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Konstantinidis S.,Gordon College | Welsh J.P.,Process Development and EngineeringMerck and Co. IncKenilworth | Roush D.J.,Process Development and EngineeringMerck and Co. IncKenilworth | Velayudhan A.,Gordon College
Biotechnology Progress | Year: 2016

The identification of feasible operating conditions during the early stages of bioprocess development is implemented frequently through High Throughput (HT) studies. These typically employ techniques based on regression analysis, such as Design of Experiments. In this work, an alternative approach, based on a previously developed variant of the Simplex algorithm, is compared to the conventional regression-based method for three experimental systems involving polishing chromatography and protein refolding. This Simplex algorithm variant was found to be more effective in identifying superior operating conditions, and in fact it reached the global optimum in most cases involving multiple optima. By contrast, the regression-based method often failed to reach the global optimum, and in many cases reached poor operating conditions. The Simplex-based method is further shown to be robust in dealing with noisy experimental data, and requires fewer experiments than regression-based methods to reach favorable operating conditions. The Simplex-variant also lends itself to the use of HT analytical methods, when they are available, which can assist in avoiding analytical bottlenecks. It is suggested that this Simplex-variant is ideally suited to rapid optimization in early-phase process development. © 2016 American Institute of Chemical Engineers.

Petroff M.G.,Process Development and EngineeringMerck and Co. Inc.Kenilworth | Bao H.,Process Development and EngineeringMerck and Co. Inc.Kenilworth | Welsh J.P.,Process Development and EngineeringMerck and Co. Inc.Kenilworth | van Beuningen - de Vaan M.,Downstream ProcessingMerck and Co. Inc.OssThe Netherlands | And 6 more authors.
Biotechnology and Bioengineering | Year: 2016

High throughput experimental strategies are central to the rapid optimization of biologics purification processes. In this work, we extend common high throughput technologies towards the characterization of a multi-column chromatography process for a monoclonal antibody (mAb). Scale-down strategies were first evaluated by comparing breakthrough, retention, and performance (yields and clearance of aggregates and host cell protein) across miniature and lab scale columns. The process operating space was then evaluated using several integrated formats, with batch experimentation to define process testing ranges, miniature columns to evaluate the operating space, and comparison to traditional scale columns to establish scale-up correlations and verify the determined operating space. When compared to an independent characterization study at traditional lab column scale, the high throughput approach identified the same control parameters and similar process sensitivity. Importantly, the high throughput approach significantly decreased time and material needs while improving prediction robustness. Miniature columns and manufacturing scale centerpoint data comparisons support the validity of this approach, making the high throughput strategy an attractive and appropriate scale-down tool for the formal characterization of biotherapeutic processes in the future if regulatory acceptance of the miniature column data can be achieved. © 2015 Wiley Periodicals, Inc.

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