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Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm

Vijayalakshmi Chelliah1, Georgia Lazarou1, Sumit Bhatnagar2

  • 1Certara UK Ltd., Sheffield, UK.

Clinical Pharmacology and Therapeutics
|July 21, 2020
PubMed

Insights

Quantitative systems pharmacology (QSP) offers a new approach to immuno-oncology (IO) drug development. By using mechanistic modeling and simulation, QSP can improve the efficiency and effectiveness of discovering novel combination therapies.

Area of Science:

  • Oncology
  • Immunology
  • Pharmacology
  • Computational Biology

Background:

  • Immuno-oncology (IO) leverages the patient's immune system to combat cancer, building on successes with PD1/PD-L1 and CTLA4 inhibitors.
  • Current IO drug development relies heavily on combination therapies, but the vast number of potential combinations and dosing regimens presents significant challenges and inefficiencies.
  • The high number of clinical trials for IO combinations may exceed patient availability, necessitating more efficient selection and validation methods.

Purpose of the Study:

  • To review current Quantitative Systems Pharmacology (QSP) approaches in immuno-oncology drug development.
  • To discuss the role of mathematical models in understanding tumor-immune system interactions.
  • To highlight the potential of QSP and virtual patients to enhance the discovery of novel IO combination therapies.

Main Methods:

  • Review of existing literature on QSP applications in oncology and IO.
  • Analysis of mathematical modeling techniques for simulating drug pharmacokinetics, target binding, and mechanisms of action.
  • Examination of QSP platform models integrating tumor and immune system biology.

Main Results:

  • QSP utilizes mechanistic modeling and simulation to predict clinical outcomes for novel combination therapies.
  • Existing mathematical models provide a foundation for understanding complex tumor-immune system dynamics.
  • Recent development of IO-specific QSP platform models shows promise for integrating biological knowledge.

Conclusions:

  • Quantitative Systems Pharmacology (QSP) offers a powerful mechanistic modeling approach to address inefficiencies in IO drug development.
  • Integrating QSP and virtual patients can significantly improve the efficiency and effectiveness of identifying promising combination therapies.
  • This approach aims to reduce attrition rates and limit the number of clinical trials required in IO.

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