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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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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...
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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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

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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.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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A Multiomic Approach Integrating Genomic and Metabolomic Data Highlights Colorectal Cancer Pathways.

Aikaterini Iliou1, Elena Chekmeneva2, Rui Climaco Pinto2,3

  • 1Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, 157 71 Athens, Greece.

Journal of Proteome Research
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

Integrating genetic and metabolomic data reveals a key colorectal cancer (CRC) risk locus in the RHPN2 gene. This intronic region influences cell growth and metabolic processes, offering new insights into CRC development.

Keywords:
GWASRho GTPasecolorectal cancermetabolomicsmultiomicsurine

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Area of Science:

  • Genetics
  • Metabolomics
  • Cancer Biology

Background:

  • Genome-wide association studies (GWAS) have identified numerous genetic variants linked to colorectal cancer (CRC) risk.
  • Understanding the functional impact of these variants is crucial for elucidating CRC pathogenesis.

Purpose of the Study:

  • To investigate the association between CRC-associated genetic variants and urinary metabolites.
  • To explore the functional role of identified genetic loci in colorectal cancer development.

Main Methods:

  • Metabolome-wide association analysis was conducted using genomic data and untargeted 1H nuclear magnetic resonance (NMR) urine metabolomics.
  • Functional experiments, including CRISPR-mediated gene editing and RNA sequencing, were performed in colon cancer cells.

Main Results:

  • Seven colorectal cancer (CRC) single-nucleotide polymorphisms (SNPs) showed statistically significant associations with urinary metabolites, including associations with sucrose, amino acids (tyrosine, leucine), and gut microbial metabolites.
  • Knockout of a specific RHPN2 intronic region (rs10411210) in colon cancer cells impaired cell growth.
  • RNA sequencing revealed extensive deregulation of genes involved in cell division and metabolic processes following RHPN2 intronic region editing.

Conclusions:

  • The integration of genetic and metabolomic data highlights the significance of the RHPN2 intronic locus in colorectal cancer (CRC) susceptibility.
  • This locus may influence CRC risk through metabolic pathways affecting metabolite excretion.