High-Throughput Drug Screening Identifies Pyrimethamine As a Potent and Selective Inhibitor of Acute Myeloid Leukemia
Bibliographic record
Abstract
Abstract Introduction: Hematopoietic stem/progenitor cell differentiation is blocked in acute myeloid leukemia (AML) resulting in cytopenias and high risk of death. Most patients with AML become resistant to treatment due to lack of effective cytotoxic and differentiation fostering compounds. High expression of MN1 confers poor prognosis to AML patients and induces resistance to cytarabine and all-trans-retinoic acid (ATRA) induced differentiation. We thus set out to identify compounds which could potentially overcome the differentiation block in AML. Methods: Based on the above concepts and in an effort to identify novel compounds which are potent inducers of differentiation and apoptosis in AML, high-throughput drug screening was employed in the MN1 leukemic model. A total of 3580 bioactive compounds were tested in duplicate at a concentration of 2.5 µM using alamar blue fluorescence as readout. As MN1 cells are resistant to ATRA (at 1µM and even 10µM ATRA), the drug screening was performed in the presence of a clinically relevant dose of ATRA (1 µM) to identify compounds that concurrently act with the cytotoxic and/or differentiating effects of ATRA. To determine whether a compound was effective as monotherapy or if it synergized with ATRA, we also performed a validation phase study in which the IC50 of each candidate compound was tested alone and in combination with ATRA. Fifty-four inhibitors were chosen from the primary screen for further validation based on presumed mechanism of action and novelty. The shortlisted compound pyrimethamine (PMT) was validated for its differentiation and apoptosis promoting effects in various murine and human AML models. Results: Our high-throughput drug screening identified 117 compounds, which reduced MN1 leukemic cell proliferation by more than 80% above the ATRA-treated control in both replicates (inhibitors), 8 borderline inhibitors (one replicate with more than 80% inhibition and one with 74 to 80% inhibition), and 35 outliers, which inhibited cell proliferation by 80% or more in only one replicate. The biologic processes most frequently targeted by the 117 inhibitors were DNA replication (n=26), microtubule assembly (n=12), NF-kB pathway (n=8), dihydrofolate reductase (DHFR, n=3) and heat shock protein 90 (HSP90). Dihydrofolate reductase inhibitors, pyrimethamine and amethopterin/methotrexate emerged as top hits from the screening and preliminary validation studies. Validation studies identified the antifolate pyrimethamine (PMT) that potently induced apoptosis and differentiation in several murine and human leukemic cell lines when administered as a single agent. The cytotoxic effects of pyrimethamine were reversed by addition of an excess of folic acid whereas induction of myeloid differentiation at higher concentrations of pyrimethamine was not mediated through DHFR inhibition. We further evaluated the effect of pyrimethamine in an in vivo xenograft mouse model by subcutaneously inducing tumors with HL60 and THP1 cell lines. Oral pyrimethamine treatment significantly reduced tumor volumes after 14, 19 and 24 days post-transplantation and at death compared to solvent treated mice (P<0.01). The effect of pyrimethamine was further assessed in primary human AML cells and normal CD34+ cells by CFC assays. Colony numbers from primary AML cells, but not normal CD34+ bone marrow cells, were significantly reduced by pyrimethamine as compared to solvent control. Thus, our study identifies pyrimethamine as a candidate drug that is a potent and specific inducer of apoptosis and differentiation with the property of specifically targeting leukemic cells. Conclusion: Our high-throughput drug screening identified pyrimethamine as a potent and specific antileukemic compound and reinforces targeting of folate metabolism as a treatment strategy in acute myeloid leukemia. Disclosures No relevant conflicts of interest to declare.
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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) | 0.001 | 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) | 0.001 | 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".