Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data

The human cytochrome P450 (CYP450) isozymes are the most important enzymes in the body to metabolize many endogenous and exogenous substances including environmental toxins and therapeutic drugs. Any unnecessary interactions between a small molecule and CYP450 isozymes may raise a potential to disarm the integrity of the protection. Accurately predicting the potential interactions between a small molecule and CYP450 isozymes is highly desirable for assessing the metabolic stability and toxicity of the molecule. The National Institutes of Health Chemical Genomics Center (NCGC) has screened a collection of over 17,000 compounds against the five major isozymes of CYP450 (1A2, 2C9, 2C19, 2D6, and 3A4) in a quantitative high throughput screening (qHTS) format. In this study, we developed support vector classification (SVC) models for these five isozymes using a set of customized generic atom types. The CYP450 data sets were randomly split into equal-sized training and test sets. The optimized SVC models exhibited high predictive power against the test sets for all five CYP450 isozymes with accuracies of 0.93, 0.89, 0.89, 0.85, and 0.87 for 1A2, 2C9, 2C19, 2D6, and 3A4, respectively, as measured by the area under the receiver operating characteristic (ROC) curves. The important atom types and features extracted from the five models are consistent with the structural preferences for different CYP450 substrates reported in the literature. We also identified novel features with significant discerning power to separate CYP450 actives from inactives. These models can be useful in prioritizing compounds in a drug discovery pipeline or recognizing the toxic potential of environmental chemicals.     

K-Screen: a free application for high throughput screening data analysis, visualization, and laboratory information management

High throughput screening (HTS) has emerged as an important technique for allowing researchers to rapidly profile very large numbers of chemicals against drug targets. As recent and future advances make HTS cheaper to perform on even larger scales, the amount of data that has to be processed, analyzed, and searched will only grow larger in size and harder for researchers to manually sift through. It is therefore an unavoidable requirement that institutions utilizing HTS technology will need to begin looking for effective solutions in the maturing area of laboratory information management systems like many other types of labs have already done. K-Screen is one such solution. Our initial goal with K-Screen was to have an integrated application environment that supported data analysis, management, and presentation so we could efficiently perform client requested screens and searches as well as generate detailed reports on the results of those. Previously, we had attempted but failed to locate an existing software suite that sufficiently addressed all our requirements. K-Screen is a web accessible application that offers the ability to host a large chemical structure library, process and store single-dose (primary) and dose response (secondary) screening data, perform searches based on screening results, plate coordinates, and structure, substructure and structure similarity. It uses heat maps and histograms to visualize screen or plate level statistics. Interfaces to external searches against PubChem and ZINC databases are also provided. We feel that these features make K-Screen a practical and effective alternative to other commercial or academic HTS LIMS systems.     

Cheminformatics aspects of high throughput screening: from robots to models: symposium summary

The “Cheminformatics aspects of high throughput screening (HTS): from robots to models” symposium was part of the computers in chemistry technical program at the American Chemical Society National Meeting in Denver, Colorado during the fall of 2011. This symposium brought together researchers from high throughput screening centers and molecular modelers from academia and industry to discuss the integration of currently available high throughput screening data and assays with computational analysis. The topics discussed at this symposium covered the data-infrastructure at various academic, hospital, and National Institutes of Health-funded high throughput screening centers, the cheminformatics and molecular modeling methods used in real world examples to guide screening and hit-finding, and how academic and non-profit organizations can benefit from current high throughput screening cheminformatics resources. Specifically, this article also covers the remarks and discussions in the open panel discussion of the symposium and summarizes the following talks on “Accurate Kinase virtual screening: biochemical, cellular and selectivity”, “Selective, privileged and promiscuous chemical patterns in high-throughput screening” and “Visualizing and exploring relationships among HTS hits using network graphs”.     

High throughput screening and QSAR-3D/CoMFA: useful tools to design predictive models of substrate specificity for biocatalysts

After a hierarchical microbial screening process, new microorganisms have been discovered that act as biocatalysts for the stereoselective oxidation of secondary alcohols or for ketone reduction. Oxidation activity is more widespread in yeasts and bacteria, while actinomycetes, filamentous fungi and yeasts present the highest reduction activities. QSAR-3D/CoMFA is an adequate technique to design predictive models of the biocatalysts' activity. In this paper CoMFA models are designed to compare the activities of the biocatalysts selected for the oxidation of alcohols and for the reduction of ketones, starting from the results obtained during the screening process. These models are useful for learning about the activity of these microorganisms and to compare the substrate specificity requirements between alcohol oxidation and ketone reduction biocatalysts.     

The application of high throughput siRNA screening technology to study host-pathogen interactions

Recent advances in high throughput screening technologies have accelerated the identification and characterization of potential factors involved in host-virus interactions, facilitating early detection and diagnosis of diseases, as well as providing promising drug targets. The last decade has seen a plethora of successful examples of high throughput screening approaches, especially siRNA screening. With support from protein interaction studies, mRNA expression profiling, and bioinformatics, siRNA screening has also been successfully utilized to identify host factors required for a number of viruses including HIV, West Nile virus and H1N1 virus. Such studies have raised the awareness of virologists, and have opened a new chapter of global analysis of host-pathogen interactions. However, to play a more defining role in prognostics, diagnostics and therapeutics for virus diseases, acknowledged drawbacks, including false positives and negatives, inherent in this technology, must be successfully addressed.     

Retroviral display and high throughput screening

Retroviruses distinguish themselves from all other mammalian viruses by their abilities to infect and propagate in mammalian cells without causing a cytopathic effect and to stably integrate their genetic information into the genome of the host cell. These unique properties make them an ideal platform for the display and directed evolution of proteins in a mammalian cell environment. This review will describe the essentials about retrovirus biology and then discuss in detail display and screening strategies that have been developed during the past 15 years of retroviral display technology.     

A Seoul-Fluor-based bioprobe for lipid droplets and its application in image-based high throughput screening

We developed a novel fluorescent bioprobe (SF44) that can specifically visualize the cellular lipid droplets in in vitro and in vivo systems and illustrated the mechanistic rationale of its fluorogenic property. Its application to image-based high throughput screening led us to the identification of a new small-molecule modulator of lipid droplet formation.     

Transitioning enantioselective indicator displacement assays for alpha-amino acids to protocols amenable to high-throughput screening

Enantioselective indicator displacement assays (eIDAs) for alpha-amino acids were conducted in a 96-well plate format to demonstrate the viability of the technique for the high-throughput screening (HTS) of enantiomeric excess (ee) values. Chiral receptors [Cu(II)(1)](2+) and [Cu(II)(2)](2+) with the indicator chrome azurol S were implemented for the eIDAs. Enantiomeric excess calibration curves were made using both receptors and then used to analyze true test samples. These results were compared to those previously obtained with a conventional UV-vis spectrophotometer, and they showed little to no loss of accuracy, while the speed of analysis was increased. A sample of valine of unknown ee was synthesized through an asymmetric reaction to produce a realistic reaction sample, which was analyzed using receptor [Cu(II)(1)](2+). The experimentally determined ee using our eIDA was compared to that obtained by chiral HPLC and (1)H NMR chiral shift reagent analysis. This gave errors of 4.7% and 12.0%, respectively. In addition to the use of ee calibration curves, an artificial neural network (ANN) was used to determine the % L-amino acid of the test samples and of the sample of valine of unknown ee from the asymmetric reaction. This method obtained errors of 5.9% and 2.2% compared to chiral HPLC and (1)H NMR chiral shift reagent analysis, respectively. The technique using calibration curves for the determination of ee on a 96-well plate allows one to determine 96 ee values in under a minute, enabling its use for HTS of asymmetric reactions with acceptable accuracy.     

Activity-aware clustering of high throughput screening data and elucidation of orthogonal structure-activity relationships

From a medicinal chemistry point of view, one of the primary goals of high throughput screening (HTS) hit list assessment is the identification of chemotypes with an informative structure-activity relationship (SAR). Such chemotypes may enable optimization of the primary potency, as well as selectivity and phamacokinetic properties. A common way to prioritize them is molecular clustering of the hits. Typical clustering techniques, however, rely on a general notion of chemical similarity or standard rules of scaffold decomposition and are thus insensitive to molecular features that are enriched in biologically active compounds. This hinders SAR analysis, because compounds sharing the same pharmacophore might not end up in the same cluster and thus are not directly compared to each other by the medicinal chemist. Similarly, common chemotypes that are not related to activity may contaminate clusters, distracting from important chemical motifs. We combined molecular similarity and Bayesian models and introduce (I) a robust, activity-aware clustering approach and (II) a feature mapping method for the elucidation of distinct SAR determinants in polypharmacologic compounds. We evaluated the method on 462 dose-response assays from the Pubchem Bioassay repository. Activity-aware clustering grouped compounds sharing molecular cores that were specific for the target or pathway at hand, rather than grouping inactive scaffolds commonly found in compound series. Many of these core structures we also found in literature that discussed SARs of the respective targets. A numerical comparison of cores allowed for identification of the structural prerequisites for polypharmacology, i.e., distinct bioactive regions within a single compound, and pointed toward selectivity-conferring medchem strategies. The method presented here is generally applicable to any type of activity data and may help bridge the gap between hit list assessment and designing a medchem strategy.     

Gene expression analysis for high throughput screening applications

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 Allegro( trade mark ) (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 C(t) 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-PCR- based 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.     

基于报告基因技术的MDR1转录抑制剂高通量筛选模型的建立和应用

MDR1基因是引起肿瘤多药耐药的主要基因,其编码的P-gp蛋白可持续将药物由胞内排出胞外以降低胞内药物浓度导致多药耐药,MDR1基因的转录抑制剂可抑制MDR1基因在癌细胞中的表达,从而逆转肿瘤多药耐药.通过克隆MDR1基因的启动子,将其插入pGL3-basic质粒构建MDR1-luc＋报告基因载体,再将重组载体转染入HepG2肝癌细胞并筛选单克隆细胞株,构建了MDR1启动子的高通量筛选模型,Z′因子为0.75;通过对中药样品库的筛选,得到两种中药提取物高良姜水提物、红豆蔻醇提物有明显耐药逆转效果,EC50值分别为高良姜水提物16.37mgL-1和红豆蔻醇提物14.96mgL-1,RT-PCR验证上述两种阳性样品具有明显的抑制MDR1基因表达的作用.以上结果为MDR1基因的转录抑制剂高通量筛选奠定了基础.     

Fluorescence-based adenylyl cyclase assay adaptable to high throughput screening

The second messenger cAMP has been implicated in numerous cellular processes such as glycogen metabolism, muscle contraction, learning and memory, and differentiation and development. Genetic evidence suggests that the enzyme that produces cAMP, adenylyl cyclase (AC), may be involved in pathogenesis in many of these cellular processes. In addition, these data suggest that membrane-bound ACs may be valuable targets for therapeutics to treat pathogenesis of these processes. The development of a robust real-time adenylyl cyclase assay that can be scalable to high-throughput screening could help in the development of novel therapeutics. Here we report a novel fluorescence-based cyclase assay using Bodipy FL GTPgammaS (BGTPgammaS). The fluorescence of the Bodipy moiety of BGTPgammaS was dramatically enhanced by incubation with the minimal catalytic core of wild-type-AC (wt-AC) and a mutant with decreased purine selectivity (mut-AC), in an AC activation-dependent manner. No increase in fluorescence was observed using Bodipy FL ATPgammaS (BATPgammaS) as substrate for either wt-AC or mut-AC. Using BGTPgammaS, forskolin, Gsalpha.GTPgammaS and the divalent cation Mn(2+) potently enhanced the rate of fluorescence increase in a concentration-dependent manner. The fluorescence enhancement of the Bodipy moiety was inhibited by known inhibitors of AC such as 2'deoxy,3'AMP and 2',5'-dideoxy-3'ATP. Furthermore, the fluorescence assay is adaptable to 96-well and 384-well multiplate format and is thus applicable to high throughput screening methodologies.     

Analysis of image-based phenotypic parameters for high throughput gene perturbation assays

Although image-based phenotypic assays are considered a powerful tool for siRNA library screening, the reproducibility and biological implications of various image-based assays are not well-characterized in a systematic manner. Here, we compared the resolution of high throughput assays of image-based cell count and typical cell viability measures for cancer samples. It was found that the optimal plating density of cells was important to obtain maximal resolution in both types of assays. In general, cell counting provided better resolution than the cell viability measure in diverse batches of siRNAs. In addition to cell count, diverse image-based measures were simultaneously collected from a single screening and showed good reproducibility in repetitions. They were classified into a few functional categories according to biological process, based on the differential patterns of hit (i.e., siRNAs) prioritization from the same screening data. The presented systematic analyses of image-based parameters provide new insight to a multitude of applications and better biological interpretation of high content cell-based assays.     

Recent advances in high throughput screening for ADME properties

With the increase in the numbers of molecules synthesized in a typical drug discovery program, as well as the large amount of information utilized in the selection of a drug candidate, there is a need for a plethora of drug metabolism and pharmacokinetic (DMPK) information to be regularly generated in discovery. Over the past decade, many in vitro, and even in vivo, DMPK screens have been developed and routinely deployed to generate this information in support of drug discovery efforts. In the past few years, newer methods, or adaptations to methods, have been published, and this review attempts to summarize these advances. In particular, advances have been reported for experimental approaches to metabolic clearance, CYP inhibition, in vivo exposure, and distribution, as well as in silico determinations of absorption, distribution, metabolism, and excretion (ADME) properties. Bioanalytical approaches aimed at optimizing analyte method development, sample preparation, and analyte detection, have also been reported. Future advances will further improve the ability to make decisions on molecules earlier in drug discovery.     

Design of a compound screening collection for use in high throughput screening

In this paper we introduce a quantitative model that relates chemical structural similarity to biological activity, and in particular to the activity of lead series of compounds in high-throughput assays. From this model we derive the optimal screening collection make up for a given fixed size of screening collection, and identify the conditions under which a diverse collection of compounds or a collection focusing on particular regions of chemical space are appropriate strategies. We derive from the model a diversity function that may be used to assess compounds for acquisition or libraries for combinatorial synthesis by their ability to complement an existing screening collection. The diversity function is linked directly through the model to the goal of more frequent discovery of lead series from high-throughput screening. We show how the model may also be used to derive relationships between collection size and probabilities of lead discovery in high-throughput screening, and to guide the judicious application of structural filters.