Data Analysis of High-Throughput Screening Results:  Application of Multidomain Clustering to the NCI Anti-HIV Data Set

Quantitative high-throughput screening (qHTS) experiments can simultaneously produce concentration-response profiles for thousands of chemicals. In a typical qHTS study, a large chemical library is subjected to a primary screen to identify candidate hits for secondary screening, validation studies, or prediction modeling. Different algorithms, usually based on the Hill equation logistic model, have been used to classify compounds as active or inactive (or inconclusive). However, observed concentration-response activity relationships may not adequately fit a sigmoidal curve. Furthermore, it is unclear how to prioritize chemicals for follow-up studies given the large uncertainties that often accompany parameter estimates from nonlinear models. Weighted Shannon entropy can address these concerns by ranking compounds according to profile-specific statistics derived from estimates of the probability mass distribution of response at the tested concentration levels. This strategy can be used to rank all tested chemicals in the absence of a prespecified model structure, or the approach can complement existing activity call algorithms by ranking the returned candidate hits. The weighted entropy approach was evaluated here using data simulated from the Hill equation model. The procedure was then applied to a chemical genomics profiling data set interrogating compounds for androgen receptor agonist activity.

Download Full-text

Alternative Statistical Parameter for High-Throughput Screening Assay Quality Assessment

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057106296498 ◽

2007 ◽

Vol 12 (2) ◽

pp. 229-234 ◽

Cited By ~ 66

Author(s):

Yunxia Sui ◽

Zhijin Wu

Keyword(s):

Data Analysis ◽

Quality Assessment ◽

High Throughput ◽

High Throughput Screening ◽

Statistical Parameter ◽

Direct Analysis ◽

Quality Parameters ◽

Screening Assay ◽

Active Compounds ◽

High Throughput Screening Assay

High-throughput screening is an essential process in drug discovery. The ability to identify true active compounds depends on the high quality of assays and proper analysis of data. The Z factor, presented by Zhang et al. in 1999, provides an easy and useful summary of assay quality and has been a widely accepted standard. However, as data analysis has undergone much improvement recently, the assessment of assay quality has not evolved in parallel. In this article, the authors study the implications of Z factor values under different conditions and link the Z factor with the power of discovering true active compounds. They discuss the different interpretations of Z factor depending on error distributions and advocate direct analysis of power as assay quality assessment. They also propose that in estimating assay quality parameters, adjustments in data analysis should be taken into account. Studying the power of identifying true “hits” gives a more direct interpretation of assay quality and may provide guidance in assay optimization on some occasions.

Download Full-text

Analysis of a High-Throughput Screening Data Set Using Potency-Scaled Molecular Similarity Algorithms

Journal of Chemical Information and Modeling ◽

10.1021/ci6005432 ◽

2007 ◽

Vol 47 (2) ◽

pp. 367-375 ◽

Cited By ~ 5

Author(s):

Ingo Vogt ◽

Jürgen Bajorath

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Molecular Similarity ◽

Data Set

Download Full-text

Hormesis in high-throughput screening of antibacterial compounds in E coli

Human & Experimental Toxicology ◽

10.1177/0960327109358917 ◽

2010 ◽

Vol 29 (8) ◽

pp. 667-677 ◽

Cited By ~ 40

Author(s):

Edward J Calabrese ◽

George R Hoffmann ◽

Edward J Stanek ◽

Marc A Nascarella

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Antibacterial Agents ◽

Threshold Model ◽

Concentration Effects ◽

Data Set ◽

E Coli ◽

Model Predictions ◽

High Throughput Study ◽

Repeat Measure

This article assesses the response below a toxicological threshold for 1888 antibacterial agents in Escherichia coli, using 11 concentrations with twofold concentration spacing in a high-throughput study. The data set had important strengths such as low variability in the control (2%—3% SD), a repeat measure of all wells, and a built-in replication. Bacterial growth at concentrations below the toxic threshold is significantly greater than that in the controls, consistent with a hormetic concentration response. These findings, along with analyses of published literature and complementary evaluations of concentration-response model predictions of low-concentration effects in yeast, indicate a lack of support for the broadly and historically accepted threshold model for responses to concentrations below the toxic threshold.

Download Full-text

Improved Statistical Methods for Hit Selection in High-Throughput Screening

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057103258285 ◽

2003 ◽

Vol 8 (6) ◽

pp. 634-647 ◽

Cited By ~ 227

Author(s):

Christine Brideau ◽

Bert Gunter ◽

Bill Pikounis ◽

Andy Liaw

Keyword(s):

Data Analysis ◽

Statistical Methods ◽

High Throughput ◽

High Throughput Screening ◽

Drug Targets ◽

Software Tool ◽

Rapid Screening ◽

Modern Drug ◽

Server Software ◽

Technical Details

High-throughput screening (HTS) plays a central role in modern drug discovery, allowing the rapid screening of large compound collections against a variety of putative drug targets. HTS is an industrial-scale process, relying on sophisticated auto mation, control, and state-of-the art detection technologies to organize, test, and measure hundreds of thousands to millions of compounds in nano-to microliter volumes. Despite this high technology, hit selection for HTS is still typically done using simple data analysis and basic statistical methods. The authors discuss in this article some shortcomings of these methods and present alternatives based on modern methods of statistical data analysis. Most important, they describe and show numerous real examples from the biologist-friendly Stat Server® HTS application (SHS), a custom-developed software tool built on the commercially available S-PLUS® and StatServer® statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables.

Download Full-text

The maturation of DNA encoded libraries: opportunities for new users

Future Medicinal Chemistry ◽

10.4155/fmc-2020-0285 ◽

2020 ◽

Author(s):

Daniel Conole ◽

James H Hunter ◽

Michael J Waring

Keyword(s):

Data Analysis ◽

Cost Effectiveness ◽

Drug Discovery ◽

High Throughput ◽

High Throughput Screening ◽

New Technology ◽

Combinatorial Libraries ◽

Library Design ◽

First Time ◽

Screening Technology

DNA-encoded combinatorial libraries (DECLs) represent an exciting new technology for high-throughput screening, significantly increasing its capacity and cost–effectiveness. Historically, DECLs have been the domain of specialized academic groups and industry; however, there has recently been a shift toward more drug discovery academic centers and institutes adopting this technology. Key to this development has been the simplification, characterization and standardization of various DECL subprotocols, such as library design, affinity screening and data analysis of hits. This review examines the feasibility of implementing DECL screening technology as a first-time user, particularly in academia, exploring the some important considerations for this, and outlines some applications of the technology that academia could contribute to the field.

Download Full-text