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Related Concept Videos

Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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...
Pharmacogenetics and Pharmacogenomics: Overview01:29

Pharmacogenetics and Pharmacogenomics: Overview

Pharmacogenetics and pharmacogenomics examine how genetic factors influence an individual's response to drugs. While pharmacogenetics focuses on the impact of specific genetic variants on drug effects, pharmacogenomics takes a broader approach, studying how genetic variation across populations contributes to differences in drug responses. These fields aim to explain why individuals may experience varying levels of efficacy or adverse reactions to the same medication.Variability in drug...
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Pharmacogenetics of Drug Metabolism: Overview

Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...
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.
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Related Experiment Video

Updated: Jun 11, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Statistical considerations in evaluating pharmacogenomics-based clinical effect for confirmatory trials.

Sue-Jane Wang1, Robert T O'Neill, Hm James Hung

  • 1Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, US FDA, Silver Spring, MD 20993, USA. suejane.wang@fda.hhs.gov

Clinical Trials (London, England)
|July 3, 2010
PubMed
Summary

Using genomic convenience samples in clinical trials can lead to significant imbalance and bias, especially in small patient groups. Careful sample size calculation is crucial to ensure reliable results and avoid false conclusions in genomic subgroup analyses.

Related Experiment Videos

Last Updated: Jun 11, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Area of Science:

  • Genomic medicine
  • Clinical trial design
  • Biostatistics

Background:

  • Current practices in randomized controlled trials often rely on genomic convenience samples.
  • This approach poses challenges in identifying genomically favorable patient subgroups.

Purpose of the Study:

  • To evaluate the risks of imbalance, confounding, bias, and statistical errors in convenience samples.
  • To determine appropriate sample sizes to mitigate these risks.
  • To emphasize the need for independent replication of subgroup findings.

Main Methods:

  • Analysis of four case examples from regulatory experiences involving convenience samples.
  • Calculation of imbalance probabilities for pre-specified genomic subgroups.
  • Illustrative drug development example to demonstrate scientific rigor and replication needs.

Main Results:

  • Convenience samples can range from 18% to 38% of intent-to-treat samples.
  • Baseline imbalance occurs with probabilities exceeding 25%.
  • Confounding factors can introduce bias, leading to false positive or negative conclusions regarding treatment efficacy.

Conclusions:

  • Pre-specification of genomic subgroup hypotheses offers limited control over Type I error.
  • Complete ascertainment of genomic samples is recommended.
  • Minimum sample sizes (e.g., 100-1350 patients) are suggested for genomic subgroups to minimize imbalance based on prevalence concerns.