Combinatorial Chemistry & High Throughput Screening - Volume 7, Issue 2, 2004

The quantitative structure-activity relationship (QSAR) of a set of 29 agonists for tyramine (TA) receptor responsible for the inhibition of sex-pheromone production in Plodia interpunctella, was analyzed using comparative receptor surface analysis (CoRSA). Using the common steric and electrostatic features of the most active members of a series of compounds, CoRSA generated a virtual receptor model, represented as points on a surface complementary to the van der Waals or Wyvill steric surface of the aligned compounds. Three-dimensional energetics descriptors were calculated from receptor surface model (RSM) / ligand interaction and these three-dimensional descriptors were used in genetic partial least squares data analysis to generate a QSAR model, giving a 3D QSAR with r2=0.969 for calibration and CV- r2=0.635 for the leave-oneout cross validation.

We have developed a method of spraying assay reagents onto a target gel in the Micro-Arrayed Compound Screening (μARCS) format. After application of target gels to compound sheets, subsequent reagents can be applied by spraying onto the target gel. The spraying method conserves on assay reagents by up to 10-fold, eliminates the need for casting additional agarose gels, and increases the throughput of a screen by 3-fold. To demonstrate the efficacy of applying the spraying method to μARCS, we screened over 600,000 compounds for inhibitors of histone deacetylase (HDAC). Commercially available HDAC substrate and reaction developer were sprayed directly onto the gel to initiate the reaction and to amplify the signal, respectively. Picks in the primary screen were retested at a density of 384 compounds per sheet in the μARCS format. IC50 values for active compounds were confirmed in a 96-well plate assay. The screen identified several small molecule inhibitors of the enzyme, including members of several classes of known HDAC inhibitors. The combination of the high-density format of μARCS, the efficiency of the spraying method, and a timed sequence of adding assay reagents permitted a screening throughput of 200,000 tests an hour. We present the details of the screening format and the analysis of the hits from the screen.

Enzymes can perform intricate regioselective and / or enantioselective chemical transformations and can accelerate reaction rates by enormous factors all under mild conditions. However, enzymes almost always present problems for use on an industrial scale. Evolutionary design approaches can be applied to the generation of stable enzymes with improved or novel catalytic activities. Directed evolution can be considered as the biotechnological equivalent of combinatorial chemistry, where the expressed proteins are the combinatorial libraries of biocatalysts. This review will focus on the search of novel biocatalysts produced by genetic engineering with modified activity and stability in different environments, substrate specificity and enantioselectivity. Methods of screening and / or selection for the desired properties will also be described. Finally, the efforts in de novo enzyme design are mentioned.

Motivation: Microarrays have allowed the expression level of thousands of genes or proteins to be measured simultaneously. Data sets generated by these arrays consist of a small number of observations (e.g., 20-100 samples) on a very large number of variables (e.g., 10,000 genes or proteins). The observations in these data sets often have other attributes associated with them such as a class label denoting the pathology of the subject. Finding the genes or proteins that are correlated to these attributes is often a difficult task since most of the variables do not contain information about the pathology and as such can mask the identity of the relevant features. We describe a genetic algorithm (GA) that employs both supervised and unsupervised learning to mine gene expression and proteomic data. The pattern recognition GA selects features that increase clustering, while simultaneously searching for features that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the features chosen by the GA contain information primarily about differences between classes in the data set. The principal component analysis routine embedded in the fitness function of the GA acts as an information filter, significantly reducing the size of the search space since it restricts the search to feature sets whose principal component plots show clustering on the basis of class. The algorithm integrates aspects of artificial intelligence and evolutionary computations to yield a smart one pass procedure for feature selection, clustering, classification, and prediction.

To meet growing needs for high throughput gene expression profiling, we established a new automated high throughput TaqMan RT-PCR method for quantitative mRNA expression analysis. In this method, the AllegroTM (Zymark) system conducts all sample tracking and liquid handling steps, and ABI PRISM® 7900 HT (Applied Biosystems) is used to conduct real-time determination of the Ct value when amplification of PCR products is first detected and accumulation of inhibitory PCR products is unlikely to occur. The ABI PRISM® 7900 HT Sequence Detection System features a real-time PCR instrument with 384- well-plate compatibility and robotic loading, and continuous wavelength detection, which enables the use of multiple fluorophores in a single reaction. The Allegro System offers an assembly line approach with a modular design that allows reconfiguration of the components to accommodate variations in the assay flow. In the present study, we have established and validated a new automated High Throughput (HT) TaqMan RT-PCRbased method for quantitative mRNA expression analysis. The data demonstrate that HT-Taqman PCR is a powerful tool that can be used for measuring low concentrations of mRNA, and is highly accurate, reproducible, and amenable to high throughput analysis. Results suggest that HT-TaqMan is a reliable method for the quantification of low-expression genes and a powerful tool with HT capability for target identification / validation, structure-activity relationship (SAR) study, compound selection for efficacy studies, and biomarker identification in drug discovery and development.

Targeting of drugs and genes to specific cell types is an emerging paradigm in the treatment of many medical conditions. However, targeting structures such as peptides are susceptible to rapid inactivation in vivo. To address this problem, novel targeting molecules can now be rapidly synthesized using a combinatorial approach. Methods to screen the large libraries created in this process are often lacking or compatible only with solution-based screening. This report describes a high-throughput cell-based method utilizing flow cytometry, capable of rapidly screening large libraries of molecules simultaneously for biological functionality and stability. In this method, each library molecule is attached to a microsphere exhibiting a unique set of optical properties, or “fingerprint”, conferring modularity and multiplex capability. We investigated the multiplex capability of our flow cytometric method to determine its capacity for high-throughput screening. Current instrumentation in our laboratory allows the screening of at least 75 unique compounds in a single well, a number comparable to available solution-based assays. In state-of-the-art configuration, however, this methodology can support the screening of up to 1875 compounds per well, achieving high-throughput potential in a single multiwell plate. We also investigated the binding capability of targeted microspheres to adherent target cells. These microspheres exhibited a 12-fold increase in binding over control, untargeted microspheres. Competitive inhibition experiments with soluble ligand confirmed the specificity of microsphere binding. Overall, the methodology proposed here is capable of quickly and effectively screening large libraries of targeting molecules using instrumentation readily available to the greater research community.

Capsazepine (CPZ, 1) is a well-known vanilloid receptor (VR1) antagonist that has been cited widely used inthe literature. However the current synthetic methods used for the total synthesis of CPZ are lengthy, involve multiple purification steps, and produce low yields. Here we describe a new and highly efficient synthesis of benzazepine 3, a synthetic precursor of CPZ, in only two steps and 59% overall yield from a commercially available tetralone 2 via a Schmidt reaction as a key step. Moreover, we apply parallel synthesis techniques to prepare CPZ and CPZ analogs. Our approach enables the possibility of preparing larger, and more diverse libraries of CPZ analogs.

The automated polymer-assisted solution phase (PASP) synthesis of a 72 member library of 2- alkylthio-benzimidazoles 16 and benzimidazolin-2-ones 17 using commercially available robotic workstations is described. By incorporating both automated aqueous work-ups, in-line scavenging and 'catch and release' protocols the desired compounds were obtained directly in good yields and excellent purities without the need for conventional chromatographic purification. The synthesis described demonstrates how both manual and automated equipment may be utilised together to provide a versatile approach that facilitates parallel compound synthesis.