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Multi‐criteria manufacturability indices for ranking high‐concentration monoclonal antibody formulations
Authors:Yang Yang  Ajoy Velayudhan  Nina F Thornhill  Suzanne S Farid
Institution:1. +44 (0)20 7594 6622+44 (0)20 7594 6606 0000-0001-7827-7585 Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, South Kensington Campus, London, UK;2. The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, University College London, Torrington Place, London, UK;3. Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, South Kensington Campus, London, UK;4. +44 (0)20 76794415+44 (0) 20 7916 3943
Abstract: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. Biotechnol. Bioeng. 2017;114: 2043–2056. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc.
Keywords:data mining  high‐concentration mAb formulation  manufacturability index  viscosity  aggregation  developability assessment
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