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

Dosage Regimens: Designs and Approaches01:28

Dosage Regimens: Designs and Approaches

405
Designing a dosage regimen, which refers to the manner of drug administration, is a complex process involving the selection of drug dose, route, and frequency. This process is underpinned by pharmacokinetic parameters derived from tests and population averages. These parameters are then tailored to patient-specific variables such as diagnosis, demographics, and allergy status. Once therapy commences, therapeutic response monitoring is critical and achieved through clinical and physical...
405
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

234
It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
234
Dosage Regimen Designs: Nomograms and Tabulations01:23

Dosage Regimen Designs: Nomograms and Tabulations

264
Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...
264
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

299
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...
299
Dose Size and Dosing Frequency: Determination Methods01:21

Dose Size and Dosing Frequency: Determination Methods

441
Determining the optimal dose size and dosing frequency in pharmacotherapy is crucial for achieving therapeutic effectiveness while minimizing adverse effects. This article explores the methodologies employed in determining these parameters, focusing on their significance and interplay to tailor dosing regimens.Dose Size: Dose size refers to the amount of a drug administered in a single dose. It is determined based on the drug's pharmacodynamics and pharmacokinetics properties and...
441
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

379
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...
379

You might also read

Related Articles

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

Sort by
Same author

Deciphering small sequence differences in T cell receptor-antigen pairing.

Nature communications·2026
Same author

Integrated analysis of phenotypic and SSR data reveals the genetic structure differentiation of wild <i>Rhododendron mariae</i> Hance populations in Guangdong and the driving factors for conservation planning.

Frontiers in plant science·2026
Same author

Enabling rapid and accurate grand discrimination of flue-cured tobacco: a near-infrared hyperspectral and machine learning approach.

Frontiers in plant science·2026
Same author

An Ultraresponsive Green Biosensor for Robust in Vivo Imaging of Synaptic Zinc Dynamics.

ACS sensors·2026
Same author

International guidelines on the diagnosis and treatment of NUT carcinoma.

Innovation (Cambridge (Mass.))·2026
Same author

High KAP1 expression promotes pleural mesothelioma cell proliferation and metastasis.

The International journal of biological markers·2026

Related Experiment Video

Updated: Mar 3, 2026

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K

A Bayesian interval dose-finding design addressingOckham's razor: mTPI-2.

Wentian Guo1, Sue-Jane Wang2, Shengjie Yang3

  • 1School of Public Health, Fudan University, PR China.

Contemporary Clinical Trials
|May 2, 2017
PubMed
Summary
This summary is machine-generated.

The modified toxicity probability interval (mTPI) method for dose finding is improved by mTPI-2, which enhances safety decisions in clinical trials. This new Bayesian design offers superior performance and a user-friendly online tool.

Keywords:
Bayes ruleCrowd sourcingDecision theoryPhase I clinical trial

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K
Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.4K

Related Experiment Videos

Last Updated: Mar 3, 2026

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K
Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.4K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacometrics

Background:

  • Interval-based Bayesian designs are gaining traction for dose-finding studies.
  • The modified toxicity probability interval (mTPI) is a prominent example.
  • Existing mTPI methods may have safety limitations due to overly sharpened probability models.

Purpose of the Study:

  • To evaluate the decision rules of the mTPI method within a Bayesian decision theory framework.
  • To address the safety concerns arising from the probability models in mTPI.
  • To introduce and demonstrate the superior performance of a novel Bayesian dose-finding design, mTPI-2.

Main Methods:

  • Formal Bayesian decision theory was used to analyze mTPI decision rules.
  • A new framework was developed to 'blunt the Ockham's razor,' modifying probability models.
  • The performance of the proposed mTPI-2 method was compared against existing approaches.

Main Results:

  • mTPI decision rules align with an optimal Bayesian decision rule.
  • Overly sharpened probability models in mTPI can lead to suboptimal safety decisions.
  • The mTPI-2 method demonstrates superior performance, particularly from a safety perspective.

Conclusions:

  • The mTPI-2 design offers an improved approach to Bayesian dose finding.
  • Blunting the Ockham's razor in probability models enhances safety in clinical trials.
  • An online tool is available for implementing and evaluating mTPI-2 designs.