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Related Experiment Videos

Model-based benefit-risk assessment: can Archimedes help?

R Krishna1

  • 1Merck Research Laboratories, Merck & Co., Inc., Whitehouse Station, New Jersey, USA. rajesh_krishna@merck.com

Clinical Pharmacology and Therapeutics
|February 19, 2009
PubMed
Summary

New FDA guidance emphasizes evaluating cardiovascular risk for Type 2 diabetes drugs. Computational tools, like Archimedes, can predict risks early in drug development to inform decisions.

Area of Science:

  • Pharmacology and Drug Development
  • Cardiovascular Risk Assessment
  • Computational Modeling in Medicine

Background:

  • US FDA issued draft guidance in December 2008 for evaluating cardiovascular risk in new antidiabetic therapies for Type 2 diabetes.
  • Recent focus on delineating cardiovascular risk reduction associated with novel antidiabetic medications.
  • Need for early prediction of cardiovascular risk in drug development.

Purpose of the Study:

  • To review computational tools for early cardiovascular risk prediction in antidiabetic drug development.
  • To propose a model-based solution for early decision-making in the drug development value chain.
  • To specifically reference the Archimedes computational model.

Main Methods:

  • Review of existing computational tools for cardiovascular risk assessment.

Related Experiment Videos

  • Focus on the Archimedes model (Kaiser Permanente) as a case study.
  • Conceptual proposal for a model-based approach to inform early drug development decisions.

    • Main Results:

    • Identification of computational tools as viable methods for early cardiovascular risk prediction.
    • Demonstration of the potential utility of models like Archimedes in the drug development process.
    • Highlighting the feasibility of integrating predictive modeling for risk assessment.

    Conclusions:

    • Computational modeling offers a pathway for early prediction of cardiovascular risk associated with antidiabetic therapies.
    • Model-based solutions can facilitate timely decisions in the drug development value chain.
    • Adoption of such tools aligns with regulatory expectations for evaluating drug safety.