Small Molecule Protein Assembly Modulators with Pan-Cancer Therapeutic Efficacy
Bibliographic record
Abstract
Abstract Two structurally-unrelated small molecule chemotypes, represented by compounds PAV-617 and PAV-951 with antiviral activity in cell culture against monkeypox virus (MPXV) and human immunodeficiency virus (HIV) respectively, were studied for anti-cancer efficacy. Each exhibited apparent pan-cancer cytotoxicity, reasonable pharmacokinetics, and non-toxicity in mice at active concentrations. Anti-tumor properties of both chemotypes, were validated in mouse xenografts against A549 human lung cancer and, for one of the chemotypes, against HT-29 colorectal cancer. The targets of these compounds are unconventional: each binds to a different transient, energy-dependent multi-protein complex containing the protein TRIM28/KAP1, an allosteric modulator known to regulate mechanisms underlying viral and nonviral disease states including cancer. Treatment with these compounds alters the target multi-protein complexes in a manner consistent with allosteric modulation as their mechanism of action. These compounds appear to remove a block, crucial for cancer survival and progression, on the homeostatic linkage of uncontrolled cellular proliferation to apoptosis. These compounds provide starting points for development of next-generation non-toxic, pan-cancer therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".