Chemistry and Biology of the Caged Garcinia Xanthones

Natural products have been a great source of many small molecule drugs for various diseases. In spite of recent advances in biochemical engineering and fermentation technologies that allow us to explore microorganisms and the marine environment as alternative sources of drugs, more than 70 % of the current small molecule therapeutics derive their structures from plants used in traditional medicine. Natural‐product‐based drug discovery relies heavily on advances made in the sciences of biology and chemistry. Whereas biology aims to investigate the mode of action of a natural product, chemistry aims to overcome challenges related to its supply, bioactivity, and target selectivity. This review summarizes the explorations of the caged Garcinia xanthones, a family of plant metabolites that possess a unique chemical structure, potent bioactivities, and a promising pharmacology for drug design and development.     

Library design practices for success in lead generation with small molecule libraries

The generation of novel structures amenable to rapid and efficient lead optimization comprises an emerging strategy for success in modern drug discovery. Small molecule libraries of sufficient size and diversity to increase the chances of discovery of novel structures make the high throughput synthesis approach the method of choice for lead generation. Despite an industry trend for smaller, more focused libraries, the need to generate novel lead structures makes larger libraries a necessary strategy. For libraries of a several thousand or more members, solid phase synthesis approaches are the most suitable. While the technology and chemistry necessary for small molecule library synthesis continue to advance, success in lead generation requires rigorous consideration in the library design process to ensure the synthesis of molecules possessing the proper characteristics for subsequent lead optimization. Without proper selection of library templates and building blocks, solid phase synthesis methods often generate molecules which are too heavy, too lipophilic and too complex to be useful for lead optimization. The appropriate filtering of virtual library designs with multiple computational tools allows the generation of information-rich libraries within a drug-like molecular property space. An understanding of the hit-to-lead process provides a practical guide to molecular design characteristics. Examples of leads generated from library approaches also provide a benchmarking of successes as well as aspects for continued development of library design practices.     

Deuterium‐ and Tritium‐Labelled Compounds: Applications in the Life Sciences

Hydrogen isotopes are unique tools for identifying and understanding biological and chemical processes. Hydrogen isotope labelling allows for the traceless and direct incorporation of an additional mass or radioactive tag into an organic molecule with almost no changes in its chemical structure, physical properties, or biological activity. Using deuterium‐labelled isotopologues to study the unique mass‐spectrometric patterns generated from mixtures of biologically relevant molecules drastically simplifies analysis. Such methods are now providing unprecedented levels of insight in a wide and continuously growing range of applications in the life sciences and beyond. Tritium (³H), in particular, has seen an increase in utilization, especially in pharmaceutical drug discovery. The efforts and costs associated with the synthesis of labelled compounds are more than compensated for by the enhanced molecular sensitivity during analysis and the high reliability of the data obtained. In this Review, advances in the application of hydrogen isotopes in the life sciences are described.     

Biosensor-based small molecule fragment screening with biolayer interferometry

Biosensor-based fragment screening is a valuable tool in the drug discovery process. This method is advantageous over many biochemical methods because primary hits can be distinguished from non-specific or non-ideal interactions by examining binding profiles and responses, resulting in reduced false-positive rates. Biolayer interferometry (BLI), a technique that measures changes in an interference pattern generated from visible light reflected from an optical layer and a biolayer containing proteins of interest, is a relatively new method for monitoring small molecule interactions. The BLI format is based on a disposable sensor that is immersed in 96-well or 384-well plates. BLI has been validated for small molecule detection and fragment screening with model systems and well-characterized targets where affinity constants and binding profiles are generally similar to those obtained with surface plasmon resonsance (SPR). Screens with challenging targets involved in protein–protein interactions including BCL-2, JNK1, and eIF4E were performed with a fragment library of 6,500 compounds, and hit rates were compared for these targets. For eIF4E, a protein containing a PPI site and a nucleotide binding site, results from a BLI fragment screen were compared to results obtained in biochemical HTS screens. Overlapping hits were observed for the PPI site, and hits unique to the BLI screen were identified. Hit assessments with SPR and BLI are described.     

瓜环类超分子药物载体的研究进展

瓜环具有“内疏水、外亲水”的特殊分子结构,可以多种有机物发生主客体相互作用而形成自组装分子胶囊或超分子实体,使其在环境、医药、化学分离、材料的改性等方面都有着广泛的应用前景。本文结合自己的工作,参考国内外文献综述了瓜环在药物载体及药物传输方面的研究进展,并就瓜环在药物领域的发展作一展望。     

An αv‐RGD Integrin Inhibitor Toolbox: Drug Discovery Insight,Challenges and Opportunities

There is a requirement for efficacious and safe medicines to treat diseases with high unmet need. The resurgence in αv‐RGD integrin inhibitor drug discovery is poised to contribute to this requirement. However, drug discovery in the αv integrin space is notoriously difficult due to the receptors being structurally very similar as well as the polar zwitterionic nature of the pharmacophore. This Review aims to guide drug discovery research in this field through an αv inhibitor toolbox, consisting of small molecules and antibodies. Small‐molecule αv tool compounds with extended profiles in αvβ1, 3, 5, 6 and 8 cell adhesion assays, with key physicochemical properties, have been collated to assist in the selection of the right tool for the right experiment. This should also facilitate an understanding of partial selectivity profiles of compounds generated in different assays across research institutions. Prospects for further αv integrin research and the critical importance of target validation are discussed, where increased knowledge of the selectivity for individual RGD αv integrins is key. Insights into the design of small‐molecule RGD chemotypes for topical or oral administration are provided and clinical findings on advanced molecules are examined.     

动态组合化学最新进展

动态组合化学是组合化学的一个新兴分支,在药物先导化合物的发现中有广阔的应用前景。在动态组合化学库中,利用靶标分子的诱导结合作用,通过可逆共价反应,能够选择性的筛选到与靶标分子存在强相互作用的优势化合物。本文按照动态组合化学方法简介、动态组合化学中的可逆共价化学、动态组合化学库的分类、动态组合化学库筛选方法的研究进展及动态组合化学在药物先导化合物发现过程中的应用等5个方面对动态组合化学进行了概述。     

Subtractive Genomics Approach to Identify Putative Drug Targets and Identification of Drug-like Molecules for Beta Subunit of DNA Polymerase III in Streptococcus Species

The prolonged use of the antibiotics over the years has transformed many organisms resistant to multiple drugs. This has made the field of drug discovery of vital importance in curing various infections and diseases. The drugs act by binding to a specific target protein of prime importance for the cell??s survival. Streptococcus agalactiae, Streptococcus pneumoniae, and Streptococcus pyogenes are the few gram positive organisms that have developed resistance to drugs. It causes pneumonia, meningitis, pharyngitis, otitis media, sinusitis, bacteremia, pericarditis, and arthritis infections. The present study was carried out to identify potential drug targets and inhibitors for beta subunit of DNA polymerase III in these three Streptococcus species that might facilitate the discovery of novel drugs in near future. Various steps were adopted to find out novel drug targets. And finally 3D structure of DNA polymerase III subunit beta was modeled. The ligand library was generated from various databases to find the most suitable ligands. All the ligands were docked using Molegro Virtual Docker and the lead molecules were investigated for ADME and toxicity.     

High-throughput screening for bioactive components from traditional Chinese medicine

Throughout the centuries, traditional Chinese medicine has been a rich resource in the development of new drugs. Modern drug discovery, which relies increasingly on automated high throughput screening and quick hit-to-lead development, however, is confronted with the challenges of the chemical complexity associated with natural products. New technologies for biological screening as well as library building are in great demand in order to meet the requirements. Here we review the developments in these techniques under the perspective of their applicability in natural product drug discovery. Methods in library building, component characterizing, biological evaluation, and other screening methods including NMR and X-ray diffraction are discussed.     

Disease-specific induced pluripotent stem cells: a platform for human disease modeling and drug discovery

The generation of disease-specific induced pluripotent stem cell (iPSC) lines from patients with incurable diseases is a promising approach for studying disease mechanisms and drug screening. Such innovation enables to obtain autologous cell sources in regenerative medicine. Herein, we report the generation and characterization of iPSCs from fibroblasts of patients with sporadic or familial diseases, including Parkinson's disease (PD), Alzheimer's disease (AD), juvenile-onset, type I diabetes mellitus (JDM), and Duchenne type muscular dystrophy (DMD), as well as from normal human fibroblasts (WT). As an example to modeling disease using disease-specific iPSCs, we also discuss the previously established childhood cerebral adrenoleukodystrophy (CCALD)- and adrenomyeloneuropathy (AMN)-iPSCs by our group. Through DNA fingerprinting analysis, the origins of generated disease-specific iPSC lines were identified. Each iPSC line exhibited an intense alkaline phosphatase activity, expression of pluripotent markers, and the potential to differentiate into all three embryonic germ layers: the ectoderm, endoderm, and mesoderm. Expression of endogenous pluripotent markers and downregulation of retrovirus-delivered transgenes [OCT4 (POU5F1), SOX2, KLF4, and c-MYC] were observed in the generated iPSCs. Collectively, our results demonstrated that disease-specific iPSC lines characteristically resembled hESC lines. Furthermore, we were able to differentiate PD-iPSCs, one of the disease-specific-iPSC lines we generated, into dopaminergic (DA) neurons, the cell type mostly affected by PD. These PD-specific DA neurons along with other examples of cell models derived from disease-specific iPSCs would provide a powerful platform for examining the pathophysiology of relevant diseases at the cellular and molecular levels and for developing new drugs and therapeutic regimens.     

MEMES: Machine learning framework for Enhanced MolEcular Screening

In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as “hits”. In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.

A novel machine learning framework based on Bayesian optimization for efficient sampling of chemical space. The framework is able to identify 90% of top-1000 hits by only sampling 6% of the complete dataset containing ∼100 million compounds.     

八元瓜环与抗癌药物甲氨蝶呤的超分子相互作用

利用紫外吸收光谱法考察了八元瓜环(Q[8])与抗癌药物甲氨蝶呤(MTX)的相互作用;同时研究了甲氨蝶呤的不同质子化形式及体系温度对相互作用的影响;计算了相应的稳定常数及热力学参数等.实验结果表明,Q[8]与MTX作用比为1∶1,包结平衡常数为(7.64±1.52)×104 L/mol;龄前热力学参数测定结果表明,上述主...     

Adsorption-Desorption Properties of Bisphosphonates

Abstract

Aminobisphosphonates have generated substantial interest recently for the treatment of bone diseases and as plant growth regulators, both alone and as a carrier for other drugs, because of the high affinity to the hydroxyapatite (HAP). We are investigating the drug delivery action of bisphosphonates for the treatment of osteoporosis and other bone diseases. Current studies include analysis of the adsorption-desorption processes of bisphosphonate drug carriers to bone mineral, as well as the design and synthesis of new bisphosphonate derivatives. The HAP adsorption data of aminomethylenebis-phosphonate (M P) (1). alendronate (2), and the prostaglandin derivative of bisphosphonate (PGE,-BP) (3) is shown on the graph.     

PASS-based prediction of metabolites detection in biological systems

ABSTRACT

Metabolite identification is an essential part of the drug discovery and development process. Experimental methods allow identifying metabolites and estimating their relative amount, but they require cost-intensive and time-consuming techniques. Computational methods for metabolite prediction are devoid of these shortcomings and may be applied at the early stage of drug discovery. In this study, we investigated the possibility of creating SAR models for the prediction of the qualitative metabolite yield (‘major’, ‘minor’, ”trace” and ”negligible”) depending on species and biological experimental systems. In addition, we have created models for prediction of xenobiotic excretion depending on its administration route for different species. The prediction is based on an algorithm of naïve Bayes classifier implemented in PASS software. The average accuracy of prediction was 0.91 for qualitative metabolite yield prediction and 0.89 for prediction of xenobiotic excretion. The created models were included as a component of MetaTox web application, which allows predicting the xenobiotic metabolism pathways (http://www.way2drug.com/mg).     

Toward designing drug-like libraries: a novel computational approach for prediction of drug feasibility of compounds

Prediction of the degree of drug-like character in small molecules is of great industrial interest. The major barrier, however, is the lack of a definition for drug-like character. We used the concept of the multilevel chemical compatibility (MLCC) between a compound and a drug library as a measure of the drug-like character of a compound. The rationale is that the local chemical environment of each atom or group of atoms in a compound largely contributes to the stability, toxicity, and metabolism in vivo. A systematic comparison of the local environments within a compound and those within the existing drugs provides a basis for determining whether and how much a compound is drug-like. We applied the MLCC calculations to four test sets: top selling drugs, compounds under biological testing prior to the preclinical test, anticancer drugs, and compounds known to have poor drug-like character. The following conclusions were obtained: (1) A convergent number of unique local structure types were found in the analysis of the library of the existing drugs. It suggests that the current drug library contains about 80% of all the viable types; therefore, discovery of a drug with new local structures is only an event of relatively small probability. (2) The method is highly selective in discerning drug-like compounds: most of the top drugs are predicted to be drug-like, about one-quarter of the biological testing compounds are drug-like, and about one-fifth of the anticancer drugs are drug-like. (3) The method also correctly predicted that none of the known problematic compounds are drug-like. (4) The method is fast enough for computational screening of virtual combinatorial chemistry libraries and databases of available compounds.     

Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4

Combinatorial synthesis and large scale screening methods are being used increasingly in drug discovery, particularly for finding novel lead compounds. Although these "random" methods sample larger areas of chemical space than traditional synthetic approaches, only a relatively small percentage of all possible compounds are practically accessible. It is therefore helpful to select regions of chemical space that have greater likelihood of yielding useful leads. When three-dimensional structural data are available for the target molecule this can be achieved by applying structure-based computational design methods to focus the combinatorial library. This is advantageous over the standard usage of computational methods to design a small number of specific novel ligands, because here computation is employed as part of the combinatorial design process and so is required only to determine a propensity for binding of certain chemical moieties in regions of the target molecule. This paper describes the application of the Multiple Copy Simultaneous Search (MCSS) method, an active site mapping and de novo structure-based design tool, to design a focused combinatorial library for the class II MHC protein HLA-DR4. Methods for the synthesizing and screening the computationally designed library are presented; evidence is provided to show that binding was achieved. Although the structure of the protein-ligand complex could not be determined, experimental results including cross-exclusion of a known HLA-DR4 peptide ligand (HA) by a compound from the library. Computational model building suggest that at least one of the ligands designed and identified by the methods described binds in a mode similar to that of native peptides.     

Towards the development of molecularly imprinted artificial receptors for the screening of estrogenic chemicals.

Molecularly imprinted polymers are prepared using various steroid compounds as the templates. The imprinted polymers can selectively re-bind the original print molecules, which leads to versatile potential applications. The feasibility of using these artificial receptors to replace their biological counterparts for preliminary screening of a chemical library is demonstrated. A steroid library composed of 22 closely related compounds is screened with an estrogen specific polymer. The print molecule is identified with accuracy and structural similarities of other members are correlated with normalized retention indices. Molecularly imprinted artificial receptors are envisioned as being useful for screening purposes in drug discovery or for identifying endocrine-disrupting chemicals.     

Discovery,structure, and chemical synthesis of disulfide-rich peptide toxins and their analogs

Disulfide bond-rich peptide toxins are promising scaffolds for the development of medicinal peptides because they possess a rigid 3D structure formed by multiple disulfide bonds. In this review, we discussed recent advances in the discovery, structural elucidation and chemical synthesis of disulfide-rich peptide toxins and their analogs.     

Incurred sample reanalysis in drug discovery bioanalysis

Bioanalysis plays a key role during the drug discovery process to generate the pharmacokinetic data to facilitate unbiased evaluation of leads, optimized leads and drug candidates. Such pharmacokinetic data are used to enable key decisions in the drug discovery process. The aim of the work is to put forward a new strategy of performing the incurred sample reanalysis for select small molecule novel chemical entities at different stages of drug discovery prior to candidate selection. Three discovery programs representing hits, leads and optimized lead candidates were selected for the incurred sample reanalysis (ISR) analysis. From each discovery program, two novel chemical entities were selected for the ISR analysis. The time points considered for ISR generally varied among the programs; however, samples coinciding with drug absorption, distribution and elimination were considered in the ISR assessment. With the exception of a single ISR value that gave a high deviation (about 63%), the observed ISR values supported the discovery bioanalytical assays. While the individual bioanalytical laboratory can draw an algorithm for selecting novel chemical entities and fixing the acceptance criteria for the ISR data, it is proposed that the percentage difference between ISR vs. original concentration for 67% of the repeat samples is contained within ±30% for discovery bioanalysis.     

Systems biology and systems chemistry: new directions for drug discovery

Improvements in drug design have historically been centered around structure-based optimization of molecule specificity for a targeted protein, in an effort to reduce unintentional binding to other proteins and off-target effects. Although the "one-to-one" drug design strategy has been successful in impairing function of targets associated with a number of diseases, recent reports of drug promiscuity, which are a potential source of adverse reactions in patients, make a case to refine the drug design strategy such that it includes an awareness of multiple interactions from both ligand and protein perspectives. Polypharmacology and chemical biology studies are amassing interaction data at rapid rates, and the integration of such data into an interpretable model requires zooming our perspective out from the single ligand-target level to the larger network-wide level. We review some of the recent developments in systems-level research for drug design and discovery, and discuss the directions that some drug design efforts are heading toward.