regulation. This binding region is poorly conserved between UGT1A and UGT2B C-terminal sequences, which otherwise exhibit high similarity. Consistently, these compounds did not bind to the C-terminal domain of UGT2B7 nor a triple mutant of UGT1A-C replaced with UGT2B7 residues in this region. Overall, we discovered a site on UGTs that can be leveraged to differentially target UGT1As and UGT2Bs, identified UGT1A4 as a therapeutic target, and found new chemical matter that binds the UGT1A C-terminus, inhibits glucuronidation and restores drug sensitivity.
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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 | 0.000 | 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 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".