7-Aminoalkoxy-Quinazolines from Epigenetic Focused Libraries Are Potent and Selective Inhibitors of DNA Methyltransferase 1

Inhibitors of epigenetic writers such as DNA methyltransferases (DNMTs) are attractive compounds for epigenetic drug and probe discovery. To advance epigenetic probes and drug discovery, chemical companies are developing focused libraries for epigenetic targets. Based on a knowledge-based approach, herein we report the identification of two quinazoline-based derivatives identified in focused libraries with sub-micromolar inhibition of DNMT1 (30 and 81 nM), more potent than S-adenosylhomocysteine. Also, both compounds had a low micromolar affinity of DNMT3A and did not inhibit DNMT3B. The enzymatic inhibitory activity of DNMT1 and DNMT3A was rationalized with molecular modeling. The quinazolines reported in this work are known to have low cell toxicity and be potent inhibitors of the epigenetic target G9a. Therefore, the quinazoline-based compounds presented are attractive not only as novel potent inhibitors of DNMTs but also as dual and selective epigenetic agents targeting two families of epigenetic writers.     

Design and Synthesis of Hsp90 Inhibitors with B-Raf and PDHK1 Multi-Target Activity

The design of multi-target ligands has become an innovative approach for the identification of effective therapeutic treatments against complex diseases, such as cancer. Recent studies have demonstrated that the combined inhibition of Hsp90 and B-Raf provides synergistic effects against several types of cancers. Moreover, it has been reported that PDHK1, which presents an ATP-binding pocket similar to that of Hsp90, plays an important role in tumor initiation, maintenance and progression, participating also to the senescence process induced by B-Raf oncogenic proteins. Based on these premises, the simultaneous inhibition of these targets may provide several benefits for the treatment of cancer. In this work, we set up a design strategy including the assembly and integration of molecular fragments known to be important for binding to the Hsp90, PDHK1 and B-Raf targets, aided by molecular docking for the selection of a set of compounds potentially able to exert Hsp90-B-Raf-PDHK1 multi-target activities. The designed compounds were synthesized and experimentally validated in vitro. According to the in vitro assays, compounds 4 a , 4 d and 4 e potently inhibited Hsp90 and moderately inhibited the PDHK1 kinase. Finally, molecular dynamics simulations were performed to provide further insights into the structural basis of their multi-target activity.     

Multi‐Objective Molecular De Novo Design by Adaptive Fragment Prioritization

We present the development and application of a computational molecular de novo design method for obtaining bioactive compounds with desired on‐ and off‐target binding. The approach translates the nature‐inspired concept of ant colony optimization to combinatorial building block selection. By relying on publicly available structure–activity data, we developed a predictive quantitative polypharmacology model for 640 human drug targets. By taking reductive amination as an example of a privileged reaction, we obtained novel subtype‐selective and multitarget‐modulating dopamine D₄ antagonists, as well as ligands selective for the sigma‐1 receptor with accurately predicted affinities. The nanomolar potencies of the hits obtained, their high ligand efficiencies, and an overall success rate of 90 % demonstrate that this ligand‐based computer‐aided molecular design method may guide target‐focused combinatorial chemistry.     

Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform

Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.     

Deorphaning the Macromolecular Targets of the Natural Anticancer Compound Doliculide

The cyclodepsipeptide doliculide is a marine natural product with strong actin‐polymerizing and anticancer activities. Evidence for doliculide acting as a potent and subtype‐selective antagonist of prostanoid E receptor 3 (EP3) is presented. Computational target prediction suggested that this membrane receptor is a likely macromolecular target and enabled immediate in vitro validation. This proof‐of‐concept study demonstrates the in silico deorphanization of phenotypic screening hits as a viable concept for future natural‐product‐inspired chemical biology and drug discovery efforts.     

Betulin,a Newly Characterized Compound in Acacia auriculiformis Bark,Is a Multi-Target Protein Kinase Inhibitor

The purpose of this work is to investigate the protein kinase inhibitory activity of constituents from Acacia auriculiformis stem bark. Column chromatography and NMR spectroscopy were used to purify and characterize betulin from an ethyl acetate soluble fraction of acacia bark. Betulin, a known inducer of apoptosis, was screened against a panel of 16 disease-related protein kinases. Betulin was shown to inhibit Abelson murine leukemia viral oncogene homolog 1 (ABL1) kinase, casein kinase 1ε (CK1ε), glycogen synthase kinase 3α/β (GSK-3 α/β), Janus kinase 3 (JAK3), NIMA Related Kinase 6 (NEK6), and vascular endothelial growth factor receptor 2 kinase (VEGFR2) with activities in the micromolar range for each. The effect of betulin on the cell viability of doxorubicin-resistant K562R chronic myelogenous leukemia cells was then verified to investigate its putative use as an anti-cancer compound. Betulin was shown to modulate the mitogen-activated protein (MAP) kinase pathway, with activity similar to that of imatinib mesylate, a known ABL1 kinase inhibitor. The interaction of betulin and ABL1 was studied by molecular docking, revealing an interaction of the inhibitor with the ABL1 ATP binding pocket. Together, these data demonstrate that betulin is a multi-target inhibitor of protein kinases, an activity that can contribute to the anticancer properties of the natural compound and to potential treatments for leukemia.     

Fragment‐Based Discovery of a Dual pan‐RET/VEGFR2 Kinase Inhibitor Optimized for Single‐Agent Polypharmacology

Oncogenic conversion of the RET (rearranged during transfection) tyrosine kinase is associated with several cancers. A fragment‐based chemical screen led to the identification of a novel RET inhibitor, Pz‐1. Modeling and kinetic analysis identified Pz‐1 as a type II tyrosine kinase inhibitor that is able to bind the “DFG‐out” conformation of the kinase. Importantly, from a single‐agent polypharmacology standpoint, Pz‐1 was shown to be active on VEGFR2, which can block the blood supply required for RET‐stimulated growth. In cell‐based assays, 1.0 nM of Pz‐1 strongly inhibited phosphorylation of all tested RET oncoproteins. At 1.0 mg kg^?1 day^?1 per os, Pz‐1 abrogated the formation of tumors induced by RET‐mutant fibroblasts and blocked the phosphorylation of both RET and VEGFR2 in tumor tissue. Pz‐1 featured no detectable toxicity at concentrations of up to 100.0 mg kg^?1, which indicates a large therapeutic window. This study validates the effectiveness and usefulness of a medicinal chemistry/polypharmacology approach to obtain an inhibitor capable of targeting multiple oncogenic pathways.     

Set-Theoretic Formalism for Treating Ligand-Target Datasets

Data on ligand–target (LT) interactions has played a growing role in drug research for several decades. Even though the amount of data has grown significantly in size and coverage during this period, most datasets remain difficult to analyze because of their extreme sparsity, as there is no activity data whatsoever for many LT pairs. Even within clusters of data there tends to be a lack of data completeness, making the analysis of LT datasets problematic. The current effort extends earlier works on the development of set-theoretic formalisms for treating thresholded LT datasets. Unlike many approaches that do not address pairs of unknown interaction, the current work specifically takes account of their presence in addition to that of active and inactive pairs. Because a given LT pair can be in any one of three states, the binary logic of classical set-theoretic methods does not strictly apply. The current work develops a formalism, based on ternary set-theoretic relations, for treating thresholded LT datasets. It also describes an extension of the concept of data completeness, which is typically applied to sets of ligands and targets, to the local data completeness of individual ligands and targets. The set-theoretic formalism is applied to the analysis of simple and joint polypharmacologies based on LT activity profiles, and it is shown that null pairs provide a means for determining bounds to these values. The methodology is applied to a dataset of protein kinase inhibitors as an illustration of the method. Although not dealt with here, work is currently underway on a more refined treatment of activity values that is based on increasing the number of activity classes.     

From Complex Natural Products to Simple Synthetic Mimetics by Computational De Novo Design

We present the computational de novo design of synthetically accessible chemical entities that mimic the complex sesquiterpene natural product (?)‐Englerin A. We synthesized lead‐like probes from commercially available building blocks and profiled them for activity against a computationally predicted panel of macromolecular targets. Both the design template (?)‐Englerin A and its low‐molecular weight mimetics presented nanomolar binding affinities and antagonized the transient receptor potential calcium channel TRPM8 in a cell‐based assay, without showing target promiscuity or frequent‐hitter properties. This proof‐of‐concept study outlines an expeditious solution to obtaining natural‐product‐inspired chemical matter with desirable properties.