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Related Concept Videos

Clinical Trials01:16

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Clinical Trials: Overview01:11

Clinical Trials: Overview

Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

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 assumptions,...

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Related Experiment Video

Updated: Jun 6, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

Dynamic, multi-level network models of clinical trials.

Marco D Sorani1, Geoffrey T Manley, J Claude Hemphill

  • 1Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94110, USA. soranim@pharmacy.ucsf.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 2, 2010
PubMed
Summary

Network analysis offers a novel approach to understanding clinical trials. This study reveals distinct network structures for nervous system diseases and behaviors/mental disorders, with high-profile trials showing unique topological features.

Related Experiment Videos

Last Updated: Jun 6, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

Area of Science:

  • Computational biology
  • Clinical informatics
  • Network science

Background:

  • Network models are increasingly applied to biological systems and clinical questions.
  • Clinical trials have traditionally used statistical methods, but network approaches are novel.
  • Data from clinicaltrials.gov registry was utilized for network construction.

Purpose of the Study:

  • To apply network analysis to clinical trials for the first time.
  • To investigate the network structures of Nervous System Diseases (NSD) and Behaviors and Mental Disorders (BMD) clinical trials.
  • To explore the relationship between network topology and clinical significance, including publication in high-profile journals.

Main Methods:

  • Collected data for 6,847 clinical trials in NSD and BMD categories.
  • Constructed disease and intervention networks using Cytoscape software.
  • Standardized nomenclature with MeSH and UMLS terms for multi-level annotation.
  • Analyzed temporal dynamics and topological features of separate BMD and NSD networks.
  • Examined a sub-network of Multiple Sclerosis and Alzheimer's trials linked to journal impact.

Main Results:

  • The BMD network exhibits a decentralized topology, not clearly reflecting its defining diseases.
  • The NSD network maintains distinct disease representation but shows emergence of new research areas.
  • High-profile clinical trials demonstrate unique network characteristics compared to others.
  • Network analysis provides insights into the evolution and structure of clinical research landscapes.

Conclusions:

  • Network analysis provides a novel framework for understanding clinical trial landscapes.
  • Distinct topological differences exist between BMD and NSD research networks.
  • Network topology correlates with clinical significance, suggesting potential for trial optimization.
  • Further research is needed to refine mathematical, clinical, and methodological aspects of network analysis in clinical trials.