The Oral Off-Patent Antimicrobial Clioquinol Inhibits the Proteasome and Induces Cell Death in AML and Myeloma Cells through a Copper-Dependent Mechanism.
Bibliographic record
Abstract
Abstract Proteasome inhibitors are used in the treatment of myeloma and are under evaluation for the treatment of other malignancies including acute myeloid leukemia (AML). Via high throughput screening for inhibitors of cyclin D2 transactivation, we identified the off-patent antimicrobial, Clioquinol (CQ) that we subsequently determined acted by inhibiting the proteasome. CQ is a copper-binding halogenated 8-hydroxyquinoline that was used in the 1950’s-1970’s as an oral anti-parasitic agent. We determined that CQ decreased expression of cyclin D2, arrested cells in the G1 phase, and increased expression of p21, p27 and the abundance of ubiquitinated proteins. This constellation of changes can be associated with inhibition of the proteasome, so we evaluated the effects of CQ on proteasomal function. AML and myeloma cell lines as well as primary AML and normal hematopoietic cells were incubated with increasing concentrations of CQ. After a 24 hour incubation, cell lysates were prepared and the chymotrypsin-like activity of the proteasome was measured by monitoring the rate of cleavage of the fluorescent substrate Suc-LLVY-AMC. In cell lines and primary AML samples, CQ inhibited chymotrypsin-like activity at low micromolar concentrations and at concentrations associated with reductions in cyclin D2, G1 arrest, and increased p21 and p27 expression. Conversely, CQ did not inhibit the proteasome in normal hematopoietic cells up to 100 μM. As CQ is a copper-binding molecule, we tested the requirement of copper for its effects on the proteasome. Supplementing CQ with copper increased CQ’s potency as a proteasome inhibitor, but also negated its preferential inhibition of the proteasome in malignant cells over normal hematopoietic cells. Of note, copper alone had no effect on proteasomal function when added to intact cells. Since inhibiting the proteasome induces cell death, we tested the effects of CQ on the viability of AML (n=6), myeloma (n=14), and solid tumor (n=5) cell lines as well as primary AML samples (n=6) and primary normal hematopoietic cells (n=3). Forty-eight hours after incubation, CQ induced cell death in 6/6 AML, 12/14 myeloma, 0/5 solid tumor, 6/7 primary AML patient samples and 0/3 normal hematopoietic cells with an IC50 <20 μM. Of note, after oral administration of CQ, trough serum concentrations of 20 μM can be achieved in patients. CQ-mediated reductions in cell viability were associated with its ability to inhibit the proteosome, as no proteasomal inhibition was detected in CQ-resistant cell lines. CQ-induced cell death was copper-dependent as supplementing copper in the medium enhanced the toxicity of CQ, while adding the strong copper chelator, tetrathiomoylbdate, abrogated CQ-induced cell death. Given the effects of CQ on AML cell lines and primary patient samples, we evaluated CQ in a mouse model of leukemia. Twice daily oral gavage with CQ prevented the growth of MDAY-D2 leukemia cells in DBA2 mice without evidence of toxicity. Thus, we have identified the off-patent anti-microbial agent CQ that inhibits the proteasome and induces apoptosis preferentially in malignant cells at clinically achievable concentrations. It acts through a copper-dependent mechanism and leverages the higher copper levels present in malignant cell. As such, CQ could be rapidly advanced into clinical trial as an oral proteasome inhibitor for the treatment of AML and myeloma.
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".