High throughput screening informatics

High throughput screening (HTS), an industrial effort to leverage developments in the areas of modern robotics, data analysis and control software, liquid handling devices, and sensitive detectors, has played a pivotal role in the drug discovery process, allowing researchers to efficiently screen millions of compounds to identify tractable small molecule modulators of a given biological process or disease state and advance them into high quality leads. As HTS throughput has significantly increased the volume, complexity, and information content of datasets, lead discovery research demands a clear corporate strategy for scientific computing and subsequent establishment of robust enterprise-wide (usually global) informatics platforms, which enable complicated HTS work flows, facilitate HTS data mining, and drive effective decision-making. The purpose of this review is, from the data analysis and handling perspective, to examine key elements in HTS operations and some essential data-related activities supporting or interfacing the screening process, and outline properties that various enabling software should have. Additionally, some general advice for corporate managers with system procurement responsibilities is offered.     

Novel statistical approach for primary high-throughput screening hit selection

The standard activity threshold-based method (the "top X" approach), currently widely used in the high-throughput screening (HTS) data analysis, is ineffective at identifying good-quality hits. We have proposed a novel knowledge-based statistical approach, driven by the hidden structure-activity relationship (SAR) within a screening library, for primary hit selection. Application to an in-house ultrahigh-throughput screening (uHTS) campaign has demonstrated it can directly identify active scaffolds containing valuable SAR information with a greatly improved confirmation rate compared to the standard "top X" method (from 55% to 85%). This approach may help produce high-quality leads and expedite the hit-to-lead process in drug discovery.     

Targeting chemical inputs and optimising HTS for agrochemical discovery

In vivo high throughput screening (HTS) has been adopted by most of the larger crop protection companies as an important tool for the discovery of new agrochemicals. There has been a paradigm shift in capabilities from screening a few thousand compounds a year to several hundred thousand and the quantity of screening sample required has fallen dramatically. The unifying goal now bringing together screens and inputs is the need to maximise the flow of useful information from HTS and thereby minimise the time taken to discover robust leads and new products. This review examines the positive changes that have occurred towards targeted design and selection of chemical inputs for agrochemical discovery over the last ten years and corresponding developments in HTS assays, data analysis and the logistics of compound storage and dispensing.     

The use of immortalized cell lines in GPCR screening: the good, bad and ugly

For most membrane-bound molecular targets, including G protein linked receptors (GPCRs), the optimal approach in drug discovery involves the use of cell based high throughput screening (HTS) technologies to identify compounds that modulate target activity. Most GPCRs have been cloned and can therefore be routinely expressed in immortalized cell lines. These cells can be easily and rapidly grown in unlimited quantities making them ideal for use in current HTS technologies. A significant advantage of this approach is that immortalized recombinant cells provide a homogenous background for expression of the target which greatly facilitates consistency in screening, thus allowing for a better understanding of the mechanism of action of the interacting compound or drug. Nonetheless, it is now evident that numerous disparities exist between the physiological environment of screening systems using recombinant cells and natural tissues. This has lead to a problem in the validity of the pharmacological data obtained using immortalized cells in as much as such cells do not always reflect the desired clinical efficacy and safety of the compounds under examination. This brief review discusses these issues and describes how they influence the discovery of drugs using modern HTS.     

Parallel and multiplexed bead-based assays and encoding strategies

Advances in high throughput screening (HTS), together with the rapid progress in combinatorial chemistry, genomic and proteomic sciences have dramatically stimulated the development of a variety tools to enable the drug discovery process to become more efficient. Major future challenges in HTS include obtaining high density and good quality data based on assays that are rapid, reliable, inexpensive, sensitive, simple and miniaturised. This paper reviews the development and role of bead-based assays for HTS including DNA and single nucleotide polymorphism (SNP) assays, particularly from a multiplex perspective and evaluating the recent advances in bead-based arrays. The encoding strategies that are commonly used in bead-based assays are highlighted, while the importance of magnetic beads in genomic and proteomic purifications is discussed. In conclusion, bead-based assays offer a powerful promising approach for many aspects of drug discovery.     

Cloud computing approaches to accelerate drug discovery value chain

Continued advancements in the area of technology have helped high throughput screening (HTS) evolve from a linear to parallel approach by performing system level screening. Advanced experimental methods used for HTS at various steps of drug discovery (i.e. target identification, target validation, lead identification and lead validation) can generate data of the order of terabytes. As a consequence, there is pressing need to store, manage, mine and analyze this data to identify informational tags. This need is again posing challenges to computer scientists to offer the matching hardware and software infrastructure, while managing the varying degree of desired computational power. Therefore, the potential of "On-Demand Hardware" and "Software as a Service (SAAS)" delivery mechanisms cannot be denied. This on-demand computing, largely referred to as Cloud Computing, is now transforming the drug discovery research. Also, integration of Cloud computing with parallel computing is certainly expanding its footprint in the life sciences community. The speed, efficiency and cost effectiveness have made cloud computing a 'good to have tool' for researchers, providing them significant flexibility, allowing them to focus on the 'what' of science and not the 'how'. Once reached to its maturity, Discovery-Cloud would fit best to manage drug discovery and clinical development data, generated using advanced HTS techniques, hence supporting the vision of personalized medicine.     

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.     

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.     

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.     

Impact of novel screening technologies on ion channel drug discovery

Ion channels are a large superfamily of membrane proteins that pass ions across membranes. They are critical to diverse physiological functions in both excitable and nonexcitable cells and underlie many diseases. As a result, they are an important target class which is proven to be highly "druggable". However, for high throughput screening (HTS), ion channels are historically difficult as a target class due to their unique molecular properties and the limitations of assay technologies that are HTS-amendable. In this article, we describe the background of ion channels and current status and challenges for ion channel drug discovery, followed by an overview of both conventional and newly emerged ion channel screening technologies. The critical impact of such new technologies on current and future ion channel drug discovery is also discussed.     

Peptide display in functional genomics

The completion of the human genome project has opened novel scientific avenues in functional genomics, structural genomics and proteomics. These areas have a common goal: the identification of all the proteins acting and cross-talking in a single cell at a defined moment of its lifecycle. The expansion of these areas in bioscience has been facilitated by the rapid development of high throughput screening (HTS) methods which has, in turn, attracted the business community to make investments in this novel business segment of biotechnology. By using these HTS methods, the hope is that novel targets will be validated much more rapidly speeding up the development of novel drugs. Numerous techniques and tools have emerged over the past decade for the identification of small target-specific molecular ligands that exploit a common feature: the exploration of molecular diversity using combinatorial methods. While chemists developed new methods for rapidly and efficiently synthesising and screening large collections of small molecules, biologists used recombinant DNA techniques for selecting displayed repertoires. To this end, the discovery of new low molecular weight peptides is becoming increasingly important, not only as molecular tools for the understanding of protein-protein interactions but also for the generation of lead compounds.     

Screening of enzyme inhibitors from traditional Chinese medicine

Traditional Chinese medicine (TCM) has been used for more than 4000 years. By comparison with large combinatorial chemistry libraries and natural products of the West for high-throughput screening (HTS) of new drugs discovery, an advantage of TCM is that the preparation has clear efficacies on the therapy of some diseases. Although the effective components are not clear, the clear efficacies of TCM have been identified for long time practice, Therefore, TCMs should be valuable lead compound libraries with a definite therapy efficacy from the viewpoint of HTS. Nevertheless, current HTS technologies are not easily adapted to investigate TCMs because they are designed for screening a relatively pure known chemical at a known concentration. In contrast, TCMs are mixtures of unknown compounds in unknown concentrations that may differ markedly between samples from different plants. This article reviews the current and future researches on the enzyme inhibitors screening from TCM.     

High-throughput screening of inhibitory potential of nine cytochrome P450 enzymes in vitro using liquid chromatography/tandem mass spectrometry

The early detection of potential drug-drug interactions is an important issue of drug discovery that has led to the development of high-throughput screening (HTS) methods for potential drug interactions. We developed a HTS method for potential interactions of inhibitory drugs for nine human P450 enzymes using cocktail incubation and tandem mass spectrometry in vitro. This new method involves incubation of two cocktail doses and single cassette analysis. The two cocktail doses in vitro were developed to minimize solvent effects and mutual drug interactions among substrates: cocktail A was composed of phenacetin for CYP1A2, coumarin for CYP2A6, paclitaxel for CYP2C8, S-mephenytoin for CYP2C19, dextromethorphan for CYP2D6, and midazolam for CYP3A4; and cocktail B was composed of three substrates including bupropion for CYP2B6, tolbutamide for CYP2C9, and chlorzoxazone for CYP2E1. In the incubation study of these cocktails, the reaction mixtures were pooled and simultaneously analyzed using liquid chromatography/tandem mass spectrometry employing a fast gradient. The method was validated by comparing the inhibition data obtained from the incubation of each individual probe substrate alone with data from the new method. The IC50 value of each inhibitor in the cocktail agreed well with that of the individual probe drug as well as with values previously reported in the literature. As a HTS method for potential interactions of the inhibition of these nine P450 enzymes, this new method will be useful in the drug discovery process and for the mechanistic understanding of drug interactions.     

Discovery of a new family of chromium ethylene polymerisation catalysts using high throughput screening methodology

Following a discovery that salicylaldimines bearing bulky ortho-phenoxy substituents and small imine substituents give very active chromium catalysts for ethylene polymerisation, High Throughput Screening (HTS) methodology has been employed to facilitate a further discovery of exceptionally active catalysts based on tridentate salicylaldimine ligands with bulky triptycenyl groups.     

Lead-like, drug-like or “Pub-like”: how different are they?

Academic and industrial research continues to be focused on discovering new classes of compounds based on HTS. Post-HTS analyses need to prioritize compounds that are progressed to chemical probe or lead status. We report trends in probe, lead and drug discovery by examining the following categories of compounds: 385 leads and the 541 drugs that emerged from them; "active" (152) and "inactive" (1488) compounds from the Molecular Libraries Initiative Small Molecule Repository (MLSMR) tested by HTS; "active" (46) and "inactive" (72) compounds from Nature Chemical Biology (NCB) tested by HTS; compounds in the drug development phase (I, II, III and launched), as indexed in MDDR; and medicinal chemistry compounds from WOMBAT, separated into high-activity (5,784 compounds with nanomolar activity or better) and low-activity (30,690 with micromolar activity or less). We examined Molecular weight (MW), molecular complexity, flexibility, the number of hydrogen bond donors and acceptors, LogP-the octanol/water partition coefficient estimated by ClogP and ALOGPS), LogSw (intrinsic water solubility, estimated by ALOGPS) and the number of Rule of five (Ro5) criteria violations. Based on the 50% and 90% distribution moments of the above properties, there were no significant difference between leads of known drugs and "actives" from MLSMR or NCB (chemical probes). "Inactives" from NCB and MLSMR were also found to exhibit similar properties. From these combined sets, we conclude that "Actives" (569 compounds) are less complex, less flexible, and more soluble than drugs (1,651 drugs), and significantly smaller, less complex, less hydrophobic and more soluble than the 5,784 high-activity WOMBAT compounds. These trends indicate that chemical probes are similar to leads with respect to some properties, e.g., complexity, solubility, and hydrophobicity.     

Past and future perspectives of synthetic peptide libraries

Combinatorial preparation and HTS of arrays of compounds have increased the speed of drug discovery. A strong impulse in this field has come by the introduction of the solid phase synthesis method that, through automation and miniaturization, has paved the way to the preparation of large collections of compounds in compact and trackable formats. Due to the well established synthetic procedures, peptides have been largely used to develop the basic concepts of combinatorial chemistry and peptide libraries are still successfully employed in screening programs. However, peptides generally do not fulfil the requirements of low conformational flexibility, stability and bioavailability needed for good drug candidates and peptide leads with high potency and selectivity are often made "druggable" by conversion to more stable structures with improved pharmacological profiles. Such an approach makes the screening of peptide libraries still a valuable tool for drug discovery. We propose here a panoramic review of the most common methods for the preparation and screening of peptide libraries and the most interesting findings of the last decade. We also report on a new approach we follow in our laboratory that is based on the use of "simplified" libraries composed by a minimum number of non-redundant amino acids for the assembly of short peptides. The choice of amino acids is dictated by diversity in lipophilicity, MW, charge and polarity. Newly identified active sequences are then modified by preparing new variants containing analogous amino acids, so that the chemical space occupied by the excluded residues can be explored. This approach offers the advantage of simplifying the synthesis and deconvolution of libraries and provides new active compounds with a molecular size similar to that of small molecules, to which they can be easily converted.     

High throughput screening technology and the small molecules modulating aging related signals

Aging and its related diseases are severe issues in modern society. Many efforts have been made to understand the mechanisms of aging and to find the ways to prevent age-related diseases. Identifying the compounds targeting aging-related signals is a challenging work because there are so many proteins and signals involved. Recently, alone with the progresses in high throughput screening (HTS) technology, increasing numbers of small molecules targeting aging-related pathologic processes have been identified. In this review, we introduce the basic workflow, classification and assay strategies of HTS technology, and sort out known small molecules identified via HTS technology by their roles in aging related diseases, such as neural degenerative diseases, diabetes and tumors. Given the fact that application of HTS on aging research is still at an early stage, we also summarize the cellular mechanisms about aging process, paralleled with the compounds which can modulate the functions of proteins important for aging signals. Finally, we briefly discuss some advanced HTS technologies for their potent applications on the discovery of anti-aging compounds. The main purpose of this review is to provide updated and useful information to those who are interested in pharmacology and HTS technology, but not familiar with aging biology, or vice versa.     

An overview of drug screening using primary and embryonic stem cells

Cellular technologies are widely used in drug discovery to treat human diseases. Most studies involve the expression of recombinant targets in immortalized cells and measure drug interactions using simple, quantifiable responses. Such cells are also amenable to high throughput screening (HTS) methods. However, the cell phenotype employed in HTS is often determined by the assay technology available, rather than the physiological relevance of the cell background. They are, therefore, suboptimal surrogates for cells that accurately reflect human diseases. Consequently, there is growing interest in adopting primary and embryonic stem cells in drug discovery. Primary cells are already used in secondary screening assays in conjunction with confocal imaging techniques, as well as in target validation studies employing, for example, gene silencing approaches. Stem cells can be grown in unlimited quantities and can be derived from transgenic animals engineered to express disease causing proteins better coupling the molecular target with function in vivo. Human stem cells also offer unique opportunities for drug discovery in that they can be directed to specific phenotypes thus providing a framework to identify tissue-selective agents. Organizing stem cells into networks resembling those in native tissues, potentially returns drug discovery back to the highly successful pharmacological methods of the past, in which organ and tissue based systems were used, but with the advantage that they can be utilized using modern HTS technologies. This emerging area will lead to discovery of compounds whose effect in vivo is more predictable thereby increasing the efficiency of drugs that ameliorate human disease.     

Fragment-based drug design: computational & experimental state of the art

Fragment-based screening is an emerging technology which is used as an alternative to high-throughput screening (HTS), and often in parallel. Fragment screening focuses on very small compounds. Because of their small size and simplicity, fragments exhibit a low to medium binding affinity (mM to μM) and must therefore be screened at high concentration in order to detect binding events. Since some issues are associated with high-concentration screening in biochemical assays, biophysical methods are generally employed in fragment screening campaigns. Moreover, these techniques are very sensitive and some of them can give precise information about the binding mode of fragments, which facilitates the mandatory hit-to-lead optimization. One of the main advantages of fragment-based screening is that fragment hits generally exhibit a strong binding with respect to their size, and their subsequent optimization should lead to compounds with better pharmacokinetic properties compared to molecules evolved from HTS hits. In other words, fragments are interesting starting points for drug discovery projects. Besides, the chemical space of low-complexity compounds is very limited in comparison to that of drug-like molecules, and thus easier to explore with a screening library of limited size. Furthermore, the "combinatorial explosion" effect ensures that the resulting combinations of interlinked binding fragments may cover a significant part of "drug-like" chemical space. In parallel to experimental screening, virtual screening techniques, dedicated to fragments or wider compounds, are gaining momentum in order to further reduce the number of compounds to test. This article is a review of the latest news in both experimental and in silico virtual screening in the fragment-based discovery field. Given the specificity of this journal, special attention will be given to fragment library design.     

Luminescent proteins from Aequorea victoria: applications in drug discovery and in high throughput analysis

Recent progress in generating a vast number of drug targets through genomics and large compound libraries through combinatorial chemistry have stimulated advancements in drug discovery through the development of new high throughput screening (HTS) methods. Automation and HTS techniques are also highly desired in fields such as clinical diagnostics. Luminescence-based assays have emerged as an alternative to radiolabel-based assays in HTS as they approach the sensitivity of radioactive detection along with ease of operation, which makes them amenable to miniaturization. Luminescent proteins provide the advantage of reduced reagent and operating costs because they can be produced in unlimited amounts through the use of genetic engineering tools. In that regard, the use of two naturally occurring and recombinantly produced luminescent proteins from the jellyfish Aequorea victoria, namely, aequorin and the green fluorescent protein (GFP), has attracted attention in a number of analytical applications in diverse research areas. Aequorin is naturally bioluminescent and has therefore, virtually no associated background signal, which allows its detection down to attomole levels. GFP has become the reporter of choice in a variety of applications given that it is an autofluorescent protein that does not require addition of any co-factors for fluorescence emission. Furthermore, the generation of various mutants of GFP with differing luminescent and spectral properties has spurred additional interest in this protein. In this review, we focus on the use of aequorin and GFP in the development of highly sensitive assays that find applications in drug discovery and in high throughput analysis.