Liposomes have long been established as versatile and biocompatible carriers for biologically active molecules. Advances in manufacturing technology have dramatically broadened their application landscape, positioning them today as effective platforms for the oral delivery of pharmacologically active compounds, nutrients, and dietary supplements. Developing effective oral liposomal formulations, however, demands more than empirical optimization. It requires a strategy that simultaneously accounts for the complex physiological environment of the gastrointestinal (GI) tract, the physicochemical profile of the encapsulated payload, and the practical realities of scalable production. This work presents an integrative framework that unifies four critical decision-making axes: the Biopharmaceutics Classification System (BCS), Lipinski's Rule of Five, log P assessment and production process constraints. By mapping BCS categories onto specific GI absorption mechanisms, this framework enables the rational engineering of liposome architecture and properties to actively exploit physiological uptake routes. If the approach is effectively applied, liposomal carriers can achieve bioavailability enhancement that is to some degree independent of the payload's intrinsic membrane permeability and markedly less susceptible to food-effect interference compared to conventional oral formulations. Critically, aligning payload BCS class and log P with manufacturing feasibility supports the rational selection of production methods and excipient systems, striking a calibrated balance among encapsulation efficiency, release kinetics, physicochemical stability, and scale-up practicality. The power of this integrated approach is illustrated through two contrasting compounds, vitamin C (highly hydrophilic, BCS Class I) and vitamin D (highly hydrophobic, BCS Class IV), representing opposite ends of the physicochemical spectrum. These case studies demonstrate that tailoring liposome composition and processing conditions to the specific payload profile and GI physiological context can yield meaningful, nutritionally relevant gains in oral bioavailability for both hydrophilic and lipophilic molecules. This framework provides a scientifically rigorous and industrially actionable foundation for the rational development of next-generation oral liposomal formulations, systems that are not only mechanistically optimized but also commercially viable, ultimately contributing to improved therapeutic and nutritional health outcomes.
As natural bioactive macromolecules isolated from various berries, Berry polysaccharides (BPs) possess excellent biocompatibility, low toxicity and diverse health-promoting properties, which has garnered extensive interest in functional food and pharmaceutical research. Nevertheless, imperfect large-scale manufacturing processes, ambiguous structure-activity relationship (SAR) and limited metabolic research substantially impede their industrial transformation and practical application. This review systematically summarizes recent research advances on BPs, clarifies their preparation technologies, multifunctional bioactivities, molecular mechanisms, SAR rules, pharmacokinetic profiles and safety assessment, and highlights the cutting-edge technologies, so as to offer theoretical support for subsequent development and utilization of BPs. This comprehensive review was conducted to integrate and critically appraise the latest research progress on BPs, covering innovative preparation technologies, biological function exploration, structural-activity correlation analysis, pharmacokinetic profiles and cutting-edge interdisciplinary translational applications. Peer-reviewed literatures focusing on the extraction and purification, bioactivity evaluation, molecular mechanistic exploration, SAR analysis, pharmacokinetics and toxicological assessment of BPs were rigorously retrieved, screened and summarized for synthetic discussion. Novel eco-friendly extraction techniques, including ultrasonic-, microwave-, enzyme-assisted extraction and membrane separation are progressively replacing traditional extraction strategies. Berry polysaccharides display diverse bioactivities such as anti-inflammation, immunomodulation, hypoglycemia, hypolipidemia, neuroprotection and gut microbiota regulation via NF-κB, PI3K/Akt, MAPK, Keap1-Nrf2 signaling pathways and short-chain fatty acid metabolism. Meanwhile, SAR studies identify the molecular weight, glycosidic linkage, branching patterns and spatial conformation as critical factors dominating their bioactivity and bioavailability. BPs exhibit low oral bioavailability but favorable safety profiles, relying primarily on intestinal microbiota metabolism. Notably, artificial intelligence, multi-omics and nanotechnology greatly accelerate the comprehensive research on BPs. This review systematically summarizes current research progress and application prospects of berry polysaccharides (BPs). It deepens the understanding of their structure-function correlations and molecular mechanisms, offering valuable references for the rational development and translational application of BPs in functional foods, pharmaceuticals and health-benificial products.
trans-Aconitic acid, an unsaturated tricarboxylic acid, possesses considerable application potential in agriculture, biomaterials, and green chemistry, owing to its distinct chemical structure and thermodynamic stability. However, its industrial-scale production has long been hampered by the limited yields of plant extraction methods, as well as the economic and environmental drawbacks associated with chemical synthesis. This review systematically outlines the advances in the production and application of trans-Aconitic acid, with a specific focus on breakthroughs in microbial manufacturing. Through systematic metabolic engineering of industrial Aspergillus terreus, a high-titer trans-Aconitic acid cell factory was successfully developed. Subsequent optimization of the fermentation medium and process parameters enabled the establishment of a robust and scalable fermentation process, which was successfully validated in a 120 m3 fermenter. In parallel, a green and efficient downstream process was designed to overcome challenges related to high solubility and complex impurity profiles of the fermentation broth, leading to the creation of the first industrial trans-Aconitic acid production line. These technological breakthroughs have facilitated commercial application of trans-Aconitic acid in two key areas: bionematicide formulations for sustainable crop protection and bio-based plasticizers of trans-Aconitates. The progress summarized in this review positions trans-Aconitic acid as a promising bio-based platform chemical and outlines a replicable route from laboratory innovation to industrial implementation for bio-derived organic acids.
Industrial, commercial, and institutional (ICI) food waste is a large, poorly quantified component of municipal solid waste. Landfill disposal of food waste results in greenhouse gas emissions and lost resources. Here, we apply a spatial-probabilistic modelling approach for estimating sector-specific ICI food waste generation and demonstrate this technique for Montreal, Canada. We identified 14,508 ICI establishments from municipal food inspection records and classified them into 14 subsectors. Point locations were matched to building footprints to acquire areal estimates for each establishment. Values for other operational variables were taken from a set of 421 Canadian ICI facility audits. A Monte Carlo approach was used to estimate uncertainty (relative standard error) for each establishment, with results summarized by establishment type and subsector. Overall, the model showed that the ICI sectors in Montreal sent 215.4 ± 7.8 kt yr-1 of food waste to landfill, with 6% of the mapped grid cells accounting for over 50% of total disposal. Food service and retail contributed 83% of the total ICI food waste, while the food manufacturing subsector had the greatest uncertainty. Areas with a higher intensity of food waste generation had consistently lower uncertainty, suggesting that they could be strategic areas to target policy interventions. Our approach draws on readily accessible building footprint data and could be adapted for cities in other regions where food waste audits are available. By explicitly modelling sector-specific and geographic variability, our model can help pinpoint where urban food waste reduction and landfill diversion strategies could be most effective, supporting efforts toward a circular bioeconomy.
ε-Poly-l-lysine (ε-PL) is transitioning from a food preservative to a bio-based cationic functional scaffold with opportunities spanning food, agriculture, and biomaterials. However, progress in the field is hampered by a persistent disconnect between titer-driven manufacturing optimization and performance-driven application development. To bridge this gap, we propose a specification-driven (spec-driven) framework that uses critical quality attributes (CQAs) to link molecular structure, industrial production, and application translation. First, we define a minimal, actionable set of ε-PL CQAs and map their molecular determinants to functional relevance. Next, we delineate the biological feasibility windows and constraints that govern these CQAs under high-throughput biosynthesis. We then synthesize integrated strain, process, and recovery engineering strategies to illustrate how CQAs can be translated into reproducible industrial specifications. Finally, application requirements are reverse-mapped to the CQA combinations most frequently required across use scenarios. We conclude that while titer remains important, further gains in titer alone are insufficient to unlock broad translation. Among the proposed CQAs, chain-length distribution, impurity profile, and manufacturing consistency are particularly critical because they determine functional performance, safety boundaries, and lot-to-lot reproducibility. Accordingly, chain-length control, impurity reduction, and integrated strain-process-downstream control represent key rate-limiting steps for developing specification-grade ε-PL product families.
Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, especially in advanced stages with limited treatment efficacy. Rhizoma Paridis (RPS), a traditional Chinese medicinal herb, has shown promising antitumor activity, yet its potential anti-GC mechanisms have not been systematically investigated. An integrated computational and experimental framework was established to explore the anti-GC activity and potential mechanisms of RPS. First, the active compounds of RPS and their corresponding targets were identified using UPLC, LC-MS analysis and machine learning (Ml)-driven data mining. Then, the key target genes were determined by the gradient boosting algorithms and their associations with GC were investigated by the deconvolution algorithms, unsupervised clustering and dimensionality reduction methods. Further, the potential compound-target interactions and the mechanism of RPS for treating GC were supported by the molecular docking, Molecular dynamics (MD) simulations, in vivo and in vitro experiments. A total of 118 phytochemicals were identified from RPS, from which 40 candidate bioactive compounds and six hub genes, i.e., BAX, BCL2, VEGFA, KDR, CASP3 and CTNNB1 were prioritized. Functional enrichment analyses suggested that these targets were mainly associated with apoptosis, angiogenesis and Wnt/β-catenin-related signaling pathways. Transcriptomic, prognostic, immune infiltration and single-cell analyses further supported their relevance in GC. Docking and MD simulations indicated favorable binding stability between representative compounds and the key targets. Experimental studies demonstrated that RPS inhibited GC cell proliferation and tumor growth, accompanied by apoptosis-related and angiogenesis-associated molecular changes. RPS exhibits significant anti-GC activity and may exert its effects through the coordinated regulation of apoptosis-, angiogenesis- and Wnt/β-catenin-associated pathways. This study provides a systems-level framework integrating computational analyses with biological validation to investigate the pharmacological effects of complex herbal medicines and offers mechanistic insights into the anti-GC potential of RPS.
Surface-layer (S-layer) proteins, forming the outermost envelope of many bacteria and archaea, exhibit extraordinary structural precision and self-assemble into two-dimensional crystalline lattices with square, hexagonal, or oblique symmetry. These monomolecular arrays, typically 5 to 25 nm in periodicity (varying by species), offer defined porosity and serve as robust biological nanoplatforms. Their innate capacity for self-assembly and molecular ordering has attracted significant attention in nanobiotechnology, vaccine development, biosensing, drug delivery, and ultrafiltration. S-layers are especially valued for their ability to mimic viral capsids, enhance antigen presentation, stabilize lipid bilayers, and provide highly organized scaffolds for enzyme immobilization and nanopatterning. Recent experimental achievements include the use of S-layer fusion proteins for mucosal vaccine delivery and the development of recombinant S-layer-based electrochemical biosensors. However, transitioning these advances to commercial-scale applications remains challenging. Limitations include the scalability of high-purity protein production, cost-effective recombinant expression, stability under harsh industrial conditions, and unresolved regulatory pathways for biologically derived nanomaterials. Additionally, synthetic alternatives present practical and economic competition. Nonetheless, interdisciplinary efforts in synthetic biology, materials science, and computational modeling are addressing these bottlenecks. Innovations such as cross-linkable domains, fusion with polymers or lipids, and predictive structure-function modeling are improving the robustness and adaptability of S-layer systems. As current research advances from theoretical potential to functional prototypes, S-layer proteins offer transformative prospects across medical, industrial, and environmental domains. This review uniquely integrates mechanistic S-layer biology with engineering-for-manufacture, protein-design workflows, and commercialization roadmaps - offering actionable protocols and benchmarks not covered in prior syntheses.
Since the early 1960s, nanotechnology has been a critical area of science, allowing for the development of sophisticated nanomaterials. Nanofibers, one of the most widely used nanotechnological drug delivery systems, have emerged as a highly versatile platform within modern pharmaceutical sciences. By combining various polymers with active herbal ingredients, these systems mimic the natural extracellular matrix and provide improved functions such as a high surface area-to-volume ratio, drug targeting, and controlled drug release. Preclinical studies demonstrate that phytochemical-containing nanofibers have improved therapeutic profiles, including higher anti-inflammatory, antioxidant, antimicrobial, and antineoplastic effects. In various biomedical applications, such as promoting tissue engineering (such as bone and nerve regeneration), limiting tumor growth, and accelerating wound closure, it was shown that nanofibers may overcome the physicochemical limitations of herbal drugs such as low solubility and bioavailability. Despite promising preclinical study results, there are significant obstacles in the way of the commercialization of herbal drug-loaded nanofibers. The difficult standardization of multi-component herbal extracts, stabilization drawbacks, and the absence of scalable industrial manufacturing equipment that can maintain repeatable characterization of nanofibers are some of the major obstacles, in addition to a lack of clinical trials. To navigate the clinical translation of nanofibers, interdisciplinary collaboration regarding quality control, safety, and regulatory pathways is strictly necessary due to the case-by-case review approach utilized by regulatory bodies such as the US Food and Drug Administration (FDA) and European Medicines Agency (EMA). This review offers a comprehensive overview of research on herbal drug-loaded nanofibers and contributes a novel perspective with regulatory and clinical translational insights. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Current Safe and Sustainable by Design (SSbD) frameworks, such as those proposed by the Joint Research Centre, support the development of safer, more sustainable substances, materials, and manufacturing processes. However, these were not designed for product developers or designers and therefore do not align with the iterative and creative nature of product development, nor with its specific needs and constraints. Broad adoption of SSbD requires tiered, resource-efficient assessments tailored to the specific needs of different professionals. The product developer's perspective is essential for creating safer, more sustainable products, enabling context-specific evaluation of chemical applications, substances of concern (SoC), and product life cycles. In a circular economy, where products, parts, and materials circulate, holistic views become critical, as emissions and exposures may increase and accumulate. Therefore, we developed the Mapping Assessment for Product Substance Safety and Sustainability (MAPSSS) tool, which integrates principles of Life Cycle Assessment (LCA) and Risk Assessment (RA) into a product development context. Through iterative development based on real-life cases of products containing SoC, we combined essential elements of RA and LCA into a product development workflow. Expert input on environmental assessment and ecotoxicology informed each iteration of the process. MAPSSS supports product developers in identifying and assessing SoC risks, trade-offs, uncertainty levels, and environmental impacts across the product life cycle. It enables the identification of risk hotspots in reference products, supporting mitigation strategies and comparison with new solutions. This represents an initial step toward a product-specific SSbD assessment tool for early design stages. The online version contains supplementary material available at 10.1007/s43615-026-01077-w.
Opportunities to manufacture sustainable masonry units with less of an impact on the environment and sufficient durability under demanding service circumstances have been made possible by the growing use of industrial by-products in construction. Nevertheless, there is still a lack of knowledge regarding the long-term chemical durability of composite interlocking bricks that incorporate various waste materials under various exposure conditions. The mechanical performance and chemical durability of composite interlocking bricks made from fly ash (FA), manufactured sand (MS), lime, gypsum, crushed rubber (CR), and granite waste powder (GWP) as partial substitutes for traditional ingredients are assessed in this study. Hydraulic compression molding was used to create two brick sizes, which were assessed using compressive strength testing, chemical exposure to 5% sodium chloride (NaCl), 5% sodium sulfate (Na2SO4), and natural seawater for up to 120 days, as well as scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) of the prepared powder mixture. While combinations with 20-30% GWP showed better resistance to sulfate and chloride intrusion, the optimized mixture with 30% GWP had the best compressive strength. The results show that the mechanical performance and chemical durability of composite interlocking bricks are greatly improved by the proper use of industrial waste materials.
DNA origami is a nanofabrication technique where a long DNA scaffold is folded using locally complementary staple strands to create predesigned two-and three-dimensional structures with desired shapes, sizes, and surface functionalities. While many of these structures have been proposed for applications in biosensing, nanorobotics, and targeted therapeutic delivery, among others, translating this technology from the bench to clinical and industrial settings faces significant challenges, especially in reproducibility, process scalability, and control precision. Microfluidic platforms offer potential solutions to these limitations by providing accurate control, process automation, and easy integration of multiple workflow steps within lab-on-chip devices. This review examines the potential role of microfluidic technologies in DNA origami production, characterization and actuation, highlighting advantages and future directions.
Fast and accurate industrial Load Station (LS) inspection is crucial in manufacturing environments. This study addresses the challenges of deploying a deep learning-based solution for LS inspection in semiconductor wafer handling, where the Load Station must be properly aligned and occlusion-free before pin pack assembly operations. A significant challenge in industrial settings is data scarcity, as collecting and annotating large amounts of datasets is often impractical due to operational constraints and the rarity of abnormal conditions. This study examines how training data size affects inspection reliability in a real-world smart manufacturing context. The experiments utilise YOLOv5 and YOLOv8 variants across five training set sizes to determine minimum data requirements for reliable deployment, evaluated under 5-fold cross-validation with multiple random seeds. Our results demonstrate that YOLOv8 achieves superior data efficiency, with only 40 samples per class (T-40), YOLOv8n achieves 0.981 ± 0.014 inspection accuracy and 0.842 ± 0.050 mean Average Precision (mAP@0.5) on a held-out real test set, while YOLOv5 requires substantially more training data to achieve comparable performance. Smaller variants (YOLOv8n, YOLOv8s) consistently outperform larger models in this data-scarce environment. An ablation study confirms that combining real and synthetic obstruction samples is essential, and the approach is validated on an operational semiconductor manufacturing dataset, providing practical, statistically grounded recommendations for deploying deep learning inspection systems when training data are limited.
Sauce-flavor Baijiu is a Chinese distilled spirit with a highly nonlinear, multivariable, and coupled pit fermentation process, complicating real-time parameter acquisition and regulation. In this study, a deep neural network (DNN) based multi-task framework was developed using industrial-scale microbial community profiles and physicochemical parameters, enabling prediction of fermentation states and in silico optimization of controllable parameters to regulate target flavor compounds. Firstly, microbial communities, physicochemical parameters, and volatile flavor compounds were analyzed across seven production rounds. The results suggested that dominant microorganisms included Acetilactobacillus, Kroppenstedtia, Pichia, Monascus, and Kazachstania. Across rounds, acidity increased while pH decreased. Total esters and alcohols increased from rounds 1-3, then declined toward round 7, with ethyl acetate and phenethyl alcohol being key discriminants. Using this seven-round fermentation-parameter dataset, a DNN framework was developed with two functions: (i) fermentation parameter prediction and (ii) targeted flavor regulation via an optimization module coupled to the trained model. Baseline comparisons and ablation experiments demonstrated that the model achieved good predictive performance (Pearson's r = 0.801; R2 = 0.690; CCC = 0.783). Evaluation using data from a subsequent production year showed encouraging predictive agreement for key parameters. As a concept validation, the DNN-guided ethyl acetate regulation strategy was evaluated in a simulated round-6 pit fermentation by inoculating with Companilactobacillus pabuli, Fructilactobacillus fructivorans, and Pichia kudriavzevii. This intervention increased ethyl acetate and ethanol levels, supporting the feasibility of the model-guided intervention direction. Overall, this work supports intelligent monitoring and flavor-oriented control in sauce-flavor Baijiu production, providing a foundation for automated manufacturing.
In nano-catalysts, constructing isostructural counterparts with precisely tunable surface and interface properties to unveil their structure-activity relationships remains a significant challenge. Herein, we report the synthesis of two atomically precise, isostructural Cu hydride nanoclusters [Cu18H17(EtPP)10]+ (Cu18-1) and [Cu18H17(TPP)10]+ (Cu18‑2), via ligand engineering. Although they share a similar metal-core structure, their surface/ interface interactions exhibit marked differences. In the CO2 electroreduction reaction, Cu18-1 exhibits 70.59% selectivity for C2H4 with an industrial-grade current density of -2.99 A·m-2, both of which are approximately twice those of Cu18-2. This enhancement originates from the weaker intramolecular interactions in Cu18‑1, which facilitate phosphine‑ligand stripping under reaction conditions, thereby exposing more metal active sites and promoting C─C coupling. The mechanism is corroborated by post‑reaction mass spectrometry, theoretical calculations, and electrochemically active surface area measurements. This work provides fresh insights into the rational design of metal-organic catalysts through ligand‑engineered regulation of intramolecular interactions.
Continuous distillation is widely used in the commodity and petrochemical industry to purify fine chemicals on massive industrial scales; however, this key separation strategy is largely unused within the pharmaceutical industry. This article investigates the development of a pilot-scale continuous distillation system for in-line removal of an excess amine reactant (i.e., tert-butylamine) when incorporated within an advanced manufacturing technology (AMT) for continuous drug substance production. Initial design parameters were simulated in ASPEN + to assess potential process solvents, setpoint parameters, and azeotrope formation. Complementary analytical characterization was performed with high performance liquid chromatography (HPLC) and gas chromatography - flame ionization detection (GC-FID). Early continuous distillation prototypes and key engineering design decisions are highlighted. The final engineered system is comprised of a + 3-foot distillation column fitted with an Allihn condenser, a 250 mL three-necked round bottom flask, and an automated system for reflux ratio (RD) control. The column is readily integrated within an advanced manufacturing system to continuously remove amine reagent during continuous manufacturing of albuterol sulfate at a 3.0 mL/min process basis, corresponding to a theoretical throughput of approximately 1,800 doses per hour based on the demonstrated process flowrate, reaction stoichiometry, and operating conditions. The application of continuous distillation offers a unique strategy for impurity removal during advanced pharmaceutical manufacturing and can be scaled to meet target throughputs. Here we provide general criteria necessary for successful implementation of similar continuous in-line distillation systems.
Colorectal cancer (CRC) is a leading cause of cancer mortality despite widespread colonoscopy screening. Colonoscopy effectiveness, which can reduce CRC risk by 90%, depends largely on adenoma detection rate (ADR), a metric strongly influenced by mucosal inspection quality during withdrawal. However, ADR miss rates remain as high as 26% due to incomplete inspection. Although simulation-based training (SBT) improves procedural skills, physical simulators lack objective assessment and feedback on inspection quality. This study aimed to develop an automated machine-learning model (ML) to objectively assess colonoscopy inspection performance and identify feedback to improve ADR. A Vision Transformer (ViT)-based ML model was developed to automatically recognize three inspection techniques during SBT required for effective inspection: clear lumen view, lateral wall, and obscured (fold) inspection. Model performance was evaluated using accuracy, precision, recall, and F1 score. The validated model was applied to videos from experts (n = 5), intermediate (n = 5), and novice residents (n = 7) performing withdrawal on a physical simulator embedded with silicone polyps. The model quantified time spent in each technique and surrounding polyp detection. Polyp detection rate (PDR) was quantified based on number of polyps detected. The model achieved high accuracy, precision, recall, and F1 score. Greater inspection time in obscured regions was significantly associated with higher PDR (r = 0.50, p = 0.041). Compared with novices, experts spent more time inspecting with a clear lumen view (p = 0.039) and deeper obscured views (p = 0.017), demonstrated higher PDR, and devoted more time to obscured regions before or after polyp detection. The ViT ML model accurately recognizes key inspection techniques and objectively differentiates inspection performance across expertise levels using time-based metrics and PDR. This enables objective assessment and targeted feedback-such as highlighting uninspected, obscured regions within physical SBT, with the potential to improve inspection performance and ADR.
Stretchable and self-adhesive epidermal electrodes and sensors with long-term stability are highly desirable for wearable applications. These electrodes and sensors typically comprise multiple components, each contributing distinct mechanical and electrical functions. However, their materials design and optimization rely heavily on time-consuming trial-and-error approaches, highlighting the need for a more effective strategy. Here, we report a data-driven composition optimization strategy integrating artificial neural network (ANN) modeling and genetic algorithm (GA) optimization for the rational design of self-adhesive epidermal electrodes/sensors. By defining optimization objectives that prioritized either high electrical conductivity and adhesion or high piezoresistive sensitivity, stretchable epidermal electrodes and sensors were developed. The optimized electrode exhibits high stretchability (∼ 177%), robust adhesion (0.10 N cm- 1), and low skin electrode contact impedance (∼ 72 kΩ at 10 Hz), enabling more stable long-term acquisition of electromyograms (EMG), electrocardiograms (ECG), and electroencephalograms (EEG) signals, compared to commercial Ag/AgCl gel electrodes. The resulting optimal sensor based on a different material composition demonstrates large stretchability (∼ 153%) and good piezoresistive sensitivity (gauge factor ∼ 4.79), enabling motion monitoring and human-machine interface demonstrations. This work highlights the effectiveness of data-driven optimization for application-specific design of wearable devices.
Escherichia coli remains the premier microbial chassis in industrial biotechnology; yet, translating laboratory-scale metabolic successes into robust, hundred-ton-scale manufacturing presents persistent multidimensional bottlenecks. This review systematically elucidates a contemporary paradigm shift in E. coli fermentation regulation, transitioning from isolated, empirical process adjustments to cross-scale, deeply coupled intelligent cybernetic frameworks. We first deconstruct the precise cross-scale links between macroscopic physicochemical parameters and intracellular biological regulatory networks, detailing how fluctuations in temperature, pH, and dissolved oxygen fundamentally reshape intracellular molecular cascades alongside transmembrane proton motive forces. To mitigate spatial heterogeneity, substrate mixing delays, and carbon catabolite repression common in large-volume bioreactors, we comprehensively evaluate advanced mitigation strategies that integrate process-level model predictive control (MPC) with dynamic synthetic biology circuits, including quorum-sensing-based growth-production decoupling, metabolite-responsive feedback loops, and optogenetic switches. Critically, we highlight the convergence of multi-scale modeling and digital twins as a frontier simulation-driven tool, which dynamically couples computational fluid dynamics (CFD) with genome-scale metabolic models (GEMs) to render the industrial fermentation "black box" transparent. Finally, we outline the evolutionary trajectory of E. coli biomanufacturing toward a digitized, intelligent, and sustainable paradigm, leveraging low-carbon C1 feedstocks and paving the way for next-generation green biomanufacturing.
Proteinogenic amino acids are important supplements in foods, feeds, and pharmaceuticals. However, the development of cost-efficient and scalable platform downstream processes for multi-ton manufacturing of amino acids is complicated by upstream process heterogeneity resulting in plethora of implementations at laboratory and industrial scale. Here, we review 18 unit operations and analyzed >50 downstream processes reported over six decades to compare separation performance, sustainability and costs, highlighting process advantages and limitations. We find that chromatography is used in >65% of downstream processes, achieving purities >98% in different setups. Sustainable and cost-effective alternatives seem to challenge this position. For example, membrane separations can achieve purities of 60-98% with reduced environmental footprint, costs, and additional options for process integration, mainly for acidic and basic amino acids. Crystallization is frequently used for formulation and purification if >50 g L-1 of amino acids with extreme pI are processed. Options to streamline current processes are discussed.
Fetal Bovine Serum (FBS) has served as the gold standard supplement in mammalian cell cultures for more than six decades due to its cocktail of growth factors, carrier proteins, hormones, and attachment factors that create an ideal proliferative microenvironment. Nevertheless, heightened regulatory pressure, ethical issues associated with the use of bovine fetuses, logistical challenges, batch-to-batch variations, and the risks of contamination by adventitious agents have driven the need to formulate synthetic, animal-origin-free (AOF) substitutes. This review examines the key technologies being explored to engineer FBS substitutes, including chemically defined media, recombinant protein fortification using precision fermentation, hydrolyzed plants, human platelet lysates (HPL), conditioned media derived from induced pluripotent stem cells (iPSCs), and genetically engineered microbial cell factories, with special consideration of their feasibility for large-scale vaccine production in Vero, BHK-21, HEK293, and MDCK cells. No single technology can presently replace FBS across all applications; however, the convergence of biologics, chemically defined media, and precision fermentation platforms promises to bridge this gap.