Discovery of novel α7 nicotinic acetylcholine receptor ligands via pharmacophoric and docking studies of benzylidene anabaseine analogs

Based on pharmacophore elucidation and docking studies on interactions of benzylidene anabaseine analogs with AChBPs and α7 nAChR, novel spirodiazepine and spiroimidazoline quinuclidine series have been designed. Binding studies revealed that some of hydrogen-bond donor containing compounds exhibit improved affinity and selectivity for the α7 nAChR subtype in comparison with most potent metabolite of GTS-21, 3-(4-hydroxy-2-methoxybenzylidene)-anabaseine. Hydrophobicity and rigidity of the ligand also contribute into its binding affinity. We also describe alternative pharmacophoric features equidistant from the carbonyl oxygen atom of the conserved Trp-148 of the principal face, which may be exploited to further design diverse focused libraries targeting the α7 nAChR.     

Elucidation of common pharmacophores from analysis of targeted metabolites transported by the multispecific drug transporter-Organic anion transporter1 (Oat1)

Organic anion transporter 1 (Oat1), first identified as NKT, is a multispecific transporter responsible for the handling of drugs and toxins in the kidney and choroid plexus, but its normal physiological role appears to be in small molecule metabolite regulation. Metabolites transported by Oat1 and which are altered in the blood and urine of the murine Oat1 knockout, may serve as templates for further drug design. This may lead to better tissue targeting of drugs or design of Oat1 inhibitors that prolong the half-life of current drugs. Due to the multispecificity of the transporter, 19 of known targeted metabolites have different chemical structures and properties that make constructing a common pharmacophore model difficult. Here we propose an approach that clustered the metabolites into four distinct groups which allowed for the construction of a consensus pharmacophore for each cluster. The screening of commercial molecular databases determined the top candidates whose interaction with Oat1 was confirmed in an experimental model of organic anion transport. Thus, these candidate selections represent potential molecules for further drug design.     

Network-Based Relating Pharmacological and Genomic Spaces for Drug Target Identification

Background

Identifying drug targets is a critical step in pharmacology. Drug phenotypic and chemical indexes are two important indicators in this field. However, in previous studies, the indexes were always isolated and the candidate proteins were often limited to a small subset of the human genome.

Methodology/Principal Findings

Based on the correlations observed in pharmacological and genomic spaces, we develop a computational framework, drugCIPHER, to infer drug-target interactions in a genome-wide scale. Three linear regression models are proposed, which respectively relate drug therapeutic similarity, chemical similarity and their combination to the relevance of the targets on the basis of a protein-protein interaction network. Typically, the model integrating both drug therapeutic similarity and chemical similarity, drugCIPHER-MS, achieved an area under the Receiver Operating Characteristic (ROC) curve of 0.988 in the training set and 0.935 in the test set. Based on drugCIPHER-MS, a genome-wide map of drug biological fingerprints for 726 drugs is constructed, within which unexpected drug-drug relations emerged in 501 cases, implying possible novel applications or side effects.

Conclusions/Significance

Our findings demonstrate that the integration of phenotypic and chemical indexes in pharmacological space and protein-protein interactions in genomic space can not only speed the genome-wide identification of drug targets but also find new applications for the existing drugs.     

Theoretical studies of Alzheimer's disease drug candidate 3-[(2,4-dimethoxy)benzylidene]-anabaseine (GTS-21) and its derivatives

Theoretical and molecular modeling studies have been conducted for understanding the details of how 3-[(2,4-dimethoxy)benzylidene]-anabaseine dihydrochloride (GTS-21) and its metabolism derivatives bind with the receptor of alpha7 nicotinic acetylcholine dimer. Good accordance with experimental results has been achieved. It was found that the van der Waals repulsion makes the dominant contribution to the binding energy. GTS-21 and its metabolites are apparently too large for the binding sites of the alpha7 dimer. To improve the effectiveness of the drug, a possible approach is to reduce its volume while maintaining the presence of the active groups. Our studies, in combination with experimental studies, will lead to a promising basis for practical drug design against Alzheimer's disease.     

Synthetic approaches to the 2009 new drugs

New drugs are introduced to the market every year and each individual drug represents a privileged structure for its biological target. These new chemical entities (NCEs) provide insights into molecular recognition and also serve as leads for designing future new drugs. This review covers the syntheses of 21 NCEs marketed in 2009.     

动物毒素及相关药物研究进展

动物毒素是指有毒动物毒器分泌的毒液中,结构和功能丰富多样的蛋白质和多肽,其具有高活力、高结构多样性、高专一性等特点, 是药物开发的重要资源。综述了动物毒素药物的特点以及进入临床药物和候选药物分子的研究进展,同时分析了我国动物毒素类药物研发 中需要重视的问题。     

Natural toxins and their therapeutic potential

Plants have been extensively investigated for exploring their therapeutic potentials, but there are comparatively scanty reports on drugs derived from animal kingdom, except for hormones. During last decade, the toxins that are used for defense by the animals, have been isolated and found useful tools for physiological and pharmacological studies, besides giving valuable leads to drug development. Toxins with interesting results have been isolated from the venoms of snakes, scorpions, spiders, snails, lizards, frogs and fish. The present review describe about some toxins as drugs and their biological activities. Some fungal, bacterial and marine toxins have also been covered in this article.     

Identification of intracellular targets of small molecular weight chemical compounds using affinity chromatography

Efforts to characterize small molecular weight chemical inhibitors of pharmacological interest tend to identify molecules with high efficiency and selectivity, to meet the two criteria required for the clinical development of a drug: efficacy and harmlessness. Drug candidates are expected to inhibit efficiently the target they have been optimized against (for example, a particular type of protein kinase). These hits are also designed to not interfere (or as little as possible) with the activity of other cellular enzymes/proteins to reduce undesired side effects. Here we discuss the use of immobilized drugs as affinity chromatography matrices to purify and identify their bona fide intracellular targets. This method not only allows the systematic investigation of the selectivity of pharmacological compounds but also the anticipation of their putative adverse effects.     

基于蛋白质受体的药物分子计算机辅助设计策略

药物分子计算机辅助设计是一种在计算机或者理论上通过构建具有一定潜在药理活性的新化学实体的分子模拟方法。近十几年来,高通量组学技术的快速发展为生物和化学药物分子设计提供了良好的数据支撑和研究契机。另外,现代社会对生物制药合理性以及作用机理理解的要求越来越高,行业普遍要求药物需要有高效、无毒或者低毒以及靶向性强等特点。随着越来越多与药物靶点相关的蛋白质结构通过实验方法解析出来,基于蛋白质受体的药物分子设计方法可行性进一步提高,其方法也变得越来越重要。基于蛋白质受体的药物分子设计方法,一般是以蛋白质以及配体的三维结构出发进行分析,这让药物分子先导物的发现更加理性化。随着相关实验数据的积累以及深度学习等算法的发展,从而可以进行更加科学的药物分子设计,这在一定程度上加快了新药研发的进程,并更有利于探索相应的分子机理。本文对基于蛋白质受体的药物分子设计方法的常用策略进行系统的回顾、总结和展望。     

The α7 Nicotinic Acetylcholine Receptor Agonist GTS-21 Improves Bacterial Clearance in Mice by Restoring Hyperoxia-Compromised Macrophage Function

Mechanical ventilation with supraphysiological concentrations of oxygen (hyperoxia) is routinely used to treat patients with respiratory distress. However, prolonged exposure to hyperoxia compromises the ability of the macrophage to phagocytose and clear bacteria. Previously, we showed that the exposure of mice to hyperoxia elicits the release of the nuclear protein high mobility group box-1 (HMGB1) into the airways. Extracellular HMGB1 impairs macrophage phagocytosis and increases the mortality of mice infected with Pseudomonas aeruginosa (PA). The aim of this study was to determine whether GTS-21 [3-(2,4 dimethoxybenzylidene)-anabaseine dihydrochloride], an α7 nicotinic acetylcholine receptor (α7nAChR) agonist, could inhibit hyperoxia-induced HMGB1 release into the airways, enhance macrophage function and improve bacterial clearance from the lungs in a mouse model of ventilator-associated pneumonia. GTS-21 (0.04, 0.4 and 4 mg/kg) or saline was systemically administered via intraperitoneal injection to mice that were exposed to hyperoxia (≥99% O₂) and subsequently challenged with PA. We found that systemic administration of 4 mg/kg GTS-21 significantly increased bacterial clearance, decreased acute lung injury and decreased accumulation of airway HMGB1. To investigate the cellular mechanism of these observations, RAW 264.7 cells, a macrophagelike cell line, were incubated with different concentrations of GTS-21 in the presence of 95% O₂. The phagocytic activity of macrophages was significantly increased by GTS-21 in a dose-dependent manner. In addition, hyperoxia-induced hyperacetylation of HMGB1 was significantly reduced in macrophages incubated with GTS-21. Furthermore, GTS-21 significantly inhibited the cytoplasmic translocation and release of HMGB1 from these macrophages. Our results indicate that GTS-21 is effective in improving bacterial clearance and reducing acute lung injury by enhancing macrophage function via inhibiting the release of nuclear HMGB1. Therefore, the α7nAChR represents a possible pharmacological target to improve the clinical outcome of patients on ventilators by augmenting host defense against bacterial infections.     

Molecular pharmacology in a simple model system: implicating MAP kinase and phosphoinositide signalling in bipolar disorder

Understanding the mechanisms of drug action has been the primary focus for pharmacological researchers, traditionally using rodent models. However, non-sentient model systems are now increasingly being used as an alternative approach to better understand drug action or targets. One of these model systems, the social amoeba Dictyostelium, enables the rapid ablation or over-expression of genes, and the subsequent use of isogenic cell culture for the analysis of cell signalling pathways in pharmacological research. The model also supports an increasingly important ethical view of research, involving the reduction, replacement and refinement of animals in biomedical research. This review outlines the use of Dictyostelium in understanding the pharmacological action of two commonly used bipolar disorder treatments (valproic acid and lithium). Both of these compounds regulate mitogen activated protein (MAP) kinase and inositol phospholipid-based signalling by unknown means. Analysis of the molecular pathways targeted by these drugs in Dictyostelium and translation of discoveries to animal systems has helped to further understand the molecular mechanisms of these bipolar disorder treatments.     

Characterization of a new Kv1.3 channel-specific blocker, J123, from the scorpion Buthus martensii Karsch

The potassium channel Kv1.3 is an attractive pharmacological target for T-cell-mediated autoimmune diseases, and specific and selective peptidic blockers of Kv1.3 channels have served as valuable therapeutic leads for treating these diseases. Here, we found a new peptide toxin, J123, with 43 amino acids including six cysteine residues by screening the venomous cDNA library of scorpion Buthus martensii Karsch, which has been used as traditional medicine in China for more than 2000 years. The sequence analysis suggested that peptide J123 constituted a new member of the alpha-KTx toxins. The electrophysiological experiments further indicated that peptide J123 has a novel pharmacological profiles: it blocked Kv1.3 channel with high potency (IC(50)=0.79nM), and exhibited good selectivity on Kv1.3 over Kv1.1 (>1000-fold) and Kv1.2 (about 30-fold), respectively. Furthermore, peptide J123 had no activity on SKCa2 and SKCa3 channels at micromolar concentration level. Based on the pharmacological activities, the possible channel-interacting surface of peptide J123 was also predicted by molecular modeling and docking. All these data not only enrich the knowledge of the structure-function relationship of the new Kv1.3-speicific peptide but also present a potential drug candidate for selectively targeting Kv1.3 channels.     

Global mapping of pharmacological space

We present the global mapping of pharmacological space by the integration of several vast sources of medicinal chemistry structure-activity relationships (SAR) data. Our comprehensive mapping of pharmacological space enables us to identify confidently the human targets for which chemical tools and drugs have been discovered to date. The integration of SAR data from diverse sources by unique canonical chemical structure, protein sequence and disease indication enables the construction of a ligand-target matrix to explore the global relationships between chemical structure and biological targets. Using the data matrix, we are able to catalog the links between proteins in chemical space as a polypharmacology interaction network. We demonstrate that probabilistic models can be used to predict pharmacology from a large knowledge base. The relationships between proteins, chemical structures and drug-like properties provide a framework for developing a probabilistic approach to drug discovery that can be exploited to increase research productivity.     

动物离子通道毒素与药物开发

就离子通道和动物离子通道毒素的来源、种类、特性及其对新药开发的意义进行了综述。各种来源的动物毒素通常分子量较小,富含二硫键,是直接作用于分子靶标（如离子通道、受体及酶）的小分子蛋白质。很多动物毒素对电压门控离子通道具有高度的专一性和有效性,具有独特、简洁的三维空间结构。其对新药的发现、设计以及寻找潜在的治疗靶标具有重要的指导意义。     

Developing hybrid molecule therapeutics for diverse enzyme inhibitory action: Active role of coumarin-based structural leads in drug discovery

Hybrid drugs featuring two or more potentially bioactive pharmacophores have been recognized as advanced and superior chemical entities to simultaneously modulate multiple drug targets of multifactorial diseases, thus overcoming the severe side effects associated with a single drug molecule. The selection of these chemical moieties to produce hybrid structures with druggable properties is generally facilitated by the observed and/or anticipated synergistic pharmacological activities of the individual molecules. In this perspective, coumarin template has extensively been studied in pursuit of structurally diverse leads for drug development due to high affinity and specificity to different molecular targets. This review highlights the most commonly exploited approaches conceptualizing the design and construction of hybrid molecules by coupling two or more individual fragments with or without an appropriate linker. In addition to the design strategies, this review also summarizes and reflects on the therapeutic potential of these hybrid molecules for diverse enzyme inhibitory action as well as their observed structure-activity relationship (SAR). Several key features of the synthesized hybrid structures that assert a profound impact on the inhibitory function have also been discussed alongside computational investigations, inhibitor molecular diversity and selectivity toward multiple drug targets. Finally, these drug discovery and development efforts should serve as a handy reference aiming to provide a useful platform for the exploration of new coumarin-based compounds with enhanced enzyme inhibitory profile.     

A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.     

A free energy based computational pathway from chemical templates to lead compounds: a case study of COX-2 inhibitors

Automation of lead compound design in silico given the structure of the protein target and a definition of its active site vies for the top of the wish list in any drug discovery programme. We present here an enumeration of steps starting from chemical templates and propose a solution at the state of the art, in the form of a system independent comprehensive computational pathway. This methodology is illustrated with cyclooxygenase-2 (COX-2) as a target. We built candidate molecules including a few Non Steroidal Anti-inflammatory Drugs (NSAIDs) from chemical templates, passed them through empirical filters to assess drug-like properties, optimized their geometries, derived partial atomic charges via quantum calculations, performed Monte Carlo docking, carried out molecular mechanics and developed free energy estimates with Molecular Mechanics Generalized Born Solvent Accessibility (MMGBSA) methodology for each of the candidate molecules. For the case of aspirin, we also conducted molecular dynamics on the enzyme, the drug and the complex with explicit solvent followed by binding free energy analysis. Collectively, the results obtained from the above studies viz. sorting of drugs from non-drugs, semi-quantitative estimates of binding free energies, amply demonstrate the viability of the strategy proposed for lead selection/design for biomolecular targets.     

Calmodulin pharmacology

Calmodulin (CaM) is a major intracellular receptor for Ca²⁺. CaM is thus a crucial receptor to consider in pharmacological modification of cellular activity. Potential mechanisms by which drugs may modify CaM effectiveness are considered in the context of its interaction with Ca²⁺ and in turn with its various effectors. Some examples of established drug mechanisms are considered. A wide range of chemical compounds representing diverse pharmacological classes are anti-CaM under some conditions. No simple relationships have been established between molecular level events and therapeutic applicability of anti-CaM compounds.     

Improving Detection of Arrhythmia Drug-Drug Interactions in Pharmacovigilance Data through the Implementation of Similarity-Based Modeling

Identification of Drug-Drug Interactions (DDIs) is a significant challenge during drug development and clinical practice. DDIs are responsible for many adverse drug effects (ADEs), decreasing patient quality of life and causing higher care expenses. DDIs are not systematically evaluated in pre-clinical or clinical trials and so the FDA U. S. Food and Drug Administration relies on post-marketing surveillance to monitor patient safety. However, existing pharmacovigilance algorithms show poor performance for detecting DDIs exhibiting prohibitively high false positive rates. Alternatively, methods based on chemical structure and pharmacological similarity have shown promise in adverse drug event detection. We hypothesize that the use of chemical biology data in a post hoc analysis of pharmacovigilance results will significantly improve the detection of dangerous interactions. Our model integrates a reference standard of DDIs known to cause arrhythmias with drug similarity data. To compare similarity between drugs we used chemical structure (both 2D and 3D molecular structure), adverse drug side effects, chemogenomic targets, drug indication classes, and known drug-drug interactions. We evaluated the method on external reference standards. Our results showed an enhancement of sensitivity, specificity and precision in different top positions with the use of similarity measures to rank the candidates extracted from pharmacovigilance data. For the top 100 DDI candidates, similarity-based modeling yielded close to twofold precision enhancement compared to the proportional reporting ratio (PRR). Moreover, the method helps in the DDI decision making through the identification of the DDI in the reference standard that generated the candidate.