Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

355
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
355
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

464
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.
464
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

113
The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
113
Pharmacogenetics of Drug Metabolism: Overview01:27

Pharmacogenetics of Drug Metabolism: Overview

150
Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...
150
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

468
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
468
Pharmacogenetics and Pharmacogenomics: Overview01:29

Pharmacogenetics and Pharmacogenomics: Overview

180
Pharmacogenetics and pharmacogenomics examine how genetic factors influence an individual's response to drugs. While pharmacogenetics focuses on the impact of specific genetic variants on drug effects, pharmacogenomics takes a broader approach, studying how genetic variation across populations contributes to differences in drug responses. These fields aim to explain why individuals may experience varying levels of efficacy or adverse reactions to the same medication.Variability in drug...
180

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

FGF21 underlies a hormetic response to metabolic stress in methylmalonic acidemia.

JCI insight·2018
Same author

Defective renal autoregulation in the chronic bile duct ligation model of liver failure.

Clinical and experimental nephrology·2018
Same author

Angiotensin II blockade causes acute renal failure in eNOS-deficient mice.

Journal of the renin-angiotensin-aldosterone system : JRAAS·2017
Same author

Profound hypothermia after adenosine kinase inhibition in A1AR-deficient mice suggests a receptor-independent effect of intracellular adenosine.

Pflugers Archiv : European journal of physiology·2016
Same author

Bombesin-like receptor 3 regulates blood pressure and heart rate via a central sympathetic mechanism.

American journal of physiology. Heart and circulatory physiology·2016
Same author

Concurrent activation of multiple vasoactive signaling pathways in vasoconstriction caused by tubuloglomerular feedback: a quantitative assessment.

Annual review of physiology·2015

Related Experiment Video

Updated: Apr 12, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.6K

One physiology does not fit all: a path from data variability to "physiogenetics"?

Jurgen Schnermann1

  • 1Kidney Disease Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland jurgens@intra.niddk.nih.gov.

American Journal of Physiology. Renal Physiology
|May 8, 2015
PubMed
Summary

Interindividual biological diversity significantly contributes to data variability in biomedical research, impacting experimental reproducibility. Understanding this variability is crucial for accurate physiological data collection and interpretation.

Keywords:
biodiversityoutlierpersonalized physiologysample size

More Related Videos

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.5K
In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
06:41

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

14.5K

Related Experiment Videos

Last Updated: Apr 12, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.6K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.5K
In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
06:41

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

14.5K

Area of Science:

  • Physiology
  • Biomedical Research
  • Genetics

Background:

  • Data variability is a significant challenge in biomedical experiments, often attributed to statistical noise or methodological limitations.
  • Current statistical methods do not analyze the underlying factors contributing to data scatter.
  • Interindividual biological diversity is a potential, under-recognized source of this variability.

Purpose of the Study:

  • To discuss evidence highlighting interindividual biological diversity as a cause of data variability in physiological research.
  • To explore the implications of recognizing nonrandom data variability for experimental practices.
  • To introduce the concept of 'physiogenetics' for studying the heritability of physiological diversity.

Main Methods:

  • Analysis of studies involving repeated sampling within the same individual to enable direct statistical comparisons.
  • Examination of physiological parameters such as fluid reabsorption, plasma renin concentration, and arterial blood pressure.
  • Review of evidence linking data variability to genetic or epigenetic modifications.

Main Results:

  • Significant differences in proximal fluid reabsorption and plasma renin concentration were observed between individuals within the same population.
  • Arterial blood pressure showed significant variation among individual mice, independent of strain and sex.
  • These findings suggest nonrandom data variability stemming from biological diversity.

Conclusions:

  • Interindividual biological diversity plays a crucial role in physiological data variability, challenging the notion of purely random noise.
  • Recognizing this variability necessitates adjustments in data collection, presentation, and interpretation in physiological studies.
  • The concept of 'physiogenetics' offers a framework for investigating the genetic basis of physiological diversity and its impact on research outcomes.