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Related Experiment Video

Updated: Jul 12, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Clopidogrel poor responders: an objective definition based on Bayesian classification.

Govinda J Weerakkody1, John T Brandt, Christopher D Payne

  • 1Lilly Research Laboratories, Eli Lilly and Co., Lilly Corporate Center, Indianapolis, IN, 46285, USA. weerakkody_govinda@lilly.com

Platelets
|September 1, 2007
PubMed
Summary
This summary is machine-generated.

This study objectively defines clopidogrel poor responders using Bayesian classification. New thresholds reveal 25-45% of subjects exhibit a poor antiplatelet response, aiding in assessing novel drug efficacy.

Related Experiment Videos

Last Updated: Jul 12, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

  • Pharmacology and Clinical Pharmacology
  • Biostatistics and Bioinformatics
  • Cardiovascular Medicine

Background:

  • Current definitions for poor response to clopidogrel's antiplatelet effects are empirical.
  • Bayesian classification offers a method for objective subject grouping based on observed responses.

Purpose of the Study:

  • To objectively define pharmacodynamic poor responders to clopidogrel.
  • To utilize Bayesian classification methodology for establishing these definitions.

Main Methods:

  • Analysis of turbidometric platelet aggregometry data from 112 healthy subjects.
  • Evaluation of change in maximum platelet aggregation (DeltaMPA) and percent inhibition of platelet aggregation (IPA) at 4-5 and 24 hours post-clopidogrel loading dose.
  • Application of Bayesian classification to define poor responders based on predefined response thresholds.

Main Results:

  • A clopidogrel poor responder was defined by specific IPA and DeltaMPA thresholds at varying ADP concentrations (5 and 20 microM).
  • Depending on ADP concentration, 25% to 45% of subjects were classified as poor responders using the new Bayesian thresholds.
  • Existing literature thresholds resulted in a wider classification range of 17% to 56% for poor responders.

Conclusions:

  • Objective thresholds for clopidogrel pharmacodynamic poor responders should incorporate agonist concentration.
  • The Bayesian classification approach provides a more consistent method for identifying poor responders.
  • These objective thresholds may assist in evaluating the pharmacodynamic response consistency of new ADP receptor antagonists.