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Published on: March 20, 2021

Pharmacodynamic modelling of biomarker data in oncology

Robert C Jackson1

  • 1Pharmacometrics Ltd., Whittlesford, Cambridge CB22 4NZ, UK.

ISRN Pharmacology
|April 24, 2012
PubMed

Insights

Pharmacodynamic (PD) biomarkers, including phosphoproteins and apoptosis markers, improve oncology clinical trial design and outcome prediction. Data modeling with pharmacokinetic (PK) models creates PK/PD models for virtual trials, optimizing drug development.

Area of Science:

  • Oncology
  • Pharmacology
  • Biomarker Discovery

Background:

  • Pharmacodynamic (PD) biomarkers are crucial for oncology drug development, guiding clinical protocol design and predicting patient outcomes.
  • Key PD biomarkers include phosphoproteins (indicating pathway inhibition) and plasma apoptosis markers (measuring tumor cell death).

Purpose of the Study:

  • To highlight the role of PD biomarkers in oncology drug development.
  • To explain how data modeling enhances the predictive power of PD biomarkers.
  • To introduce the concept of virtual clinical trials using PK/PD and disease models.

Main Methods:

  • Utilizing phosphoprotein analysis in biopsy samples to assess target engagement.
  • Measuring plasma biomarkers of apoptosis to quantify tumor cell killing.
  • Integrating pharmacokinetic (PK) and PD models to create PK/PD models.
  • Incorporating biomarkers of drug toxicity into models for selectivity and efficacy prediction.
  • Developing disease models to enable virtual clinical trials for in silico assessment of trial designs.

Main Results:

  • PD biomarkers, particularly phosphoproteins and apoptosis markers, provide critical insights into drug activity in early clinical trials.
  • Data modeling, especially the integration of PK and PD data, significantly enhances the predictive capabilities of biomarkers.
  • PK/PD models, when combined with disease models, facilitate the creation of virtual clinical trials.
  • Virtual clinical trials allow for the in silico evaluation of multiple trial designs, aiding in the selection of optimal experimental protocols.

Conclusions:

  • PD biomarkers are essential tools for advancing oncology drug development by improving preclinical to clinical translation and outcome prediction.
  • PK/PD modeling, incorporating toxicity and efficacy biomarkers, enables robust prediction of drug selectivity and therapeutic potential.
  • The development of virtual clinical trials through PK/PD and disease modeling offers a powerful platform for optimizing clinical trial design and accelerating drug evaluation.

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