Journal
BIOTECHNOLOGY AND BIOENGINEERING
Volume 114, Issue 9, Pages 2043-2056Publisher
WILEY
DOI: 10.1002/bit.26329
Keywords
data mining; high-concentration mAb formulation; manufacturability index; viscosity; aggregation; developability assessment
Categories
Funding
- UK Engineering & Physical Sciences Research Council (EPSRC) for the EPSRC Centre for Innovative Manufacturing in Emergent Macromolecular Therapies [EP/I033270/1]
- Consortium of Industrial and Governmental Users
- EPSRC [EP/J019798/1, EP/I033270/1, EP/P006485/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/P006485/1, EP/I033270/1, EP/J019798/1] Funding Source: researchfish
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The need for high-concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high-concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high-concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analyzing published high-throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions, and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi-criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behavior during UF/DF. Multi-criteria decision-making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF. (c) 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc.
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