From Serendipity to Rational Identification of the 5,6,7,8-Tetrahydrobenzo[4,5]thieno[2,3- d ]pyrimidin-4(3 H )-one Core as a New Chemotype of AKT1 Inhibitors for Acute Myeloid Leukemia.
Andrea AstolfiFrancesca MilanoDeborah PalazzottiJose BreaMaria Chiara PismataroMariangela MorlandoOriana TabarriniMaria Isabel LozaSerena MassariMaria Paola MartelliMaria Letizia BarrecaPublished in: Pharmaceutics (2022)
Acute myeloid leukemia (AML) is a heterogeneous hematopoietic malignancy whose prognosis is globally poor. In more than 60% of AML patients, the PI3K/AKTs/mTOR signaling pathway is aberrantly activated because of oncogenic driver alterations and further enhanced by chemotherapy as a mechanism of drug resistance. Against this backdrop, very recently we have started a multidisciplinary research project focused on AKT1 as a pharmacological target to identify novel anti-AML agents. Indeed, the serendipitous finding of the in-house compound T187 as an AKT1 inhibitor has paved the way to the rational identification of new active small molecules, among which T126 has emerged as the most interesting compound with IC 50 = 1.99 ± 0.11 μM, ligand efficiency of 0.35, and a clear effect at low micromolar concentrations on growth inhibition and induction of apoptosis in AML cells. The collected results together with preliminary SAR data strongly indicate that the 5,6,7,8-tetrahydrobenzo[4,5]thieno[2,3- d ]pyrimidin-4(3 H )-one derivative T126 is worthy of future biological experiments and medicinal chemistry efforts aimed at developing a novel chemical class of AKT1 inhibitors as anti-AML agents.
Keyphrases
- acute myeloid leukemia
- signaling pathway
- induced apoptosis
- cell proliferation
- allogeneic hematopoietic stem cell transplantation
- pi k akt
- cell cycle arrest
- end stage renal disease
- quality improvement
- epithelial mesenchymal transition
- newly diagnosed
- ejection fraction
- endoplasmic reticulum stress
- oxidative stress
- cell death
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- machine learning
- transcription factor
- big data
- acute lymphoblastic leukemia