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Updated: Aug 5, 2025
Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids
Published on: June 8, 2019
Circulating tumor DNA: Opportunities and challenges for pharmacometric approaches
Benjamin Ribba1, Andreas Roller1, Hans-Joachim Helms1
1Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffman-La Roche Ltd, Basel, Switzerland.
Abstract:
To support further development of model-informed drug development approaches leveraging circulating tumor DNA (ctDNA), we performed an exploratory analysis of the relationships between treatment-induced changes to ctDNA levels, clinical response and tumor size dynamics in patients with cancer treated with checkpoint inhibitors and targeted therapies. This analysis highlights opportunities for pharmacometrics approaches such as for optimizing sampling design strategies. It also highlights challenges related to the nature of the data and associated variability overall emphasizing the importance of mechanistic modeling studies of the underlying biology of ctDNA processes such as shedding, release and clearance and their relationships with tumor size dynamic and treatment effects.
Insights
This study explored circulating tumor DNA (ctDNA) changes in cancer patients receiving targeted therapies and checkpoint inhibitors. Findings suggest pharmacometrics can optimize sampling but highlight the need for mechanistic modeling of ctDNA dynamics.
Area of Science:
- Oncology
- Pharmacometrics
- Molecular Biology
Background:
- Circulating tumor DNA (ctDNA) shows promise as a biomarker in cancer treatment.
- Understanding ctDNA dynamics alongside clinical response is crucial for drug development.
- Model-informed drug development (MIDD) can leverage ctDNA data.
Purpose of the Study:
- To explore relationships between ctDNA changes, clinical response, and tumor size dynamics.
- To identify opportunities for pharmacometrics in optimizing ctDNA sampling strategies.
- To highlight challenges and the need for mechanistic modeling of ctDNA biology.
Main Methods:
- Exploratory analysis of ctDNA levels, clinical response, and tumor size.
- Analysis of data from cancer patients treated with checkpoint inhibitors and targeted therapies.
- Focus on pharmacometrics and mechanistic modeling approaches.
Main Results:
- Treatment-induced ctDNA changes correlate with clinical response and tumor size dynamics.
- Pharmacometrics offers potential for optimizing sampling strategies for ctDNA.
- Data variability and complexity present challenges for modeling.
Conclusions:
- Mechanistic modeling of ctDNA shedding, release, and clearance is essential.
- Integrating ctDNA dynamics with tumor size and treatment effects requires robust models.
- Further research into ctDNA biology will enhance model-informed drug development.
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