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AAPS PharmSciTech

, Volume 19, Issue 5, pp 2185–2194 | Cite as

Optimization of a Coupling Process for Insulin Degludec According to a Quality by Design (QbD) Paradigm

  • Lei Nie
  • Mingming Hu
  • Xu Yan
  • Tingting Guo
  • Haibin Wang
  • Sheng Zhang
  • Haibin Qu
Research Article

Abstract

This case study described a successful application of the quality by design (QbD) principles to a coupling process development of insulin degludec. Failure mode effects analysis (FMEA) risk analysis was first used to recognize critical process parameters (CPPs). Five CPPs, including coupling temperature (Temp), pH of desB30 solution (pH), reaction time (Time), desB30 concentration (Conc), and molar equivalent of ester per mole of desB30 insulin (MolE), were then investigated using a fractional factorial design. The curvature effect was found significant, indicating the requirement of second-order models. Afterwards, a central composite design was built with an augmented star and center points study. Regression models were developed for the CPPs to predict the purity and yield of predegludec using above experimental data. The R2 and adjusted R2 were higher than 96 and 93% for the two models respectively. The Q2 values were more than 80% indicating a good predictive ability of models. MolE was found to be the most significant factor affecting both yield and purity of predegludec. Temp, pH, and Conc were also significant for predegludec purity, while Time appeared to remarkably influence the yield model. The multi-dimensional design space and normal operating region (NOR) with a robust setpoint were determined using a probability-based Monte-Carlo simulation method. The verified experimental results showed that the design space was reliable and effective. This study enriches the understanding of acetylation process and is instructional to other complicated operations in biopharmaceutical engineering.

KEY WORDS

quality by design design of experiment insulin degludec acylation Monte-Carlo simulation 

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Copyright information

© American Association of Pharmaceutical Scientists 2018

Authors and Affiliations

  • Lei Nie
    • 1
  • Mingming Hu
    • 2
  • Xu Yan
    • 1
  • Tingting Guo
    • 2
  • Haibin Wang
    • 2
  • Sheng Zhang
    • 1
  • Haibin Qu
    • 1
  1. 1.Pharmaceutical Informatics Institute, College of Pharmaceutical SciencesZhejiang UniversityHangzhouChina
  2. 2.Zhejiang Hisun Pharmaceutical Co., Ltd.TaizhouChina

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