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Related Concept Videos

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Introduction to Test of Independence01:21

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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
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Hypothesis Test for Test of Independence01:16

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The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
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The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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Related Experiment Video

Updated: Jan 27, 2026

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry
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Label-free absolute protein quantification with data-independent acquisition.

Bing He1, Jian Shi1, Xinwen Wang1

  • 1Department of Clinical Pharmacy, University of Michigan, Ann Arbor, MI 48109, United States of America.

Journal of Proteomics
|March 19, 2019
PubMed
Summary

We developed a new data-independent acquisition (DIA) method using the TPA algorithm (DIA-TPA) for label-free absolute protein quantification. This method accurately quantifies proteins, even those with shared peptides, in human liver samples.

Keywords:
Absolute protein quantificationData dependent acquisitionData independent acquisitionLivers

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Absolute Quantification of Cell-Free Protein Synthesis Metabolism by Reversed-Phase Liquid Chromatography-Mass Spectrometry
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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Biochemistry

Background:

  • Data-independent acquisition (DIA) is widely used for relative protein quantification.
  • A robust, label-free method for absolute protein quantification (APQ) using DIA is not yet established.
  • Accurate protein quantification is crucial for understanding biological processes and drug metabolism.

Purpose of the Study:

  • To present a novel DIA-based label-free APQ method, termed DIA-TPA.
  • To validate the DIA-TPA method for absolute protein expression profiling in human liver samples.
  • To demonstrate the capability of DIA-TPA in quantifying proteins, including those with shared peptides.

Main Methods:

  • Developed and applied the DIA-TPA algorithm for absolute protein quantification.
  • Conducted DIA and data-dependent acquisition (DDA) experiments on 36 human liver microsome and S9 samples.
  • Validated DIA-TPA accuracy against stable isotope labeling by amino acids in cell culture (SILAC) assays and compared with DDA-TPA.

Main Results:

  • DIA-TPA quantified approximately twice as many proteins as MS1-based DDA-TPA.
  • Protein concentrations determined by DIA-TPA and DDA-TPA were comparable.
  • DIA-TPA results for carboxylesterase 1 concentrations were consistent with SILAC-based proteomic assays.
  • Successfully applied DIA-TPA to quantify drug-metabolizing enzymes, including those with shared peptides.

Conclusions:

  • The DIA-TPA method provides a robust and accurate approach for label-free absolute protein quantification using DIA.
  • DIA-TPA significantly increases protein coverage compared to DDA-based methods.
  • The method's ability to handle shared peptides expands its applicability for comprehensive proteome analysis.