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Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune-Oncology Therapies: A Tutorial for

Georgia Lazarou1, Vijayalakshmi Chelliah1, Ben G Small1

  • 1Certara QSP, Certara UK Limited, Sheffield, UK.

Clinical Pharmacology and Therapeutics
|January 20, 2020
PubMed

Insights

Omics data can enhance cancer drug development by improving mechanistic models. Integrating these resources aids in optimizing dosing and treatment strategies for immuno-oncology therapies.

Area of Science:

  • Oncology
  • Pharmacology
  • Bioinformatics

Background:

  • Molecular biology techniques generate vast "omics" data from clinical oncology samples.
  • Omics data are primarily used for discovering therapeutic targets and biomarkers.
  • The potential of omics data to refine mechanistic models for clinical pharmacology is underappreciated.

Purpose of the Study:

  • To discuss the integration of omics data into mechanistic models for immuno-oncology drug development.
  • To illustrate the application of omics data with a Cancer Immunity Cycle (CIC) model for non-small cell lung carcinoma.
  • To highlight the impact of omics-informed mechanistic models on quantitative pharmacology decisions.

Main Methods:

  • Reviewing omics data resources applicable to mechanistic modeling.
  • Integrating transcriptomics data from clinical samples to infer tumor microenvironment composition.
  • Calibrating a minimal clinical model of the Cancer Immunity Cycle (CIC).

Main Results:

  • Demonstrated a method to parameterize CIC mechanistic models using omics data.
  • Showcased the calibration of a CIC model for non-small cell lung carcinoma using transcriptomics.
  • Highlighted the potential for virtual trial simulations using Quantitative Systems Pharmacology (QSP) platforms.

Conclusions:

  • Omics data integration is crucial for advancing mechanistic models in immuno-oncology.
  • Quantitative Systems Pharmacology platforms, informed by omics data, will increasingly impact cancer immunotherapy development.
  • Virtual trial simulations offer a powerful tool for optimizing drug development decisions.

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