Advertisement

CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints

  • Alexander SedykhEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1473)

Abstract

The nature of high-throughput screening (HTS) puts certain limits on optimal test conditions for each particular sample, therefore, on top of usual data normalization, additional parsing is often needed to account for incomplete read outs or various artifacts that arise from signal interferences.

CurveP is a heuristic, user-tunable, curve-cleaning algorithm that attempts to find a minimum set of corrections, which would give a monotonic dose–response curve. After applying the corrections, the algorithm proceeds to calculate a set of numeric features, which can be used as a fingerprint characterizing the sample, or as a vector of independent variables (e.g., molecular descriptors in case of chemical substances testing). The resulting output can be a part of HTS data analysis or can be used as input for a broad spectrum of computational applications, such as Quantitative Structure-Activity Relationship (QSAR) modeling, computational toxicology, bio- and cheminformatics.

Key words

Nonparametric fitting Monotonicity Heuristics 

References

  1. 1.
    Pereira DA, Williams JA (2007) Origin and evolution of high throughput screening. Br J Pharmacol 152:53–61CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Inglese J, Auld DS, Jadhav A et al (2006) Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries. Proc Natl Acad Sci U S A 103:11473–11478CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Hsieh JH, Sedykh A, Huang R et al (2015) A data analysis pipeline accounting for artifacts in Tox21 quantitative high-throughput screening assays. J Biomol Screen 20:887–897CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Sedykh A, Zhu H, Tang H et al (2011) Use of in vitro HTS-derived concentration–response Data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity. Environ Health Perspect 119:364–370CrossRefPubMedGoogle Scholar
  5. 5.
    Lock EF, Abdo N, Huang R et al (2012) Quantitative high-throughput screening for chemical toxicity in a population-based in vitro model. Toxicol Sci 126:578–588CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Low Y, Sedykh A, Fourches D et al (2013) Integrative chemical-biological read-across approach for chemical hazard classification. Chem Res Toxicol 26:1199–1208CrossRefPubMedGoogle Scholar
  7. 7.
    Sprague B, Shi Q, Kim MT et al (2014) Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers. J Comput Aided Mol Des 28:631–646CrossRefPubMedGoogle Scholar
  8. 8.
    Sedykh A, Low Y, Lock E et al. (2012) Using population-based dose-response cytotoxicity data for in silico prediction of rat acute toxicity. Abstracts of Papers, 51th SOT National meeting, San Francisco, CA, March 11–15Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Multicase Inc.BeachwoodUSA

Personalised recommendations