Realization of digital twin for dynamic control toward sample variation of ion exchange chromatography in antibody separation.

Digital twin (DT) is a virtual and digital representation of physical objects or processes. In this paper, this concept is applied to dynamic control of the collection window in the ion exchange chromatography (IEC) toward sample variations. A possible structure of a feedforward model-based control DT system was proposed. Initially, a precise IEC mechanistic model was established through experiments, model fitting, and validation. The average root mean square error (RMSE) of fitting and validation was 8.1% and 7.4%, respectively. Then a model-based gradient optimization was performed, resulting in a 70.0% yield with a remarkable 11.2% increase. Subsequently, the DT was established by systematically integrating the model, chromatography system, online high-performance liquid chromatography, and a server computer. The DT was validated under varying load conditions. The results demonstrated that the DT could offer an accurate control with acidic variants proportion and yield difference of less than 2% compared to the offline analysis. The embedding mechanistic model also showed a positive predictive performance with an average RMSE of 11.7% during the DT test under >10% sample variation. Practical scenario tests indicated that tightening the control target could further enhance the DT robustness, achieving over 98% success rate with an average yield of 72.7%. The results demonstrated that the constructed DT could accurately mimic real-world situations and perform an automated and flexible pooling in IEC. Additionally, a detailed methodology for applying DT was summarized.

The identification of a robust leucine dehydrogenase from a directed soil metagenome for efficient synthesis of L-2-aminobutyric acid.

L-2-aminobutyric acid (L-2-ABA) is a chiral precursor for the synthesis of anti-epileptic drug levetiracetam and anti-tuberculosis drug ethambutol. Asymmetric synthesis of L-2-ABA by leucine dehydrogenases has been widely developed. However, the limitations of natural enzymes, such as poor stability, low catalytic efficiency, and inhibition of high-concentration substrates, limit large-scale applications. Herein, by directed screening of a metagenomic library from unnatural amino acid-enriched environments, a robust leucine dehydrogenase, TvLeuDH, was identified, which exhibited high substrate tolerance and excellent enzymatic activity towards 2-oxobutyric acid. In addition, TvLeuDH has strong affinity for NADH. Subsequently, a three-enzyme co-expression system containing L-threonine deaminase, TvLeuDH, and glucose dehydrogenase was established. By optimizing reaction conditions, 1.5 M L-threonine could be converted to L-2-ABA with a 99% molar conversion rate and a space-time yield of 51.5 g·L-1 ·h-1 . In this process, no external coenzyme was added. The robustness of TvLeuDH allowed the reaction to be performed without the addition of extra salt as the buffer, demonstrating the simplest reaction system currently reported. These unique properties for the efficient and environmentally friendly production of chiral amino acids make TvLeuDH a particularly promising candidate for industrial applications, which reveals the great potential of directed metagenomics for industrial biotechnology.

Model-assisted process design for better evaluation and scaling up of continuous downstream bioprocessing.

Continuous process is a promising alternative for tradition batch process in biomanufacturing, which has higher productivity and lower material consumption. However, despite the maturation of necessary technologies for continuous process, there are few discussion about optimization of full continuous process. One possible reason is that the continuous process is such a complex and interacted process that the traditional experiment-based optimization approach is not sufficient anymore. To address that problem, the process simulation tool SuperPro Designer and continuous capture chromatography model were integrated into a model-assisted design platform for better development of continuous process. The influences of different continuous capture operation modes and sub-lot number under various upstream conditions were analyzed for pilot-scale production. The best combination of operation mode and sub-lot number were determined for the highest equipment utilization without any conflict. After the process optimization, the equipment utilization of continuous process was significantly improved to 27.3%. Then, a pilot-scale case study was carried out to validate the proposed approach. The results showed that the scaling up and process design of continuous process were successful. No time conflict and process failure happened and the final product met the release specification. Finally, the cost of goods was evaluated with SuperPro Designer, and the results showed a 17.4% cost reduction for optimized continuous downstream process compared to batch process, which is promoting for the future industrial applications.

Model-based evaluation and model-free strategy for process development of three-column periodic counter-current chromatography.

Multi-column counter-current chromatography is an advanced technology used for continuous capture processes to improve process productivity, resin capacity utilization and product consistency. However, process development is difficult due to process complexity. In this work, some general and convenient guidances for three-column periodic counter-current chromatography (3C-PCC) were developed. Boundaries and distributions of operating windows of 3C-PCC processes were clarified by model-based predictions. Interactive effects of feed concentration (c0), resin properties (qmax and De), recovery and regeneration times (tRR) were evaluated over a wide range for maximum productivity (Pmax). Furthermore, variation of Pmax was analyzed considering the constraint factors (capacity utilization target and flow rate limitation). The plateau value of Pmax was determined by qmax and tRR. The operating conditions for Pmax were controlled by qmax, tRR and c0 interactively, and a critical concentration existed to judge whether the operating conditions of Pmax under constraints. Based on the comprehensive understanding on 3C-PCC processes, a model-free strategy was proposed for process development. The optimal operating conditions could be determined based on a set of breakthrough curves, which was used to optimize process performance and screen resins. The approach proposed was validated using monoclonal antibody (mAb) capture with a 3C-PCC system under various mAb and feed concentrations. The results demonstrated that a thorough model-based process understanding on multi-column counter-current chromatography is important and could improve process development and establish a model-free strategy for more convenient applications.