Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks

Abstract

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. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:696-707, 2017.

Chromatographic parameter determination for complex biological feedstocks

“…Mechanistic modelling was applied as described in Ref. . All correlations to determine relevant parameters are shown in Table .…”

Distinct and Quantitative Validation Method for Predictive Process Modelling in Preparative Chromatography of Synthetic and Bio-Based Feed Mixtures Following a Quality-by-Design (QbD) Approach

“…The derived model and the parameter set is afterwards suitable for detailed engineering of both batch and continuous chromatography. For modelling chromatography, all types of model approaches have been presented [18,19], from stage models [18,20,21] to mechanistic models [22][23][24][25][26][27]. Among the most common ones is the general rate model and its derivatives/simplifications [28][29][30][31], which is explained in the chromatography modelling section.…”

High-order approximation of chromatographic models using a nodal discontinuous Galerkin approach

“…High order methods that aim to obtain a desired resolution using the smallest number of degrees of freedom (DOFs) possible have been developed during the last decades. The introduction of such methods in the field of chromatography is important since they have the potential to significantly reduce the required computational effort when solving large-scale [13,14], dynamic [15] or large parameter estimation [16,17] optimization problems which are gaining increasing practical importance due to the allowance of fast and cheap process development.…”