Simplex‐based optimization of numerical and categorical inputs in early bioprocess development: Case studies in HT chromatography

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.     

Chromatography process development in the quality by design paradigm I: Establishing a high‐throughput process development platform as a tool for estimating “characterization space” for an ion exchange chromatography step

This article describes the development of a high‐throughput process development (HTPD) platform for developing chromatography steps. An assessment of the platform as a tool for establishing the “characterization space” for an ion exchange chromatography step has been performed by using design of experiments. Case studies involving use of a biotech therapeutic, granulocyte colony‐stimulating factor have been used to demonstrate the performance of the platform. We discuss the various challenges that arise when working at such small volumes along with the solutions that we propose to alleviate these challenges to make the HTPD data suitable for empirical modeling. Further, we have also validated the scalability of this platform by comparing the results from the HTPD platform (2 and 6 μL resin volumes) against those obtained at the traditional laboratory scale (resin volume, 0.5 mL). We find that after integration of the proposed correction factors, the HTPD platform is capable of performing the process optimization studies at 170‐fold higher productivity. The platform is capable of providing semi‐quantitative assessment of the effects of the various input parameters under consideration. We think that platform such as the one presented is an excellent tool for examining the “characterization space” and reducing the extensive experimentation at the traditional lab scale that is otherwise required for establishing the “design space.” Thus, this platform will specifically aid in successful implementation of quality by design in biotech process development. This is especially significant in view of the constraints with respect to time and resources that the biopharma industry faces today. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29: 403–414, 2013