An impurity characterization based approach for the rapid development of integrated downstream purification processes

Abstract

In this study, we describe a new approach for the characterization of process‐related impurities along with an in silico tool to generate orthogonal, integrated downstream purification processes for biological products. A one‐time characterization of process‐related impurities from product expression in Pichia pastoris was first carried out using linear salt and pH gradients on a library of multimodal, salt‐tolerant, and hydrophobic charge induction chromatographic resins. The Reversed‐phase ultra‐performance liquid chromatography (UPLC) analysis of the fractions from these gradients was then used to generate large data sets of impurity profiles. A retention database of the biological product was also generated using the same linear salt and pH gradients on these resins, without fraction collection. The resulting two data sets were then analyzed using an in silico tool, which incorporated integrated manufacturing constraints to generate and rank potential three‐step purification sequences based on their predicted purification performance as well as whole‐process “orthogonality” for impurity removal. Highly ranked sequences were further examined to identify templates for process development. The efficacy of this approach was successfully demonstrated for the rapid development of robust integrated processes for human growth hormone and granulocyte‐colony stimulating factor.

Document Details

Document Type
Pub Defense Publication
Publication Date
May 04, 2018
Source ID
10.1002/bit.26718

Entities

People

  • Chaz Goodwine
  • J Christopher Love
  • Kerry R. Love
  • Laura E Crowell
  • Nicholas Vecchiarello
  • Steven M Cramer
  • Steven M. Timmick

Organizations

  • Defense Advanced Research Projects Agency
  • Massachusetts Institute of Technology
  • Naval Information Warfare Systems Command
  • Rensselaer Polytechnic Institute

Tags

Readers

  • Analytical Chemistry
  • Computational Modeling and Simulation
  • Molecular and Cellular Biochemistry