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The nuclear farnesoid X receptor (FXR) and the enzyme soluble epoxide hydrolase (sEH) are validated molecular targets to treat metabolic disorders such as non‐alcoholic steatohepatitis (NASH). Their simultaneous modulation in vivo has demonstrated a triad of anti‐NASH effects and thus may generate synergistic efficacy. Here we report dual FXR activators/sEH inhibitors derived from the anti‐asthma drug Zafirlukast. Systematic structural optimization of the scaffold has produced favorable dual potency on FXR and sEH while depleting the original cysteinyl leukotriene receptor antagonism of the lead drug. The resulting polypharmacological activity profile holds promise in the treatment of liver‐related metabolic diseases.
Diversity-oriented synthesis (DOS) is a rich source for novel lead structures in Medicinal Chemistry. In this study, we present a DOS-compatible method for synthesis of compounds bearing a free thiol moiety. The procedure relies on Rh(II)-catalyzed coupling of dithiols to diazo building blocks. The synthetized library was probed against metallo-β-lactamases (MBLs) NDM-1 and VIM-1. Biochemical and biological evaluation led to identification of novel potent MBL inhibitors with antibiotic adjuvant activity.
Over the past two decades the “one drug – one target – one disease” concept became the prevalent paradigm in drug discovery. The main idea of this approach is the identification of a single protein target whose inhibition leads to a successful treatment of the examined disease. The predominant assumption is that highly selective ligands would avoid unwanted side effects caused by binding to secondary non-therapeutic targets. In recent years the results of post-genomic and network biology showed that proteins rarely act in isolated systems but rather as a part of a highly connected network [1]. In addition this connectivity leads to more robust systems that cannot be interfered by the inhibition of a single target of that network and consequently might not lead to the desired therapeutic effect [2]. Furthermore studies prove that robust systems are rather affected by weak inhibitions of several parts than by a complete inhibition of a single selected element of that system [3]. Therefore there is an increasing interest in developing drugs that take effect on multiple targets simultaneously but is concurrently a great challenge for medicinal chemists. There has to be a sufficient activity on each target as well as an adequate pharmacokinetic profile [4]. Early design strategies tried to link the pharmacophors of known inhibitors, however these methods often lead to high molecular weight and low ligand efficacy. We present a new rational approach based on a retrosynthetic combinatorial analysis procedure [5] on approved ligands of multiple targets. These RECAP fragments are used to design a large combinatorial library containing molecules featuring chemical properties of each ligand class. The molecules are further validated by machine learning models, like random forests and self-organizing maps, regarding their activity on the targets of interest.
Gout is the most common arthritic disease in human but was long neglected and therapeutic options are not satisfying. However, with the recent approval of the urate transporter inhibitor lesinurad, gout treatment has experienced a major innovation. Here we show that lesinurad possesses considerable modulatory potency on peroxisome proliferator-activated receptor γ (PPARγ). Since gout has a strong association with metabolic diseases such as type 2 diabetes, this side-activity appears as very valuable contributing factor to the clinical efficacy profile of lesinurad. Importantly, despite robustly activating PPARγ in vitro, lesinurad lacked adipogenic activity, which seems due to differential coactivator recruitment and is characterized as selective PPARγ modulator (sPPARγM).
The transcription factor Tal1 is a critical activator or repressor of gene expression in hematopoiesis and leukaemia. The mechanism by which Tal1 differentially influences transcription of distinct genes is not fully understood. Here we show that Tal1 interacts with the peptidylarginine deiminase IV (PADI4). We demonstrate that PADI4 can act as an epigenetic coactivator through influencing H3R2me2a. At the Tal1/PADI4 target gene IL6ST the repressive H3R2me2a mark triggered by PRMT6 is counteracted by PADI4, which augments the active H3K4me3 mark and thus increases IL6ST expression. In contrast, at the CTCF promoter PADI4 acts as a repressor. We propose that the influence of PADI4 on IL6ST transcription plays a role in the control of IL6ST expression during lineage differentiation of hematopoietic stem/progenitor cells. These results open the possibility to pharmacologically influence Tal1 in leukaemia.
The prediction of protein–ligand interactions and their corresponding binding free energy is a challenging task in structure-based drug design and related applications. Docking and scoring is broadly used to propose the binding mode and underlying interactions as well as to provide a measure for ligand affinity or differentiate between active and inactive ligands. Various studies have revealed that most docking software packages reliably predict the binding mode, although scoring remains a challenge. Here, a diverse benchmark data set of 99 matched molecular pairs (3D-MMPs) with experimentally determined X-ray structures and corresponding binding affinities is introduced. This data set was used to study the predictive power of 13 commonly used scoring functions to demonstrate the applicability of the 3D-MMP data set as a valuable tool for benchmarking scoring functions.
Die Komplementarität der molekularen Oberflächen und der Pharmakophorpunkte ist ein verbreiteter Konzept im rechnergestützen Moleküldesign. Diesem Konzept folgend wurde die Software SQUIRREL neu entwickelt und in der Programmiersprache Java implemetiert. Die Software generiert die Vorschläge für den bioisosteren Ersatz von Molekülen und Molekülfragmenten. SQUIRREL kombiniert Oberflächen- und Pharmakophoreigenschaften bioaktiver Substanzen und kann im virtuellen Screening und fragment-basierten de novo Design eingesetzt werden. In einer prospektiven Studie wurde SQUIRREL verwendet, um neue selektive PPARalpha-Agonisten aus einer kommerziellen Moleküldatenbank zu identifizieren. Die Software lieferte eine potente Substanz (EC50 = 44 nM) mit über 100facher Selektivität gegenüber PPARgamma. In einer zweiten Studie wurde eine Leitstruktur de novo generiert und synthetisiert. Als Ausgangstruktur diente der bekannte PPARalpha-Agonist GW590735. Während des Designvorgangs wurden zwei Teilstrukturen, die für die Aktivität von GW590735 verantwortlich sind, durch bioisostere Gruppen ersetzt, die von SQUIRRELnovo vorgeschlagen wurden. Die neue Leitstruktur aktiviert PPARalpha in einem zellbasierten Reportergen-Testsystem bei einem EC50 von 0.51 µM.
We developed the Pharmacophore Alignment Search Tool (PhAST), a text-based technique for rapid hit and lead structure searching in large compound databases. For each molecule, a two-dimensional graph of potential pharmacophoric points (PPPs) is created, which has an identical topology as the original molecule with implicit hydrogen atoms. Each vertex is coloured by a symbol representing the corresponding PPP. The vertices of the graph are canonically labelled. The symbols associated with the vertices are combined to a so-called PhAST-Sequence beginning with the vertex with the lowest canonical label. Due to the canonical labelling the created PhAST-Sequence is characteristic for each molecule. For similarity assessment, PhAST-Sequences are compared using the sequence identity in their global pairwise alignment. The alignment score lies between 0 (no similarity) and 1 (identical PhAST-Sequences). In order to use global pairwise sequence alignment, a score matrix for pharmacophoric symbols was developed and gap penalties were optimized. PhAST performed comparably and sometimes superior to other similarity search tools (CATS2D, MOE pharmacophore quadruples) in retrospective virtual screenings using the COBRA collection of drugs and lead structures. Most importantly, the PhAST alignment technique allows for the computation of significance estimates that help prioritize a virtual hit list.
Shape complementarity is a compulsory condition for molecular recognition. In our 3D ligand-based virtual screening approach called SQUIRREL, we combine shape-based rigid body alignment with fuzzy pharmacophore scoring. Retrospective validation studies demonstrate the superiority of methods which combine both shape and pharmacophore information on the family of peroxisome proliferator-activated receptors (PPARs). We demonstrate the real-life applicability of SQUIRREL by a prospective virtual screening study, where a potent PPARalpha agonist with an EC50 of 44 nM and 100-fold selectivity against PPARgamma has been identified...
Two methods for the fast, fragment-based combinatorial molecule assembly were developed. The software COLIBREE® (Combinatorial Library Breeding) generates candidate structures from scratch, based on stochastic optimization [1]. Result structures of a COLIBREE design run are based on a fixed scaffold and variable linkers and side-chains. Linkers representing virtual chemical reactions and side-chain building blocks obtained from pseudo-retrosynthetic dissection of large compound databases are exchanged during optimization. The process of molecule design employs a discrete version of Particle Swarm Optimization (PSO) [2]. Assembled compounds are scored according to their similarity to known reference ligands. Distance to reference molecules is computed in the space of the topological pharmacophore descriptor CATS [3]. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor (PPAR gamma) selective agonists. In a second approach, we developed the formal grammar Reaction-MQL [4] for the in silico representation and application of chemical reactions. Chemical transformation schemes are defined by functional groups participating in known organic reactions. The substructures are specified by the linear Molecular Query Language (MQL) [5]. The developed software package contains a parser for Reaction-MQL-expressions and enables users to design, test and virtually apply chemical reactions. The program has already been used to create combinatorial libraries for virtual screening studies. It was also applied in fragmentation studies with different sets of retrosynthetic reactions and various compound libraries.
There is a renewed interest in pseudoreceptor models which enable computational chemists to bridge the gap of ligand- and receptor-based drug design. We developed a pseudoreceptor model for the histamine H4 receptor (H4R) based on five potent antagonists representing different chemotypes. Here we present the selection of potential ligand binding pockets that occur during molecular dynamics (MD) simulations of a homology-based receptor model. We present a method for prioritizing receptor models according to their match with the consensus ligand-binding mode represented by the pseudoreceptor. In this way, ligand information can be transferred to receptor-based modelling. We use Geometric Hashing to match three-dimensional points in Cartesion space. This allows for the rapid translation- and rotation-free comparison of atom coordinates, which also permits partial matching. The only prerequisite is a hash table, which uses distance triplets as hash keys. Each time a distance triplet occurring in the candidate point set which corresponds to an existing key, the match is represented by a vote of the respective key. Finally, the global match of both point sets can be easily extracted by selection of voted distance triplets. The results revealed a preferred ligand-binding pocket in H4R, which would not have been identified using an unrefined homology model of the protein. The key idea was to rely on ligand information by pseudoreceptor modelling.
Protein kinases are targets for drug development. Dysregulation of kinase activity leads to various diseases, e.g. cancer, inflammation, diabetes. Human polo-like kinase 1 (Plk1), a serine/threonine kinase, is a cancer-relevant gene and a potential drug target which attracts increasing attention in the field of cancer therapy. Plk1 is a key player in mitosis and modulates entry into mitosis and the spindle checkpoint at the meta-/anaphase transition. Plk1 overexpression is observed in various human tumors, and it is a negative prognostic factor for cancer patients. The same catalytical mechanism and the same co-substrate (ATP) lead to the problem of inhibitor selectivity. A strategy to solve this problem is represented by targeting the inactive conformation of kinases. Kinases undergo conformational changes between active and inactive conformation and thus an additional hydrophobic pocket is created in the inactive conformation where the surrounding amino acids are less conserved. A "homology model" of the inactive conformation of Plk1 was constructed, as the crystal structure in its inactive conformation is unknown. A crystal structure of Aurora A kinase served as template structure. With this homology model a receptor-based pharmacophore search was performed using SYBYL7.3 software. The raw hits were filtered using physico-chemical properties. The resulting hits were docked using Gold3.2 software, and 13 candidates for biological testing were manually selected. Three compounds of the 13 tested exhibit anti-proliferative effects in HeLa cancer cells. The most potent inhibitor, SBE13, was further tested in various other cancer cell lines of different origins and displayed EC50 values between 12 microM and 39 microM. Cancer cells incubated with SBE13 showed induction of apoptosis, detected by PARP (Poly-Adenosyl-Ribose-Polymerase) cleavage, caspase 9 activation and DAPI staining of apoptotic nuclei.
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 Protein kinases are important targets for drug development. The almost identical protein folding of kinases and the common co-substrate ATP leads to the problem of inhibitor selectivity. Type II inhibitors, targeting the inactive conformation of kinases, occupy a hydrophobic pocket with less conserved surrounding amino acids. Human polo-like kinase 1 (Plk1) represents a promising target for approaches to identify new therapeutic agents. Plk1 belongs to a family of highly conserved serine/threonine kinases, and is a key player in mitosis, where it modulates the spindle checkpoint at metaphase/anaphase transition. Plk1 is over-expressed in all today analyzed human tumors of different origin and serves as a negative prognostic marker in cancer patients. The newly identified inhibitor, SBE13, a vanillin derivative, targets Plk1 in its inactive conformation. This leads to selectivity within the Plk family and towards Aurora A. This selectivity can be explained by docking studies of SBE13 into the binding pocket of homology models of Plk1, Plk2 and Plk3 in their inactive conformation. SBE13 showed anti-proliferative effects in cancer cell lines of different origins with EC50 values between 5 microM and 39 microM and induced apoptosis. Increasing concentrations of SBE13 result in increasing amounts of cells in G2/M phase 13 hours after double thymidin block of HeLa cells. The kinase activity of Plk1 was inhibited with an IC50 of 200 pM. Taken together, we could show that carefully designed structure-based virtual screening is well-suited to identify selective type II kinase inhibitors targeting Plk1 as potential anti-cancer therapeutics.
Metabolic syndrome (MetS) is a highly prevalent disease cluster worldwide. It requires polypharmacological treatment of the single conditions including type II diabetes, hypertension, and dyslipidemia, as well as the associated comorbidities. The complex treatment regimens with various drugs lead to drug-drug interactions and inadequate patient adherence, resulting in poor management of the disease. Multi-target approaches aim at reducing the polypharmacology and improving the efficacy. This review summarizes the medicinal chemistry efforts to develop multi-target ligands for MetS. Different combinations of pharmacological targets in context of in vivo efficacy and future perspective for multi-target drugs in MetS are discussed.
Designed polypharmacology presents as an attractive strategy to increase therapeutic efficacy in multi-factorial diseases by a directed modulation of multiple involved targets with a single molecule. Such an approach appears particularly suitable in non-alcoholic steatohepatitis (NASH) which involves hepatic steatosis, inflammation and fibrosis as pathological hallmarks. Among various potential pharmacodynamic mechanisms, activation of the farnesoid X receptor (FXRa) and inhibition of leukotriene A4 hydrolase (LTA4Hi) hold promise to counteract NASH according to preclinical and clinical observations. We have developed dual FXR/LTA4H modulators as pharmacological tools, enabling evaluation of this polypharmacology concept to treat NASH and related pathologies. The optimized FXRa/LTA4Hi exhibits well-balanced dual activity on the intended targets with sub-micromolar potency and is highly selective over related nuclear receptors and enzymes rendering it suitable as tool to probe synergies of dual FXR/LTA4H targeting.
Nuclear receptors (NRs) activate transcription of target genes in response to binding of ligands to their ligand-binding domains (LBDs). Typically, in vitro assays use either gene expression or the recruitment of coactivators to the isolated LBD of the NR of interest to measure NR activation. However, this approach ignores that NRs function as homo- as well as heterodimers and that the LBD harbors the main dimerization interface. Cofactor recruitment is thereby interconnected with oligomerization status as well as ligand occupation of the partnering LBD through allosteric cross talk. Here we present a modular set of homogeneous time-resolved FRET–based assays through which we investigated the activation of PPARγ in response to ligands and the formation of heterodimers with its obligatory partner RXRα. We introduced mutations into the RXRα LBD that prevent coactivator binding but do not interfere with LBD dimerization or ligand binding. This enabled us to specifically detect PPARγ coactivator recruitment to PPARγ:RXRα heterodimers. We found that the RXRα agonist SR11237 destabilized the RXRα homodimer but promoted formation of the PPARγ:RXRα heterodimer, while being inactive on PPARγ itself. Of interest, incorporation of PPARγ into the heterodimer resulted in a substantial gain in affinity for coactivator CBP-1, even in the absence of ligands. Consequently, SR11237 indirectly promoted coactivator binding to PPARγ by shifting the oligomerization preference of RXRα toward PPARγ:RXRα heterodimer formation. These results emphasize that investigation of ligand-dependent NR activation should take NR dimerization into account. We envision these assays as the necessary assay tool kit for investigating NRs that partner with RXRα.
Dual- or multi-target ligands have gained increased attention in the past years due to several advantages, including more simple pharmacokinetic and phamarcodynamic properties compared to a combined application of several drugs. Furthermore multi-target ligands often possess improved efficacy. We present a new approach for the discovery of dual-target ligands using aligned pharmacophore models combined with a shape-based scoring. Starting with two sets of known active compounds for each target, a number of different pharmacophore models is generated and subjected to pairwise graph-based alignment using the Kabsch-Algorithm. Since a compound may be able to bind to different targets in different conformations, the algorithm aligns pairs of pharmacophore models sharing the same features which are not necessarily at the exactly same spatial distance. Using the aligned models, a pharmacophore search on a multi-conformation-database is performed to find compounds matching both models. The potentially “dual” ligands are scored by a shape-based comparison with the known active molecules using ShaEP.
Using this approach, we performed a prospective fragment-based virtual screening for dual 5-LO/sEH inhibitors. Both enzymes play an important role in the arachidonic acid cascade and are involved in inflammatory processes, pain, cardiovascular diseases and allergic reactions. Beside several new selective inhibitors we were able to find a compound inhibiting both enzymes in low micromolar concentrations. The results indicate that the idea of aligned pharmacophore models can be successfully employed for the discovery of dual-target ligands.
Several lines of evidence suggest the ligand-sensing transcription factor Nurr1 as a promising target to treat neurodegenerative diseases. Nurr1 modulators to validate and exploit this therapeutic potential are rare, however. To identify novel Nurr1 agonist chemotypes, we have employed the Nurr1 activator amodiaquine as template for microscale analogue library synthesis. The first set of analogues was based on the 7-chloroquiolin-4-amine core fragment of amodiaquine and revealed superior N-substituents compared to diethylaminomethylphenol contained in the template. A second library of analogues was subsequently prepared to replace the chloroquinolineamine scaffold. The two sets of analogues enabled a full scaffold hop from amodiaquine to a novel Nurr1 agonist sharing no structural features with the lead but comprising superior potency on Nurr1. Additionally, pharmacophore modeling based on the entire set of active and inactive analogues suggested key features for Nurr1 agonists.
Endocannabinoids are important lipid-signaling mediators. Both protective and deleterious effects of endocannabinoids in the cardiovascular system have been reported but the mechanistic basis for these contradicting observations is unclear. We set out to identify anti-inflammatory mechanisms of endocannabinoids in the murine aorta and in human vascular smooth muscle cells (hVSMC). In response to combined stimulation with cytokines, IL-1β and TNFα, the murine aorta released several endocannabinoids, with anandamide (AEA) levels being the most significantly increased. AEA pretreatment had profound effects on cytokine-induced gene expression in hVSMC and murine aorta. As revealed by RNA-Seq analysis, the induction of a subset of 21 inflammatory target genes, including the important cytokine CCL2 was blocked by AEA. This effect was not mediated through AEA-dependent interference of the AP-1 or NF-κB pathways but rather through an epigenetic mechanism. In the presence of AEA, ATAC-Seq analysis and chromatin-immunoprecipitations revealed that CCL2 induction was blocked due to increased levels of H3K27me3 and a decrease of H3K27ac leading to compacted chromatin structure in the CCL2 promoter. These effects were mediated by recruitment of HDAC4 and the nuclear corepressor NCoR1 to the CCL2 promoter. This study therefore establishes a novel anti-inflammatory mechanism for the endogenous endocannabinoid AEA in vascular smooth muscle cells. Furthermore, this work provides a link between endogenous endocannabinoid signaling and epigenetic regulation.
The arachidonic acid cascade is a key player in inflammation, and numerous well-established drugs interfere with this pathway. Previous studies have suggested that simultaneous inhibition of 5-lipoxygenase (5-LO) and soluble epoxide hydrolase (sEH) results in synergistic anti-inflammatory effects. In this study, a novel prototype of a dual 5-LO/sEH inhibitor KM55 was rationally designed and synthesized. KM55 was evaluated in enzyme activity assays with recombinant enzymes. Furthermore, activity of KM55 in human whole blood and endothelial cells was investigated. KM55 potently inhibited both enzymes in vitro and attenuated the formation of leukotrienes in human whole blood. KM55 was also tested in a cell function-based assay. The compound significantly inhibited the LPS-induced adhesion of leukocytes to endothelial cells by blocking leukocyte activation.