Abstract
Recently, a grid compatible Simplex variant has been demonstrated to identify optima consistently and rapidly in challenging high throughput (HT) applications in early bioprocess development. Here, this method is extended by deploying it to multi‐objective optimization problems. Three HT chromatography case studies are presented, each posing challenging early development situations and including three responses which were amalgamated by the adoption of the desirability approach. The suitability of a design of experiments (DoE) methodology per case study, using regression analysis in addition to the desirability approach, was evaluated for a large number of weights and in the presence of stringent and lenient performance requirements. Despite the adoption of high‐order models, this approach had low success in identification of the optimal conditions. For the deployment of the Simplex approach, the deterministic specification of the weights of the merged responses was avoided by including them as inputs in the formulated multi‐objective optimization problem, facilitating this way the decision making process. This, and the ability of the Simplex method to locate optima, rendered the presented approach highly successful in delivering rapidly operating conditions, which belonged to the Pareto set and offered a superior and balanced performance across all outputs compared to alternatives. Moreover, its performance was relatively independent of the starting conditions and required sub‐minute computations despite its higher order mathematical functionality compared to DoE techniques. These evidences support the suitability of the grid compatible Simplex method for early bioprocess development studies involving complex data trends over multiple responses. © 2018 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:1393–1406, 2018
Highlights
The elucidation and definition of the design space of a process is often the culmination of development efforts including the completion of experiments across multiple scales
Regression analysis is used in a design of experiments (DoE) approach to scout combinations of inputs so as to determine how they affect a set of objective functions, or outputs/responses
The desirability approach was used in three case studies in high throughput (HT) chromatography to investigate simultaneously the effects of pH, Conductivity, and Load on outputs including yield, and host cell protein (HCP) and host cell DNA clearance
Summary
The elucidation and definition of the design space of a process is often the culmination of development efforts including the completion of experiments across multiple scales. Multivariate data analysis is emerging as a tool for multivariate statistical process control and for deconvoluting chromatograms in downstream processing.[5] Here, regression analysis is used in a DoE approach to scout combinations of inputs so as to determine how they affect a set of objective functions, or outputs/responses. These can include quantities such as yield, impurity, and contaminant levels, and so on. A multi-objective optimization problem can be defined and be addressed by overlaying the response surfaces of the considered outputs so as to obtain windows of operation in a graphical fashion.[6]
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