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Quantitative Systems Pharmacology Modeling in Immuno-Oncology: Hypothesis Testing, Dose Optimization, and Efficacy
Hanwen Wang1, Theinmozhi Arulraj1, Alberto Ippolito1
1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Abstract:
Despite an increasing number of clinical trials, cancer is one of the leading causes of death worldwide in the past decade. Among all complex diseases, clinical trials in oncology have among the lowest success rates, in part due to the high intra- and inter-tumoral heterogeneity. There are more than a thousand cancer drugs and treatment combinations being investigated in ongoing clinical trials for various cancer subtypes, germline mutations, metastasis, etc. Particularly, treatments relying on the (re)activation of the immune system have become increasingly present in the clinical trial pipeline. However, the complexities of the immune response and cancer-immune interactions pose a challenge to the development of these therapies. Quantitative systems pharmacology (QSP), as a computational approach to predict tumor response to treatments of interest, can be used to conduct in silico clinical trials with virtual patients (and emergent use of digital twins) in place of real patients, thus lowering the time and cost of clinical trials. In line with improved mechanistic understanding of the human immune system and promising results from recent cancer immunotherapy, QSP models can play critical roles in model-informed drug development in immuno-oncology. In this chapter, we discuss how QSP models were designed to serve different study objectives, including hypothesis testing, dose optimization, and efficacy prediction, via case studies in immuno-oncology.
Insights
Quantitative systems pharmacology (QSP) models accelerate cancer drug development by simulating in silico clinical trials. These computational tools predict tumor response, reducing time and cost for novel immuno-oncology therapies.
Area of Science:
- Computational biology
- Pharmacology
- Immunology
Background:
- Cancer remains a leading cause of death, with oncology clinical trials facing low success rates due to tumor heterogeneity.
- Immuno-oncology therapies are increasingly prevalent but complex, posing challenges for drug development.
- Quantitative Systems Pharmacology (QSP) offers a computational approach to predict treatment responses.
Purpose of the Study:
- To explore the application of QSP models in immuno-oncology drug development.
- To demonstrate how QSP facilitates in silico clinical trials, virtual patients, and digital twins.
- To highlight QSP's role in hypothesis testing, dose optimization, and efficacy prediction for cancer therapies.
Main Methods:
- Utilizing Quantitative Systems Pharmacology (QSP) as a computational modeling approach.
- Conducting in silico clinical trials with virtual patients to simulate treatment outcomes.
- Applying QSP models to address various study objectives in immuno-oncology.
Main Results:
- QSP models can effectively predict tumor response to cancer treatments.
- In silico trials using QSP can significantly reduce the time and cost associated with traditional clinical trials.
- QSP models are valuable tools for model-informed drug development in immuno-oncology.
Conclusions:
- QSP models are critical for advancing immuno-oncology drug development.
- The use of QSP enables more efficient and cost-effective clinical trial simulations.
- QSP facilitates hypothesis testing, dose optimization, and efficacy prediction, accelerating the development of novel cancer immunotherapies.
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