BET Inhibitor Target Selectivity Database
BRD2 and BRD4 have fundamentally distinct functions — BRD2 prepares genes for activation while BRD4 triggers transcription — meaning that pan-BET inhibitors have been disrupting gene regulation in counterproductive ways, and the field now needs selective BRD4 inhibitors.
BET Inhibitor Target Selectivity Database
BRD2 and BRD4 have fundamentally distinct functions — BRD2 prepares genes for activation while BRD4 triggers transcription — meaning that pan-BET inhibitors have been disrupting gene regulation in counterproductive ways, and the field now needs selective BRD4 inhibitors.
Build a comprehensive, publicly accessible database of BET bromodomain inhibitors organized by their selectivity profile (pan-BET, BRD4-selective, BRD2-selective, tandem bromodomain-selective), with structured data on preclinical efficacy by cancer type, clinical trial status, and mechanistic rationale. A key feature would be a selectivity comparison tool that visualizes BRD2/BRD4/BRD3 affinity profiles side by side for all cataloged compounds.
The database would include a 'failure analysis' module documenting pan-BET inhibitor clinical trials that underperformed, linking each to the possible mechanistic explanation now provided by BRD2/BRD4 functional distinctions. This would help drug developers and researchers understand not just which compounds exist but why certain selectivity profiles might be superior for specific indications.
A research synthesis section would aggregate all evidence for differential BRD2/BRD4 functions — gene regulation studies, structural data, cancer dependency analyses — providing a unified reference for researchers designing next-generation BET inhibitors.
BET inhibitors represent a significant investment by the pharmaceutical industry, and the explanation for their clinical failures is now within reach. A centralized selectivity database would accelerate the transition from pan-BET to selective BRD4 inhibition, potentially rescuing a drug class with strong preclinical rationale in MYC-driven cancers (AML, TNBC, neuroblastoma, and many more).
Who Is This For?
Medicinal chemists developing BET inhibitors, oncology drug development teams, cancer biologists studying transcriptional regulation, and clinical researchers evaluating which BET programs to advance.
Skills & Tools Needed
- Medicinal chemistry / cheminformatics (selectivity data curation)
- Web database development
- Knowledge of BET bromodomain biology and transcription regulation
- Clinical trial data integration
- Data visualization for selectivity profiling (radar charts, heatmaps)
Feasibility
medium — Compound selectivity data exists in published literature and patent filings; the main effort is systematic curation and a clean database interface — achievable for a small team with chemistry and web development skills.
Inspired by: Scientists finally uncover why promising cancer drugs keep failing
