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A Review of Modeling Approaches to Predict Drug Response in Clinical Oncology
1Department of Pharmacology, Yonsei University College of Medicine, Seoul, Korea. kspark@yuhs.ac.
Abstract:
Model-based approaches have emerged as important tools for quantitatively understanding temporal relationships between drug dose, concentration, and effect over the course of treatment, and have now become central to optimal drug development and tailored drug treatment. In oncology, the therapeutic index of a chemotherapeutic drug is typically narrow and a full dose-response relationship is not available, often because of treatment failure. Noting the benefits of model-based approaches and the low therapeutic index of oncology drugs, in recent years, modeling approaches have been increasingly used to streamline oncologic drug development through early identification and quantification of dose-response relationships. With this background, this report reviews publications that used model-based approaches to evaluate drug treatment outcome variables in oncology therapeutics, ranging from tumor size dynamics to tumor/biomarker time courses and survival response.
Insights
Model-based approaches are crucial for understanding drug effects in cancer treatment. This review highlights their use in oncology drug development to quantify dose-response relationships and improve patient outcomes.
Area of Science:
- Pharmacometrics and Systems Pharmacology
- Oncology Drug Development
- Quantitative Pharmacology
Background:
- Model-based approaches are vital for understanding drug dose, concentration, and effect over time.
- Oncology drugs often have a narrow therapeutic index, limiting full dose-response data availability.
- Modeling is increasingly used to streamline cancer drug development by quantifying dose-response relationships early.
Purpose of the Study:
- To review publications utilizing model-based approaches in oncology therapeutics.
- To evaluate how these models assess drug treatment outcome variables.
- To cover tumor size, biomarker dynamics, and survival response in cancer treatment.
Main Methods:
- Literature review of publications employing model-based approaches.
- Analysis of studies focusing on oncology drug treatment outcomes.
- Categorization of outcomes including tumor size, biomarker kinetics, and survival data.
Main Results:
- Model-based methods are increasingly applied in oncology drug development.
- These approaches aid in early identification and quantification of dose-response relationships.
- Diverse outcome variables like tumor dynamics and survival are evaluated using these models.
Conclusions:
- Model-based approaches are essential for optimizing cancer drug development.
- Quantifying dose-response relationships early improves therapeutic strategies.
- These quantitative methods enhance the understanding of drug efficacy and patient outcomes in oncology.
