Linked Life Data

21160066

Source:http://linkedlifedata.com/resource/pubmed/id/21160066

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2011-1-10
pubmed:abstractText
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.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1552-454X
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
16
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1-14
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Identifying actives from HTS data sets: practical approaches for the selection of an appropriate HTS data-processing method and quality control review.
pubmed:affiliation
University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA. tos8@pitt.edu
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural