Fine tuning for success in structure-based virtual screening.

Structure-based virtual screening plays a significant role in drug-discovery. The method virtually docks millions of compounds from corporate or public libraries into a binding site of a disease-related protein structure, allowing for the selection of a small list of potential ligands for experimental testing. Many algorithms are available for docking and assessing the affinity of compounds for a targeted protein site. The performance of affinity estimation calculations is highly dependent on the size and nature of the site, therefore a rationale for selecting the best protocol is required. To address this issue, we have developed an automated calibration process, implemented in a Knime workflow. It consists of four steps: preparation of a protein test set with structures and models of the target, preparation of a compound test set with target-related ligands and decoys, automatic test of 24 scoring/rescoring protocols for each target structure and model, and graphical display of results. The automation of the process combined with execution on high performance computing resources greatly reduces the duration of the calibration phase, and the test of many combinations of algorithms on various target conformations results in a rational and optimal choice of the best protocol. Here, we present this tool and exemplify its application in setting-up an optimal protocol for SBVS against Retinoid X Receptor alpha.

Dual Targeting of Histone Methyltransferase G9a and DNA-Methyltransferase 1 for the Treatment of Experimental Hepatocellular Carcinoma.

Epigenetic modifications such as DNA and histone methylation functionally cooperate in fostering tumor growth, including that of hepatocellular carcinoma (HCC). Pharmacological targeting of these mechanisms may open new therapeutic avenues. We aimed to determine the therapeutic efficacy and potential mechanism of action of our dual G9a histone-methyltransferase and DNA-methyltransferase 1 (DNMT1) inhibitor in human HCC cells and their crosstalk with fibrogenic cells. The expression of G9a and DNMT1, along with that of their molecular adaptor ubiquitin-like with PHD and RING finger domains-1 (UHRF1), was measured in human HCCs (n = 268), peritumoral tissues (n = 154), and HCC cell lines (n = 32). We evaluated the effect of individual and combined inhibition of G9a and DNMT1 on HCC cell growth by pharmacological and genetic approaches. The activity of our lead compound, CM-272, was examined in HCC cells under normoxia and hypoxia, human hepatic stellate cells and LX2 cells, and xenograft tumors formed by HCC or combined HCC+LX2 cells. We found a significant and correlative overexpression of G9a, DNMT1, and UHRF1 in HCCs in association with poor prognosis. Independent G9a and DNMT1 pharmacological targeting synergistically inhibited HCC cell growth. CM-272 potently reduced HCC and LX2 cells proliferation and quelled tumor growth, particularly in HCC+LX2 xenografts. Mechanistically, CM-272 inhibited the metabolic adaptation of HCC cells to hypoxia and induced a differentiated phenotype in HCC and fibrogenic cells. The expression of the metabolic tumor suppressor gene fructose-1,6-bisphosphatase (FBP1), epigenetically repressed in HCC, was restored by CM-272. Conclusion: Combined targeting of G9a/DNMT1 with compounds such as CM-272 is a promising strategy for HCC treatment. Our findings also underscore the potential of differentiation therapy in HCC.

Towards the understanding of the activity of G9a inhibitors: an activity landscape and molecular modeling approach.

In this work, we analyze the structure-activity relationships (SAR) of epigenetic inhibitors (lysine mimetics) against lysine methyltransferase (G9a or EHMT2) using a combined activity landscape, molecular docking and molecular dynamics approach. The study was based on a set of 251 G9a inhibitors with reported experimental activity. The activity landscape analysis rapidly led to the identification of activity cliffs, scaffolds hops and other active an inactive molecules with distinct SAR. Structure-based analysis of activity cliffs, scaffold hops and other selected active and inactive G9a inhibitors by means of docking followed by molecular dynamics simulations led to the identification of interactions with key residues involved in activity against G9a, for instance with ASP 1083, LEU 1086, ASP 1088, TYR 1154 and PHE 1158. The outcome of this work is expected to further advance the development of G9a inhibitors.

Information Retrieval and Text Mining Technologies for Chemistry.

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes.

A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions. It integrates a range of text mining, named entity recognition and information extraction components. LimTox relies on machine-learning, rule-based, pattern-based and term lookup strategies. This system processes scientific abstracts, a set of full text articles and medical agency assessment reports. Although the main focus of LimTox is on adverse liver events, it enables also basic searches for other organ level toxicity associations (nephrotoxicity, cardiotoxicity, thyrotoxicity and phospholipidosis). This tool supports specialized search queries for: chemical compounds/drugs, genes (with additional emphasis on key enzymes in drug metabolism, namely P450 cytochromes-CYPs) and biochemical liver markers. The LimTox website is free and open to all users and there is no login requirement. LimTox can be accessed at: http://limtox.bioinfo.cnio.es.

Targeting the anion exchanger 2 with specific peptides as a new therapeutic approach in B lymphoid neoplasms.

Regulatory T (Treg) cells can weaken antitumor immune responses, and inhibition of their function appears to be a promising therapeutic approach in cancer patients. Mice with targeted deletion of the gene encoding the Cl-/HCO3- anion exchanger AE2 (also termed SLC4A2), a membrane-bound carrier involved in intracellular pH regulation, showed a progressive decrease in the number of Treg cells. We therefore challenged AE2 as a potential target for tumor therapy, and generated linear peptides designed to bind the third extracellular loop of AE2, which is crucial for its exchange activity. Peptide p17AE2 exhibited optimal interaction ability and indeed promoted apoptosis in mouse and human Treg cells, while activating effector T-cell function. Interestingly, this linear peptide also induced apoptosis in different types of human leukemia, lymphoma and multiple myeloma cell lines and primary malignant samples, while it showed only moderate effects on normal B lymphocytes. Finally, a macrocyclic AE2 targeting peptide exhibiting increased stability in vivo was effective in mice xenografted with B-cell lymphoma. These data suggest that targeting the anion exchanger AE2 with specific peptides may represent an effective therapeutic approach in B-cell malignancies.

Development and Validation of Molecular Overlays Derived from Three-Dimensional Hydrophobic Similarity with PharmScreen.

Molecular alignment is a standard procedure for three-dimensional (3D) similarity measurements and pharmacophore elucidation. This process is influenced by several factors, such as the physicochemical descriptors utilized to account for the molecular determinants of biological activity and the reference templates. Relying on the hypothesis that the maximal achievable binding affinity for a drug-like molecule is largely due to desolvation, we explore a novel strategy for 3D molecular overlays that exploits the partitioning of molecular hydrophobicity into atomic contributions in conjunction with information about the distribution of hydrogen-bond (HB) donor/acceptor groups. A brief description of the method, as implemented in the software package PharmScreen, including the derivation of the fractional hydrophobic contributions within the quantum mechanical version of the Miertus-Scrocco-Tomasi (MST) continuum model, and the procedure utilized for the optimal superposition between molecules, is presented. The computational procedure is calibrated by using a data set of 402 molecules pertaining to 14 distinct targets taken from the literature and validated against the AstraZeneca test, which comprises 121 experimentally derived sets of molecular overlays. The results point out the suitability of the MST-based hydrophobic parameters for generating molecular overlays, as correct predictions were obtained for 94%, 79%, and 54% of the molecules classified into easy, moderate, and hard sets, respectively. Moreover, the results point out that this accuracy is attained at a much lower degree of identity between the templates used by hydrophobic/HB fields and electrostatic/steric ones. These findings support the usefulness of the hydrophobic/HB descriptors to generate complementary overlays that may be valuable to rationalize structure-activity relationships and for virtual screening campaigns.

Two Affinity Sites of the Cannabinoid Subtype 2 Receptor Identified by a Novel Homogeneous Binding Assay.

Endocannabinoids act on G protein-coupled receptors that are considered potential targets for a variety of diseases. There are two different cannabinoid receptor types: ligands for cannabinoid type 2 receptors (CB2Rs) show more promise than those for cannabinoid type 1 receptors (CB1Rs) because they lack psychotropic actions. However, the complex pharmacology of these receptors, coupled with the lipophilic nature of ligands, is delaying the translational success of medications targeting the endocannabinoid system. We here report the discovery and synthesis of a fluorophore-conjugated CB2R-selective compound, CM-157 (3-[[4-[2-tert-butyl-1-(tetrahydropyran-4-ylmethyl)benzimidazol-5-yl]sulfonyl-2-pyridyl]oxy]propan-1-amine), which was useful for pharmacological characterization of CB2R by using a time-resolved fluorescence resonance energy transfer assay. This methodology does not require radiolabeled compounds and may be undertaken in homogeneous conditions and in living cells (i.e., without the need to isolate receptor-containing membranes). The affinity of the labeled compound was similar to that of the unlabeled molecule. Time-resolved fluorescence resonance energy transfer assays disclosed a previously unreported second affinity site and showed conformational changes in CB2R forming receptor heteromers with G protein-coupled receptor GPR55, a receptor for l-α-lysophosphatidylinositol. The populations displaying subnanomolar and nanomolar affinities were undisclosed in competitive assays using a well known cannabinoid receptor ligand, AM630 (1-[2-(morpholin-4-yl)ethyl]-2-methyl-3-(4-methoxybenzoyl)-6-iodoindole), and TH-chrysenediol, not previously tested on binding to cannabinoid receptors. Variations in binding parameters upon formation of dimers with GPR55 may reflect decreases in binding sites or alterations of the quaternary structure of the macromolecular G protein-coupled receptor complexes. In summary, the homogeneous binding assay described here may serve to better characterize agonist binding to CB2R and to identify specific properties of CB2R on living cells.

Novel Scaffold FingerPrint (SFP): applications in scaffold hopping and scaffold-based selection of diverse compounds.

A novel 2D Scaffold FingerPrint (SFP) for mining ring fragments is presented. The rings are described not only by their topology, shape, and pharmacophoric features (hydrogen-bond acceptors and donors, their relative locations, sp3 carbons, and chirality) but also by the position and nature of their growing vectors because they play a critical role from the drug discovery perspective. SFP can be used (i) to identify alternative chemotypes to a reference ring either in a visual mode or by running quantitative similarity searches and (ii) in chemotype-based diversity selections. Two retrospective case studies focused on melanin concentrating hormone 1-receptor antagonists (MCH-R1) and phosphodiesterase-5 inhibitors (PDE5) demonstrate the capability of this method for identifying novel structurally different and synthetically accessible chemotypes. Good enrichment factor (155 and 219) and recall values (46% and 73%) are found within the first 100 ranked hits (0.3% of screened database). Our 2D SFP descriptor outperforms well-validated current gold-standard 2D fingerprints (ECFP_6) and 3D approaches based on shape and electrostatic similarity. Scaffold-based selection of diverse compounds has a critical impact on corporate library design and compound acquisitions; thus, a novel strategy is introduced that uses diverse scaffold selections using this SFP descriptor combined with R-group selection at the different substitution sites. Both approaches are available as part of an interactive web-based application that requires minimal input and no computational knowledge by medicinal chemists.

Epigenetic-based differentiation therapy for Acute Myeloid Leukemia.

Despite the development of novel therapies for acute myeloid leukemia, outcomes remain poor for most patients, and therapeutic improvements are an urgent unmet need. Although treatment regimens promoting differentiation have succeeded in the treatment of acute promyelocytic leukemia, their role in other acute myeloid leukemia subtypes needs to be explored. Here we identify and characterize two lysine deacetylase inhibitors, CM-444 and CM-1758, exhibiting the capacity to promote myeloid differentiation in all acute myeloid leukemia subtypes at low non-cytotoxic doses, unlike other commercial histone deacetylase inhibitors. Analyzing the acetylome after CM-444 and CM-1758 treatment reveals modulation of non-histone proteins involved in the enhancer-promoter chromatin regulatory complex, including bromodomain proteins. This acetylation is essential for enhancing the expression of key transcription factors directly involved in the differentiation therapy induced by CM-444/CM-1758 in acute myeloid leukemia. In summary, these compounds may represent effective differentiation-based therapeutic agents across acute myeloid leukemia subtypes with a potential mechanism for the treatment of acute myeloid leukemia.

Imidazo[1,2-a]pyrazines as novel PI3K inhibitors.

Phosphoinositide-3-kinase (PI3K) is an important target for cancer therapeutics due to the deregulation of its signaling pathway in a wide variety of human cancers. We describe herein a novel series of imidazo[1,2-a]pyrazines as PI3K inhibitors.

Identification of ETP-46321, a potent and orally bioavailable PI3K α, δ inhibitor.

Phosphoinositide-3-kinase (PI3K) is an important target for cancer therapeutics due to the deregulation of this signaling pathway in a wide variety of human cancers. Herein, we describe the optimization of imidazo [1,2-a] pyrazines, which allow us to identify compound 14 (ETP-46321), with potent biochemical and cellular activity and good pharmacokinetic properties (PK) after oral dosing. ETP-46321 PK/PD studies showed time dependent downregulation of AKT(Ser473) phosphorylation, which correlates with compound levels in tumor tissue and demonstrating to be efficacious in a GEMM mouse tumor model driven by a K-Ras(G12V) oncogenic mutation. Treatment with ETP-46321 resulted in significant tumor growth inhibition.

Rapid identification of ETP-46992, orally bioavailable PI3K inhibitor, selective versus mTOR.

Phosphoinositide-3-kinases (PI3K) are a family of lipid kinases mediating numerous cell processes such as proliferation, migration and differentiation. PI3K is an important target for cancer therapeutics due to the deregulation of this signaling pathway in a wide variety of human cancers. Herein, we describe the rapid identification of ETP-46992, within 2-aminocarbonyl imidazo [1,2-a] pyrazine series, with suitable pharmacokinetic (PK) properties that allows the establishment of mechanism of action and efficacy in vivo studies. ETP-46992 showed tumor growth inhibition in a GEMM mouse tumor model driven by a K-Ras(G12V) oncogenic mutation and in tumor xenograft models with PI3K pathway deregulated (BT474).

Biologically relevant chemical space navigator: from patent and structure-activity relationship analysis to library acquisition and design.

A new and versatile visualization tool, based on a descriptor accounting for ligand-receptor interactions (LiRIf), is introduced for guiding medicinal chemists in analyzing the R-groups from a congeneric series. Analysis is performed in a reference-independent scenario where the whole biologically relevant chemical space (BRCS) is represented. Using a real project-based data set, we show the impact of this tool on four key navigation strategies for the drug discovery process. First, this navigator analyzes competitors' patents, including a comparison of patents coverage and the identification of the most frequent fragments. Second, the tool analyzes the structure-activity relationship (SAR) leading to the representation of reference-independent activity landscapes that enable the identification not only of critical ligand-receptor interactions (LRI) and substructural features but also of activity cliffs. Third, this navigator enables comparison of libraries, thus selecting commercially available molecules that complement unexplored spaces or areas of interest. Finally, this tool also enables the design of new analogues, which is based on reaction types and the exploration purpose (focused or diverse), selecting the most appropriate reagents.

Using novel descriptor accounting for ligand-receptor interactions to define and visually explore biologically relevant chemical space.

The definition and pragmatic implementation of biologically relevant chemical space is critical in addressing navigation strategies in the overlapping regions where chemistry and therapeutically relevant targets reside and, therefore, also key to performing an efficient drug discovery project. Here, we describe the development and implementation of a simple and robust method for representing biologically relevant chemical space as a general reference according to current knowledge, independently of any reference space, and analyzing chemical structures accordingly. Underlying our method is the generation of a novel descriptor (LiRIf) that converts structural information into a one-dimensional string accounting for the plausible ligand-receptor interactions as well as for topological information. Capitalizing on ligand-receptor interactions as a descriptor enables the clustering, profiling, and comparison of libraries of compounds from a chemical biology and medicinal chemistry perspective. In addition, as a case study, R-groups analysis is performed to identify the most populated ligand-receptor interactions according to different target families (GPCR, kinases, etc.), as well as to evaluate the coverage of biologically relevant chemical space by structures annotated in different databases (ChEMBL, Glida, etc.).

Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.

The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.

A toolkit for covalent docking with GOLD: from automated ligand preparation with KNIME to bound protein-ligand complexes.

Motivation: Current covalent docking tools have limitations that make them difficult to use for performing large-scale structure-based covalent virtual screening (VS). They require time-consuming tasks for the preparation of proteins and compounds (standardization, filtering according to the type of warheads), as well as for setting up covalent reactions. We have developed a toolkit to help accelerate drug discovery projects in the phases of hit identification by VS of ultra-large covalent libraries and hit expansion by exploration of the binding of known covalent compounds. With this application note, we offer the community a toolkit for performing automated covalent docking in a fast and efficient way. Results: The toolkit comprises a KNIME workflow for ligand preparation and a Python program to perform the covalent docking of ligands with the GOLD docking engine running in a parallelized fashion. Availability and implementation: The KNIME workflow entitled 'Evotec_Covalent_Processing_forGOLD.knwf' for the preparation of the ligands is available in the KNIME Hub https://hub.knime.com/emilie_pihan/spaces. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Design and Synthesis of Novel Epigenetic Inhibitors Targeting Histone Deacetylases, DNA Methyltransferase 1, and Lysine Methyltransferase G9a with In Vivo Efficacy in Multiple Myeloma.

Concomitant inhibition of key epigenetic pathways involved in silencing tumor suppressor genes has been recognized as a promising strategy for cancer therapy. Herein, we report a first-in-class series of quinoline-based analogues that simultaneously inhibit histone deacetylases (from a low nanomolar range) and DNA methyltransferase-1 (from a mid-nanomolar range, IC50 < 200 nM). Additionally, lysine methyltransferase G9a inhibitory activity is achieved (from a low nanomolar range) by introduction of a key lysine mimic group at the 7-position of the quinoline ring. The corresponding epigenetic functional cellular responses are observed: histone-3 acetylation, DNA hypomethylation, and decreased histone-3 methylation at lysine-9. These chemical probes, multitarget epigenetic inhibitors, were validated against the multiple myeloma cell line MM1.S, demonstrating promising in vitro activity of 12a (CM-444) with GI50 of 32 nM, an adequate therapeutic window (>1 log unit), and a suitable pharmacokinetic profile. In vivo, 12a achieved significant antitumor efficacy in a xenograft mouse model of human multiple myeloma.

Chemical interrogation of FOXO3a nuclear translocation identifies potent and selective inhibitors of phosphoinositide 3-kinases.

Activation of the phosphoinositide 3-kinase (PI3K)/Akt signaling pathway is one the most frequent genetic events in human cancer. A cell-based imaging assay that monitored the translocation of the Akt effector protein, Forkhead box O (FOXO), from the cytoplasm to the nucleus was employed to screen a collection of 33,992 small molecules. The positive compounds were used to screen kinases known to be involved in FOXO translocation. Pyrazolopyrimidine derivatives were found to be potent FOXO relocators as well as biochemical inhibitors of PI3Kalpha. A combination of virtual screening and molecular modeling led to the development of a structure-activity relationship, which indicated the preferred substituents on the pyrazolopyrimidine scaffold. This leads to the synthesis of ETP-45658, which is a potent and selective inhibitor of phosphoinositide 3-kinases and demonstrates mechanism of action in tumor cell lines and in vivo in treated mice.

A novel FTY720 analogue targets SET-PP2A interaction and inhibits growth of acute myeloid leukemia cells without inducing cardiac toxicity.

Acute myeloid leukemia (AML) is an aggressive disease associated with very poor prognosis. Most patients are older than 60 years, and in this group only 5-15% of cases survive over 5 years. Therefore, it is urgent to develop more effective targeted therapies. Inactivation of protein phosphatase 2 A (PP2A) is a recurrent event in AML, and overexpression of its endogenous inhibitor SET is detected in ~30% of patients. The PP2A activating drug FTY720 has potent anti-leukemic effects; nevertheless, FTY720 induces cardiotoxicity at the anti-neoplastic dose. Here, we have developed a series of non-phosphorylable FTY720 analogues as a new therapeutic strategy for AML. Our results show that the lead compound CM-1231 re-activates PP2A by targeting SET-PP2A interaction, inhibiting cell proliferation and promoting apoptosis in AML cell lines and primary patient samples. Notably, CM-1231 did not induce cardiac toxicity, unlike FTY720, in zebrafish models, and reduced the invasion and aggressiveness of AML cells more than FTY720 in zebrafish xenograft models. In conclusion, CM-1231 is safer and more effective than FTY720; therefore, this compound could represent a novel and promising approach for treating AML patients with SET overexpression.