In-line fiber optical sensor for detection of IgG aggregates in affinity chromatography.

Therapeutic monoclonal antibodies (mAbs) are critical for treatment of a wide range of diseases. Immunoglobulin G (IgG) is the most predominant form of mAb but is prone to aggregation during production. Detection and removal of IgG aggregates are time-consuming and laborious. Chromatography is central for purification of biopharmaceuticals in general and essential in the production of mAbs. Protein purification systems are usually equipped with detectors for monitoring pH, UV absorbance, and conductivity, to facilitate optimization and control of the purification process. However, specific in-line detection of the target products and contaminating species, such as aggregates, is currently not possible using convectional techniques. Here we show a novel fiber optical in-line sensor, based on localized surface plasmon resonance (LSPR), for specific detection of IgG and IgG aggregates during affinity chromatography. A flow cell with a Protein A sensor chip was connected to the outlet of the affinity column connected to three different chromatography systems operating at lab scale to pilot scale. Samples containing various IgG concentrations and aggregate contents were analyzed in-line during purification on a Protein A column using both pH gradient and isocratic elution. Because of avidity effects, IgG aggregates showed slower dissociation kinetics than monomers after binding to the sensor chips. Possibilities to detect aggregate concentrations below 1 % and difference in aggregate content smaller than 0.3 % between samples were demonstrated. In-line detection of aggregates can circumvent time-consuming off-line analysis and facilitate automation and process intensification.

Process integrated biosensors for real-time monitoring of antibodies for automated affinity purification.

Therapeutic monoclonal antibodies (mAbs) provide new means for treatments of a wide range of diseases and comprise a large fraction of all new approved drugs. Production of mAbs is expensive compared to conventional drug production, primarily due to the complex processes involved. The affinity purification step is dominating the cost of goods in mAb manufacturing. Process intensification and automation could reduce costs, but the lack of real-time process analytical technologies (PAT) complicates this development. We show a specific and robust fiber optical localized surface plasmon resonance (LSPR) sensor technology that is optimized for in-line product detection in the effluent in affinity capture steps. The sensor system comprises a flow cell and a replaceable sensor chip functionalized with biorecognition elements for specific analyte detection. The high selectivity of the sensor enable detection of mAbs in complex sample matrices at concentrations below 2.5 µg mL-1. In place regeneration of the sensor chips allowed for continuous monitoring of multiple consecutive chromatographic separation cycles. Excellent performance was obtained at different purification scales with flow rates up to 200 mL min-1. This sensor technology facilitates efficient column loading, optimization, and control of chromatography systems, which can pave the way for continuous operation and automation of protein purification steps.

Control of specific carbon dioxide production in a fed-batch culture producing recombinant protein using a soft sensor.

The feeding of a fed-batch cultivation producing recombinant protein was controlled by a soft sensor set-up. It was assumed that the control approach could be based on the cell's production of carbon dioxide and that this parameter indicates the metabolic state occurring at induced protein expression. The soft sensor used the on-line signals from a carbon dioxide analyser and a near-infrared (NIR) probe for biomass to estimate the specific production rate qCO2. Control experiments were carried out with various qCO2 set-points where we observe that the feeding of nutrients to the culture could easily be controlled and resulted in a decreased variability compared to uncontrolled cultivations. We therefore suggest that this control approach could serve as an alternative to other commonly applied methods such as controlling the cell's overflow metabolism of acetate or the cell's specific growth rate. However, further studies of the internal metabolic fluxes of CO2 during protein expression would be recommended for a more precise characterization of the relationship between qCO2 and protein expression in order to fully interpret the control behaviour.

A soft sensor for bioprocess control based on sequential filtering of metabolic heat signals.

Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of the microbial culture and from that the specific growth rate and active biomass concentration were derived. By applying sequential digital signal filtering, the soft sensor was made more robust for industrial practice with cultures generating low metabolic heat in environments with high noise level. The estimated specific growth rate signal obtained from the three stage sequential filter allowed controlled feeding of substrate during the fed-batch phase of the production process. The biomass and growth rate estimates from the soft sensor were also compared with an alternative sensor probe and a capacitance on-line sensor, for the same variables. The comparison showed similar or better sensitivity and lower variability for the metabolic heat soft sensor suggesting that using permanent temperature sensors of a bioreactor is a realistic and inexpensive alternative for monitoring and control. However, both alternatives are easy to implement in a soft sensor, alone or in parallel.

Operator training in recombinant protein production using a structured simulator model.

Model-based operator training simulators (OTS) could be powerful tools for virtual training of operational procedures and skills of production personnel in recombinant protein processes. The applied model should describe critical events in the bioprocess so accurately that the operators' ability to observe and alertly act upon these events is trained with a high degree of efficiency. In this work is shown how this is accomplished in a structured multi-compartment model for the production of a recombinant protein in an Escherichia coli fed-batch process where in particular the induction procedure, the stress effects and overflow metabolism were highlighted. The structured model was applied on the OTS platform that virtually simulated the operational bioreactor procedures in real or accelerated time. Evaluation of training using the model-based OTS showed that trained groups of operators exhibited improved capability compared with the untrained groups when subsequently performing real laboratory scale cultivations. The results suggest that this model-based OTS may provide a valuable resource for enhancing operator skills in large scale recombinant protein manufacturing.

Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein.

A soft sensor approach is described for controlling metabolic overflow from mixed-acid fermentation and glucose overflow metabolism in a fed-batch cultivation for production of recombinant green fluorescence protein (GFP) in Escherichia coli. The hardware part of the sensor consisted of a near-infrared in situ probe that monitored the E. coli biomass and an HPLC analyzer equipped with a filtration unit that measured the overflow metabolites. The computational part of the soft sensor used basic kinetic equations and summations for estimation of specific rates and total metabolite concentrations. Two control strategies for media feeding of the fed-batch cultivation were evaluated: (1) controlling the specific rates of overflow metabolism and mixed-acid fermentation metabolites at a fixed pre-set target values, and (2) controlling the concentration of the sum of these metabolites at a set level. The results indicate that the latter strategy was more efficient for maintaining a high titer and low variability of the produced recombinant GFP protein.