Downstream process development is a major area of importance within the field of bioengineering. During the design of such a downstream process, important decisions have to be made regarding the type of unit operations as well as their sequence and their operating conditions. Current computational approaches addressing these issues either show a high level of simplification or struggle with computational speed. Therefore, this article presents a new approach that combines detailed mechanistic models and speed-enhancing artificial neural networks. This approach was able to simultaneously optimize a process with three different chromatographic columns toward yield with a minimum purity of 99.9%. The addition of artificial neural networks greatly accelerated this optimization. Due to high computational speed, the approach is easily extendable to include more unit operations. Therefore, it can be of great help in the acceleration of downstream process development.

Knowledge-based development of chromatographic separation processes requires efficient techniques to determine the physicochemical properties of the product and the impurities to be removed. These characterization techniques are usually divided into approaches that determine molecular properties, such as charge, hydrophobicity and size, or molecular interactions with auxiliary materials, commonly in the form of adsorption isotherms. In this study we demonstrate the application of a three-dimensional liquid chromatography approach to a clarified cell homogenate containing a therapeutic enzyme. Each separation dimension determines a molecular property relevant to the chromatographic behavior of each component. Matching of the peaks across the different separation dimensions and against a high-resolution reference chromatogram allows to assign the determined parameters to pseudo-components, allowing to determine the most promising technique for the removal of each impurity. More detailed process design using mechanistic models requires isotherm parameters. For this purpose, the second dimension consists of multiple linear gradient separations on columns in a high-throughput screening compatible format, that allow regression of isotherm parameters with an average standard error of 8%.

Integration of fermentation and downstream steps is required to improve the sustainability of industrial biotechnology processes. In this context, a new integrated process for β-lactam nuclei production is proposed, which has been theoretically investigated and conceptually designed. This process is an integration of fermentation and enzymatic deacylation of adipyl-7- aminodeacetoxycephalosporanic acid (adADCA) in one reactor producing 7-aminodeacetoxycephalosporanic acid (ADCA) directly from glucose. Although the deacylation equilibrium is unfavorable at the fermentation pH, it is pulled to completion because ADCA starts to crystallize and the liberated side chain, adipic acid (AA), is consumed by the fermentation. Therefore the integrated process requires much less AA than the nonintegrated process. In addition, the new process will lead to a reduction in number of downstream processing units, should avoid the use of acids and bases for pH shifts, and might lead to a reduction in waste salts production. A conceptual process was designed including an economic and technical analysis. The design goal was to produce 2000 tons/year of ADCA of 99% purity. Rigorous simulations were performed to evaluate different process options. Among the economic advantages of the new integrated process are 13.4% lower capital investments and 7.8% lower manufacturing costs. An analogous process for 6-aminopenicillanic acid (APA) is not feasible due to chemical degradation of this product.