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Related Concept Videos

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

893
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...
893
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

302
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...
302
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

61
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
61
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

695
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...
695
Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu01:29

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

83
Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
83
Pharmacokinetic–Pharmacodynamic Relationship: Problems01:24

Pharmacokinetic–Pharmacodynamic Relationship: Problems

57
The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...
57

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Related Experiment Video

Updated: Mar 12, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Harnessing Big Data for Systems Pharmacology.

Lei Xie1,2, Eli J Draizen3,4, Philip E Bourne3,5

  • 1Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065;

Annual Review of Pharmacology and Toxicology
|November 5, 2016
PubMed
Summary

Systems pharmacology modeling (SPM) integrates diverse data for drug discovery. Big data analytics and AI offer solutions to challenges in developing interpretable and actionable multiscale models for predicting drug responses.

Keywords:
NIH Commonscloud computingcomputational modelingdata sciencemachine learningsemantic websystems biologysystems pharmacology modeling

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Area of Science:

  • Computational Biology
  • Pharmacology
  • Data Science

Background:

  • Systems pharmacology aims to understand drug actions holistically.
  • Drug discovery and clinical practice benefit from mechanistic and predictive models.
  • Integrating multi-scale data (genetic to environmental) is crucial for systems pharmacology modeling (SPM).

Purpose of the Study:

  • To review emergent issues in systems pharmacology modeling (SPM).
  • To discuss potential solutions for SPM challenges using big data technology and analytics.
  • To highlight the role of SPM in generating hypotheses and gaining knowledge from complex biological data.

Main Methods:

  • Integration of multi-scale biological data (genomics, omics).
  • Application of big data technologies and advanced analytics.
  • Collaboration between domain experts and integration of heterogeneous models (biophysics, ML, semantic web).

Main Results:

  • Explosion of omics data and big data technologies enable computational models.
  • Challenges exist in model management, integration, translation, and knowledge integration.
  • Emergent issues in SPM are being addressed by advancements in high-throughput techniques, cloud computing, and data science.

Conclusions:

  • SPM requires interdisciplinary collaboration and integration of diverse models.
  • Big data analytics and AI are key to overcoming SPM challenges.
  • Future SPM will be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable (FAIR-R-IA).