Advanced control strategies for continuous capture of monoclonal antibodies based upon biolayer interferometry.

The semi and fully continuous production of monoclonal antibodies (mAbs) has been gaining traction as a lower cost, and efficient production of mAbs to broaden patient access. To be truly flexible and adaptive to process demands, the industry has lacked sufficient advanced control strategies. The variation of the upstream product concentration typically cannot be handled by the downstream capture step, which is configured for a constant feed concentration and fixed binding capacity. This inflexibility leads to losses of efficiency and product yield. This study shows that these challenges can be overcome by a novel advanced control strategy concept that includes dynamic control throughout a perfusion bioreactor, with cell retention by alternating tangential flow, integrated with simulated moving bed (SMB) multi-column chromatography. The automation workflow and advanced control strategy were implemented through the use of a visual programming development environment. This enabled dynamic flow control across the upstream and downstream process integrated with a dynamic column loading of the SMB. A sensor prototype, based on continuous biolayer interferometry measurements was applied to detect mAb breakthrough within the last column flow-through to manage column switching. This novel approach provided higher specificity and lower background signal compared to commonly used spectroscopy methods, resulting in an optimized resin utilization while simultaneously avoiding product loss. The dynamic loading was found to provide a twofold increase of the mAb concentration in the eluate compared to a conservative approach with a predefined recipe with similar impurity removal. This concept shows that advanced control strategies can lead to significant process efficiency and yield improvement.

Online deployment of an O-PLS model for dielectric spectroscopy-based inline monitoring of viable cell concentrations in Chinese hamster ovary cell perfusion cultivations.

Viable cell concentration (VCC) is an essential parameter that is required to support the efficient cultivation of mammalian cells. Although commonly determined using at-line or off-line analytics, in-line capacitance measurements represent a suitable alternative method for the determination of VCC. In addition, these latter efforts are complimentary with the Food and Drug Administration's initiative for process analytical technologies (PATs). However, current applications for online determination of the VCC often rely on single frequency measurements and corresponding linear regression models. It has been reported that this may be insufficient for application at all stages of a mammalian cell culture processes due to changes in multiple cell parameters over time. Alternatively, dielectric spectroscopy, measuring capacitance at multiple frequencies, in combination with multivariate mathematical models, has proven to be more robust. However, this has only been applied for retrospective data analysis. Here, we present the implementation of an O-PLS model for the online processing of multifrequency capacitance signals and the on-the-fly integration of the models' VCC results into a supervisory control and data acquisition (SCADA) system commonly used for cultivation observation and control. This system was evaluated using a Chinese hamster ovary (CHO) cell perfusion process.

Towards an automated approach for smart sterility test examination.

As new technologies emerge, deep learning applications are often integral parts of new products as features and often as differentiating benefits. This is especially notable in commercial consumer products in everyday applications, such as voice assistants or streaming content recommendation systems. Due to the power and applicability of these deep learning technologies significant efforts are being directed to the development and integration of appropriate models into science and engineering applications to supplant analogue systems that may be highly prone to human error. Here we present an innovative, low-cost approach to advance sterility assessment workflows that are required and regulated within drug release/manufacturing processes. The model system leverages off-the-shelf hardware as well as deep learning models to detect and classify different microbial contaminations in test containers. The paired hardware and software tools were evaluated in experiments using common model organisms (C. sporogenes, P. aeruginosa, S. aureus). With this approach we were able to detect all three test organisms across 40 experiments, furthermore we were capable of classifying the present organisms with an average classification accuracy of over 87%.

Human-Device Interaction in the Life Science Laboratory.

The interaction of the human user with equipment and software is a central aspect of the work in the life science laboratory. The enhancement of the usability and intuition of software and hardware products, as well as holistic interaction solutions are a demand from all stakeholders in the scientific laboratory who desire more efficient workflows. Shorter training periods, parallelization of workflows, improved data integrity, and enhanced safety are only a few advantages innovative intuitive human-device-interfaces can bring. With recent advances in artificial intelligence (AI), the availability of smart devices, as well as unified communication protocols, holistic interaction solutions are on the rise. Future interaction in the laboratory will not be limited to pushing mechanical buttons on equipment. Instead, the interplay between voice, gestures, and innovative hard- and software components will drive interactions in the laboratory into a more streamlined future.

A Machine Vision Approach for Bioreactor Foam Sensing.

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

Introducing a Virtual Assistant to the Lab: A Voice User Interface for the Intuitive Control of Laboratory Instruments.

The introduction of smart virtual assistants (VAs) and corresponding smart devices brought a new degree of freedom to our everyday lives. Voice-controlled and Internet-connected devices allow intuitive device controlling and monitoring from all around the globe and define a new era of human-machine interaction. Although VAs are especially successful in home automation, they also show great potential as artificial intelligence-driven laboratory assistants. Possible applications include stepwise reading of standard operating procedures (SOPs) and recipes, recitation of chemical substance or reaction parameters to a control, and readout of laboratory devices and sensors. In this study, we present a retrofitting approach to make standard laboratory instruments part of the Internet of Things (IoT). We established a voice user interface (VUI) for controlling those devices and reading out specific device data. A benchmark of the established infrastructure showed a high mean accuracy (95% ± 3.62) of speech command recognition and reveals high potential for future applications of a VUI within the laboratory. Our approach shows the general applicability of commercially available VAs as laboratory assistants and might be of special interest to researchers with physical impairments or low vision. The developed solution enables a hands-free device control, which is a crucial advantage within the daily laboratory routine.

Additive manufactured customizable labware for biotechnological purposes.

Yet already developed in the 1980s, the rise of 3D printing technology did not start until the beginning of this millennium as important patents expired, which opened the technology to a whole new group of potential users. One of the first who used this manufacturing tool in biotechnology was Lücking et al. in 2012, demonstrating potential uses 1, 2. This study shows applications of custom-built 3D-printed parts for biotechnological experiments. It gives an overview about the objects' computer-aided design (CAD) followed by its manufacturing process and basic studies on the used printing material in terms of biocompatibility and manageability. Using the stereolithographic (SLA) 3D-printing technology, a customizable shake flask lid was developed, which was successfully used to perform a bacterial fed-batch shake flask cultivation. The lid provides Luer connectors and tube adapters, allowing both sampling and feeding without interrupting the process. In addition, the digital blueprint the lid is based on, is designed for a modular use and can be modified to fit specific needs. All connectors can be changed and substituted in this CAD software-based file. Hence, the lid can be used for other applications, as well. The used printing material was tested for biocompatibility and showed no toxic effects neither on mammalian, nor on bacteria cells. Furthermore an SDS-PAGE-comb was drawn and printed and its usability evaluated to demonstrate the usefulness of 3D printing for everyday labware. The used manufacturing technique for the comb (multi jet printing, MJP) generates highly smooth surfaces, allowing this application.

A smart device application for the automated determination of E. coli colonies on agar plates.

The manual counting of colonies on agar plates to estimate the number of viable organisms (so-called colony-forming units-CFUs) in a defined sample is a commonly used method in microbiological laboratories. The automation of this arduous and time-consuming process through benchtop devices with integrated image processing capability addresses the need for faster and higher sample throughput and more accuracy. While benchtop colony counter solutions are often bulky and expensive, we investigated a cost-effective way to automate the colony counting process with smart devices using their inbuilt camera features and a server-based image processing algorithm. The performance of the developed solution is compared to a commercially available smartphone colony counter app and the manual counts of two scientists trained in biological experiments. The comparisons show a high accuracy of the presented system and demonstrate the potential of smart devices to displace well-established laboratory equipment.

Protein-Protein Interactions, Not Substrate Recognition, Dominate the Turnover of Chimeric Assembly Line Polyketide Synthases.

The potential for recombining intact polyketide synthase (PKS) modules has been extensively explored. Both enzyme-substrate and protein-protein interactions influence chimeric PKS activity, but their relative contributions are unclear. We now address this issue by studying a library of 11 bimodular and 8 trimodular chimeric PKSs harboring modules from the erythromycin, rifamycin, and rapamycin synthases. Although many chimeras yielded detectable products, nearly all had specific activities below 10% of the reference natural PKSs. Analysis of selected bimodular chimeras, each with the same upstream module, revealed that turnover correlated with the efficiency of intermodular chain translocation. Mutation of the acyl carrier protein (ACP) domain of the upstream module in one chimera at a residue predicted to influence ketosynthase-ACP recognition led to improved turnover. In contrast, replacement of the ketoreductase domain of the upstream module by a paralog that produced the enantiomeric ACP-bound diketide caused no changes in processing rates for each of six heterologous downstream modules compared with those of the native diketide. Taken together, these results demonstrate that protein-protein interactions play a larger role than enzyme-substrate recognition in the evolution or design of catalytically efficient chimeric PKSs.