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Related Concept Videos

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

733
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

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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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Factors Affecting Drug Response: Overview01:21

Factors Affecting Drug Response: Overview

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When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
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Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

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Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Related Experiment Video

Updated: Jul 13, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

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Towards more accurate pharmacogenomic variant effect predictions.

Yoomi Park1,2, Volker Lauschke3,4,5

  • 1Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, South Korea.

Pharmacogenomics
|October 17, 2023
PubMed
Summary

Accurate interpretation of genetic variants is crucial for personalized medicine. Combining multiplexed assays, structure-based predictions, and biobank data can improve pharmacogenomic effect predictions.

Keywords:
genomic medicinepersonalized drug responseprecision medicinevariant of unknown significance

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Area of Science:

  • Pharmacogenomics
  • Genetics
  • Computational Biology

Background:

  • Accurate interpretation of genetic variants is a major challenge in translating pharmacogenomic data into clinical practice.
  • Current methods often struggle to predict the precise effects of genetic variations on drug response.

Discussion:

  • An integrated approach combining multiplexed assays, structure-based predictions, and biobank data offers a promising solution.
  • This multi-faceted strategy can enhance the accuracy of predicting the functional impact of genetic variants.

Key Insights:

  • Multiplexed assays provide high-throughput variant screening.
  • Structure-based predictions offer insights into protein function and variant effects.
  • Biobank data supply real-world evidence for variant impact and clinical correlations.

Outlook:

  • Developing more accurate pharmacogenomic effect predictors is essential for advancing personalized medicine.
  • This integrated methodology can accelerate the clinical implementation of pharmacogenomics.
  • Future research should focus on refining these predictors and validating them in diverse populations.