Figure 1: Approaches for Quality Assurance in Biopharmaceutical Production (A) Quality by testing approach – after process steps samples are drawn to determine product quality and quantity in the laboratory. As this is a retrospective approach, it does not allow any process control and leads to failure prone production even though it is costly and time consuming. (B) Quality by Design approach – the process is monitored in real-time and therefore product quality and quantity is known at each stage of the process. If the process is performed within the set specifications, the product will finally have the desired quality. Real-time monitoring of the process allows process control and therefore enhances product quality and reduces time and material consumption for offline analysis.
Then the subsequent quality by design initiative’s main aims (Q8-Q11 quality guidelines of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) (The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH),) should “ensure that all sources of variability affecting a process are identified, explained, and managed by appropriate measures. This enables the finished medicine to consistently meet its predefined characteristics from the start”. This considers the raw material variations and all variations of the process parameters. Furthermore, recently a guidance has been released to recommend continuous integrated biomanufacturing. This guidance also recommends an in depth process understanding but does not recommend process control. The guideline of real-time release has been developed because “under specific circumstances an appropriate combination of process controls (critical process parameters) together with pre-defined material attributes may provide greater assurance of product quality than end product testing and the context as such be an integral part of the control strategy principle”. Furthermore, “RTRT may be applied during the stages of manufacture of chemical and biological products resulting in the elimination of all, or certain, specific tests in the specifications of the finished active substance or finished medicinal product”. The ultimate goals must be to monitor and control the process in real-time so that we are able to efficiently control the variability of raw materials, variability of the process parameters and in the inherent variability of the biological system(Wasalathanthri et al., 2020). If we want to control the process, we need real-time information on quantity, purity and potency, - the critical quality attributes (CQA) - because only then can we control the critical process parameters. This does not differ from current approaches, but everything will be real-time instead of retrospective. It is obvious that we do not have dedicated sensors for quantity, purity and potency. We must use indirect methods, but there is a reluctance in industry and academia to apply soft sensors. A soft sensor (Luttmann et al., 2012; Mandenius et al., 2015; Roch et al., 2016), also known as a virtual sensor, is not a real existing sensor, but a simulation of interdependencies between representative measured variables and a target variable such as product concentration, aggregate content, dsDNA content, or a biological activity of a biopharmaceutical for instance. Then the soft sensors can be used to control a system(Ender et al., 2003). Especially in downstream processing when executed in batch we have limited possibility to really control the process. Therefore, a further incentive for continuous integrated manufacturing beyond the economic gain and improvement of the environmental footprint is the ability to fully control and automate a process and to arrive at system runs autonomously. Such a system would also contribute to a digital twin of a manufacturing plant, but such a twin is not required for an automated process. Process control is already well established in upstream processing but not in downstream processing. Control of oxygen and pH has been already done in the very early days of production of biopharmaceuticals in bioreactors. In an advanced bioreactor control systems air, O2, CO2, in the off gas and dissolved oxygen and temperature are measured in-line together with NIR, Raman, turbidity and capacitance to infer nutrient concentration and waste products or on-line sampling is conducted measured it directly by at-line enzymatic assays or HPLC (Zhao et al., 2015).
In advanced downstream processing, chromatographic workstations are equipped with UV 280 flow cell, pH, conductivity probes (Carta et al., 2010). Advanced sensors such as NIR, AT -FTIR, Raman multiangle light scattering, and dynamic light scattering are not routinely used as sensors for downstream process monitoring, although they would provide very useful information that goes far beyond the standard information. The signal from such sensors yields a spectrum that must be further interpreted using a so-called deconvolution process, for example, to assign a particular wavelength to a molecular property. These chemometric approaches were developed simultaneously with the development of the sensor hardware. Recently, it was found that the peak profile itself could be used to correlate it with certain properties of the process fluid or biomolecule, such as the glycosylation pattern of antibodies, aggregate content, or truncated variants. In such a case, the entire spectrum is used instead of a specific wavelength, and if the spectral information is trained with off-line data, it can be considered a soft sensor(Feidl et al., 2019; Kornecki et al., 2018).
Decades ago, bioprocessing originally started with fixed time/volume processes and even processes are performed without a single sensor. The next stage is threshold-based processing based on either a single sensor or multiple sensors. More sophisticated methods for using sensor data include chemometric methods, advanced statistical methods, and hybrid models (Figure 2). Hybrid models and model predictive control are the culmination of a monitoring and control system that ultimately enables real-time release.
Fixed Time/Volume