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Enregistrement W2553355410 · doi:10.1182/blood.v124.21.2304.2304

High-Throughput Drug Screening Identifies Pyrimethamine As a Potent and Selective Inhibitor of Acute Myeloid Leukemia

2014· article· en· W2553355410 sur OpenAlexaff
Amit Sharma, Nidhi Jyotsana, Courteney K. Lai, Anuhar Chaturvedi, Kerstin Görlich, Cecilia Murphy, Jan Blanchard, Arnold Ganser, Eric D. Brown, John A. Hassell, R. Keith Humphries, Michael Morgan, Michael Heuser

Notice bibliographique

RevueBlood · 2014
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueRetinoids in leukemia and cellular processes
Établissements canadiensMcMaster UniversityBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésCytarabineMyeloid leukemiaPharmacologyTretinoinLeukemiaRetinoic acidDifferentiation therapyMyeloidAcute promyelocytic leukemiaCancer researchMedicineImmunologyBiologyCell culture

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,716

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,004
Tête enseignante GPT0,216
Écart entre enseignants0,212 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2014
Routes d'admission1
Résumé présentoui

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