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The Role of PD Biomarkers in Biosimilar Development - To Get the Right Answer One Must First Ask the Right Question
Gillian R Woollett1, Joseph P Park1, Jihyun Han1
1Samsung Bioepis Co., LTD., Incheon, Korea.
Pharmacodynamic (PD) biomarkers may not replace phase III studies for biosimilar approval. Pharmacokinetic (PK) studies and analytical assessments are most sensitive for detecting product differences, answering core biosimilarity questions effectively.
Area of Science:
- Biopharmaceutical Development
- Regulatory Science
- Biomarker Research
Background:
- A workshop explored pharmacodynamic (PD) biomarkers for biosimilar development and regulatory approval.
- The discussion considered PD biomarkers potentially replacing Phase III studies.
- However, sponsor motivation, feasibility, and regulatory risks were less explored.
Purpose of the Study:
- To evaluate the role and efficiency of PD biomarkers in biosimilar development.
- To analyze the comparative value of PD biomarkers versus pharmacokinetic (PK) and analytical studies.
- To consider the practical aspects and regulatory implications for biosimilar sponsors.
Main Methods:
- Review of concepts discussed at a US FDA and Duke Margolis workshop.
- Analysis of the established sensitivity of pharmacokinetic (PK) studies for biosimilarity assessment.
- Consideration of the parallel nature of biosimilar development timelines versus originator products.
Main Results:
- Pharmacokinetic (PK) studies and analytical assessments are highly sensitive for detecting differences between biosimilar and reference products.
- These methods already address the core questions of biosimilarity.
- Additional information from PD biomarkers or Phase III studies may offer reassurance but not fundamentally new actionable data.
Conclusions:
- The primary value in biosimilarity assessment lies in analytical and PK studies.
- PD biomarkers and Phase III studies may not be essential for establishing biosimilarity if PK and analytical data are robust.
- Optimizing biosimilar development requires understanding current methodologies and future possibilities, focusing on parallel processing and essential data generation.
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