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Innovative biologics, including cell therapeutics, virus-like particles, exosomes,recombinant proteins, and peptides, seem likely to substitute monoclonal antibodies as the maintherapeutic entities in manufacturing over the next decades. This molecular variety causes agrowing need for a general change of methods as well as mindset in the process development stage,as there are no platform processes available such as those for monoclonal antibodies. Moreover,market competitiveness demands hyper-intensified processes, including accelerated decisionstoward batch or continuous operation of dedicated modular plant concepts. This indicates gaps inprocess comprehension, when operation windows need to be run at the edges of optimization. Inthis editorial, the authors review and assess potential methods and begin discussing possiblesolutions throughout the workflow, from process development through piloting to manufacturingoperation from their point of view and experience. Especially, the state-of-the-art for modeling inred biotechnology is assessed, clarifying differences and applications of statistical, rigorousphysical-chemical based models as well as cost modeling. “Digital-twins” are described and effortsvs. benefits for new applications exemplified, including the regulation-demanded QbD (quality bydesign) and PAT (process analytical technology) approaches towards digitalization or industry 4.0based on advanced process control strategies. Finally, an analysis of the obstacles and possiblesolutions for any successful and efficient industrialization of innovative methods from processdevelopment, through piloting to manufacturing, results in some recommendations. A centralquestion therefore requires attention: Considering that QbD and PAT have been required byauthorities since 2004, can any biologic manufacturing process be approved by the regulatoryagencies without being modeled by a “digital-twin” as part of the filing documentation?

Physico-chemical modelling and predictive simulation are becoming key for modern process engineering. Rigorous models rely on the separation of different effects (e.g., fluid dynamics, kinetics, mass transfer) by distinct experimental parameter determination on lab-scale. The equations allow the transfer of the lab-scale data to any desired scale, if characteristic numbers like e.g., Reynolds, Péclet, Sherwood, Schmidt remain constant and fluid-dynamics of both scales are known and can be described by the model. A useful model has to be accurate and therefore match the experimental data at different scales and combinations of process and operating parameters. Besides accuracy as one quality attribute for the modelling depth, model precision also has to be evaluated. Model precision is considered as the combination of modelling depth and the influence of experimental errors in model parameter determination on the simulation results. A model is considered appropriate if the deviation of the simulation results is in the same order of magnitude as the reproducibility of the experimental data to be substituted by the simulation. Especially in natural product extraction, the accuracy of the modelling approach can be shown through various studies including different feedstocks and scales, as well as process and operating parameters. Therefore, a statistics-based quantitative method for the assessment of model precision is derived and discussed in detail in this paper to complete the process engineering toolbox. Therefore a systematic workflow including decision criteria is provided.

The focus of pharmaceutical product development lies on assuring excellent product quality at the end of a cost-efficient process. The Quality-by-Design (QbD) concept shifts the focus from quality assurance through testing to quality control by process understanding, resulting in very robust processes with high quality product. QbD was originally intended by authorities for biologics, where product quality proven completely by analytics is not desired. Product quality has to be controlled by means of appropriate processes and operations as well.These demands were developed in order to improve patients' safety by optimal drug quality at more efficient manufacturing processes reducing costs for healthcare systems. Furthermore, production of biologics includes feedstock variability and complex multi-step manufacturing processes in batch operation with variable lots -condition, which apply to botanicals as well.The use of rigorous (physico-chemical) process modeling in combination with QbD results in a high degree of process understanding. This offers, contrary to popular prejudices, great benefit for manufacturers with little extra effort during development.The methodical QbD-based approach is pursued to develop a process for extraction and purification of 10-deacetylbaccatin III from yew needles. A short history and key elements of the QbD-based application are introduced.The line of argument for basic process conception is described and initial risk assessment is shown. Typical raw material variation and vaporization are identified as causes of process variability, therefore, the implications to subsequent process steps are pointed out. Finally, influences of load and flow rate on the chromatographic separation of 10-deacetylbaccatin III are shown to exemplify sensitivity of purification design.

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