MScreen: An Integrated Compound Management and High-Throughput Screening Data Storage and Analysis System

High-throughput screening (HTS) has historically been used by the pharmaceutical industry to rapidly test hundreds of thousands of compounds to identify potential drug candidates. More recently, academic groups have used HTS to identify new chemical probes or small interfering RNA (siRNA) that can serve as experimental tools to examine the biology or physiology of novel proteins, processes, or interactions. HTS presents a significant challenge with the vast and complex nature of data generated. This report describes MScreen, a Web-based, open-source cheminformatics application for chemical library and siRNA plate management, primary HTS and dose-response data handling, structure search, and administrative functions. Each project in MScreen can be secured with passwords or shared in an open-information environment that enables collaborators to easily compare data from many screens, providing a useful means to identify compounds with desired selectivity. Unique features include compound, substance, mixture, and siRNA plate creation and formatting; automated dose-response fitting and quality control (QC); and user, target, and assay method administration. MScreen provides an effective means to facilitate HTS information handling and analysis in the academic setting so that users can efficiently view their screening data and evaluate results for follow-up.     

Identifying actives from HTS data sets: practical approaches for the selection of an appropriate HTS data-processing method and quality control review

High-throughput screening (HTS) has achieved a dominant role in drug discovery over the past 2 decades. The goal of HTS is to identify active compounds (hits) by screening large numbers of diverse chemical compounds against selected targets and/or cellular phenotypes. The HTS process consists of multiple automated steps involving compound handling, liquid transfers, and assay signal capture, all of which unavoidably contribute to systematic variation in the screening data. The challenge is to distinguish biologically active compounds from assay variability. Traditional plate controls-based and non-controls-based statistical methods have been widely used for HTS data processing and active identification by both the pharmaceutical industry and academic sectors. More recently, improved robust statistical methods have been introduced, reducing the impact of systematic row/column effects in HTS data. To apply such robust methods effectively and properly, we need to understand their necessity and functionality. Data from 6 HTS case histories are presented to illustrate that robust statistical methods may sometimes be misleading and can result in more, rather than less, false positives or false negatives. In practice, no single method is the best hit detection method for every HTS data set. However, to aid the selection of the most appropriate HTS data-processing and active identification methods, the authors developed a 3-step statistical decision methodology. Step 1 is to determine the most appropriate HTS data-processing method and establish criteria for quality control review and active identification from 3-day assay signal window and DMSO validation tests. Step 2 is to perform a multilevel statistical and graphical review of the screening data to exclude data that fall outside the quality control criteria. Step 3 is to apply the established active criterion to the quality-assured data to identify the active compounds.     

High Content Analysis of Primary Macrophages Hosting Proliferating Leishmania Amastigotes: Application to Anti-leishmanial Drug Discovery

Background/Objectives

Human leishmaniases are parasitic diseases causing severe morbidity and mortality. No vaccine is available and numerous factors limit the use of current therapies. There is thus an urgent need for innovative initiatives to identify new chemotypes displaying selective activity against intracellular Leishmania amastigotes that develop and proliferate inside macrophages, thereby causing the pathology of leishmaniasis.

Methodology/Principal Findings

We have developed a biologically sound High Content Analysis assay, based on the use of homogeneous populations of primary mouse macrophages hosting Leishmania amazonensis amastigotes. In contrast to classical promastigote-based screens, our assay more closely mimics the environment where intracellular amastigotes are growing within acidic parasitophorous vacuoles of their host cells. This multi-parametric assay provides quantitative data that accurately monitors the parasitic load of amastigotes-hosting macrophage cultures for the discovery of leishmanicidal compounds, but also their potential toxic effect on host macrophages. We validated our approach by using a small set of compounds of leishmanicidal drugs and recently published chemical entities. Based on their intramacrophagic leishmanicidal activity and their toxicity against host cells, compounds were classified as irrelevant or relevant for entering the next step in the drug discovery pipeline.

Conclusions/Significance

Our assay represents a new screening platform that overcomes several limitations in anti-leishmanial drug discovery. First, the ability to detect toxicity on primary macrophages allows for discovery of compounds able to cross the membranes of macrophage, vacuole and amastigote, thereby accelerating the hit to lead development process for compounds selectively targeting intracellular parasites. Second, our assay allows discovery of anti-leishmanials that interfere with biological functions of the macrophage required for parasite development and growth, such as organelle trafficking/acidification or production of microbicidal effectors. These data thus validate a novel phenotypic screening assay using virulent Leishmania amastigotes growing inside primary macrophage to identify new chemical entities with bona fide drug potential.     

Chemical compound navigator: a web-based chem-BLAST, chemical taxonomy-based search engine for browsing compounds

A novel technique to annotate, query, and analyze chemical compounds has been developed and is illustrated by using the inhibitor data on HIV protease-inhibitor complexes. In this method, all chemical compounds are annotated in terms of standard chemical structural fragments. These standard fragments are defined by using criteria, such as chemical classification; structural, chemical, or functional groups; and commercial, scientific or common names or synonyms. These fragments are then organized into a data tree based on their chemical substructures. Search engines have been developed to use this data tree to enable query on inhibitors of HIV protease (http://xpdb.nist.gov/hivsdb/hivsdb.html). These search engines use a new novel technique, Chemical Block Layered Alignment of Substructure Technique (Chem-BLAST) to search on the fragments of an inhibitor to look for its chemical structural neighbors. This novel technique to annotate and query compounds lays the foundation for the use of the Semantic Web concept on chemical compounds to allow end users to group, sort, and search structural neighbors accurately and efficiently. During annotation, it enables the attachment of "meaning" (i.e., semantics) to data in a manner that far exceeds the current practice of associating "metadata" with data by creating a knowledge base (or ontology) associated with compounds. Intended users of the technique are the research community and pharmaceutical industry, for which it will provide a new tool to better identify novel chemical structural neighbors to aid drug discovery.     

Detection of oligonucleotide-ligand complexes by ESI-MS (DOLCE-MS) as a component of high throughput screening

With the advent of combinatorial chemistry and high throughput screening, a major bottleneck in the pharmaceutical industry has changed from quickly finding active compounds to limiting them to a manageable number for proper follow-up. With hundreds to thousands of active compounds identified by a multitude of biological screens, there need to be rapid and unambiguous methods for eliminating false positive, toxic, or otherwise difficult compounds from further scrutiny. We have used electrospray ionization mass spectrometry as a rapid screening method to identify compounds from viral screens that yield a positive assay response by interaction with DNA rather than inhibiting the target enzyme. Both the sample preparation and data acquisition have been automated, allowing the screening of all hits from relevant biological screens (up to 1,000/week). The assay was validated using several known DNA intercalators and minor groove binders. These "standards" and many but not all of our "active compounds" were shown to form noncovalent complexes with a variety of different DNA:DNA and DNA:RNA duplexes.     

The Privileged Chemical Space Predictor (pcsp): A computer program that identifies privileged chemical space from screens of modularly assembled chemical libraries

Modularly assembled combinatorial libraries are often used to identify ligands that bind to and modulate the function of a protein or a nucleic acid. Much of the data from screening these compounds, however, is not efficiently utilized to define structure–activity relationships (SAR). If SAR data are accurately constructed, it can enable the design of more potent binders. Herein, we describe a computer program called Privileged Chemical Space Predictor (pcsp) that statistically determines SAR from high-throughput screening (HTS) data and then identifies features in small molecules that predispose them for binding a target. Features are scored for statistical significance and can be utilized to design improved second generation compounds or more target-focused libraries. The program’s utility is demonstrated through analysis of a modularly assembled peptoid library that previously was screened for binding to and inhibiting a group I intron RNA from the fungal pathogen Candida albicans.     

Distance-Based Genome Rearrangement Phylogeny

Evolution operates on whole genomes through direct rearrangements of genes, such as inversions, transpositions, and inverted transpositions, as well as through operations, such as duplications, losses, and transfers, that also affect the gene content of the genomes. Because these events are rare relative to nucleotide substitutions, gene order data offer the possibility of resolving ancient branches in the tree of life; the combination of gene order data with sequence data also has the potential to provide more robust phylogenetic reconstructions, since each can elucidate evolution at different time scales. Distance corrections greatly improve the accuracy of phylogeny reconstructions from DNA sequences, enabling distance-based methods to approach the accuracy of the more elaborate methods based on parsimony or likelihood at a fraction of the computational cost. This paper focuses on developing distance correction methods for phylogeny reconstruction from whole genomes. The main question we investigate is how to estimate evolutionary histories from whole genomes with equal gene content, and we present a technique, the empirically derived estimator (EDE), that we have developed for this purpose. We study the use of EDE on whole genomes with identical gene content, and we explore the accuracy of phylogenies inferred using EDE with the neighbor joining and minimum evolution methods under a wide range of model conditions. Our study shows that tree reconstruction under these two methods is much more accurate when based on EDE distances than when based on other distances previously suggested for whole genomes. Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users. [Reviewing Editor: Dr. Martin Kreitman]     

Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets

Virtual screening is one of the major tools used in computer-aided drug discovery. In structure-based virtual screening, the scoring function is critical to identifying the correct docking pose and accurately predicting the binding affinities of compounds. However, the performance of existing scoring functions has been shown to be uneven for different targets, and some important drug targets have proven especially challenging. In these targets, scoring functions cannot accurately identify the native or near-native binding pose of the ligand from among decoy poses, which affects both the accuracy of the binding affinity prediction and the ability of virtual screening to identify true binders in chemical libraries. Here, we present an approach to discriminating native poses from decoys in difficult targets for which several scoring functions failed to correctly identify the native pose. Our approach employs Discrete Molecular Dynamics simulations to incorporate protein-ligand dynamics and the entropic effects of binding. We analyze a collection of poses generated by docking and find that the residence time of the ligand in the native and nativelike binding poses is distinctly longer than that in decoy poses. This finding suggests that molecular simulations offer a unique approach to distinguishing the native (or nativelike) binding pose from decoy poses that cannot be distinguished using scoring functions that evaluate static structures. The success of our method emphasizes the importance of protein-ligand dynamics in the accurate determination of the binding pose, an aspect that is not addressed in typical docking and scoring protocols.     

Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The discovery of novel bioactive molecules advances our systems‐level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold‐hopping compounds. Through a machine‐learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G‐protein‐coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand‐screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.     

The cell cycle hypothesis of Alzheimer's disease: suggestions for drug development

The cell cycle of hypothesis of neural dysfunction in chronic neurodegenerative conditions such as Alzheimer's disease (AD) offers a unified approach to understanding both existing and novel strategies for drug development. At the present time, a ligand based approach is a pragmatic solution for identifying new chemical leads on which to base future discovery and optimisation. We have pursued a ligand based approach on the basis of public domain data to identify existing compounds capable of abrogating the cell cycle at the G0-G1 interface. Selected on this basis, irrespective of the tissue under study, we identified several classes of compounds as potential chemical leads. Of these compounds, at least ten have already been shown to be neuroprotective in animal models of acute neurodegeneration. Such compounds could form the basis of a screening exercise after development of suitable screening tools. Progressing of chemical leads through such an approach will be more efficient if future leads display relevant "drug-like" properties. Further, drug development in this arena should take account of the special concerns raised by targeting an elderly population. This will involve accounting for frequent polypharmacy in the aging population, and age-related alterations in physiology.     

Surfactant Protein D Contributes to Ocular Defense against Pseudomonas aeruginosa in a Murine Model of Dry Eye Disease

Dry eye disease can cause ocular surface inflammation that disrupts the corneal epithelial barrier. While dry eye patients are known to have an increased risk of corneal infection, it is not known whether there is a direct causal relationship between these two conditions. Here, we tested the hypothesis that experimentally-induced dry eye (EDE) increases susceptibility to corneal infection using a mouse model. In doing so, we also examined the role of surfactant protein D (SP-D), which we have previously shown is involved in corneal defense against infection. Scopolamine injections and fan-driven air were used to cause EDE in C57BL/6 or Black Swiss mice (wild-type and SP-D gene-knockout). Controls received PBS injections and were housed normally. After 5 or 10 days, otherwise uninjured corneas were inoculated with 10⁹ cfu of Pseudomonas aeruginosa strain PAO1. Anesthesia was maintained for 3 h post-inoculation. Viable bacteria were quantified in ocular surface washes and corneal homogenates 6 h post-inoculation. SP-D was measured by Western immunoblot, and corneal pathology assessed from 6 h to 4 days. EDE mice showed reduced tear volumes after 5 and 10 days (each by ∼75%, p<0.001) and showed fluorescein staining (i.e. epithelial disruption). Surprisingly, there was no significant difference in corneal pathology between EDE mice and controls (∼10–14% incidence). Before bacterial inoculation, EDE mice showed elevated SP-D in ocular washes. After inoculation, fewer bacteria were recovered from ocular washes of EDE mice (<2% of controls, p = 0.0004). Furthermore, SP-D knockout mice showed a significant increase in P. aeruginosa corneal colonization under EDE conditions. Taken together, these data suggest that SP-D contributes to corneal defense against P. aeruginosa colonization and infection in EDE despite the loss of barrier function to fluorescein.     

Spindle disturbances in mammalian cells. III. Toxicity, c-mitosis and aneuploidy with 22 different compounds. Specific and unspecific mechanisms

Early investigations have shown that many chemically different compounds can cause disturbances of the spindle function (c-mitosis) in eukaryotic cells and that there is an unspecific (physical) mechanism based on the partitioning of the compound into cellular hydrophobic compartments. This suggests that the approach should be quantitative when testing compounds for this type of activity in vitro; effect/no effect is not the most pertinent question. The present study demonstrates how a set of reference compounds can be used in attempts to identify compounds that act by a more specific (chemical) mechanism to disturb the spindle function. All experiments were performed with an established cell line (V79 Chinese hamster). The results suggest that there is a good qualitative coupling in these cells between c-mitosis and aneuploidy with chemical treatment. Among compounds that are particularly active in relation to their lipophilic character are some chlorophenols, caffeine, diamide, diethyl maleate, 1-chloro-2,4-dinitrobenzene and tertiary butylhydroperoxide. This points to Ca2+-sequestering by mitochondria and/or cellular pH regulation (chlorophenols), Ca2+ release and sequestering by the endoplasmic reticulum (caffeine), enzymatic conjugation to glutathione (diethyl maleate, chlorodinitrobenzene) and hydroperoxide metabolism (t-butylhydroperoxide) as important target functions for specific activity.     

High-Throughput Screening for the Identification of New Therapeutic Options for Metastatic Pheochromocytoma and Paraganglioma

Drug repurposing or repositioning is an important part of drug discovery that has been growing in the last few years for the development of therapeutic options in oncology. We applied this paradigm in a screening of a library of about 3,800 compounds (including FDA-approved drugs and pharmacologically active compounds) employing a model of metastatic pheochromocytoma, the most common tumor of the adrenal medulla in children and adults. The collection of approved drugs was screened in quantitative mode, testing the compounds in compound-titration series (dose-response curves). Analysis of the dose-response screening data facilitated the selection of 50 molecules with potential bioactivity in pheochromocytoma cells. These drugs were classified based on molecular/cellular targets and signaling pathways affected, and selected drugs were further validated in a proliferation assay and by flow cytometric cell death analysis. Using meta-analysis information from molecular targets of the top drugs identified by our screening with gene expression data from human and murine microarrays, we identified potential drugs to be used as single drugs or in combination. An example of a combination with a synergistic effect is presented. Our study exemplifies a promising model to identify potential drugs from a group of clinically approved compounds that can more rapidly be implemented into clinical trials in patients with metastatic pheochromocytoma or paraganglioma.     

A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays

The ability to identify active compounds (3hits2) from large chemical libraries accurately and rapidly has been the ultimate goal in developing high-throughput screening (HTS) assays. The ability to identify hits from a particular HTS assay depends largely on the suitability or quality of the assay used in the screening. The criteria or parameters for evaluating the 3suitability2 of an HTS assay for hit identification are not well defined and hence it still remains difficult to compare the quality of assays directly. In this report, a screening window coefficient, called 3Z-factor,2 is defined. This coefficient is reflective of both the assay signal dynamic range and the data variation associated with the signal measurements, and therefore is suitable for assay quality assessment. The Z-factor is a dimensionless, simple statistical characteristic for each HTS assay. The Z-factor provides a useful tool for comparison and evaluation of the quality of assays, and can be utilized in assay optimization and validation.     

Using clustering techniques to improve hit selection in high-throughput screening

A typical modern high-throughput screening (HTS) operation consists of testing thousands of chemical compounds to select active ones for future detailed examination. The authors describe 3 clustering techniques that can be used to improve the selection of active compounds (i.e., hits). They are designed to identify quality hits in the observed HTS measurements. The considered clustering techniques were first tested on simulated data and then applied to analyze the assay inhibiting Escherichia coli dihydrofo-late reductase produced at the HTS laboratory of McMaster University.     

Open innovation for phenotypic drug discovery: The PD2 assay panel

Phenotypic lead generation strategies seek to identify compounds that modulate complex, physiologically relevant systems, an approach that is complementary to traditional, target-directed strategies. Unlike gene-specific assays, phenotypic assays interrogate multiple molecular targets and signaling pathways in a target "agnostic" fashion, which may reveal novel functions for well-studied proteins and discover new pathways of therapeutic value. Significantly, existing compound libraries may not have sufficient chemical diversity to fully leverage a phenotypic strategy. To address this issue, Eli Lilly and Company launched the Phenotypic Drug Discovery Initiative (PD(2)), a model of open innovation whereby external research groups can submit compounds for testing in a panel of Lilly phenotypic assays. This communication describes the statistical validation, operations, and initial screening results from the first PD(2) assay panel. Analysis of PD(2) submissions indicates that chemical diversity from open source collaborations complements internal sources. Screening results for the first 4691 compounds submitted to PD(2) have confirmed hit rates from 1.6% to 10%, with the majority of active compounds exhibiting acceptable potency and selectivity. Phenotypic lead generation strategies, in conjunction with novel chemical diversity obtained via open-source initiatives such as PD(2), may provide a means to identify compounds that modulate biology by novel mechanisms and expand the innovation potential of drug discovery.