The capacity to locate efficiently a subset of experimental conditions necessary for the identification of an operating envelope is a key objective in many studies. We have shown previously how this can be performed by using the simplex algorithm and this paper now extends the approach by augmenting the established simplex method to form a novel hybrid experimental simplex algorithm (HESA) for identifying 'sweet spots' during scouting development studies. The paper describes the new algorithm and illustrates its use in two bioprocessing case studies conducted in a 96-well filter plate format. The first investigates the effect of pH and salt concentration on the binding of green fluorescent protein, isolated from Escherichia coli homogenate, to a weak anion exchange resin and the second examines the impact of salt concentration, pH and initial feed concentration upon the binding capacities of a FAb', isolated from E. coli lysate, to a strong cation exchange resin. Compared with the established algorithm, HESA was better at delivering valuable information regarding the size, shape and location of operating 'sweet spots' that could then be further investigated and optimized with follow up studies. To test how favorably these features of HESA compared with conventional DoE (design of experiments) methods, HESA results were also compared with approaches including response surface modeling experimental designs. The results show that HESA can return 'sweet spots' that are equivalently or better defined than those obtained from DoE approaches. At the same time the deployment of HESA to identify bioprocess-relevant operating boundaries was accompanied by comparable experimental costs to those of DoE methods. HESA is therefore a viable and valuable alternative route for identifying 'sweet spots' during scouting studies in bioprocess development.
The High Throughput (HT) investigation of chromatographic separations is an important element of downstream bioprocess development due to the importance of chromatography as a technique for achieving stringent regulatory requirements on product purity. Various HT formats for chromatography exist, but the miniature column approach has characteristics resembling large scale packed bed column chromatography the most. The operation of such columns on robotic stations can be automated, but this is not always a straightforward procedure; the robotic manipulations are highly dependent on the settings of each experiment and the standard commands of the supporting software may not provide readily the required flexibility and accessibility for "plug and play" functionality. These can limit the potential of this technique in laboratories engaging on HT activities. In this work, we present an application which aims to overcome this challenge by providing end-users with a flexible operation of the miniature column technique on an automated liquid handler. The application includes a script which is written on Freedom EVOware, and is supplemented by custom compiled executables. Here, the manipulations carried out by the application are described in detail and its functionality is demonstrated through typical experiments based on bind and elute miniature column chromatography. The application is shown to allow for the unsupervised "on-the-fly" programming of the robotic station and to ultimately make the technique accessible to non-automation experts. This application is therefore well suited to simplifying development activities based on the robotic deployment of the miniature column chromatography technique.
Bioprocess development studies often involve the investigation of numerical and categorical inputs via the adoption of Design of Experiments (DoE) techniques. An attractive alternative is the deployment of a grid compatible Simplex variant which has been shown to yield optima rapidly and consistently. In this work, the method is combined with dummy variables and it is deployed in three case studies wherein spaces are comprised of both categorical and numerical inputs, a situation intractable by traditional Simplex methods. The first study employs in silico data and lays out the dummy variable methodology. The latter two employ experimental data from chromatography based studies performed with the filter‐plate and miniature column High Throughput (HT) techniques. The solute of interest in the former case study was a monoclonal antibody whereas the latter dealt with the separation of a binary system of model proteins. The implemented approach prevented the stranding of the Simplex method at local optima, due to the arbitrary handling of the categorical inputs, and allowed for the concurrent optimization of numerical and categorical, multilevel and/or dichotomous, inputs. The deployment of the Simplex method, combined with dummy variables, was therefore entirely successful in identifying and characterizing global optima in all three case studies. The Simplex‐based method was further shown to be of equivalent efficiency to a DoE‐based approach, represented here by D‐Optimal designs. Such an approach failed, however, to both capture trends and identify optima, and led to poor operating conditions. It is suggested that the Simplex‐variant is suited to development activities involving numerical and categorical inputs in early bioprocess development.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.