Establishment of a Design Space for Biopharmaceutical Purification Processes Using DoE

Abstract

Recent trends in the pharmaceutical sector are changing the way protein purification processes are designed and executed, moving from operating the process in a fixed point to allowing a permissible region in the operating space known as design space. This trend is driving product development to design quality into the manufacturing process (Quality by Design) and not to rely exclusively on testing quality in the product. A typical purification step has numerous operating parameters that can impact its performance. Therefore, optimization and robustness analysis in purification processes can be time-consuming since they are mainly grounded on experimental work. A valuable approach consists in the combination of an adequate risk analysis technique for selecting the relevant factors influencing process performance and the design of experiment methodology. The latter allows for many process variables which can be studied at the same time; thus, the number of tests will be reduced in comparison with the conventional approach based on trial and error. These multivariate studies permit a detailed exploration in the experimental range and lay the foundation of Quality by Design principles application. This article outlines a recommended sequence of activities toward the establishment of an expanded design space for a purification process.

Quality by design‐steered development and validation of analytical and bioanalytical methods for raloxifene: Application of Monte Carlo simulations and variance inflation factor

“…Method validation was subsequently conducted following MODR verification. The corresponding 3D response surface and respective 2D contour graphs were drawn, and the resultant factor–response relationship(s) was deciphered graphically (Amadeo et al, 2014).…”

“…response surface and respective 2D contour graphs were drawn, and the resultant factor-response relationship(s) was deciphered graphically (Amadeo et al, 2014).…”

Purification optimization for a recombinant single-chain variable fragment against type 1 insulin-like growth factor receptor (IGF-1R) by using design of experiment (DoE)

“…Moreover, it often fails to predict the genuine optimal conditions because the setup just covers a limited part of the experiment space and does not include the combined effects of all the variables involved. These shortcomings can be overcome by design of experiment (DoE), which is a statistics-based technique for designing experiments and analyzing the information obtained [8]. The advantage of this technique is that a wide range of experimental parameters or variables could be varied systematically and simultaneously, enabling obtainment of sufficient information using minimum number of experiments.…”

“…Furthermore, DoE allows detection of interactive effects of different factors, thus ensuring the reliability and robustness of the optimal conditions obtained. In recent years, a number of study cases have proved the high efficiency of DoE approach on protein purification optimization [8][9][10].…”