Plate-based diversity subset screening generation 2: an improved paradigm for high-throughput screening of large compound files

High-throughput screening (HTS) is an effective method for lead and probe discovery that is widely used in industry and academia to identify novel chemical matter and to initiate the drug discovery process. However, HTS can be time consuming and costly and the use of subsets as an efficient alternative to screening entire compound collections has been investigated. Subsets may be selected on the basis of chemical diversity, molecular properties, biological activity diversity or biological target focus. Previously, we described a novel form of subset screening: plate-based diversity subset (PBDS) screening, in which the screening subset is constructed by plate selection (rather than individual compound cherry-picking), using algorithms that select for compound quality and chemical diversity on a plate basis. In this paper, we describe a second-generation approach to the construction of an updated subset: PBDS2, using both plate and individual compound selection, that has an improved coverage of the chemical space of the screening file, whilst only selecting the same number of plates for screening. We describe the validation of PBDS2 and its successful use in hit and lead discovery. PBDS2 screening became the default mode of singleton (one compound per well) HTS for lead discovery in Pfizer.     

Nanomaterials in Cerebrovascular Disease Diagnose and Treatment

Cerebrovascular diseases (CVDs) are among the most serious diseases with high mortality and disability rates. The prevalent diagnosis and treatment methods of CVDs include imaging and interventional therapy. With the development of nanotechnology, large numbers of nanomaterials have been applied to the diagnosis and treatment of CVDs, mainly including carbon nanotubes, quantum dots, fullerenes, and dendrimers. In this review, the applications of nanomaterials in the field of diagnosis and treatment of CVDs, mainly including drug target delivery, imaging, therapy, endovascular treatment, and angiogenesis, are summarized. The applications of nanomaterials in the field of CVD are almost in the laboratory, and more effort is needed for clinical translation. The aim of this review is to provide useful information for future research and equipment development.     

Exploring the chemical space of peptides for drug discovery: a focus on linear and cyclic penta-peptides

Peptide and peptide-like structures are regaining attention in drug discovery. Previous studies suggest that bioactive peptides have diverse structures and may have physicochemical properties attractive to become hit and lead compounds. However, chemoinformatic studies that characterize such diversity are limited. Herein, we report the physicochemical property profile and chemical space of four synthetic linear and cyclic combinatorial peptide libraries. As a case study, the analysis was focused on penta-peptides. The chemical space of the peptide and N-methylated peptides libraries was compared to compound data sets of pharmaceutical relevance. Results indicated that there is a major overlap in the chemical space of N-methylated cyclic peptides with inhibitors of protein–protein interactions and macrocyclic natural products available for screening. Also, there is an overlap between the chemical space of the synthetic peptides with peptides approved for clinical use (or in clinical trials), and to other approved drugs that are outside the traditional chemical space. Results further support that synthetic penta-peptides are suitable compounds to be used in drug discovery projects.     

Prediction of compounds’ biological function (metabolic pathways) based on functional group composition

Efficient in silico screening approaches may provide valuable hints on biological functions of the compound-candidates, which could help to screen functional compounds either in basic researches on metabolic pathways or drug discovery. Here, we introduce a machine learning method (Nearest Neighbor Algorithm) based on functional group composition of compounds to the analysis of metabolic pathways. This method can quickly map small chemical molecules to the metabolic pathway that they likely belong to. A set of 2,764 compounds from 11 major classes of metabolic pathways were selected for study. The overall prediction rate reached 73.3%, indicating that functional group composition of compounds was really related to their biological metabolic functions.     

Multidimensional chromatography coupled with mass spectrometry for target-based screening

Summary The synthesis of structural analogs and the process of drug discovery have evolved dramatically through recent advances in solid-phase synthesis reagents and automated screening systems. As molecular diversity strategies emerge, the need for automated target-based selection of lead candidates becomes equally important. Multidimensional automated chromatographic techniques coupled to electrospray ionization mass spectrometry facilitate the selection process and provide maximum characterization information in a single screening run. The capture of tightly bound affinity leads by target biomolecules, followed by subsequent release and high-resolution separation with sensitive detection, significantly reduces the time required to identify and characterize lead compounds. This automated multidimensional chromatographic approach coupled with mass spectrometry, Selectronics, was used with several organic and natural libraries to demonstrate an automated target-based screening technique to select for high-affinity binders as potential lead compounds.Abbreviations ESI electrospray ionization - HPLC high-performance liquid chromatography - HTS high-throughput screening - ESI-TOF electrospray ionization time-of-flight - SAR structure-activity relationship     

Extraction of natural products using supercritical fluids and pressurized liquids assisted by ultrasound: Current status and trends

Natural products are a source of a wide range of chemical compounds, from pigments to bioactive compounds, which can be extracted and used in different applications. Due to consumer awareness, the interest in natural compounds significantly increased in the last decades, prompting the search for more efficient and environmentally friendly extraction techniques and methods. Pressurized liquids and fluids (sub and supercritical) are being explored to extract natural compounds within the green process concept. The combination of these techniques with ultrasound has emerged as an alternative to intensify the extraction process efficiently. In this context, this work presents a comprehensive review and current insights into the use of high-pressure systems, specifically supercritical fluid extraction and pressurized liquid extraction assisted by ultrasound, as emerging technologies for extracting bioactive compounds from natural products. The extraction mechanisms, applications, and the influence of operational parameters in the process are addressed, in addition to an analysis of the main challenges to be overcome for widespread application.     

核磁共振波谱在药物发现中的应用

核磁共振波谱通过检测组成有机化合物分子的原子核跃迁而得到反映核性质的参数以及周围化学环境对这些参数的影响规律. 这些参数的相关内容包含了极其详尽的有机化合物分子结构和分子间相互作用的信息,并构成了核磁共振结构解析和生物靶分子-配体相互作用研究的理论基础. 在生物医药研发领域内,科研院所和公司企业的研发工作者们一直在努力探索利用核磁共振波谱监测生物靶分子-配体相互作用作为药物发现工具的潜能. 本文旨在针对核磁共振波谱在药物发现过程中活性化合物筛选的最新研究进展进行综述.     

Selective MAO-B inhibitors: a lesson from natural products

Monoamine oxidases (MAOs) are mitochondrial bound enzymes, which catalyze the oxidative deamination of monoamine neurotransmitters. Inside the brain, MAOs are present in two isoforms: MAO-A and MAO-B. The activity of MAO-B is generally higher in patients affected by neurodegenerative diseases like Alzheimer’s and Parkinson’s. Therefore, the search for potent and selective MAO-B inhibitors is still a challenge for medicinal chemists. Nature has always been a source of inspiration for the discovery of new lead compounds. Moreover, natural medicine is a major component in all traditional medicine systems. In this review, we present the latest discoveries in the search for selective MAO-B inhibitors from natural sources. For clarity, compounds have been classified on the basis of structural analogy or source: flavonoids, xanthones, tannins, proanthocyanidins, iridoid glucosides, curcumin, alkaloids, cannabinoids, and natural sources extracts. MAO inhibition values reported in the text are not always consistent due to the high variability of MAO sources (bovine, pig, rat brain or liver, and human) and to the heterogeneity of the experimental protocols used.     

Recent synthetic strategies toward the synthesis of spirocyclic compounds comprising six-membered carbocyclic/heterocyclic ring systems

Molecular Diversity - Spirocyclic compounds fascinate the synthetic chemists due to their privileged ring system and efficacy in drug discovery. Many natural compounds comprise spirocyclic moiety...     

NMR在天然产物选择性分离与结构研究中的应用

核磁共振(NMR)是天然产物化学研究的重要手段. 本工作将核磁共振技术作为选择性提取分离的监测手段,依据目标化合物的NMR谱学特征,以¹H NMR监控和指导从植物体中寻找和得到二萜类化合物收到了较好的效果. 并运用多种核磁共振技术对分离得到的3种二萜类化合物(jolkinolide A、jolkinolide B和17-hydro-xyjolkinolide B)进行了结构解析.     

Recent development of two chitinase inhibitors,Argifin and Argadin,produced by soil microorganisms

Chitin, the second most abundant polysaccharide in nature, occurs in fungi, some algae and many invertebrates, including insects. Thus, chitin synthesis and degradation could represent specific targets for fungicides and insecticides. Chitinases hydrolyze chitin into oligomers of N-acetyl-d-glucosamine at key points in the life cycles of organisms, consequently, chitinase inhibitors have become subject of increasing interest. This review covers the development of two chitinase inhibitors of natural origin, Argifin and Argadin, isolated from the cultured broth of microorganisms in our laboratory. In particular, the practical total synthesis of these natural products, the synthesis of lead compounds via computer-aided rational molecular design, and discovery methods that generate only highly-active compounds using a kinetic target(chitinase)-guided synthesis approach (termed in situ click chemistry) are described.     

Interrogating the druggable genome with structural informatics

Summary Structural genomics projects are producing protein structure data at an unprecedented rate. In this paper, we present the Target Informatics Platform (TIP), a novel structural informatics approach for amplifying the rapidly expanding body of experimental protein structure information to enhance the discovery and optimization of small molecule protein modulators on a genomic scale. In TIP, existing experimental structure information is augmented using a homology modeling approach, and binding sites across multiple target families are compared using a clique detection algorithm. We report here a detailed analysis of the structural coverage for the set of druggable human targets, highlighting drug target families where the level of structural knowledge is currently quite high, as well as those areas where structural knowledge is sparse. Furthermore, we demonstrate the utility of TIP's intra- and inter-family binding site similarity analysis using a series of retrospective case studies. Our analysis underscores the utility of a structural informatics infrastructure for extracting drug discovery-relevant information from structural data, aiding researchers in the identification of lead discovery and optimization opportunities as well as potential “off-target” liabilities.     

Pharmacophore-based screening and drug repurposing exemplified on glycogen synthase kinase-3 inhibitors

The current study was conducted to elaborate a novel pharmacophore model to accurately map selective glycogen synthase kinase-3 (GSK-3) inhibitors, and perform virtual screening and drug repurposing. Pharmacophore modeling was developed using PHASE on a data set of 203 maleimides. Two benchmarking validation data sets with focus on selectivity were assembled using ChEMBL and PubChem GSK-3 confirmatory assays. A drug repurposing experiment linking pharmacophore matching with drug information originating from multiple data sources was performed. A five-point pharmacophore model was built consisting of a hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic (H), and two rings (RR). An atom-based 3D quantitative structure–activity relationship (QSAR) model showed good correlative and satisfactory predictive abilities (training set \({R}^{2}= 0.904\); test set: \({Q}^{2}= 0.676\); whole data set: stability \(s = 0.803\)). Virtual screening experiments revealed that selective GSK-3 inhibitors are ranked preferentially by Hypo-1, but fail to retrieve nonselective compounds. The pharmacophore and 3D QSAR models can provide assistance to design novel, potential GSK-3 inhibitors with high potency and selectivity pattern, with potential application for the treatment of GSK-3-driven diseases. A class of purine nucleoside antileukemic drugs was identified as potential inhibitor of GSK-3, suggesting the reassessment of the target range of these drugs.     

Future pathways for combinatorial chemistry

Summary Investment in combinatorial chemistry (combichem) in the pharmaceutical industry is being driven by the need for increased efficiency. Results from pioneers in the field have demonstrated where mixture or discrete compound synthesis is useful, and what mixture sizes and compound concentrations are appropriate. To make the techniques of combichem of general utility in drug discovery, a broad range of advances is still required. Conversion of organic chemistry to solid phase conditions is key, as are developments in linkers and resins. Library design methodology requires further development. Combinatorial biosynthesis of focused libraries of natural products holds great promise for capitalising on hardwon natural product leads. Miniaturisation of screens is required to reduce the cost of screening combinatorial libraries. Developments in the processes preceding and following synthesis are required to enable the flow of increased numbers of compounds without new bottlenecks developing. The impact of combinatorial chemistry will be greatly enhanced by synergy with ongoing parallel developments in genetic technologies, screening technologies and bioinformatics.     

Exploratory research on bioactive natural products with a focus on biological phenomena

The discovery of new basic compounds holds the key for advancing material sciences. We have focused on the identification and characterization of natural key compounds that control biologically and physiologically intriguing phenomena. The discovery of new bioactive molecules, facilitated by a deeper understanding of nature, should advance our knowledge of biological processes and lead to new strategies to treat disease. The structure and function of natural compounds are sometimes unexpectedly original. Based on our past experience and results, we have carried out research to find new directions for compound exploration by directly learning from dynamic biological phenomena in the field, and have succeeded in creating a new research field in biological molecular sciences.     

Quantum Chemical Study on the adsorption of metformin drug on the surface of pristine,Si- and Al-doped (5, 5) SWCNTs

Adsorption properties of metformin (MF) drug onto pristine, Si- and Al-doped (5, 5) armchair single-wall carbon nanotubes (SWCNTs) were studied using density functional theory (DFT) calculations at the B3LYP and ωB97XD methods with the standard 6–311 G** basis set. The most stable geometries of the MF drug molecule onto pristine, Si- and Al-doped (5, 5) CNTs were selected and evaluated in the gaseous and aqueous environments. We calculated the natural bond orbitals (NBO), Frontier molecular orbital (FMO), density of states (DOS) and molecular electrostatic potential (MEP) of systems upon adsorption of MF drug. It was found that the reaction of MF drug with pure SWCNT is physisorption in nature, while high chemisorption can be achieved by using Al- and Si-doped SWCNTs. Despite Al-doped SWCNT provides stronger adsorption, however the change in the energy gap of Si-doped SWCNT is more pronounced. It is predicted that MF drug incorporating Si-doped SWCNT can be extended as drug delivery system.     

Consensus QSAR model for identifying novel H5N1 inhibitors

Due to the importance of neuraminidase in the pathogenesis of influenza virus infection, it has been regarded as the most important drug target for the treatment of influenza. Resistance to currently available drugs and new findings related to structure of the protein requires novel neuraminidase 1 (N1) inhibitors. In this study, a consensus QSAR model with defined applicability domain (AD) was developed using published N1 inhibitors. The consensus model was validated using an external validation set. The model achieved high sensitivity, specificity, and overall accuracy along with low false positive rate (FPR) and false discovery rate (FDR). The performance of model on the external validation set and training set were comparable, thus it was unlikely to be overfitted. The low FPR and low FDR will increase its accuracy in screening large chemical libraries. Screening of ZINC library resulted in 64,772 compounds as probable N1 inhibitors, while 173,674 compounds were defined to be outside the AD of the consensus model. The advantage of the current model is that it was developed using a large and diverse dataset and has a defined AD which prevents its use on compounds that it is not capable of predicting. The consensus model developed in this study is made available via the free software, PaDEL-DDPredictor.