From Peptide Process Variability to Phase III Manufacturing Readiness

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In this case study, explore how Neuland applied a structured, data-driven process optimization approach to a late-stage peptide API program. By evaluating multiple process variables and their interactions through Design of Experiments, the team strengthened understanding of the manufacturing process, addressed scale-up risks, and established a more reliable foundation for Phase III peptide API supply.

Key Takeaways

• Applied Design of Experiments (DoE) to evaluate multiple process variables.
• Identified parameter interactions affecting process performance and product quality.
• Improved process understanding for robust and reproducible manufacturing.
• Focused optimization on critical process parameters and quality attributes.
• Reduced reliance on one-factor-at-a-time experimentation.
• Strengthened scale-up readiness for Phase III peptide API manufacturing.
• Supported process transferability and an evidence-based control strategy.

This case study highlights the value of combining peptide process expertise with statistical experimental design. A DoE-driven approach can reveal parameter interactions that conventional single-variable studies may miss, helping development teams build a more robust, reproducible, and scalable peptide manufacturing process.

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