Integration of NMR and high-throughput screening

NMR-based screening has become a powerful method for the identification and analysis of low-molecular weight organic compounds that bind to protein targets and can be utilized in drug discovery programs. In particular, heteronuclear NMR-based screening can yield information about both the affinity and binding location of potential lead compounds. In addition, heteronuclear NMR-based screening has wide applications in complementing and facilitating conventional high-throughout screening programs. This article will describe several strategies for the integration of NMR-based screening and high-throughput screening. The marriage of these two techniques promises to be of tremendous benefit in the triage of hits that come from HTS, and can aid the medicinal chemist in the identification of quality leads that have high potential for further optimization.     

Chip-based high-throughput screening of herbal medicines

At present, high-throughput screening (HTS) programs in drug discovery rely mainly on compound libraries from combinational chemistry. Similarly, natural flora has been used as a prominent origin for new and potent herbal drugs. Herbal medicines have been used worldwide for thousands of years to cure many diseases. As such, herbal secondary metabolites show a remarkable structural diversity that supplements chemically synthesized compound analogs in drug discovery screening. Unfortunately, there is often a considerable deterioration in the quality of herbal drugs in such screening programs as there are time-consuming manual processes involved in the isolation of active ingredients from the highly complex mixtures of herbal plant products. The quality and quantity of herbal samples are critical for the success of HTS programs. In the recent past, there have been substantial improvements in HTS due to the miniaturization and integration of microchip (e.g., Herbochip(?), DNA chip, protein chip, cell chip, etc.)-based technologies so as to design herbal drugs that compete with synthetic drug analogs. Here we will review various technologies used for HTS of herbal medicines. Finally, we will summarize our efforts to develop a novel chip-based HTS assay to explore the antioxidant and radioprotective properties of herbal plants.     

Gas chromatographic high-throughput screening techniques in catalysis

Discovering highly efficient catalysts is of great scientific and economical interest. Advances in high-throughput assays in combination with sophisticated analytical techniques have increased the rapidity with which catalysts can be identified and optimized. Understanding how kinetics in the mechanism of catalysis is controlled by structural parameters is essential for a directed design of catalysts. To identify such rate-controlling elementary steps and to develop and refine models, comprehensive experimental kinetic data of a broad variety of substrates are necessary. In the present article concepts of high-throughput screening techniques in catalysis using gas chromatography are reviewed in a survey covering the period from 1998 to 2007. To cover also the origins of concepts and groundbreaking experiments in this research area milestones going back to 1950 are also reviewed. The first part of the review will focus on off-line gas chromatographic analysis, the second part on on-line gas chromatographic analysis covering sequential, parallelized and high-throughput multiplexing gas chromatography. The third part presents recent advances in the integration of chemical transformation and analysis in gas chromatography. The present review article describes the state-of-the-art, scope and limitations, and applications of these different high-throughput screening approaches.     

Retrospective analysis of an experimental high-throughput screening data set by recursive partitioning

With the emergence of combinatorial chemistry, whether based on parallel, mixture, solution, or solid phase chemistry, it is now possible to generate large numbers of diverse or focused compound libraries. In this paper we aim to demonstrate that it is possible to design targeted libraries by applying nonparametric statistical methods, recursive partitioning in particular, to large data sets containing thousands of compounds and their associated biological data. Moreover, when applied to an experimental high-throughput screening (HTS) data set, our data strongly suggest that this method can improve the hit rate of our primary screens (about 4- to 5-fold) while increasing screening efficiency: less than one-fifth of the complete selection needs to be screened in order to identify about 75% of all actives present.     

Automated information extraction and structure-activity relationship analysis of cytochrome P450 substrates

Information on CYP-chemical interactions was comprehensively explored by a text-mining technique, to confirm our previous structure-activity relationship model for CYP substrates (Yamashita et al. J. Chem. Inf. Model. 2008, 48, 364-369). The text-mining technique is based on natural language processing and can extract chemical names and their interaction patterns according to sentence context. After chemicals were automatically extracted and classified into CYP substrates, inhibitors, and inducers, 709 substrates were retrieved from the PubChem database and categorized as 216, 145, 136, 217, 156, and 379 substrates for CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4, respectively. Although the previous classification model was developed using data from only 161 compounds, the model classified the substrates found by text-mining analysis with reasonable accuracy. This confirmed the validity of both the multi-objective classification model for CYP substrates and the text-mining procedure.     

Establishing the structure-activity relationship of teixobactin

The updated studies have been summarized to provide structure-activity relationship of teixobactin.     

Binary quantitative structure-activity relationship (QSAR) analysis of estrogen receptor ligands

The use of high throughput screening (HTS) to identify lead compounds has greatly challenged conventional quantitative structure-activity relationship (QSAR) techniques that typically correlate structural variations in similar compounds with continuous changes in biological activity. A new QSAR-like methodology that can correlate less quantitative assay data (i.e., "active" versus "inactive"), as initially generated by HTS, has been introduced. In the present study, we have, for the first time, applied this approach to a drug discovery problem; that is, the study of the estrogen receptor ligands. The binding affinities of 463 estrogen analogues were transformed into a binary data format, and a predictive binary QSAR model was derived using 410 estrogen analogues as a training set. The model was applied to predict the activity of 53 estrogen analogues not included in the training set. An overall accuracy of 94% was obtained.     

高通量药物筛选现代检测技术研究进展

高通量药物筛选是发现创新药物的重要技术途径.高通量筛选结果必须通过适当的检测方法才能反映出来,检测技术是实现高通量药物筛选的基础.本文综述了近年来有关光学分析、色谱分析、热分析、电化学分析、质谱、核磁共振等现代检测技术在高通量药物筛选研究中的进展.     

Application of liposome based sensors in high-throughput screening systems

High throughout screening is an approach based on the concept which assumes that when sufficiently large library of compounds are tested, the chance of discovering a new active compound is increased. In order to meet this expectation, proper testing criteria need to be devised. Those criteria should be related to the fate of a compound in the organism to have any predictive power. Not long ago, the main criteria were based exclusively on parameters defined by the maximum activity (QSAR). In this system the activity criteria have not been included therefore the compound ability to reach the target is not accounted for. Considering that, the construction of yet another set of parameters has been initiated (QSPR). The parameters are in fact semi-empirical numbers which need to be tested on real, physical models. Whereas the activity tests are straightforward, the pharmacokinetic ones are difficult and controversial. One such parameter describes the critical property of an active compound, namely its ability to cross biological membranes. This review describes new concepts in the determination of the permeability coefficient with the help of methods which are based on liposome biosensors. Two methods using fluorescence probes incorporated in the lipid bilayer of liposome are described in detail and compared to other currently available techniques.     

Comparative study of machine-learning and chemometric tools for analysis of in-vivo high-throughput screening data

High-throughput screening (HTS) has become a central tool of many pharmaceutical and crop-protection discovery operations. If HTS screening is carried out at the level of the intact organism, as is commonly done in crop protection, this strategy has the potential of uncovering a completely new mechanism of actions. The challenge in running a cost-effective HTS operation is to identify ways in which to improve the overall success rate in discovering new biologically active compounds. To this end, we describe our efforts directed at making full use of the data stream arising from HTS. This paper describes a comparative study in which several machine learning and chemometric methodologies were used to develop classifiers on the same data sets derived from in vivo HTS campaigns and their predictive performances compared in terms of false negative and false positive error profiles.     

Discovery of a new efficient chiral ligand for copper-catalyzed enantioselective Michael additions by high-throughput screening of a parallel library

A combinatorial library of 125 chiral Schiff base ligands 5 was synthesized with the use of solution-phase parallel synthesis and solid-phase extraction (SPE) techniques to scavenge excess reagents and reaction by-products and avoid chromatography. The synthetic methodology coupled five N-Boc-protected beta-amino sulfonyl chlorides 1a-e with five different amines 2f-j to give 25 N-Boc sulfonamides 3, which were in turn deprotected and coupled with five salicylaldehydes 4p-t to give 125 ligands 5 in good yields and of sufficient purity to be used in ligand-catalyzed reactions. These ligands were tested in the copper-catalyzed conjugate addition of dialkyl zinc to cyclic and acyclic enones. A multisubstrate high-throughput screening of the library was performed with an equimolar mixture of 2-cyclohexenone and 2-cycloheptenone (9 and 10, respectively, 0.2 mmol total), with 5.5 mol% ligand 5 (0.011 mmol) and 5 mol% Cu(OTf)2 (OTf= OSO2CF3) (0.010 mmol) in 1:1 toluene/ hexane at - 20 degrees C. From the screening of the library, 5bhr was identified as the best ligand, which yielded 3-ethylcyclohexanone (12) and 3-ethylcycloheptanone (13) in 82% and 81% ee, respectively, and complete conversions. Under optimized conditions (2.75 mol% 5bhr, 2.5 mol% copper(i) triflate, toluene as reaction solvent), improved results were obtained for 12 (90% ee, 93% yield) and for 13 (91% ee, 95% yield). Selected ligands 5 were also tested in the addition of Me2Zn to 2-cyclohexenone (9, ee up to 79%), of Et2Zn to 2-cyclopentenone (11, ee up to 80%) and to acyclic enones 16 and 17 (ee up to 50%).     

Three-dimensional quantitative structure-activity relationship analysis of the new potent sulfonylureas using comparative molecular similarity indices analysis

The present study describes the implementation of a new three-dimensional quantitative structure-activity relationship (3D-QSAR) technique: comparative molecular similarity indices analysis (CoMSIA) to a set of novel herbicidal sulfonylureas targeted acetolactate synthase. Field expressions in terms of similarity indices in CoMSIA were applied instead of the usually used Lennard-Jones and Coulomb-type potentials in CoMFA. Two different kinds of alignment techniques including field-fit alignment and atom-by-atom fits were used to produce the molecular aggregate. The results indicated that those two alignment rules generated comparative 3D-QSAR models with similar statistical significance. However, from the predictive ability of the test set, the models from the alignment after maximal steric and electrostatic optimization were slightly better than those from the simple atom-by-atom fits. Moreover, systematic variations of some parameters in CoMSIA were performed to search the best 3D-QSAR model. A significant cross-validated q2 was obtained, indicating the predictive potential of the model for the untested compounds; meanwhile the predicted biological activities of the five compounds in the test set were in good agreement with the experimental values. The CoMSIA coefficient contour plots identified several key features explaining the wide range of activities, which were very valuable for us in tracing the properties that really matter and getting insight into the potential mechanisms of the intermolecular interactions between inhibitor and receptor, especially with respect to the design of new compounds.     

Fluorescence assays for high-throughput screening of protein kinases

Protein kinases comprise one of the most important group of targets for drug discovery research today. Methods to identify novel kinase inhibitors by high-throughput screening have evolved rapidly in recent years. An important aspect is the availability of fluorescent probes that can be applied in a homogeneous, or mix-and-measure, assay format. Here, we illustrate the application of fluorescence read-out technologies for kinase targets in light of our own experiences in assay development and high-throughput screening.     

Setup for high-throughput impedance screening of gas-sensing materials

This paper reports on the setup for a high-throughput impedance measurement system that allows rapid screening of the electrical and dielectrical properties of solid-state sample libraries in variable atmospheres and temperatures. Using multielectrode arrays, most time-consuming steps in the workflow are parallelized. In addition, an approach for automated data evaluation of impedance spectra is presented. For reasons of verification of robust measuring results and reproducibility, screening results of a sample library composed of doped indium(III) oxide as a resistive-type gas-sensing material are discussed on the basis of the determined sensitivities focusing temperature and testing gas gradients.     

Optical chemical biosensors for high-throughput screening of drugs

Optical biosensors have been commercially available since the early 1990s, and have been used extensively in many areas of research in the life sciences. Optical biosensors developed for drug analysis generally exploit the high selectivity of the antigen-antibody and drug-protein interaction. Optical biosensors can be made based on optical diffraction or electro-chemiluminescence. High-throughput screening, (HTS) which includes automated preparation of a large number of samples and then screening of their properties in multi-well plates, improves the efficiency of research in many scientific areas, e.g., catalyst screening, food processing, chemical synthesis, drug discovery, absorption, distribution, metabolism, and excretion and toxicological and cell based screening. The three most common detection techniques used in HTS are UV-VIS absorbance, fluorescence and luminescence. In this review, we summarize some recent trends and developments in the construction of optical chemical biosensors used in high-throughput screening of drugs. Also, we have included environmental, biological and other medical applications of biosensors.     

Strategic pooling of compounds for high-throughput screening.

Bringing new medicines to the market depends on the rapid discovery of new and effective drugs, often initiated through the biological testing of many thousands of compounds in high-throughput screening (HTS). Mixing compounds together into pools for screening is one way to accelerate this process and reduce costs. This paper contains both theoretical and experimental data which suggest that careful selection of compounds to be pooled together is necessary in order to reduce the risk of reactivity between compounds within the pools.