Identification of New FLT3 Inhibitors That Potently Inhibit AML Cell Lines via an Azo Click-It/Staple-It Approach

Ma, Xiaochu; Zhou, Jie; Wang, Changhao; Carter-Cooper, Brandon; Yang, Fan; Larocque, Elizabeth; Fine, Jonathan; Tsuji, Genichiro; Chopra, Gaurav; Lapidus, Rena G.; Sintim, Herman O.

doi:10.1021/acsmedchemlett.6b00468

Cited by 21 publications

(26 citation statements)

References 23 publications

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“…The resulting chemo-proteome interactions can be interrogated to study polypharmacology 19 and investigate the effect drugs and agents have on protein classes in a disease-specific context 19,22 . In previous works, we have used the algorithm presented herein to combat Ebola 20 , determine the toxicity of potential diabetes therapeutics 21 , and rank the affinity of kinase inhibitors for the treatment of Acute Myeloid Leukemia 23 .…”

Section: Introductionmentioning

confidence: 99%

CANDOCK: Chemical atomic network based hierarchical flexible docking algorithm using generalized statistical potentials

Fine

Konc

Samudrala

et al. 2018

Preprint

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Small molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations, such as, improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g. cofactors, metal-ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions and cofactors interactions in the binding pocket that are traditionally ignored by current methods.We evaluate the algorithm on the PDBbind and Astex proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks.Finally, we identify the best selector and ranker potential functions, such that, the statistical score of best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best docked pose with biological activity.

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Section: Introductionmentioning

confidence: 99%

CANDOCK: Chemical atomic network based hierarchical flexible docking algorithm using generalized statistical potentials

Fine

Konc

Samudrala

et al. 2018

Preprint

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“…We have previously reported that the alkynyl aminoisoquinolines and aminonaphthyridines analogs are privileged kinase inhibitors with potent activities against FLT3-driven acute myeloid leukemia. [18,19] Our success in developing alkynyl aminoisoquinolines and aminonaphthyridines as FLT3 inhibitors prompted us to evaluate our compounds against other kinases and to endow the compounds with moieties that are commonly found in drugs (Figure 3).…”

Section: Resultsmentioning

confidence: 99%

“…This decrease in ABL1(T315I) inhibition could be due to steric clash between the larger Rg roup and the isoleucine at position 315 of the mutant enzyme.S ubstitution of the piperazine moiety with a morpholine (compound 17)o rp yrrolidine (compound 18)d e-creasedA BL1i nhibition. However,w hen substituted with ad imethylamine (19), the ABL1 inhibition remained the same (Figure 7B). Compounds 17-19 were poor inhibitors of the mutant ABL1 forms, ABL1(T315I) and ABL1(E255K).…”

Section: Aminoisoquinolinesand Aminonaphthyridines Potently Inhibit Amentioning

confidence: 88%

“…Our group has been interested in developing alkynyl aminoisoquinolines and alkynylaminonaphthyridine as FLT3 inhibitors for the treatment of acute myeloid leukemia. [18,19] Some of our FLT3 inhibitors, such as HSN286 and compound 1 (see Figure 2) contain aryl alkynyl benzamide moiety, which is also found in ponatinib. Because ponatinib also inhibits ABL1, in addition to FLT3, we wondered if our compounds were also ABL1 inhibitors.…”

Section: Introductionmentioning

confidence: 99%

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Alkynylnicotinamide‐Based Compounds as ABL1 Inhibitors with Potent Activities against Drug‐Resistant CML Harboring ABL1(T315I) Mutant Kinase

et al. 2018

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The introduction of imatinib into the clinical scene revolutionized the treatment of chronic myelogenous leukemia (CML). The overall eight-year survival rate for CML has increased from about 6 % in the 1970s to over 90 % in the imatinib era. However, about 20 % of CML patients harbor primary or acquired resistance to tyrosine kinase inhibitors. ABL1 point mutations in the BCR-ABL1 fusion protein, such as ABL1(T315I), typically emerge after prolonged kinase inhibitor treatment. Ponatinib (AP24534) is currently the only approved CML drug that is active against the ABL1(T315I) mutation. However, ponatinib has severe cardiovascular toxicities; hence, there have been efforts to find safer CML drugs that work against ABL1 secondary mutations. We reveal that isoquinoline- or naphthyridine-based compounds, such as HSN431, HSN576, HSN459, and HSN608 potently inhibit the enzymatic activities of ABL1, ABL1(T315I), and ABL1(E255K). These compounds inhibit the proliferation of ABL1-driven CML cell lines, K652 and KCL22 as well as the drug-resistant cell line, KCL22-IR, which harbors the secondary mutated ABL1(T315I) kinase.

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“…29 Recently, it has been shown that molecular sub-graph features incorporated through a GNN and protein features encoded by their sequence can be combined to predict if a compound can target a given protein. 24 Inspired by this work and based on our interest in developing methods for drug design and immunology [29][30][31][32][33][34][35][36] , we have developed a new machine learning model to predict if a compound can inhibit the PD-1/PD-L1 interaction. Our method replaces the protein sequence features with docking scores representing the free energy of binding and due to this global energetic interaction of the small molecule in the binding pocket, we have termed this model as an "Energy Graph Neural Network" (EGNN).…”

Section: Introductionmentioning

confidence: 99%

Combined Molecular Graph Neural Network and Structural Docking Selects Potent Programmable Cell Death Protein 1/Programmable Death-Ligand 1 (PD-1/PD-L1) Small Molecule Inhibitors

Pr¹,

Kp²,

Ja³

et al. 2020

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The Programmable Cell Death Protein 1/Programmable Death-Ligand 1 (PD-1/PD-L1) interaction is an immune checkpoint utilized by cancer cells to enhance immune suppression. There exists a huge need to develop small molecules drugs that are fast acting, cheap, and readily bioavailable compared to antibodies. Unfortunately, synthesizing and validating large libraries of small-molecule to inhibit PD-1/PD-L1 interaction in a blind manner is a both time-consuming and expensive. To improve this drug discovery pipeline, we have developed a machine learning methodology trained on patent data to identify, synthesize and validate PD-1/PD-L1 small molecule inhibitors. Our model incorporates two features: docking scores to represent the energy of binding (E) as a global feature and sub-graph features through a graph neural network (GNN) to represent local features. This Energy-Graph Neural Network (EGNN) model outperforms traditional machine learning methods as well as a simple GNN with an average F1 score of 0.997 (± 0.004) suggesting that the topology of the small molecule, the structural interaction in the binding pocket, and chemical diversity of the training data are all important considerations for enhancing model performance. A Bootstrapped EGNN model was used to select compounds for synthesis and experimental validation with predicted high and low potency to inhibit PD-1/PD-L1 interaction. The new potent inhibitor, (4-((3-(2,3-dihydrobenzo[<i>b</i>][1,4]dioxin-6-yl)-2-methylbenzyl)oxy)-2,6-dimethoxybenzyl)-D-serine, is a hybrid of two known bioactive scaffolds, and has an IC<sub>50</sub> values of 339.9 nM that is comparatively better than the known bioactive compound. We conclude that our EGNN model can identify active molecules designed by scaffold hopping, a well-known medicinal chemistry technique and will be useful to identify new potent small molecule inhibitors for specific targets.

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Identification of New FLT3 Inhibitors That Potently Inhibit AML Cell Lines via an Azo Click-It/Staple-It Approach

Cited by 21 publications

References 23 publications

CANDOCK: Chemical atomic network based hierarchical flexible docking algorithm using generalized statistical potentials

CANDOCK: Chemical atomic network based hierarchical flexible docking algorithm using generalized statistical potentials

Alkynylnicotinamide‐Based Compounds as ABL1 Inhibitors with Potent Activities against Drug‐Resistant CML Harboring ABL1(T315I) Mutant Kinase

Combined Molecular Graph Neural Network and Structural Docking Selects Potent Programmable Cell Death Protein 1/Programmable Death-Ligand 1 (PD-1/PD-L1) Small Molecule Inhibitors

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