A comprehensive study in PAT-applications for a QbD-compliant development of continuous biopharmaceutical production

The development of continuously operated integrated pharmaceutical production processes needs a tremendous expenditure. Beside the installation of a full-scale production, scale-down concepts are essential to meet the QbD-specifications of the FDA. In this presentation the surrounding PAT-field of such a plant for production of potential Malaria vaccines (shown in ICB I and ICB II) is discussed in order to create model based QbD-compliant strategies. This includes fully automated processing, global process monitoring with additional classical and spectroscopic measurement procedures including enhanced data processing and MVDA. A full-scope model of the plant allows an in-silico development of process control. The two-stage upstream line is scaled-down in a sixfold sequential/parallel operated bioreactor plant including flow analysis at-line measurements for substrates- and target protein-detection. This plant allows a fully automated simultaneous DoE-process optimization and identification of CPP-Critical Process Parameters. The DoE-model and Monte Carlo simulations create also the Design Space and the Control Space of QbD-production. Similar methods are used in the down-stream area for optimization of cyclic protein purification. These methods are applied with an AEKTAT avant which is developed especially for DoE. The main focus of the work lies on the development of a global MVDA-based monitoring system for biotechnological variables like cell mass, glycerol-, ammonium-, total secreted-, and target protein-concentration but also on the evaluation of process quality (reproducibility) of the running processes. Applications of NIR-, Raman-, and 2D-Fluorescence-Spectroscopy and the appropriate PLSR-modeling leads to a partly success. This was improved by using the nonlinear SVR-Support Vector-machine Regression. However, a MVDA-application with only classical process variables from agitation, aeration, temperature, feeding, pH, pO2, and process balances creates astonishing results in a satisfying bio-monitoring up to the on-line detection of the secreted target protein concentration. The quality of running processes was evaluated with a GB-Golden Batch approach. The GB-tunnel was created with data from QbD-conformed process courses and then used for an on-line observation and prediction of actual first principal components. A MPC-Model Predictive Control was also implemented in order to avoid a leaving of the GB-tunnel by correction of process set-points. These methods open the way to an on-line release of pharmaceutical products

Monitoring and control of reproducibility in quasi-continuous integrated production processes of Active Pharmaceutical Ingredients

The development of integrated production processes include the combination and transformation of current batch oriented unit operations into linked sequential/parallel production strategies. The presented process starts with a two-stage upstream consisting of cell cultivation and subsequent protein production, which in turn results in a five step downstream process, consisting of cell clarification via a separator, retention of cellular debris using microfiltration, concentration of the secreted product by ultrafiltration with subsequent buffer exchange through diafiltration, followed by a final purification using column chromatography. The three main operations cell breeding, protein production and the complete downstream line are running in series, but also in parallel with a one-day-offset each. Such strategies were developed at HAW Hamburg [Luttmann et al., 2015]. To achieve reproducible process conditions, the process development was done in accordance with the known industry guidelines from FDA and ICH regarding QbD and PAT. In this context the identification of optimal Design Spaces and Control Spaces was in the foreground. The in-line measurement of important media components and cell physiological parameters as well as the on-line evaluation of process reproducibility, are remaining unsolved problems in industrial biotechnology - irrespective of whether a process is operated batch wise or continuously. A way out of this dilemma can be found by on-line MVDA data processing. This paper describes a comprehensive application of MVDA in process monitoring and control using the example of an integrated production of potential Malaria vaccines with Pichia pastoris. Cell mass, glycerol and secreted target proteins as well as cell internal AOX content are measured with NIR-, Raman- and 2D-Fluorescence-spectrometry. Here, intensive off-line analysis of the concerned process variables form the foundation for the training of spectral observations as well as for the evaluation of cell specific reaction rates from routinely measured on-line variables with MVDA-investigations. The main approach of MVDA was an on-line monitoring of reproducibility of involved unit operations. This was achieved by off-line modeling of Golden Batch tunnels and on-line evaluation of the process trajectories using SIPAT® and SIMCA® software tools. On top of this, on-line process prediction and on-line Golden Batch control were implemented. The prediction is based on IBR-Imputation by Regression (SIMCA® Q) and the control of processes evolving outside their Golden Batch limitations is based on BOBYQA-Bound Optimization by Quadratic Approximation (SIMCA® online). Such methods for process monitoring and control of quasi continuous pharmaceutical production pave the way for Real Time Release of APIs. All approaches have been approved and tested in real industrial-like production processes which have been performed over several weeks