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Transcription Factor Inhibitors: From “Undruggable” to Drug Discovery Reality

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Transcription factors (TFs) sit at the control nodes of oncogenic gene-expression programs—driving proliferation, survival, lineage plasticity, and therapy resistance across tumor types. Decades of genetics and genomics established TFs as recurrent cancer drivers and dependencies, yet their flat, dynamic interfaces long branded them “undruggable.” That view has shifted: systematic catalogs clarified the landscape of ~1,600 human sequence-specific TFs and their motifs, enabling sharper target selection, while advances in structural biology, chemical biology, and modality design produced genuine clinical proofs of concept—from direct allosteric inhibition of HIF-2α (belzutifan) to first-in-human macromolecular MYC inhibitors (OMO-103). Together these milestones mark a transition from aspiration to practice, with TFs now central to precision oncology strategies and combination regimens that aim to rewire malignant transcription at its source.

Why transcription factors were once “hard to drug” – and what changed

Transcription factors (TFs) regulate gene programs and usually lack deep pockets; they use broad protein-protein and protein-DNA interfaces and often contain intrinsically disordered regions. Structural biology, chemical biology, and new modalities have shifted the odds: we now have approved drugs that modulate TF output directly or indirectly, credible clinical signals for TF-directed agents, and maturing toolkits to discover and optimize TF binders (Bushweller 2019, Lambert 2018).

Structural & computational enablement

Cryo-EM, X-ray, NMR/HDX-MS, AlphaFold2-Multimer, and MD simulations reveal cryptic or allosteric pockets and hot spots on TF complexes (e.g. KIX, BTB, PAS, and nuclear receptors). These methods now routinely guide screen design and hit optimization, including for flat PPI surfaces and disordered regions (Bushweller 2019, Su 2021).

What counts as a “Transcription Factor Inhibitor”?

A transcription factor inhibitor can (1) block TF:DNA binding, (2) disrupt TF:cofactor PPI, (3) block TF:TF dimerization, (4) intercalate with DNA to block TF binding, or (5) inhibit subcellular shuttling of TF E(Figure 1A). Alternatively, TFs can be degraded through the proteasome degradation system either using PROTACs (Figure 1B) or molecular glue degraders. Most importantly, degradation already has human proof-of-concept: the IMiDs lenalidomide/pomalidomide recruit CRBN to gdegrade IKZF1/IKZF3 – bona fide TFs – hereby reprogramming myeloma cells. These agents prove that pharmacologic TF degradation is clinically feasible (Krönke 2013).

Figure 1: Different ways for small molecule transcription factor inhibition. A) Traditional ways for small molecule transcription factor inhibition and B) Transcription factor inhibition by PROTACs

Direct clinical and translational examples. HIF-2α (a TF dimer subunit) has an approved small molecule (belzutifan) for VHL-disease RCC (Jonasch 2021), and first-in-human TEAD palmitoylation inhibitors show activity in NF2-mutant tumors (Yap, 2023). A MYC-dominant negative mini-protein (OMO-103) delivered target engagement and early clinical signals (Garralda 2024). PROTACs that degrade STAT3 demonstrate potent clinical activity (Zhou 2019).

Modalities & case studies (small molecules to macrocycles)

  • Orthosteric/allosteric small molecules. HIF-2α (belzutifan, approved), TEAD palmitoylation inhibitors (e.g., VT3989, Ph 1), and PPI modulators like BCL6 (79-6; FX1) or CREB (KG-501; 666-15) illustrate tractable TF surfaces and cofactors (Jonasch 2021, Yap 2023, Cerchietti 2011, Cardenas 2016, Best 2004).
  • Targeted protein degradation. Besides CRBN-recruiting IMiDs (IKZF1/3), STAT3 PROTACs (e.g., SD-36) exemplify degrader strategies against TFs and co-regulators (Krönke 2013).

  • Beyond small molecules. Constrained peptides, mini-proteins, macrocycles, and peptidomimetics expand reach to large PPIs. Omomyc (OMO-103), a myc dominant-negative mini-protein, achieved clinical target engagement and disease stabilization in a subset of patients (Gerralda 2024).

  • Indirect TF pathway control. BET bromodomain inhibitors collapse super-enhancer programs (e.g. myc), CDK7/9 inhibitors throttle transcriptional pause-release/elongation, and CBP/p300 HAT inhibitors down-tune coactivator acetylation—all of which modulate TF-driven outputs. The KEAP1–NRF2 PPI inhibitors (e.g., KI-696) illustrate TF activation via PPI blockade, useful for oxidative-stress and inflammatory contexts.

How to find a Transcription Factor Inhibitor: assays that work

Biochemical assays for transcription factor screening

For TF:DNA disruption or TF:PPI modulation, choose a primary assay with an orthogonal biophysics confirm:

  • Fluorescence polarization/anisotropy (FP/FA) using labelled DNA (or peptide cofactor) is robust and HTS-friendly; it tolerates inner-filter effects better than many readouts. Confirm hits by SPR/BLI/ITC (Hall 2016).
  • AlphaScreen/AlphaLISA proximity assays scale to UH-TS. For example, a miniaturized AlphaScreen found inhibitors of the HMGA2:DNA interaction (Su 2020).
  • Reporter assays (luciferase/SEAP) encode pathway-level TF activity; paired with CRISPRi/a or degrader controls, they triage direct vs indirect mechanisms. (Tao 2023)

Assay interferences & counterscreens (critical “hygiene”).
Guard against DNA intercalators/groove binders (run ethidium displacement, calf-thymus DNA counterscreens), colloidal aggregators (add low % detergent, DLS), and redox/fluorescence artifacts (redox scavengers; absorbance/fluorescence scans). Validate with SPR/BLI/ITC and enforce tag-switched controls. Close the loop with in-cell target engagement before chemistry scale-up (Hall 2016).

Cell-based target engagement (label-free or energy-transfer)

NanoBRET/NanoBiT target-engagement assays quantify compound binding in living cells; CETSA-MS profiles proteome-wide thermal shifts to reveal on- and off-targets and PD markers. These are particularly valuable for disordered or PPI-rich TFs where classical occupancy assays struggle. 

DNA-encoded library (DEL) transcription factor inhibitor screening

DEL selections enable billions of compounds to be tested for TF binding at modest cost. Best practices for TFs: include polyanionic competitors (poly(dI–dC), heparin) and high-salt washes to suppress nonspecific capture by the DNA barcode; run tag-flipped negative selections and DNA-only baits; stabilize the biologically relevant TF complex (e.g., TEAD with palmitate, TF+cofactor). Prioritize hits with count-aware statistics (Poisson/Bayesian), then re-synthesize off-DNA for SPR/BLI and cell-based assays (Gironda-Martínez 2021). 

Modern DEL analytics (including uncertainty-aware and 3D-aware ML) help denoise count data and surface chemotypes likely to validate after off-DNA synthesis.

Where DEL fits:

DELs complement medium-throughput transcription factor screening funnels (FP/Alpha/SPR) and can seed degrader ligand discovery (e.g., KEAP1-binding warheads). For practical context on platform variants, see vendor technical notes (e.g., Vipergen’s overview).

Translational realities: resistance, biomarkers, combinations

TF networks rewire: bypass TFs, enhancer switching, and paralog compensation are common. Track occupancy (ChIP-qPCR/seq on pharmacodynamic loci), PD gene signatures, and circulating tumor DNA for pathway lesions. Combinations are rational: MDM2 antagonists with DNA-damage agents, TEAD inhibitors with FAK/MAPK blockers, STAT3 degraders with JAK inhibitors (Gounder 2023).

Disease areas beyond oncology

While oncology dominates, TF modulation is relevant in autoimmune/inflammatory disease (NF-κB, STATs, AHR), fibrosis (YAP/TAZ-TEAD; SMADs), metabolic/cardio-renal (NR TFs like PPARs/FXR), and neurology/virology (IRFs, NF-κB). Several indirect modulators (BET, CDKs, CBP/p300) already have broad preclinical/clinical footprints across these indications (Filippakopoulos 2010, Bacon 2019).

Most important TF drug targets

Target/Complex Modality Representative agent(s) Clinical Stage One-line rationale
HIF-2α (EPAS1:ARNT) Allosteric small molecule Belzutifan (MK-6482) Approved Directly inhibits HIF-2α dimer function. Strong precedent for TF targeting. (Jonasch 2021).
TEAD (YAP/TAZ–TEAD) Palmitoylation inhibitors VT3989 Phase 1 Blocks YAP/TAZ transcriptional output. Activity in NF2-mutant tumors (Yap 2023).
MYC/MAX Mini-protein/peptidomimetic OMO-103 (Omomyc) Phase 1 First clinical-stage direct MYC inhibitor with target engagement (Garralda 2023).
p53/MDM2 PPI antagonist Milademetan Phase 3 completed Reactivates p53. Defined biomarker population (MDM2-amp). (Gounder 2023).
IKZF1/IKZF3 Molecular glues (CRBN) Lenalidomide, Pomalidomide Approved Clinically validated TF degradation drives myeloma efficacy (Krönke 2013).
STAT3 PROTAC degrader SD-36 Preclinical Potent degradation of STAT3 with strong in vivo activity. (Zhou 2019).
BCL6 (BTB corepressor hub) PPI disruptor 79-6, FX1 Preclinical Disrupts BTB:corepressor binding. Regression in DLBCL models. (Cerchietti 2010).
CREB:CBP/p300 (KIX) PPI antagonist KG-501, 666-15 Preclinical Blocks coactivator recruitment. Robust pathway inhibition in vivo. (Best 2004).
NF-κB Indirect pathway modulators Multiple Mixed Central inflammatory/oncogenic TF. Druggable via upstream nodes. (Verzella 2022).
ER (ESR1) Orthosteric antagonists, SERDs Tamoxifen class, etc. Approved Canonical TF target with decades of clinical validation. (Tremont 2017).
AR (NR3C4) Antagonists Enzalutamide Approved Improves survival in mHSPC. Classic nuclear receptor TF. (Davis 2019).
NRF2 (NFE2L2) KEAP1–NRF2 PPI inhibitors (activators) KI-696 (tool) Preclinical Keap1 PPI inhibitors elevate cytoprotective NRF2 programs. (Dinkova-Kostova 2023).
SMAD2/3/4 Interface modulators. Degraders (emerging) Various Preclinical Central to TGF-β signaling in fibrosis and cancer. (Bushweller 2019).
AP-1 (FOS/JUN) PPI/disruption. Degrader concepts Emerging Preclinical Oncogenic bZIP factors. Rich interface biology. (Bushweller 2019).
ETS family (ERG/ETV1/ETV6) DNA-binding/PPI strategies Emerging Preclinical Fusion-driven oncogenes. Tractable in principle via PPIs/DNA mimicry. (Bushweller 2019).

Practical blueprint for transcription factor inhibitor screening

  1. Start biochemical, finish biophysical. Use FP/FA or AlphaScreen for throughput, then SPR/BLI/ITC to confirm direct binding and establish mechanism (DNA vs cofactor vs allosteric) (Hall 2016, Su 2020). 
  2. Counterscreens early. DNA intercalation/groove-binding, colloidal aggregation, and redox fluorescence artifacts account for many false positives; bake in detergent, polyanions, and orthogonal readouts (Hall 2016).
  3. Go cellular quickly. Move to NanoBRET target engagement and CETSA-MS to verify in-cell binding and explore selectivity (Robers 2015, Savitski 2014).
  4. Use DEL for breadth, ML for triage. DEL campaigns with TF-specific safeguards (above) plus uncertainty-aware analytics markedly improve triage to off-DNA and cell follow-up (Gironda-Martínez 2021, Lim 2022).
  5. Lean on structure. Where possible, co-crystallize or use cryo-EM/NMR/HDX-MS to map pockets and hot spots, then iterate chemistry against those constraints (Bushweller 2019).

Final takeaway

  • Undruggable no more: There are approved and clinical stage precedents for TF modulation.
  • Effective transcription factor inhibitor screening blends robust biochemical assays, orthogonal biophysics, and in-cell engagement, with DELs and ML providing breadth and precision.
  • Expect combinations and biomarker-driven development to be central as programs move from bench to bedside.

FAQ

How do you screen for a transcription factor inhibitor efficiently?

Begin with a biochemical primary—e.g., fluorescence polarization for TF:DNA or AlphaScreen for TF:PPI—because they scale and are mechanism-specific. Immediately add counterscreens for DNA intercalation and colloidal aggregation, then confirm direct binding with SPR/BLI/ITC. Move quickly to cellular target engagement (NanoBRET or CETSA-MS) to avoid chasing artifacts. For breadth, run a DNA-encoded library (DEL) selection with TF-specific safeguards, then re-synthesize off-DNA for orthogonal validation.

What assay artifacts most often derail transcription factor screening—and how do I avoid them?
False positives often stem from DNA intercalators/groove binders, colloidal aggregators, or redox/fluorescence quirks. Use calf-thymus DNA or ethidium displacement counterscreens; include low-% detergent and monitor by DLS; add redox scavengers and verify spectra. Always run orthogonal biophysics (SPR/BLI/ITC) and tag-switched controls, then establish in-cell engagement with NanoBRET or CETSA before heavy chemistry investment. Alternatively DEL screening provides means to not use assays with a biochemical response hereby allowing for discovery of binders of the TF directly.
Are there real clinical precedents for targeting transcription factors?
Yes. The clearest is TF degradation in the clinic: lenalidomide and pomalidomide recruit CRBN to degrade IKZF1/IKZF3 in multiple myeloma. Belzutifan directly inhibits HIF-2α, and TEAD palmitoylation inhibitors have entered the clinic with early signs of activity, while a myc mini-protein (OMO-103) achieved target engagement and early responses. Collectively, they validate multiple routes to modulate TF activity in patients.
Where do indirect approaches (BET, CDK7/9, CBP/p300) fit next to “true” TF inhibitors?
They’re complementary. BET inhibitors collapse enhancer-driven programs (e.g., myc), CDK7/9 inhibitors throttle transcriptional pausing/elongation, and CBP/p300 HAT inhibitors reduce co-activator acetylation—all dampen TF output without binding the TF itself. In practice, programs often pursue both direct and indirect levers, then combine with pathway agents (e.g., p53/MDM2, FAK/MAPK) to overcome adaptive rewiring and boost durability.

References

  1. Bacon, C. W. and D’Orso, I., CDK9: a signaling hub for transcriptional control, Transcription, 10 (2), 57-75. https://doi.org/10.1080/21541264.2018.1523668 
  2. Best, J. L. et. al., Identification of small-molecule antagonists that inhibit an activator:coactivator interaction, Proc Nat Ac Sci U S A, 101 (51), 17622-17627 (2004). https://doi.org/10.1073/pnas.0406374101 
  3. Bushweller, J. H., Nat Rev Cancer, 19, 611-624 (2019). https://doi.org/10.1038/s41568-019-0196-7 
  4. Cardenas, M. G. et. al. Rationally designed BCL6 inhibitors target activated B cell diffuse large B cell lymphoma, J Clin Invest, 126 (9), 3351-3362 (2016). https://doi.org/10.1172/jci85795 
  5. Cerchietti, L. C. et. al. A small molecule inhibitor of BCL6 kills DLBCL cells in vitro and in vivo, Cancer Cell, 17 (4), 400-411 (2010). https://doi.org/10.1016/j.ccr.2009.12.050 
  6. Davis, I. D. et. al., Enzalutamide with Standard First-Line Therapy in Metastatic Prostate Cancer, N Engl J Med, 381, 121-131 (2019). https://www.nejm.org/doi/10.1056/NEJMoa1903835 
  7. Dinkova-Kostova, A. T. et. al., Advances and challenges in therapeutic targeting of NRF2, Trends Pharmacol Sci, 44 (3), 173-149. https://doi.org/10.1016/j.tips.2022.12.003 
  8. Filippakopoulos, P. et. al., Selective inhibition of BET bromodomains, Nature, 468 (7327), 1067-1073 (2010). https://doi.org/10.1038/nature09504 
  9. Gerralda, E. et. al., MYC targeting by OMO-103 in solid tumors: a phase 1 trial, Nat Med, 30, 762-771 (2024). https://doi.org/10.1038/s41591-024-02805-1 
  10. Gironda-Martínez, A. et. al., DNA-Encoded Chemical Libraries: A Comprehensive Review with Succesful Stories and Future Challenges, ACS Pharmacol Transl Sci, 4 (4), 1265-1279 (2021). https://doi.org/10.1021/acsptsci.1c00118 
  11. Gounder, M. M. et. al., A First-in-Human Phase I Study of Milademetan, an MDM2 Inhibitor, in Patients With Advanced Liposarcoma, Solid Tumors, or Lymphomas, 41 (9), 1714-1724 (2023). https://doi.org/10.1200/jco.22.01285 
  12. Hall, M. D. et. al. Fluorescence polarization assays in high-throughput screening and drug discovery: a review, Methods Appl Fluoresc. 4 (2), 022001 (2016). https://doi.org/10.1088/2050-6120/4/2/022001 
  13. Jonasch, E. et. al. Belzutifan for Renal Cell Carcinoma in von Hippel–Lindau Disease, N Engl. J. Med, 385 (22), 2036-2046 (2021). DOI: doi.org/10.1056/NEJMoa2103425 
  14. Krönke, J. et. al. Lenalidomide Causes Selective Degradation of IKZF1 and IKZF3 in Multiple Myeloma Cells, Science, 343 (6168), 301-305 (2013).  https://doi.org/10.1126/science.1244851 
  15. Lambert, S. A. et. al., The Human Transcription Factors, Cell, 172 (4), 650-665 (2018). https://doi.org/10.1016/j.cell.2018.01.029 
  16. Lim, K. S. et. al., Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss function, arXiv, 2108, 12471 (2022). https://doi.org/10.48550/arXiv.2108.12471 
  17. Robers, M. B. et. al., Target engagement and drug residence time can be observed in living cells with BRET, Nat Commun, 6, 10091 (2015). https://doi.org/10.1038/ncomms10091 
  18. Savitski, M. M. et. al., Tracking cancer drugs in living cells by thermal profiling of the proteome, Science, 346 (6205), 1255784 (2014). https://doi.org/10.1126/science.1255784 
  19. Su, B. G. and Henley, M. J., Drugging Fuzzy Complexes in Transcription, Front Mol Biosci, 8, 795743 (2021). https://doi.org/10.3389/fmolb.2021.795743 
  20. Su, L. et. al., Identification of HMGA2 inhibitors by AlphaScreen-based ultra-high-throughput screening assays, Sci Rep, 10, 18850 (2020). https://doi.org/10.1038/s41598-020-75890-0 
  21. Tao, Z. and Wu, X. Targeting transcription factors in cancer: from “undruggable” to “druggable”, Methods Mol Biol, 2594, 107-131 (2023). https://doi.org/10.1007/978-1-0716-2815-7_9 
  22. Tremont, A. et. al., Endocrine Therapy for Early Breast Cancer: Updated Review, Ochsner J, 17, 405-411 (2017). 
  23. Verzella, D. et. al., The NF-κB Pharmacopeia: Novel Strategies to Subdue an Intractable Target, Biomedicines, 10 (9), 2233 (2022). https://doi.org/10.3390/biomedicines10092233 
  24. Xie, F. et. al. Identification of a Potent Inhibitor of CREB-Mediated Gene Transcription with Efficacious in Vivo Anticancer Activity, J Med Chem, 58 (12), 5075-5087 (2015). https://doi.org/10.1021/acs.jmedchem.5b00468 
  25. Yap, T. A. et. al. Abstract CT006: First-in-class, first-in-human phase 1 trial of VT3989, an inhibitor of yes-associated protein (YAP)/transcriptional enhancer activator domain (TEAD), in patients (pts) with advanced solid tumors enriched for malignant mesothelioma and other tumors with neurofibromatosis 2 (NF2) mutations, Cancer Res, 83, CT006 (2023). https://doi.org/10.1158/1538-7445.AM2023-CT006 
  26. Zhou, H, et. al., Structure-Based Discovery of SD-36 as a Potent, Selective, and Efficacious PROTAC Degrader of STAT3 Protein, J Med Chem, 2019, 62 (24), 11280-11300 (2019). https://doi.org/10.1021/acs.jmedchem.9b01530 

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