Modeling of continuous slug flow cooling crystallization towards pharmaceutical applications

The rising trend towards continuous production in the field of small-scale crystallization has generated many creative concepts for apparatuses for the production of active pharmaceutical ingredients. One of these promising apparatuses is the Slug Flow Crystallizer (SFC), which enables the adjustment of the particle size distribution and the achievement of high yields through its alternating slug flow. To realize and understand the crystallization inside the SFC, high experimental effort has been necessary until now. Therefore, a mechanistic model considering the hydrodynamics of slug flow, the energy and mass balances, and the crystallization phenomena of growth and agglomeration inside the apparatus was developed. Its purpose is to improve the understanding of the process, estimate the effects of operating parameters on target properties, and predict crystallization behavior for different substance systems with minimal experimental effort. Successful modeling was validated with experimental results for the substance system l-alanine/water. Furthermore, the robustness of the model was evaluated, and guidelines were presented, enabling the transfer of the model to new substance systems

Polymer-grade bio-monomers from oleochemicals by combining homogeneous catalysis and selective product crystallization in an integrated process

The homogeneously catalyzed methoxycarbonylation of bio-based methyl 10-undecenoate (C11-DME) produces linear 1,12-dimethyl dodecanedioate (l-C12-DME). Subsequent selective product crystallization from the reaction mixture with downstream filtration and washing allows for the generation of the bio-monomer in polymer grade quality (>99.9%). This effective purification enables its direct use, e.g., for bio-based polyamides, without further purification. It separates the expensive homogeneous catalyst dissolved in the liquid phase in its active state for efficient catalyst recycling. We present the complex interactions of process parameters regarding reaction and crystallization-based purification in an integrated catalyst recycling process. Furthermore, we demonstrate that purification of l-C12-DME with >99.9% purity over multiple consecutive recycling runs is possible. However, as the crystallization is highly sensitive towards changing concentrations of by-products and particularly unreacted substrates, this high purity is only achieved by maintaining a stable composition in the reaction mixture using a newly developed system for precise conversion control in the reaction step

Acute cytomegalovirus infection modulates the intestinal microbiota and targets intestinal epithelial cells

Primary and recurrent cytomegalovirus (CMV) infections frequently cause CMV colitis in immunocompromised as well as inflammatory bowel disease (IBD) patients. Additionally, colitis occasionally occurs upon primary CMV infection in patients who are apparently immunocompetent. In both cases, the underlying pathophysiologic mechanisms are largely elusive - in part due to the lack of adequate access to specimens. We employed the mouse cytomegalovirus (MCMV) model to assess the association between CMV and colitis. During acute primary MCMV infection of immunocompetent mice, the gut microbial composition was affected as manifested by an altered ratio of the Firmicutes to Bacteroidetes phyla. Interestingly, these microbial changes coincided with high-titer MCMV replication in the colon, crypt hyperplasia, increased colonic pro-inflammatory cytokine levels, and a transient increase in the expression of the antimicrobial protein Regenerating islet-derived protein 3 gamma (Reg3γ). Further analyses revealed that murine and human intestinal epithelial cell lines, as well as primary intestinal crypt cells and organoids represent direct targets of CMV infection causing increased cell death. Accordingly, in vivo MCMV infection disrupted the intestinal epithelial barrier and increased apoptosis of intestinal epithelial cells. In summary, our data show that CMV transiently induces colitis in immunocompetent hosts by altering the intestinal homeostasis

From Lab to Pilot Scale: Commissioning of an Integrated Device for the Generation of Crystals

Fast time-to-market, increased efficiency, and flexibility of production processes are major motivators for the development of integrated, continuous apparatuses with short changeover times. Following this trend, the modular belt crystallizer was developed and characterized in lab scale with the model system sucrose-water. Based on the promising results, the plant concept was upscaled and commissioned in industrial environment. The results are presented within the scope of this work. Starting from small seed crystals in solution, it was possible to grow, separate, and dry product particles. Further, the conducted experiments demonstrated that it is feasible to transfer the results from laboratory to pilot scale, which in turn enables accelerated process design as well as development

Identification of herbal teas and their compounds eliciting antiviral activity against SARS-CoV-2 in vitro

Background: The SARS-CoV-2/COVID-19 pandemic has inflicted medical and socioeconomic havoc, and despite the current availability of vaccines and broad implementation of vaccination programs, more easily accessible and cost-effective acute treatment options preventing morbidity and mortality are urgently needed. Herbal teas have historically and recurrently been applied as self-medication for prophylaxis, therapy, and symptom alleviation in diverse diseases, including those caused by respiratory viruses, and have provided sources of natural products as basis for the development of therapeutic agents. To identify affordable, ubiquitously available, and effective treatments, we tested herbs consumed worldwide as herbal teas regarding their antiviral activity against SARS-CoV-2. Results: Aqueous infusions prepared by boiling leaves of the Lamiaceae perilla and sage elicit potent and sustained antiviral activity against SARS-CoV-2 when applied after infection as well as prior to infection of cells. The herbal infusions exerted in vitro antiviral effects comparable to interferon-β and remdesivir but outperformed convalescent sera and interferon-α2 upon short-term treatment early after infection. Based on protein fractionation analyses, we identified caffeic acid, perilla aldehyde, and perillyl alcohol as antiviral compounds. Global mass spectrometry (MS) analyses performed comparatively in two different cell culture infection models revealed changes of the proteome upon treatment with herbal infusions and provided insights into the mode of action. As inferred by the MS data, induction of heme oxygenase 1 (HMOX-1) was confirmed as effector mechanism by the antiviral activity of the HMOX-1-inducing compounds sulforaphane and fraxetin. Conclusions: In conclusion, herbal teas based on perilla and sage exhibit antiviral activity against SARS-CoV-2 including variants of concern such as Alpha, Beta, Delta, and Omicron, and we identified HMOX-1 as potential therapeutic target. Given that perilla and sage have been suggested as treatment options for various diseases, our dataset may constitute a valuable resource also for future research beyond virology

Continuous Isolation of Particles with Varying Aspect Ratios up to Thin Needles Achieving Free-Flowing Products

The continuous vacuum screw filter (CVSF) for small-scale continuous product isolation of suspensions was operated for the first time with cuboid-shaped and needle-shaped particles. These high aspect ratio particles are very common in pharmaceutical manufacturing processes and provide challenges in filtration, washing, and drying processes. Moreover, the flowability decreases and undesired secondary processes of attrition, breakage, and agglomeration may occur intensively. Nevertheless, in this study, it is shown that even cuboid and needle-shaped particles (l-alanine) can be processed within the CVSF preserving the product quality in terms of particle size distribution (PSD) and preventing breakage or attrition effects. A dynamic image analysis-based approach combining axis length distributions (ALDs) with a kernel-density estimator was used for evaluation. This approach was extended with a quantification of the center of mass of the density-weighted ALDs, providing a measure to analyze the preservation of the inlet PSD statistically. Moreover, a targeted residual moisture below 1% could be achieved by adding a drying module (Tdry = 60 °C) to the modular setup of the CVSF

Continuous Isolation of Particles with Varying Aspect Ratios up to Thin Needles Achieving Free-Flowing Products

The continuous vacuum screw filter (CVSF) for small-scale continuous product isolation of suspensions was operated for the first time with cuboid-shaped and needle-shaped particles. These high aspect ratio particles are very common in pharmaceutical manufacturing processes and provide challenges in filtration, washing, and drying processes. Moreover, the flowability decreases and undesired secondary processes of attrition, breakage, and agglomeration may occur intensively. Nevertheless, in this study, it is shown that even cuboid and needle-shaped particles (l-alanine) can be processed within the CVSF preserving the product quality in terms of particle size distribution (PSD) and preventing breakage or attrition effects. A dynamic image analysis-based approach combining axis length distributions (ALDs) with a kernel-density estimator was used for evaluation. This approach was extended with a quantification of the center of mass of the density-weighted ALDs, providing a measure to analyze the preservation of the inlet PSD statistically. Moreover, a targeted residual moisture below 1% could be achieved by adding a drying module (Tdry = 60 °C) to the modular setup of the CVSF

Modeling of Continuous Slug Flow Cooling Crystallization towards Pharmaceutical Applications

The rising trend towards continuous production in the field of small-scale crystallization has generated many creative concepts for apparatuses for the production of active pharmaceutical ingredients. One of these promising apparatuses is the Slug Flow Crystallizer (SFC), which enables the adjustment of the particle size distribution and the achievement of high yields through its alternating slug flow. To realize and understand the crystallization inside the SFC, high experimental effort has been necessary until now. Therefore, a mechanistic model considering the hydrodynamics of slug flow, the energy and mass balances, and the crystallization phenomena of growth and agglomeration inside the apparatus was developed. Its purpose is to improve the understanding of the process, estimate the effects of operating parameters on target properties, and predict crystallization behavior for different substance systems with minimal experimental effort. Successful modeling was validated with experimental results for the substance system l-alanine/water. Furthermore, the robustness of the model was evaluated, and guidelines were presented, enabling the transfer of the model to new substance systems

Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization

Crystalline particle properties, which are defined throughout the crystallization process chain, are strongly tied to the quality of the final product bringing along the need of detailed particle characterization. The most important characteristics are the size, shape and purity, which are influenced by agglomeration. Therefore, a pure size determination is often insufficient and a deep level evaluation regarding agglomerates and primary crystals bound in agglomerates is desirable as basis to increase the quality of crystalline products. We present a promising deep learning approach for particle characterization in crystallization. In an end-to-end fashion, the interactions and processing steps are minimized. Based on instance segmentation, all crystals containing single crystals, agglomerates and primary crystals in agglomerates are detected and classified with pixel-level accuracy. The deep learning approach shows superior performance to previous image analysis methods and reaches a new level of detail. In experimental studies, L-alanine is crystallized from aqueous solution. A detailed description of size and number of all particles including primary crystals is provided and characteristic measures for the level of agglomeration are given. This can lead to a better process understanding and has the potential to serve as cornerstone for kinetic studies

Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization

Crystalline particle properties, which are defined throughout the crystallization process chain, are strongly tied to the quality of the final product bringing along the need of detailed particle characterization. The most important characteristics are the size, shape and purity, which are influenced by agglomeration. Therefore, a pure size determination is often insufficient and a deep level evaluation regarding agglomerates and primary crystals bound in agglomerates is desirable as basis to increase the quality of crystalline products. We present a promising deep learning approach for particle characterization in crystallization. In an end-to-end fashion, the interactions and processing steps are minimized. Based on instance segmentation, all crystals containing single crystals, agglomerates and primary crystals in agglomerates are detected and classified with pixel-level accuracy. The deep learning approach shows superior performance to previous image analysis methods and reaches a new level of detail. In experimental studies, L-alanine is crystallized from aqueous solution. A detailed description of size and number of all particles including primary crystals is provided and characteristic measures for the level of agglomeration are given. This can lead to a better process understanding and has the potential to serve as cornerstone for kinetic studies