Identification of a dual FLT3 and MNK2 inhibitor for acute myeloid leukemia treatment using a structure-based virtual screening approach.

Fms-like tyrosine kinase 3 (FLT3) is considered a promising therapeutic target for acute myeloid leukemia (AML) in the clinical. However, monotherapy with FLT3 inhibitor is usually accompanied by drug resistance. Dual inhibitors might be therapeutically beneficial to patients with AML due to their ability to overcome drug resistance. Mitogen-activated protein kinase (MAPK)-interacting kinases (MNKs) phosphorylate eukaryotic translation initiation factor 4E (eIF4E), which brings together the RAS/RAF/ERK and PI3K/AKT/mTOR oncogenic pathways. Therefore, dual inhibition of FLT3 and MNK2 might have an additive effect against AML. Herein, a structure-based virtual screening approach was performed to identify dual inhibitors of FLT3 and MNK2 from the ChemDiv database. Compound K783-0308 was identified as a dual inhibitor of FLT3 and MNK2 with IC50 values of 680 and 406 nM, respectively. In addition, the compound showed selectivity for both FLT3 and MNK2 in a panel of 82 kinases. The structure-activity relationship analysis and common interactions revealed interactions between K783-0308 analogs and FLT3 and MNK2. Furthermore, K783-0308 inhibited MV-4-11 and MOLM-13 AML cell growth and induced G0/G1 cell cycle arrest. Taken together, the dual inhibitor K783-0308 showed promising results and can be potentially optimized as a lead compound for AML treatment.

Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases.

The inhibitors of two isoforms of mitogen-activated protein kinase-interacting kinases (i.e., MNK-1 and MNK-2) are implicated in the treatment of a number of diseases including cancer. This work reports, for the first time, a multi-target (or multi-tasking) in silico modeling approach (mt-QSAR) for probing the inhibitory potential of these isoforms against MNKs. Linear and non-linear mt-QSAR classification models were set up from a large dataset of 1892 chemicals tested under a variety of assay conditions, based on the Box-Jenkins moving average approach, along with a range of feature selection algorithms and machine learning tools, out of which the most predictive one (>90% overall accuracy) was used for mechanistic interpretation of the likely inhibition of MNK-1 and MNK-2. Considering that the latter model is suitable for virtual screening of chemical libraries-i.e., commercial, non-commercial and in-house sets, it was made publicly accessible as a ready-to-use FLASK-based application. Additionally, this work employed a focused kinase library for virtual screening using an mt-QSAR model. The virtual hits identified in this process were further filtered by using a similarity search, in silico prediction of drug-likeness, and ADME profiles as well as synthetic accessibility tools. Finally, molecular dynamic simulations were carried out to identify and select the most promising virtual hits. The information gathered from this work can supply important guidelines for the discovery of novel MNK-1/2 inhibitors as potential therapeutic agents.