Statistical models for identifying frequent hitters in high throughput screening

Abstract High throughput screening (HTS) interrogates compound libraries to find those that are “active” in an assay. To better understand compound behavior in HTS, we assessed an existing binomial survivor function (BSF) model of “frequent hitters” using 872 publicly available HTS data sets. We found large numbers of “infrequent hitters” using this model leading us to reject the BSF for identifying “frequent hitters.” As alternatives, we investigated generalized logistic, gamma, and negative binomial distributions as models for compound behavior. The gamma model reduced the proportion of both frequent and infrequent hitters relative to the BSF. Within this data set, conclusions about individual compound behavior were limited by the number of times individual compounds were tested (1–1613 times) and disproportionate testing of some compounds. Specifically, most tests (78%) were on a 309,847-compound subset (17.6% of compounds) each tested ≥ 300 times. We concluded that the disproportionate retesting of some compounds represents compound repurposing at scale rather than drug discovery. The approach to drug discovery represented by these 872 data sets characterizes the assays well by challenging them with many compounds while each compound is characterized poorly with a single assay. Aggregating the testing information from each compound across the multiple screens yielded a continuum with no clear boundary between normal and frequent hitting compounds.

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A Fully Automated High-Throughput Flow Cytometry Screening System Enabling Phenotypic Drug Discovery

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555218773086 ◽

2018 ◽

Vol 23 (7) ◽

pp. 697-707 ◽

Cited By ~ 2

Author(s):

John Joslin ◽

James Gilligan ◽

Paul Anderson ◽

Catherine Garcia ◽

Orzala Sharif ◽

...

Keyword(s):

Flow Cytometry ◽

Drug Discovery ◽

High Throughput ◽

High Throughput Screening ◽

Screening System ◽

Preclinical Development ◽

Drug Identification ◽

Therapeutic Setting ◽

Compound Libraries ◽

Automated Platform

The goal of high-throughput screening is to enable screening of compound libraries in an automated manner to identify quality starting points for optimization. This often involves screening a large diversity of compounds in an assay that preserves a connection to the disease pathology. Phenotypic screening is a powerful tool for drug identification, in that assays can be run without prior understanding of the target and with primary cells that closely mimic the therapeutic setting. Advanced automation and high-content imaging have enabled many complex assays, but these are still relatively slow and low throughput. To address this limitation, we have developed an automated workflow that is dedicated to processing complex phenotypic assays for flow cytometry. The system can achieve a throughput of 50,000 wells per day, resulting in a fully automated platform that enables robust phenotypic drug discovery. Over the past 5 years, this screening system has been used for a variety of drug discovery programs, across many disease areas, with many molecules advancing quickly into preclinical development and into the clinic. This report will highlight a diversity of approaches that automated flow cytometry has enabled for phenotypic drug discovery.

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Drug discovery from Nature: automated high-quality sample preparation

Journal of Automated Methods and Management in Chemistry ◽

10.1155/s1463924600000249 ◽

2000 ◽

Vol 22 (5) ◽

pp. 149-157 ◽

Cited By ~ 12

Author(s):

Ralf Thiericke

Keyword(s):

Drug Discovery ◽

Sample Preparation ◽

High Throughput ◽

High Throughput Screening ◽

Solid Phase ◽

Structural Diversity ◽

Synthetic Compound ◽

Natural Origin ◽

Screening Programs ◽

Compound Libraries

Secondary metabolites from plants, animals and microorganisms have been proven to be an outstanding source for new and innovative drugs and show a striking structural diversity that supplements chemically synthesized compounds or libraries in drug discovery programs. Unfortunately, extracts from natural sources are usually complex mixtures of compounds:: often generated in time consuming and for the most part manual processes. As quality and quantity of the provided samples play a pivotal role in the success of high-throughput screening programs this poses serious problems. In order to make samples of natural origin competitive with synthetic compound libraries, we devised a novel, automated sample preparation procedure based on solid-phase extraction (SPE). By making use of a modified Zymark RapidTrace®SPE workstation an easy-to-handle and effective fractionation method has been developed which allows the generation of highquality samples from natural origin, fulfilling the requirements of an integration into high-throughput screening programs.

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High-Density Miniaturized Thermal Shift Assays as a General Strategy for Drug Discovery

CrossRef Listing of Deleted DOIs ◽

10.1177/108705710100600609 ◽

2001 ◽

Vol 6 (6) ◽

pp. 429-440 ◽

Cited By ~ 573

Author(s):

Michael W. Pantoliano ◽

Eugene C. Petrella ◽

Joseph D. Kwasnoski ◽

Victor S. Lobanov ◽

James Myslik ◽

...

Keyword(s):

Drug Discovery ◽

Ligand Binding ◽

High Throughput ◽

High Throughput Screening ◽

Screening Strategy ◽

General Strategy ◽

General Applicability ◽

High Throughput Drug Screening ◽

Compound Libraries ◽

Thermal Shift

More general and universally applicable drug discovery assay technologies are needed in order to keep pace with the recent advances in combinatorial chemistry and genomics-based target generation. Ligand-induced conformational stabilization of proteins is a well-understood phenomenon in which substrates, inhibitors, cofactors, and even other proteins provide enhanced stability to proteins on binding. This phenomenon is based on the energetic coupling of the ligand-binding and protein-melting reactions. In an attempt to harness these biophysical properties for drug discovery, fully automated instrumentation was designed and implemented to perform miniaturized fluorescence-based thermal shift assays in a microplate format for the high throughput screening of compound libraries. Validation of this process and instrumentation was achieved by investigating ligand binding to more than 100 protein targets. The general applicability of the thermal shift screening strategy was found to be an important advantage because it circumvents the need to design and retool new assays with each new therapeutic target. Moreover, the miniaturized thermal shift assay methodology does not require any prior knowledge of a therapeutic target's function, making it ideally suited for the quantitative high throughput drug screening and evaluation of targets derived from genomics.

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BioAssay Ontology Annotations Facilitate Cross-Analysis of Diverse High-Throughput Screening Data Sets

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057111400191 ◽

2011 ◽

Vol 16 (4) ◽

pp. 415-426 ◽

Cited By ~ 33

Author(s):

Stephan C. Schürer ◽

Uma Vempati ◽

Robin Smith ◽

Mark Southern ◽

Vance Lemmon

Keyword(s):

Drug Discovery ◽

High Throughput ◽

High Throughput Screening ◽

Data Sets ◽

Large Set ◽

Chemical Probes ◽

Great Flexibility ◽

Data Repositories ◽

Data Format ◽

Entry Points

High-throughput screening data repositories, such as PubChem, represent valuable resources for the development of small-molecule chemical probes and can serve as entry points for drug discovery programs. Although the loose data format offered by PubChem allows for great flexibility, important annotations, such as the assay format and technologies employed, are not explicitly indexed. The authors have previously developed a BioAssay Ontology (BAO) and curated more than 350 assays with standardized BAO terms. Here they describe the use of BAO annotations to analyze a large set of assays that employ luciferase- and β-lactamase–based technologies. They identified promiscuous chemotypes pertaining to different subcategories of assays and specific mechanisms by which these chemotypes interfere in reporter gene assays. Results show that the data in PubChem can be used to identify promiscuous compounds that interfere nonspecifically with particular technologies. Furthermore, they show that BAO is a valuable toolset for the identification of related assays and for the systematic generation of insights that are beyond the scope of individual assays or screening campaigns.

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Outlier Mining in High Throughput Screening Experiments

CrossRef Listing of Deleted DOIs ◽

10.1177/108705710200700406 ◽

2002 ◽

Vol 7 (4) ◽

pp. 341-351 ◽

Cited By ~ 16

Author(s):

Michael F.M. Engels ◽

Luc Wouters ◽

Rudi Verbeeck ◽

Greet Vanhoof

Keyword(s):

Data Mining ◽

Biological Activity ◽

High Throughput ◽

High Throughput Screening ◽

False Negative ◽

Data Sets ◽

Data Set ◽

Structure Information ◽

Screening Experiments ◽

Mining Procedure

A data mining procedure for the rapid scoring of high-throughput screening (HTS) compounds is presented. The method is particularly useful for monitoring the quality of HTS data and tracking outliers in automated pharmaceutical or agrochemical screening, thus providing more complete and thorough structure-activity relationship (SAR) information. The method is based on the utilization of the assumed relationship between the structure of the screened compounds and the biological activity on a given screen expressed on a binary scale. By means of a data mining method, a SAR description of the data is developed that assigns probabilities of being a hit to each compound of the screen. Then, an inconsistency score expressing the degree of deviation between the adequacy of the SAR description and the actual biological activity is computed. The inconsistency score enables the identification of potential outliers that can be primed for validation experiments. The approach is particularly useful for detecting false-negative outliers and for identifying SAR-compliant hit/nonhit borderline compounds, both of which are classes of compounds that can contribute substantially to the development and understanding of robust SARs. In a first implementation of the method, one- and two-dimensional descriptors are used for encoding molecular structure information and logistic regression for calculating hits/nonhits probability scores. The approach was validated on three data sets, the first one from a publicly available screening data set and the second and third from in-house HTS screening campaigns. Because of its simplicity, robustness, and accuracy, the procedure is suitable for automation.

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Artificial Neural Network—Based Analysis of High-Throughput Screening Data for Improved Prediction of Active Compounds

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057109351312 ◽

2009 ◽

Vol 14 (10) ◽

pp. 1236-1244 ◽

Cited By ~ 5

Author(s):

Swapan Chakrabarti ◽

Stan R. Svojanovsky ◽

Romana Slavik ◽

Gunda I. Georg ◽

George S. Wilson ◽

...

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Back Propagation ◽

Large Data ◽

Classification Performance ◽

Data Sets ◽

Data Set ◽

Active Compounds ◽

Physicochemical Features ◽

Artificial Neural

Artificial neural networks (ANNs) are trained using high-throughput screening (HTS) data to recover active compounds from a large data set. Improved classification performance was obtained on combining predictions made by multiple ANNs. The HTS data, acquired from a methionine aminopeptidases inhibition study, consisted of a library of 43,347 compounds, and the ratio of active to nonactive compounds, R A/N, was 0.0321. Back-propagation ANNs were trained and validated using principal components derived from the physicochemical features of the compounds. On selecting the training parameters carefully, an ANN recovers one-third of all active compounds from the validation set with a 3-fold gain in R A/N value. Further gains in RA/N values were obtained upon combining the predictions made by a number of ANNs. The generalization property of the back-propagation ANNs was used to train those ANNs with the same training samples, after being initialized with different sets of random weights. As a result, only 10% of all available compounds were needed for training and validation, and the rest of the data set was screened with more than a 10-fold gain of the original RA/N value. Thus, ANNs trained with limited HTS data might become useful in recovering active compounds from large data sets.

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Natural Products in High Throughput Screening: Automated High-Quality Sample Preparation

CrossRef Listing of Deleted DOIs ◽

10.1177/108705719900400104 ◽

1999 ◽

Vol 4 (1) ◽

pp. 15-25 ◽

Cited By ~ 35

Author(s):

Ingrid Schmid ◽

Isabel Sattler ◽

Susanne Grabley ◽

Ralf Thiericke

Keyword(s):

Drug Discovery ◽

Sample Preparation ◽

High Throughput ◽

High Throughput Screening ◽

Solid Phase ◽

Structural Diversity ◽

High Quality ◽

Synthetic Compound ◽

Natural Origin ◽

Compound Libraries

At present, compound libraries from combinatorial chemistry are the major source for high throughput screening (HTS) programs in drug discovery. On the other hand, nature has been proven to be an outstanding source for new and innovative drugs. Secondary metabolites from plants, animals, and microorganisms show a striking structural diversity that supplements chemically synthesized compounds or libraries in drug discovery programs. Unfortunately, extracts from natural sources are usually complex mixtures of compounds, often generated in time-consuming and, for the most part, manual processes. Because quality and quantity of the provided samples play a pivotal role in the success of HTS programs, this poses serious problems. In order to make samples of natural origin competitive with synthetic compound libraries, we devised a novel, automated sample preparation procedure based on solid-phase extraction (SPE). By making use of modified Zymark (Hopkinton, MA) RapidTrace® SPE workstations, we developed an easy-to-handle and effective fractionation method that generates high-quality samples from natural origin, fulfilling the requirements for an integration in high throughput drug discovery programs.

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The MALDI TOF E2/E3 ligase assay as an universal tool for drug discovery in the ubiquitin pathway

10.1101/224600 ◽

2017 ◽

Author(s):

Virginia De Cesare ◽

Clare Johnson ◽

Victoria Barlow ◽

James Hastie ◽

Axel Knebel ◽

...

Keyword(s):

Drug Discovery ◽

High Throughput ◽

High Throughput Screening ◽

E3 Ligase ◽

Ubiquitin Pathway ◽

E3 Ligases ◽

Maldi Tof ◽

Compound Libraries ◽

Ubiquitin System ◽

Conjugating Enzymes

AbstractIn many diseases, components of the ubiquitin system - such as E2/E3 ligases and deubiquitylases - are dysregulated. The ubiquitin system has therefore become an emergent target for the treatment of a number of diseases, including cancer, neurodegeneration and autoimmunity. Despite of the efforts in this field, primary screenings of compound libraries to individuate new potential therapeutic molecules targeting the ubiquitin pathway have been strongly limited by the lack of robust and fast high-throughput assays. Here we report the first label-free high-throughput screening (HTS) assay for ubiquitin E2 conjugating enzymes and E3 ligases based on Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight (MALDI TOF) mass spectrometry. The MALDI TOF E2/E3 assay allows us to test E2 conjugating enzymes and E3 ligases for their ubiquitin transfer activity, to identify E2/E3 active pairs, inhibitor potency and specificity and to screen compound libraries in vitro without synthesis of chemical or fluorescent probes. We demonstrate that the MALDI TOF E2/E3 assay is a universal tool for drug discovery screening in the ubiquitin pathway as it is suitable for working with all E3 ligase families and requires a reduced amount of reagents, compared to standard biochemical assays.

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