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Related Concept Videos

Dosage Regimens: Designs and Approaches01:28

Dosage Regimens: Designs and Approaches

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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...
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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...
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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...
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Bioavailability studies are essential for understanding how a drug is absorbed, distributed, metabolized, and excreted in the body. These studies assess the extent and rate at which the active pharmaceutical agent becomes available at the site of action. The design of bioavailability studies can involve single-dose or multiple-dose regimens, each with distinct advantages and limitations.Single-dose studies are the preferred approach due to their simplicity and reduced drug exposure for...
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Dosage Interval and Administration Route: Determination Methods01:19

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A medication’s effectiveness largely depends on its appropriate dosage and the route of administration. Dosage ensures that a sufficient drug concentration is maintained in the bloodstream to elicit the desired therapeutic effect without causing toxicity. The route of administration affects the drug's bioavailability, rate of absorption, and onset of action, which are crucial for achieving optimal therapeutic outcomes. Drug dosage calculations are critical to tailoring therapy to...
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High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
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Bayesian optimal interval design for dose finding in drug-combination trials.

Ruitao Lin1, Guosheng Yin1

  • 1Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong.

Statistical Methods in Medical Research
|July 17, 2015
PubMed
Summary

This study introduces a Bayesian optimal interval design for drug combination trials. This novel, model-free approach simplifies dose finding and demonstrates comparable performance to complex model-based designs.

Keywords:
Dose findingdrug combinationinterval designmaximum tolerated dosenonparametric methodtoxicity contour

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacology

Background:

  • Interval designs offer simplicity and desirable properties in clinical trials.
  • Dose-finding in drug-combination trials presents unique challenges.
  • Existing model-based designs often require complex pre-phase evaluations.

Purpose of the Study:

  • To develop a Bayesian optimal interval design for dose finding in drug-combination trials.
  • To propose a nonparametric, model-free allocation rule for efficient dose escalation.
  • To enhance robustness and ease of implementation in early-phase drug development.

Main Methods:

  • A Bayesian approach is utilized for optimal interval design.
  • An allocation rule maximizes the posterior probability of toxicity within a target interval.
  • The design is nonparametric, avoiding model assumptions and pre-phase requirements.

Main Results:

  • The proposed two-dimensional interval design exhibits convergence properties for large samples.
  • Simulation studies confirm robust finite-sample performance across various scenarios.
  • A modification for estimating toxicity contours using parallel paths was introduced.

Conclusions:

  • The Bayesian optimal interval design is a robust and simpler alternative for drug-combination dose finding.
  • The method shows performance comparable to model-based designs with significantly easier implementation.
  • This approach facilitates more efficient and accessible early-phase drug development.