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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Stratified Sampling Method01:16

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Convenience Sampling Method00:55

Convenience Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
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Guided Sample Pooling in Human Mass Balance Studies: A Recommended Strategic Decision Framework.

Filip Cuyckens1, Wenying Li2, Adam M Auclair3

  • 1Johnson & Johnson, Beerse, Belgium.

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|December 17, 2025
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Summary

Human mass balance studies use sample pooling to improve efficiency in metabolite profiling. This strategy helps maximize data quality for drug absorption and excretion pathways, even with limited subjects.

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Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Analytical Chemistry

Background:

  • Radiolabeled human mass balance studies are essential for elucidating drug absorption, distribution, metabolism, and excretion (ADME) pathways.
  • Metabolite profiling requires quantifying all drug-related entities in biological matrices, which is often labor-intensive.

Purpose of the Study:

  • To propose a new paradigm for metabolite profiling in human mass balance studies using optimized sample pooling strategies.
  • To provide guidance and decision trees for integrating individual and pooled sample schemes to maximize data quality and resource efficiency.

Main Methods:

  • Development of integrated sample pooling strategies combining individual and pooled sample analysis.
  • Utilizing extended liquid chromatography with sensitive detection methods (e.g., scintillation counting, AMS).
  • Consolidating existing knowledge and discussions from the IQ Consortium mass balance working group.

Main Results:

  • Sample pooling strategies enhance efficiency and data integrity in metabolite profiling.
  • Pooling allows for concentration of low-radioactivity samples, improving metabolite profile quality.
  • Decision trees facilitate informed choices between individual and pooled sample analysis based on study needs.

Conclusions:

  • Optimized sample pooling is a valuable approach to improve efficiency and data quality in human mass balance studies.
  • The proposed strategies address the challenges of limited statistical power and resource constraints.
  • This work provides a cohesive framework for metabolite profiling decisions in drug development.