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Related Concept Videos

Dose Response Curve: Conventional Versus Nonmonotonic01:21

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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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Dose-Response Relationship: Overview01:03

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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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The potency of a drug is the measure of its ability to produce a biological response and can be compared by looking at the half-maximum effective concentration or EC50 values of different drugs. A lower EC50 value indicates higher potency of the drug. In the dose–response curve of two antihypertensive drugs, candesartan and irbesartan, a significant difference is observed in their EC50 values. A lower EC50 value for candesartan indicates that it is more potent than irbesartan, as it...
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Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and...
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A drug’s dosage and pharmacokinetic properties determine how quickly it acts, how intense its effects are, and how long it lasts. Higher doses increase drug concentration at receptor sites, producing a hyperbolic curve when pharmacologic response is plotted against drug dose. Converting this scale to a log-linear format results in a sigmoidal curve, better representing dose–response relationships.For drugs following a one-compartment model, the pharmacologic response is directly...
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Analysis of Population Pharmacokinetic Data01:12

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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC
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Robust dose-response curve estimation applied to high content screening data analysis.

Thuy Tuong Nguyen1, Kyungmin Song2, Yury Tsoy3

  • 1University of California, Davis, USA.

Source Code for Biology and Medicine
|January 24, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a robust algorithm for automated sigmoid curve fitting, improving outlier detection and data weighting for dose-response experiments. The new method significantly outperforms existing software like MATLAB and GraphPad Prism.

Keywords:
Curve fittingDose response curveHigh content screeningOutlier detectionSigmoidal functionWeighting function

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Area of Science:

  • Computational Biology
  • Biostatistics
  • Pharmacometrics

Background:

  • Automated sigmoidal curve fitting presents significant challenges for large datasets.
  • Accurate dose-response curve analysis is crucial in various scientific fields.

Purpose of the Study:

  • To develop a robust algorithm for automated sigmoid dose-response curve fitting.
  • To enhance outlier detection and data point weighting in curve fitting algorithms.

Main Methods:

  • Developed a novel algorithm estimating four key parameters: floor, window, shift, and slope.
  • Integrated outlier detection during the initialization step, adjusting derivative and error estimation.
  • Improved data point weighting using mean calculation within Tukey's biweight function.

Main Results:

  • The algorithm successfully fitted sigmoid dose-response curves across a large experimental dataset (19,236 experiments).
  • The proposed method demonstrated superior performance compared to established curve fitting functions.

Conclusions:

  • The developed algorithm offers a more accurate and robust approach to automated sigmoid curve fitting.
  • This method surpasses the performance of current standard software, including MATLAB's nlinfit and GraphPad's Prism.