Selective alkylation of pyrazoles could solve a challenge in chemistry and streamline synthesis of important molecules. Here we report catalyst‐controlled pyrazole alkylation by a cyclic two‐enzyme cascade. In this enzymatic system, a promiscuous enzyme uses haloalkanes as precursors to generate non‐natural analogs of the common cosubstrate S‐adenosyl‐l‐methionine. A second engineered enzyme transfers the alkyl group in highly selective C−N bond formations to the pyrazole substrate. The cosubstrate is recycled and only used in catalytic amounts. Key is a computational enzyme‐library design tool that converted a promiscuous methyltransferase into a small enzyme family of pyrazole‐alkylating enzymes in one round of mutagenesis and screening. With this enzymatic system, pyrazole alkylation (methylation, ethylation, propylation) was achieved with unprecedented regioselectivity (>99 %), regiodivergence, and in a first example on preparative scale.

Die selektive Alkylierung von Pyrazolen ist eine Herausforderung in der Chemie und könnte die Synthese wichtiger Moleküle vereinfachen. In dieser Arbeit berichten wir über eine katalysatorgesteuerte Alkylierung von Pyrazolen durch eine cyclische Kaskadenreaktion mit zwei Enzymen. In diesem enzymatischen System nutzt ein promiskuitives Enzym Halogenalkane als Ausgangsstoffe, um nicht‐natürliche Analoga des Cosubstrats S‐Adenosyl‐l‐Methionin zu synthetisieren. Ein zweites engineertes Enzym überträgt die Alkylgruppen in einer hochselektiven C−N‐Bindungsknüpfung auf das Pyrazol‐Substrat. Das Cosubstrat wird regeneriert und nur in katalytischen Mengen eingesetzt. Für das Enzym‐Engineering wurde eine computerbasierte Methode verwendet, um eine Mutantenbibliothek in silico zu entwickeln. In einer Runde von Mutagenese und Screening wurde somit eine promiskuitive Methyltransferase in eine kleine Pyrazol‐alkylierende Enzymfamilie umgewandelt. Mit diesem bienzymatischen System konnte die Alkylierung von Pyrazolen (Methylierung, Ethylierung, Propylierung) mit bislang unerreichter Regioselektivität (>99 %), Regiodivergenz und in einem ersten Beispiel in präparativem Maßstab gezeigt werden.

Intra-experimental factor setting shifts in intensified design of experiments (iDoE) enhance understanding of bioproduction processes by capturing their dynamics and are thus essential to fulfill quality by design (QbD) ambitions. Determining the influence of process history on the cellular responses, often referred to as memory effect, is fundamental for accurate predictions. However, the current iDoE designs do not explicitly consider nor quantify the influence of process history. Therefore, we propose the one-factor-multiple-columns (OFMC)-format for iDoE planning. This format explicitly describes stage-dependent factor effects and potential memory effects as across-stage interactions (ASIs) during a bioprocess. To illustrate its utility, an OFMC-iDoE that considers the characteristic growth phases during a fed-batch process was planned. Data were analyzed using ordinary least squares (OLS) regression as previously described via stage-wise analysis of the time series and compared to direct modeling of end-of-process outcomes enabled by the OFMC-format. This article aims to provide the reader with a framework on how to plan and model iDoE data and highlights how the OFMC-format simplifies planning, and data acquisition, eases modeling and gives a straightforward quantification of potential memory effects. With the proposed OFMC-format, optimization of bioprocesses can leverage which factor settings are most beneficial in which state of the mammalian culture and thus elevate performance and quality to the next level.

Applying a single parameter set to describe complex mammalian kinetics often is too simplistic, as it fails to capture sensitive cell‐to‐environment interactions that may be exploited to optimize production performance. To resolve this time dependency, intra‐experimental parameter shifts as part of design of dynamic experiments (DoDE) can be performed to study mammalian growth and production kinetics in fed‐batch processes. This enables growth phase‐dependent optimization, aligned with cellular requirements. Here, we provide a comprehensive, head‐to‐head comparison of our phase‐dependent optimization approach with intra‐experimental shifts of process parameters to a static optimization that retains parameter settings through the entire bioprocess. Showcasing a monoclonal antibody production process development scenario, the study examines growth phase‐dependent effects of temperature (T) and dissolved oxygen (DO) together with time‐invariant parameters for feed and seeding cell density. While the static optimization suggests settings near the center of the design space, phase‐dependent optimization finds an optimum by shifting T and DO between the exponential growth, transition, and production phases. Overall, the phase‐wise optimized process gives an experimentally validated ~30% increase in product titer while maintaining comparable product quality. Furthermore, the approach breaks the correlation between product titer and acidic charged variants: both depend on T but at different timeframes. Additionally, DoDE uncovers a crucial interaction between T and DO, with low T and high DO during the exponential growth phase, leading to strong lactate accumulation. The data demonstrate the advantages of phase‐dependent optimization enabled by DoDE. The results may serve as a good practice example for follow‐up research.