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The correlation between a drug's dosage and its impact on a biological system is a cornerstone of pharmacology and toxicology. Conventional dose–response curves, which include graded and quantal relationships, are key to this understanding. Graded dose–response curves depict the spectrum of a biological reaction to different doses within an individual, indicating that as the drug dosage increases, so does the intensity of the response. On the other hand, quantal dose–response...
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CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints.

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

Alexander Sedykh1

  • 1Multicase Inc., 23811 Chagrin Blvd., Ste 305,, Beachwood, OH, 44122, USA. Sedykh@multicase.com.

Methods in Molecular Biology (Clifton, N.J.)
|August 13, 2016
PubMed
Summary
This summary is machine-generated.

High-throughput screening (HTS) data often requires cleaning due to artifacts. CurveP is a new algorithm that corrects dose-response curves, generating features for computational analysis and QSAR modeling.

Keywords:
HeuristicsMonotonicityNonparametric fitting

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Area of Science:

  • Bioinformatics
  • Computational Chemistry
  • Drug Discovery

Background:

  • High-throughput screening (HTS) generates large datasets but faces challenges with optimal test conditions, leading to incomplete readouts and signal interferences.
  • Standard data normalization in HTS is often insufficient to address artifacts and data quality issues inherent in the screening process.

Purpose of the Study:

  • To introduce CurveP, a novel heuristic algorithm designed for cleaning dose-response curves obtained from HTS.
  • To enable the generation of reliable numerical features from corrected curves for downstream computational applications.

Main Methods:

  • CurveP employs a user-tunable approach to identify and apply a minimal set of corrections to achieve monotonic dose-response curves.
  • The algorithm processes raw HTS data, correcting for artifacts and generating a set of quantitative features.

Main Results:

  • The corrected dose-response curves are rendered monotonic, improving data quality.
  • The algorithm outputs a feature set that can serve as a sample fingerprint or a vector of independent variables.

Conclusions:

  • CurveP provides an effective method for enhancing the quality of HTS data by cleaning dose-response curves.
  • The generated features are valuable inputs for quantitative structure-activity relationship (QSAR) modeling, computational toxicology, and broader bio- and cheminformatics applications.