The implementation of continuous processing in the biopharmaceutical industry is hindered by the scarcity of process analytical technologies (PAT). To monitor and control a continuous process, PAT tools will be crucial to measure real-time product quality attributes such as protein aggregation. Miniaturizing these analytical techniques can increase measurement speed and enable faster decision-making. A fluorescent dye (FD)-based miniaturized sensor has previously been developed: a zigzag microchannel which mixes two streams under 30 s. Bis-ANS and CCVJ, two established FDs, were employed in this micromixer to detect aggregation of the biopharmaceutical monoclonal antibody (mAb). Both FDs were able to robustly detect aggregation levels starting at 2.5%. However, the real-time measurement provided by the microfluidic sensor still needs to be implemented and assessed in an integrated continuous downstream process. In this work, the micromixer is implemented in a lab-scale integrated system for the purification of mAbs, established in an ÄKTA™ unit. A viral inactivation and two polishing steps were reproduced, sending a sample of the product pool after each phase directly to the microfluidic sensor for aggregate detection. An additional UV sensor was connected after the micromixer and an increase in its signal would indicate that aggregates were present in the sample. The at-line miniaturized PAT tool provides a fast aggregation measurement, under 10 min, enabling better process understanding and control.

The lack of process analytical technologies able to provide real-time information and process control over a biopharmaceutical process has long impaired the transition to continuous biomanufacturing. For the monoclonal antibody (mAb) production, aggregate formation is a major critical quality attribute (CQA) with several known process parameters (i.e., protein concentration and agitation) influencing this phenomenon. The development of a real-time tool to monitor aggregate formation is then crucial to gain control and achieve a continuous processing. Due to an inherent short operation time, miniaturized biosensors placed after each step can be a powerful solution. In this work, the development of a fluorescent dye-based microfluidic sensor for fast at-line PAT is described, using fluorescent dyes to examine possible mAb size differences. A zigzag microchannel, which provides 90% of mixing efficiency under 30 s, coupled to an UV–Vis detector, and using four FDs, was studied and validated. With different generated mAb aggregation samples, the FDs Bis-ANS and CCVJ were able to robustly detect from, at least, 2.5% to 10% of aggregation. The proposed FD-based micromixer is then ultimately implemented and validated in a lab-scale purification system, demonstrating the potential of a miniaturized biosensor to speed up CQAs measurement in a continuous process.

Biopharmaceuticals are becoming increasingly important in modern healthcare. Monoclonal antibodies (mAb) are one of the most widely used therapeutic proteins and are important for the treatment of cancer and autoimmune diseases, among others. After cell culture there are still large amounts of other impurities (e.g. host cell proteins) in solution. Chromatography is usually the first purification step, allowing to increase purity and reduce volume. This comes associated with high costs and chromatography accounts for a significant portion of total production costs for therapeutic proteins. Chromatographic process development may be time consuming and use large amounts of resins. Therefore, there is increased interest in finding cheaper techniques for chromatographic process development without compromising accuracy. This paper presents a highly sophisticated microfluidic chip approach for efficient adsorption isotherm determinations compared to current chromatographic process development. Implementation of an image analysis software ensures that chromatographic resin volume is accurately determined. The adsorption isotherm performance of microfluidics was compared to the robotic Liquid-handling Station (LHS) and labor intensive Eppendorf tubes. The microfluidic chip allows a 15-fold volume reduction and resin consumptions as low as 100/200 nl (200/100-fold reduction). The microfluidic chip performed comparably to the other miniaturized techniques, using less liquid and resin volume. For process development of expensive products (e.g. monoclonal antibodies), miniaturization (provided by the microfluidic chip) proved to be the most cost effective alternative whereas for less valuable products (e.g. lysozyme) automation (provided by the LHS) was the most cost effective alternative.

A major challenge in the transition to continuous biomanufacturing is the lack of process analytical technology (PAT) tools which are able to collect real-time information on the process and elicit a response to facilitate control. One of the critical quality attributes (CQAs) of interest during monoclonal antibodies production is aggregate formation. The development of a real-time PAT tool to monitor aggregate formation is then crucial to have immediate feedback and process control. Miniaturized sensors placed after each unit operation can be a powerful solution to speed up an analytical measurement due to their characteristic short reaction time. In this work, a micromixer structure capable of mixing two streams is presented, to be employed in the detection of mAb aggregates using fluorescent dyes. Computational fluid dynamics (CFD) simulations were used to compare the mixing performance of a series of the proposed designs. A final design of a zigzag microchannel with 45° angle was reached and this structure was subsequently fabricated and experimentally validated with colour dyes and, later, with a FITC-IgG molecule. The designed zigzag micromixer presents a mixing index of around 90%, obtained in less than 30 seconds. Therefore, a micromixer channel capable of a fast and efficient mixing is hereby demonstrated, to be used as a real-time PAT tool for a fluorescence based detection of protein aggregation.

Process development in the biotech industry leads to investments around hundred of millions of dollars. It is important to mitigate costs without neglecting the quality of process development. Biopharmaceutical process development is important for companies to develop new processes and be first to market, improve a pre-established process, or start manufacturing a product available by patent expiry (biosimilars). Laboratory automation enables methodical and standardized process development. Miniaturization and parallelization empower laboratories to screen several experimental conditions and define operating windows for purification processes, improving process robustness. Together, they allow for fast and accurate process development in a fraction of the time and cost of nonminiaturized/nonparallel process development approaches. The most widely used High-Throughput Screening technique is a liquid-handling station and microfluidics is taking its first steps in process development. Both are attractive scale-down tools for the characterization of bioprocesses and allow thousands of experiments to be performed per day. High-Throughput Process Development (HTPD) has helped to achieve major breakthroughs in process optimization, both for upstream and downstream processing. Continuous processing is the next step in process development which leads to cost reduction, higher productivity and better quality control; the integration of upstream and downstream processes is seen as a major challenge. In this review, we will focus on the state-of-the-art of miniaturized techniques for process development in the biotechnology industry, and how automation and miniaturization drive process development. A comparison between liquid-handling stations and microfluidics is made and an indication is given of which tools are still lacking for HTPD in the context of Integrated Continuous Biomanufacturing.

The transition to continuous biomanufacturing is considered the next step to reduce costs and improve process robustness in the biopharmaceutical industry, while also improving productivity and product quality. The platform production process for monoclonal antibodies (mAbs) is eligible for continuous processing to lower manufacturing costs due to patent expiration and subsequent growing competition. One of the critical quality attributes of interest during mAb purification is aggregate formation, with several processing parameters and environmental factors known to influence antibody aggregation. Therefore, a real-time measurement to monitor aggregate formation is crucial to have immediate feedback and process control and to achieve a continuous downstream processing. Miniaturized biosensors as an in-line process analytical technology tool could play a pivotal role to facilitate the transition to continuous manufacturing. In this review, miniaturization of already well-established methods to detect protein aggregation, such as dynamic light scattering, Raman spectroscopy and circular dichroism, will be extensively evaluated for the possibility of providing a real-time measurement of mAb aggregation. The method evaluation presented in this review shows which limitations of each analytical method still need to be addressed and provides application examples of each technique for mAb aggregate characterization. Additionally, challenges related to miniaturization are also addressed, such as the design of the microfluidic chip and the microfabrication material. The evaluation provided in this review shows why the development of microfluidic biosensors is considered the key for real-time measurement of mAb aggregates and how it can contribute to the transition to a continuous processing.

Finding the best purification process is a challenging task. Recently, mechanistic models that can accelerate the development of chromatographic unit operations, the most important purification units, became widely available. In previous work, several chromatographic models have been linked together to simulate and optimize integrated processes. However, considering only chromatographic steps may lead to a suboptimal process. Consequently, the aim of this study was to include models for ultra- and diafiltration units into the optimization approach to account for buffer exchange steps before or between chromatography units. This approach was applied to an industrial case, the purification of a monoclonal antibody, where cation exchange, hydrophobic interaction and mixed mode were the possible chromatographic separation modes. It turned out that only the duration of the total filtration step and the duration of the ultrafiltration step were crucial variables for the optimization of the ultra- and diafiltration steps. The ‘best’ in silico purification process was found based on the performance criteria yield and solvent usage. The purity was required to be at least 99.9%.

The application of mechanistic models for chromatography requires accurate model parameters. Especially for complex feedstocks such as a clarified cell harvest, this can still be an obstacle limiting the use of mechanistic models. Another commonly encountered obstacle is a limited amount of sample material and time to determine all needed parameters. Therefore, this study aimed at implementing an approach on a robotic liquid handling system that starts directly with a complex feedstock containing a monoclonal antibody. The approach was tested by comparing independent experimental data sets with predictions generated by the mechanistic model using all parameters determined in this study. An excellent agreement between prediction and experimental data was found verifying the approach. Thus, it can be concluded that RoboColumns with a bed volume of 200 μL can well be used to determine isotherm parameters for predictions of larger scale columns. Overall, this approach offers a new way to determine crucial model input parameters for mechanistic modelling of chromatography for complex biological feedstocks.

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.

Knowledge-based development of chromatographic separation processes requires efficient techniques to determine the physicochemical properties of the product and the impurities to be removed. These characterization techniques are usually divided into approaches that determine molecular properties, such as charge, hydrophobicity and size, or molecular interactions with auxiliary materials, commonly in the form of adsorption isotherms. In this study we demonstrate the application of a three-dimensional liquid chromatography approach to a clarified cell homogenate containing a therapeutic enzyme. Each separation dimension determines a molecular property relevant to the chromatographic behavior of each component. Matching of the peaks across the different separation dimensions and against a high-resolution reference chromatogram allows to assign the determined parameters to pseudo-components, allowing to determine the most promising technique for the removal of each impurity. More detailed process design using mechanistic models requires isotherm parameters. For this purpose, the second dimension consists of multiple linear gradient separations on columns in a high-throughput screening compatible format, that allow regression of isotherm parameters with an average standard error of 8%.