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A novel coiled flow inversion reactor (CFIR) improves process productivity and performance.
Historically, various industries such as petroleum, steel, automobile, and fast-moving consumer goods have shifted from batch to continuous processing. The major motivation behind this shift has been the need to meet increasing demands, reduce processing costs, and most importantly produce products with consistent quality and higher productivity (1–2). The biotherapeutics industry is presently undergoing a similar transition (3–5).
While the technology for continuous upstream processing (perfusion) has been explored for more than a decade, technologies that can enable continuous downstream processing are relatively few (6, 7). In an attempt to perform various downstream unit operations continuously, researchers have explored various novel technologies and reactor configurations. Different continuous flow reactors, such as a continuously stirred tank reactor (8,9) and a tubular reactor (10,11) have been explored for carrying out biotech unit operations that involve handling of protein dispersions in continuous flow.
Recently, a novel coiled flow inverter-based plug flow reactor called the coiled flow inversion reactor (CFIR) has been proposed (12). CFIR technology has been successfully used for performing unit operations such as continuous refolding (13), continuous precipitation (14), and continuous viral inactivation (15). It has been demonstrated that using a CFIR can result in a possible 10-20-fold increase in productivity and a 30–70% decrease in manufacturing cost (13,14,16).
The CFIR reactor configuration consists of helical coils bent at equidistant right angles (17). The design includes several straight helix modules, and after each straight helix module, the coil direction is changed by a 90° bend. The difference between the CFIR and the straight helical tube (Figure 1A) is the presence of these bends after each coil (Figure 1B).
The helical structure of a CFIR induces a secondary flow pattern called Dean vortices because of centrifugal forces. These Dean vortices enhance radial mixing in the reactor, which results in a narrow residence time distribution (RTD) even under laminar flow. Radial mixing is further enhanced by implementing equidistant right angle bends that change the direction of the centrifugal force, thus inducing new Dean vortices that drastically enhance radial mixing and further narrow the RTD. The strength of the secondary flow pattern is typically characterized mathematically in the form of a dimensionless number called the Dean number (De). This number is defined in Equation 1 as:
where Re is the Reynolds number, λ is the inversion ratio ( λ =d/D), d is the diameter of the tube, and D is the diameter of the helix. It has been demonstrated that increasing the Dean number up to 3 narrows the RTD curve in a CFIR (17). Thus, to obtain efficient cross-sectional mixing and a narrow RTD in a CFIR, this minimum value of the Dean number must be maintained in the reactor by design. Hence, selection of the tube inner diameter and the helix diameter has to be made in a way that De > 3. Researchers have proposed that the narrowest RTD can be achieved when the CFIR is operated at Dean number De > 3, bend angle of 90°, modified torsion parameter (T*) > 500, minimum pitch distance, and at least two turns in one coil (18).
Because most downstream bioprocessing unit operations operate in a laminar-flow regime, a CFIR offers a narrower RTD and better cross-sectional mixing, thereby emulating plug flow better than what is achieved with a simple straight tube or a helix (17). Further, a CFIR offers the ability to vary critical design attributes to customize the design to any given process. Scale up of a CFIR has also been demonstrated, where CFI has been used as a heat exchanger at a pilot-plant scale (19). Inline addition and modification at any point along the reactor length is also possible, which allows various process strategies. In addition, a CFIR is amenable to a single-use construction, and the narrower RTD makes it suitable for implementation of effective control schemes in the continuous bioprocessing train. The reactor can also be seen as a modular unit connected seamlessly with other unit operations in a continuous bioprocessing train. Effectively, a CFIR can contribute toward the development of an integrated continuous bioprocessing platform.
Case study 1: Continuous refolding using a CFIR
A novel use of a CFIR for refolding of granulocyte colony-stimulating factor (GCSF) has been proposed (12,13). An existing batch refolding process was effectively transferred into continuous mode using a dynamic inline mixer connected to a CFIR (see Figure 2). To optimize the continuous process in a CFIR, a full-factorial design of experiments (DOE) study was performed. The effect of parameters including pH, dilution, and dithiothreitol (DTT): cysteine ratio was examined. Product purity and productivity were measured with respect to time both for continuous refolding and the corresponding batch refolding. The continuous process was found to be at par with the industrial batch process in terms of critical process parameters such as percentage of native protein (84.2%), oxidized protein (10–12%), reduced protein (~0.1%), and aggregates (<0.5%).
More importantly, the performance of the developed process was found to result in 15 times higher reactor-specific productivity due to elimination of shutdown, cleaning, and filling steps. This enhancement in productivity is also because the proposed continuous process, due to its mixing features, allowed for operation at lower dilutions and thus at higher protein concentrations (0.38 mg/mL with the continuous process vs. 0.19 mg/mL with the batch process). The continuous process also resulted in a decrease in the cost of purification as well as in the required size of the refolding vessel due to the reduction in process volume and faster refolding. The proposed CFIR was shown to seamlessly enable continuous bioprocessing, taking input from an inclusion-body solubilizing unit and providing the output to a suitable continuous purification step.
Case study 2: Continuous viral inactivation in antibody manufacturing at low pH
Researchers have also demonstrated application of a coiled flow inverter (CFI) for continuous low pH viral inactivation (15). The researchers presented two different design approaches for continuous viral inactivation-the logarithmic reduction value (LRV) approach and the minimum residence time (MRT) approach. While the LRV approach targets the same reduction as obtained in the corresponding batch operation, the MRT approach guarantees a residence time for all molecules entering continuous viral inactivation that matches or exceeds the inactivation time of the batch inactivation step. The viral reduction and the monomer loss during inactivation was characterized for both approaches. Moreover, an empirical approach to correlate the CFI design parameters to the Bodenstein number was proposed. By using a linear regression model, a correlation between the Bodenstein number and the two CFI design parameters (the number of 90° bends and the curvature ratio) was created. The resulting model allowed for designing the CFI according to the inactivation time and the chosen design approach. The study clearly demonstrated that continuously operated viral inactivation at low pH value inside a CFI reactor is a promising method for continuous antibody processing (20) that can be effectively integrated into the downstream train (see Figure 3).
Case study 3: Use of a CFIR for continuous precipitation of process related impurities from a clarified cell culture supernatant
Researchers have successfully used the CFIR for continuous precipitation of clarified cell culture supernatant (CCCS) (14). Three different impurity precipitation protocols (pH precipitation, caprylic acid, and calcium chloride [CaCl2] precipitation) were performed in a CFIR with appropriate modifications in the flow path of the precipitant (see Figure 4). The pH precipitation protocol was optimized for batch as well as continuous mode using a DOE study. An improved clearance of host cell DNA (52X vs. 39X in batch), improved clearance of host cell proteins (HCP) (7X vs. 6X in batch), and comparable recovery (90% vs. 91.5% in batch) were reported. Caprylic acid and CaCl2 precipitation protocols were directly adopted from literature and the clearance of host cell DNA, HCP, and product recovery were found to be comparable or better in a CFIR than in batch operations. Further, it has also been demonstrated that use of a CFIR does not cause any undesirable impact on product quality (isoform distribution and percentage of high-molecular weight isoforms). However, the use of a CFIR has been shown to offer key benefits such as enhanced productivity (6–16X), consistent product quality, smaller facility footprint, fewer hold steps, increased equipment utilization, and lower manufacturing cost.
Continuous downstream processing for manufacturing of biotherapeutics is in an early development stage, and significant efforts are required for its successful implementation. Development of new technologies is likely to play a vital role toward simplification and facilitating continuous processing for manufacturing of biotherapeutics. Researchers have proposed various new technologies for continuous operation, both in upstream and downstream processing. Use of a CFIR has emerged as one such promising technology that has the capability to contribute to development of an integrated continuous bioprocessing platform.
Anurag S. Rathore is a professor in the Department of Chemical Engineering at the Indian Institute of Technology Delhi, a consultant at Biotech CMC, and a member of BioPharm International’s Editorial Advisory Board, Tel. +91.9650770650, email@example.com; Nikhil Kateja is a graduate student in the Department of Chemical Engineering, Indian Institute of Technology Delhi.
Vol. 29, No. 12
When referring to this article, please cite it as A. Rathore and N. Kateja, "A Coiled Flow Inversion Reactor Enables Continuous Processing," BioPharm International 29 (12) 2016.