An Intercompany Perspective on Practical Experiences of Predicting, Optimizing and Analyzing High Concentration Biologic Therapeutic Formulations

溶解度 生化工程 粘度 赋形剂 理论(学习稳定性) 计算机科学 化学 工艺工程 材料科学 色谱法 工程类 有机化学 机器学习 复合材料
作者
Preeti G Desai,Patrick Garidel,Francisca Owusu Gbormittah,Douglas E. Kamen,Brittney J. Mills,Chakravarthy Narasimhan,Shubhadra N. Singh,Elaine S. E. Stokes,Erika R Walsh
出处
期刊:Journal of Pharmaceutical Sciences [Elsevier]
卷期号:112 (2): 359-369 被引量:3
标识
DOI:10.1016/j.xphs.2022.11.020
摘要

Developing high-dose biologic drugs for subcutaneous injection often requires high-concentration formulations and optimizing viscosity, solubility, and stability while overcoming analytical, manufacturing, and administration challenges. To understand industry approaches for developing high-concentration formulations, the Formulation Workstream of the BioPhorum Development Group, an industry-wide consortium, conducted an inter-company collaborative exercise which included several surveys. This collaboration provided an industry perspective, experience, and insight into the practicalities for developing high-concentration biologics. To understand solubility and viscosity, companies desire predictive tools, but experience indicates that these are not reliable and experimental strategies are best. Similarly, most companies prefer accelerated and stress stability studies to in-silico or biophysical-based prediction methods to assess aggregation. In addition, optimization of primary container-closure and devices are pursued to mitigate challenges associated with high viscosity of the formulation. Formulation strategies including excipient selection and application of studies at low concentration to high-concentration formulations are reported. Finally, analytical approaches to high concentration formulations are presented. The survey suggests that although prediction of viscosity, solubility, and long-term stability is desirable, the outcome can be inconsistent and molecule dependent. Significant experimental studies are required to confirm robust product definition as modeling at low protein concentrations will not necessarily extrapolate to high concentration formulations.
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