The following report with recommendations is the result of an expert panel meeting on soft sensor applications in bioprocess engineering that was organized by the Measurement, Monitoring, Modelling and Control (M3C) Working Group of the European Federation of Biotechnology ‐ Section of Biochemical Engineering Science (ESBES). The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for the furthering of the successful development of soft sensor methods in bioprocess engineering research and for industrial applications, in particular with focus on biopharmaceutical applications. It concludes with a set of recommendations, which highlight current prospects for the extended use of soft sensors and those areas requiring development.

Modulating resource allocation in bacteria to redirect metabolic building blocks to the formation of recombinant proteins rather than biomass formation remains a grand challenge in biotechnology. Here, we present a novel approach for improved recombinant protein production (RPP) using Escherichia coli (E. coli) by decoupling recombinant protein synthesis from cell growth. We show that cell division and host mRNA transcription can be successfully inhibited by coexpression of a bacteriophagederived E. coli RNA polymerase (RNAP) inhibitor peptide and that genes overtranscribed by the orthogonal T7 RNAP can finally account to >55% of cell dry mass (CDM). This RNAP inhibitor peptide binds the E. coli RNAP and therefore prevents σ-factor 70 mediated formation of transcriptional qualified open promoter complexes. Thereby, the transcription of σ-factor 70 driven host genes is inhibited, and metabolic resources can be exclusively utilized for synthesis of the protein of interest (POI). Here, we mimic the late phase of bacteriophage infection by coexpressing a phage-derived xenogeneic regulator that reprograms the host cell and thereby are able to significantly improve RPP under industrial relevant fed-batch process conditions at bioreactor scale. We have evaluated production of several different recombinant proteins at different scales (from microscale to 20 L fed-batch scale) and have been able to improve total and soluble proteins yields up to 3.4fold in comparison to the reference expression system E. coli BL21(DE3). This novel approach for growth-decoupled RPP has profound implications for biotechnology and bioengineering and helps to establish more cost-effective and generic manufacturing processes for biologics and biomaterials.

Process analytical technology (PAT), the regulatory initiative for building in quality to pharmaceutical manufacturing, has a great potential for improving biopharmaceutical production.The recommended analytical tools for building in quality, multivariate data analysis, mechanistic modeling, novel models for interpretation of systems biology data and new sensor technologies for cellular states, are instrumental in exploiting this potential. Industrial biopharmaceutical production has gradually become dependent on large-scale processes using sensitive mammalian cell cultures. This further emphasizes the need for improved PAT solutions. We summarize recent progress in this area based on an expert workshop held at the 8 th European Symposium on Biochemical Engineering Sciences (Bologna, 2010), and highlight new opportunities for exploiting PAT when applied in biopharmaceutical production. We conclude with recommendations for advancing PAT applications in the biopharmaceutical industry.

The industrial production of complex biopharmaceuticals using recombinant mammalian cell lines is still mainly built on a quality by testing approach, which is represented by fixed process conditions and extensive testing of the end-product. In 2004 the FDA launched the process analytical technology initiative, aiming to guide the industry towards advanced process monitoring and better understanding of how critical process parameters affect the critical quality attributes. Implementation of process analytical technology into the bio-production process enables moving from the quality by testing to a more flexible quality by design approach. The application of advanced sensor systems in combination with mathematical modelling techniques offers enhanced process understanding, allows on-line prediction of critical quality attributes and subsequently real-time product quality control. In this review opportunities and unsolved issues on the road to a successful quality by design and dynamic control implementation are discussed. A major focus is directed on the preconditions for the application of model predictive control for mammalian cell culture bioprocesses. Design of experiments providing information about the process dynamics upon parameter change, dynamic process models, on-line process state predictions and powerful software environments seem to be a prerequisite for quality by control realization. Keywords: Biopharmaceutical production · Design of experiments · Mathematical modelling · Process analytical technology · Soft-sensingCorrespondence: Dr. Gerald Striedner, Department of Biotechnology (DBT), University of Natural Resources and Life Sciences (BOKU), Muthgasse 18, 1190 Vienna, Austria E-mail: gerald.striedner@boku.ac.at Abbreviations: CHO, Chinese hamster ovary; CPP, critical process parameter; CQA, critical quality attribute; DCS, distributed control system; DoE, design of experiments; FDA, Food and Drug Administration; iDoE, intensified design of experiments; MBDoE, model based design of experiments; MPC, model predictive control; MVDA, multivariate data analysis; PAT, process analytical technology; QbD, quality by design; SCADA, supervisory control and data acquisition; TPP, target product profile

We describe a prokaryotic expression system using the autoproteolytic function of N(pro) from classical swine fever virus. Proteins or peptides expressed as N(pro) fusions are deposited as inclusion bodies. On in vitro refolding by switching from chaotropic to kosmotropic conditions, the fusion partner is released from the C-terminal end of the autoprotease by self-cleavage, leaving the target protein with an authentic N terminus. A tailor-made N(pro) mutant called EDDIE, with increased in vitro and decreased in vivo cleavage rates, has enabled us to express proinsulin, domain-D of staphylococcal protein A, hepcidin, interferon-alpha1, keratin-associated protein 10-4, green fluorescent protein, inhibitorial peptide of senescence-evasion-factor, monocyte chemoattractant protein-1 and toxic gyrase inhibitor, among others. This N(pro) expression system can be used as a generic tool for the high-level production of recombinant toxic peptides and proteins (up to 12 g/l) in Escherichia coli without the need for chemical or enzymatic removal of the fusion tag.