BGB-45035, a selective IRAK4 CDAC, demonstrated fast IRAK4 degradation and strong signaling inhibition, and superior efficacy in preclinical disease models 2529
Bibliographic record
Abstract
Abstract Description IRAK4 is the key kinase downstream of toll-like receptors (TLRs) and interleukin-1 receptor (IL-1R), both of which have been implicated in multiple immune and inflammatory diseases. We have identified BGB-45035 as a novel and highly selective Cereblon-based IRAK4 CDAC (chimeric degradation activating compound), and compared it with KT-474, the leading IRAK4 degrader being developed clinically. BGB-45035 achieved maximum IRAK4 degradation at 5 h while KT-474 reached maximum degradation at 24 h, with stronger downstream MAPK pathway inhibition. The faster and deeper degradation kinetics induced by BGB-45035 was further illustrated using a real-time degradation reporter assay system. To compare the biological effect of the two compounds when maximum IRAK4 degradation could be achieved, BGB-45035 and KT-474 were administrated to mice via a pre-treatment regimen. BGB-45035 achieved stronger inhibition of CXCL1 induced by hIL-36g, and better efficacy in IL-36-induced skin inflammatory disease model and in IMQ-induced psoriasis model. In rat CIA model, BGB-45035 showed robust efficacy in alleviating disease development. Furthermore, BGB-45035 demonstrated better efficacy than KT-474 in IL-33-induced skin inflammatory disease model with stronger inhibition of ear IL-5 and Th2 cells in ear draining lymph node. These data enlightened the contribution of faster and deeper degradation to robust biological effects of IRAK4 degraders. Topic Categories Therapeutic Approaches to Autoimmunity (THER)
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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".