Automatic procedure for modelling, calibration, and optimization of a three-component chromatographic separation.

The current landscape of biopharmaceutical production necessitates an ever-growing set of tools to meet the demands for shorter development times and lower production costs. One path towards meeting these demands is the implementation of digital tools in the development stages. Mathematical modelling of process chromatography, one of the key unit operations in the biopharmaceutical downstream process, is one such tool. However, obtaining parameter values for such models is a time-consuming task that grows in complexity with the number of compounds in the mixture being purified. In this study, we tackle this issue by developing an automated model calibration procedure for purification of a multi-component mixture by linear gradient ion exchange chromatography. The procedure was implemented using the Orbit software (Lund University, Department of Chemical Engineering), which both generates a mathematical model structure and performs the experiments necessary to obtain data for model calibration. The procedure was extended to suggest operating points for the purification of one of the components in the mixture by means of multi-objective optimization using three different objectives. The procedure was tested on a three-component protein mixture and was able to generate a calibrated model capable of reproducing the experimental chromatograms to a satisfactory degree, using a total of six assays. An additional seventh experiment was performed to validate the model response under one of the suggested optimum conditions, respecting a 95 % purity requirement. All of the above was automated and set in motion by the push of a button. With these results, we have taken a step towards fully automating model calibration and thus accelerating digitalization in the development stages of new biopharmaceuticals.

Methodology for fast development of digital solutions in integrated continuous downstream processing.

The methodology for production of biologics is going through a paradigm shift from batch-wise operation to continuous production. Lot of efforts are focused on integration, intensification, and continuous operation for decreased foot-print, material, equipment, and increased productivity and product quality. These integrated continuous processes with on-line analytics become complex processes, which requires automation, monitoring, and control of the operation, even unmanned or remote, which means bioprocesses with high level of automation or even autonomous capabilities. The development of these digital solutions becomes an important part of the process development and needs to be assessed early in the development chain. This work discusses a platform that allows fast development, advanced studies, and validation of digital solutions for integrated continuous downstream processes. It uses an open, flexible, and extendable real-time supervisory controller, called Orbit, developed in Python. Orbit makes it possible to communicate with a set of different physical setups and on the same time perform real-time execution. Integrated continuous processing often implies parallel operation of several setups and network of Orbit controllers makes it possible to synchronize complex process system. Data handling, storage, and analysis are important properties for handling heterogeneous and asynchronous data generated in complex downstream systems. Digital twin applications, such as advanced model-based and plant-wide monitoring and control, are exemplified using computational extensions in Orbit, exploiting data and models. Examples of novel digital solutions in integrated downstream processes are automatic operation parameter optimization, Kalman filter monitoring, and model-based batch-to-batch control.

Automated quality analysis in continuous downstream processes for small-scale applications.

Development of integrated, continuous biomanufacturing (ICB) processes brings along the challenge of streamlining the acquisition of data that can be used for process monitoring, product quality testing and process control. Manually performing sample acquisition, preparation, and analysis during process and product development on ICB platforms requires time and labor that diverts attention from the development itself. It also introduces variability in terms of human error in the handling of samples. To address this, a platform for automatic sampling, sample preparation and analysis for use in small-scale biopharmaceutical downstream processes was developed. The automatic quality analysis system (QAS) consisted of an ÄKTA Explorer chromatography system for sample retrieval, storage, and preparation, as well as an Agilent 1260 Infinity II analytical HPLC system for analysis. The ÄKTA Explorer system was fitted with a superloop in which samples could be stored, conditioned, and diluted before being sent to the injection loop of the Agilent system. The Python-based software Orbit, developed at the department of chemical engineering at Lund university, was used to control and create a communication framework for the systems. To demonstrate the QAS in action, a continuous capture chromatography process utilizing periodic counter-current chromatography was set up on an ÄKTA Pure chromatography system to purify the clarified harvest from a bioreactor for monoclonal antibody production. The QAS was connected to the process to collect two types of samples: 1) the bioreactor supernatant and 2) the product pool from the capture chromatography. Once collected, the samples were conditioned and diluted in the superloop before being sent to the Agilent system, where both aggregate content and charge variant composition were determined using size-exclusion and ion-exchange chromatography, respectively. The QAS was successfully implemented during a continuous run of the capture process, enabling the acquisition of process data with consistent quality and without human intervention, clearing the path for automated process monitoring and data-based control.

Optimization of integrated chromatography sequences for purification of biopharmaceuticals.

With continued development of integrated and continuous downstream purification processes, tuning and optimization become increasingly complicated with additional parameters and codependent variables over the sequence. This article offers a novel perspective of nonlinear optimization of integrated sequences with regard to individual column sizes, flow rates, and scheduling. The problem setup itself is a versatile tool to be used in downstream design which is demonstrated in two case studies: a four-column integrated sequence and a continuously loaded twin-capture setup with five columns.