Smart process development: Application of machine-learning and integrated process modeling for inclusion body purification processes.

The development of a biopharmaceutical production process usually occurs sequentially, and tedious optimization of each individual unit operation is very time-consuming. Here, the conditions established as optimal for one-step serve as input for the following step. Yet, this strategy does not consider potential interactions between a priori distant process steps and therefore cannot guarantee for optimal overall process performance. To overcome these limitations, we established a smart approach to develop and utilize integrated process models using machine learning techniques and genetic algorithms. We evaluated the application of the data-driven models to explore potential efficiency increases and compared them to a conventional development approach for one of our development products. First, we developed a data-driven integrated process model using gradient boosting machines and Gaussian processes as machine learning techniques and a genetic algorithm as recommendation engine for two downstream unit operations, namely solubilization and refolding. Through projection of the results into our large-scale facility, we predicted a twofold increase in productivity. Second, we extended the model to a three-step model by including the capture chromatography. Here, depending on the selected baseline-process chosen for comparison, we obtained between 50% and 100% increase in productivity. These data show the successful application of machine learning techniques and optimization algorithms for downstream process development. Finally, our results highlight the importance of considering integrated process models for the whole process chain, including all unit operations.

Chemoaffinity material for plasmid DNA analysis by high-performance liquid chromatography with condition-dependent switching between isoform and topoisomer selectivity.

Plasmid DNA may exist in three isoforms, the linear, open-circular (oc, "nicked"), and covalently closed circular (ccc, "supercoiled") form. We have recently reported on the chromatographic separation of supercoiled plasmid topoisomers on cinchona-alkaloid modified silica-based stationary phases. Herein, we present a selectivity switching mechanism to achieve separation of isoforms and/or supercoiled topoisomers using the very same chromatographic column and system. While salt gradient elution facilitates topoisomer separation, the supercoiled species are eluting as a single peak upon elution by a mixed pH and organic modifier gradient, still well separated from the other isoforms. We have found that a mobile phase pH value near the pI of the zwitterionic adsorbent surface leads to full recovery of all plasmid DNA isoforms, which is a major issue when using anion exchange-based resins. Furthermore, the observed elution pattern, oc < linear < ccc, is constant upon changes of mobile phase composition, gradient slope, and plasmid size. The remarkable isoform selectivity found on quinine-based selectors is explained by van't Hoff plots, revealing a different binding mechanism between the supercoiled plasmid on one hand and the oc and linear isoforms on the other hand.