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Related Concept Videos

Targeted Cancer Therapies02:57

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
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Published on: July 25, 2020

Model-based drug development applied to oncology.

Jeffrey S Barrett1, Manish Gupta, John T Mondick

  • 1Laboratory for Applied PK/PD, Clinical Pharmacology & Therapeutics Division, The Children's Hospital of Philadelphia, USA . barrettj@E-mail.chop.edu.

Expert Opinion on Drug Discovery
|March 19, 2013
PubMed
Summary
This summary is machine-generated.

Model-based drug development (MBDD) organizes complex data for drug approval. This approach uses quantitative relationships to improve decision-making, especially in challenging fields like oncology drug development.

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Area of Science:

  • Pharmaceutical Sciences
  • Drug Development
  • Biotechnology

Background:

  • Conventional drug development paradigms struggle with data qualification and decision-making transparency.
  • The complexity of drug development data streams requires a more organized approach.
  • Oncology drug development faces unique challenges, including high attrition rates and multimodal therapies.

Purpose of the Study:

  • To introduce Model-based Drug Development (MBDD) as a method for organizing complex data in drug development.
  • To highlight MBDD's potential to improve decision-making throughout the drug development pipeline.
  • To explore the application of MBDD in oncology, addressing specific therapeutic area challenges.

Main Methods:

  • MBDD constructs quantitative relationships connecting data from discrete experiments.
  • These relationships are used to address critical questions at various development stages.
  • Scenarios are explored to facilitate optimal decision-making in drug development.

Main Results:

  • MBDD offers a structured framework for managing and interpreting drug development data.
  • The approach enhances transparency in data qualification and decision-making processes.
  • MBDD can lead to more rational explanations for development decisions, particularly in oncology.

Conclusions:

  • MBDD provides a robust framework for organizing complex drug development data.
  • Its application can lead to more informed decision-making and optimized clinical development programs.
  • MBDD holds significant promise for improving drug development efficiency and patient care, especially in oncology.