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Related Concept Videos

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...
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Determination of Multiple Dosing Parameters: Loading and Maintenance Doses01:25

Determination of Multiple Dosing Parameters: Loading and Maintenance Doses

A loading dose is an essential pharmacological strategy to rapidly achieve the target plasma drug concentration necessary for an immediate therapeutic effect. This approach is especially critical for drugs characterized by slow absorption or extended half-lives, where delaying therapeutic plasma levels could compromise treatment outcomes. By administering a loading dose, clinicians ensure a prompt onset of drug action, even for agents with complex pharmacokinetic profiles.Achieving steady-state...
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...
Dosage Regimen: Individualization01:24

Dosage Regimen: Individualization

Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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 relationship...

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An adaptive model switching approach for phase I dose-finding trials.

Takashi Daimon1, Sarah Zohar

  • 1Department of Biostatistics, Hyogo College of Medicine, 1-1 Mukogawacho, Nishinomiya City, Hyogo 663-8501, Japan. daimon@hyo-med.ac.jp

Pharmaceutical Statistics
|June 27, 2013
PubMed
Summary

This study introduces flexible model-switching designs for phase I cancer trials, improving maximum tolerated dose (MTD) estimation by allowing model selection during the trial. This approach reduces risks associated with incorrect model choices, enhancing dose-finding accuracy.

Keywords:
Bayesian inferenceadaptive designcancer clinical trialmaximum tolerated dosemodel comparisonmodel selectionphase I

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacology

Background:

  • Model-based designs for phase I dose-finding studies typically use a single statistical model.
  • Selecting the most appropriate model *a priori* is challenging and critical for accurate maximum tolerated dose (MTD) estimation.
  • Model misspecification can lead to suboptimal dose allocation and MTD identification failures.

Purpose of the Study:

  • To develop a method for sequential model selection in phase I dose-finding trials, eliminating the need for pre-trial model specification.
  • To introduce and evaluate model-switching designs that adaptively choose the best-fitting model during the trial.
  • To compare the performance of model-switching designs against traditional fixed-model designs.

Main Methods:

  • Utilized posterior predictive checks for model assessment.
  • Employed the deviance information criterion (DIC) for model comparison.
  • Developed two novel model-switching designs for phase I cancer trials.
  • Conducted simulation studies to compare model-switching designs with fixed-model designs.

Main Results:

  • The proposed model-switching designs demonstrated an advantage in reducing risks associated with poor dose allocation.
  • Model-switching designs showed a reduced likelihood of failing to identify the true MTD compared to fixed-model designs.
  • Adaptive model selection during the trial improved the reliability of MTD estimation.

Conclusions:

  • Sequential model selection and switching offer a more robust approach to phase I dose-finding.
  • These adaptive designs mitigate risks stemming from potential model misspecification.
  • The findings support the use of dynamic model selection for improved clinical decision-making in early-phase oncology trials.