Advanced oxidation processes: Performance, advantages, and scale-up of emerging technologies.

Advanced oxidation processes (AOPs) are promising technologies for partial or complete mineralization of contaminants of emerging concern by highly reactive hydroxyl, hydroperoxyl, superoxide, and sulphate radicals. Detailed investigations and reviews have been reported for conventional AOP systems that have been installed in full-scale wastewater treatment plants. However, recent efforts have focused on the peroxymonosulphate, persulphate, catalytic ozonation, ultrasonication and hydrodynamic cavitation, gamma radiation, electrochemical oxidation, modified Fenton, and plasma-assisted AOPs. This critical review presents the detailed mechanisms of emerging AOP technologies, their performance for treatment of contaminants of emerging concern, the relative advantages and disadvantages of each technology, and the remaining challenges to scale-up and implementation. Among the evaluated technologies, the modified electrochemical oxidation, gamma radiation, and plasma-assisted systems demonstrated the greatest potential for successful and sustainable implementation in wastewater treatment due to their environmental safety, compatibility, and efficient transformation of contaminants of emerging concern by a variety of reactive species. The other emerging AOP systems were also promising, but additional scale-up trials and a deeper understanding of their reaction kinetics in complex wastewater matrices are necessary to determine the technical and economic feasibility of full-scale processes.

A CFD model for predicting protein aggregation in low-pH virial inactivation for mAb production.

Significant amounts of soluble product aggregates were observed in the low-pH viral inactivation (VI) operation during an initial scale-up run for an immunoglobulin-G 4 (IgG4) monoclonal antibody (mAb IgG4-N1). Being earlier in development, a scale-down model did not exist, nor was it practical to use costly Protein A eluate (PAE) for testing the VI process at scale, thus, a computational fluid dynamics (CFD)-based high-molecular weight (HMW) prediction model was developed for troubleshooting and risk mitigation. It was previously reported that the IgG4-N1 molecules upon exposure to low pH tend to change into transient and partially unfolded monomers during VI acidification (i.e., VIA) and form aggregates after neutralization (i.e., VIN). Therefore, the CFD model reported here focuses on the VIA step. The model mimics the continuous addition of acid to PAE and tracks acid distribution during VIA. Based on the simulated low-pH zone (≤pH 3.3) profiles and PAE properties, the integrated low-pH zone (ILPZ) value was obtained to predict HMW level at the VI step. The simulations were performed to examine the operating parameters, such as agitation speed, acid addition rate, and protein concentration of PAE, of the pilot scale (50-200 L) runs. The conditions with predictions of no product aggregation risk were recommended to the real scale-up runs, resulted in 100% success rate of the consecutive 12 pilot-scale runs. This study demonstrated that the CFD-based HMW prediction model could be used as a tool to facilitate the scale up of the low-pH VI process directly from bench to pilot/production scale.

Progression of continuous downstream processing of monoclonal antibodies: Current trends and challenges.

Rapid advances in intensifying upstream processes for biologics production have left downstream processing as a bottleneck in the manufacturing scheme. Biomanufacturers are pursuing continuous downstream process development to increase efficiency and flexibility, reduce footprint and cost of goods, and improve product consistency and quality. Even after successful laboratory trials, the implementation of a continuous process at manufacturing scale is not easy to achieve. This paper reviews specific challenges in converting each downstream unit operation to a continuous mode. Key elements of developing practical strategies for overcoming these challenges are detailed. These include equipment valve complexity, favorable column aspect ratio, protein-A resin selection, quantitative assessment of chromatogram peak size and shape, holistic process characterization approach, and a customized process economic evaluation. Overall, this study provides a comprehensive review of current trends and the path forward for implementing continuous downstream processing at the manufacturing scale.

Overproduction of L-tryptophan via simultaneous feed of glucose and anthranilic acid from recombinant Escherichia coli W3110: Kinetic modeling and process scale-up.

L-tryptophan is an essential amino acid widely used in food and pharmaceutical industries. However, its production via Escherichia coli fermentation suffers severely from both low glucose conversion efficiency and acetic acid inhibition, and to date effective process control methods have rarely been explored to facilitate its industrial scale production. To resolve these challenges, in the current research an engineered strain of E. coli was used to overproduce L-tryptophan. To achieve this, a novel dynamic control strategy which incorporates an optimized anthranilic acid feeding into a dissolved oxygen-stat (DO-stat) glucose feeding framework was proposed for the first time. Three original contributions were observed. Firstly, compared to previous DO control methods, the current strategy was able to inhibit completely the production of acetic acid, and its glucose to L-tryptophan yield reached 0.211 g/g, 62.3% higher than the previously reported. Secondly, a rigorous kinetic model was constructed to simulate the underlying biochemical process and identify the effect of anthranilic acid on both glucose conversion and L-tryptophan synthesis. Finally, a thorough investigation was conducted to testify the capability of both the kinetic model and the novel control strategy for process scale-up. It was found that the model possesses great predictive power, and the presented strategy achieved the highest glucose to L-tryptophan yield (0.224 g/g) ever reported in large scale processes, which approaches the theoretical maximum yield of 0.227 g/g. This research, therefore, paves the way to significantly enhance the profitability of the investigated bioprocess.

Hot Melt Extrusion: Development of an Amorphous Solid Dispersion for an Insoluble Drug from Mini-scale to Clinical Scale.

The objective of the study was to develop an amorphous solid dispersion (ASD) for an insoluble compound X by hot melt extrusion (HME) process. The focus was to identify material-sparing approaches to develop bioavailable and stable ASD including scale up of HME process using minimal drug. Mixtures of compound X and polymers with and without surfactants or pH modifiers were evaluated by hot stage microscopy (HSM), polarized light microscopy (PLM), and modulated differential scanning calorimetry (mDSC), which enabled systematic selection of ASD components. Formulation blends of compound X with PVP K12 and PVP VA64 polymers were extruded through a 9-mm twin screw mini-extruder. Physical characterization of extrudates by PLM, XRPD, and mDSC indicated formation of single-phase ASD's. Accelerated stability testing was performed that allowed rapid selection of stable ASD's and suitable packaging configurations. Dissolution testing by a discriminating two-step non-sink dissolution method showed 70-80% drug release from prototype ASD's, which was around twofold higher compared to crystalline tablet formulations. The in vivo pharmacokinetic study in dogs showed that bioavailability from ASD of compound X with PVP VA64 was four times higher compared to crystalline tablet formulations. The HME process was scaled up from lab scale to clinical scale using volumetric scale up approach and scale-independent-specific energy parameter. The present study demonstrated systematic development of ASD dosage form and scale up of HME process to clinical scale using minimal drug (∼500 g), which allowed successful clinical batch manufacture of enabled formulation within 7 months.

Towards a seamless product and process development workflow for recombinant proteins produced by plant molecular farming.

Plant molecular farming (PMF) has been promoted as a fast, efficient and cost-effective alternative to bacteria and animal cells for the production of biopharmaceutical proteins. Numerous plant species have been tested to produce a wide range of drug candidates. However, PMF generally lacks a systematic, streamlined and seamless workflow to continuously fill the product pipeline. Therefore, it is currently unable to compete with established platforms in terms of routine, throughput and horizontal integration (the rapid translation of product candidates to preclinical and clinical development). Individual management decisions, limited funding and a lack of qualified production capacity can hinder the execution of such projects, but we also lack suitable technologies for sample handling and data management. This perspectives article will highlight current bottlenecks in PMF and offer potential solutions that combine PMF with existing technologies to build an integrated facility of the future for product development, testing, manufacturing and clinical translation. Ten major bottlenecks have been identified and are discussed in turn: automated cloning and simplified transformation options, reproducibility of bacterial cultivation, bioreactor integration with automated cell handling, options for rapid mid-scale candidate and product manufacturing, interconnection with (group-specific or personalized) clinical trials, diversity of (post-)infiltration conditions, development of downstream processing platforms, continuous process operation, compliance of manufacturing conditions with biosafety regulations, scaling requirements for cascading biomass.

The Effect of Formulation Variables on the Manufacturability of Clopidogrel Tablets via Fluidized Hot-Melt Granulation-From the Lab Scale to the Pilot Scale.

Solid pharmaceutical formulations with class II active pharmaceutical ingredients (APIs) face dissolution challenges due to limited solubility, affecting in vivo behavior. Robust computational tools, via data mining, offer valuable insights into product performance, complementing traditional methods and aiding in scale-up decisions. This study utilizes the design of experiments (DoE) to understand fluidized hot-melt granulation manufacturing technology. Exploratory data analysis (MVDA) highlights similarities and differences in tablet manufacturability and dissolution profiles at both the lab and pilot scales. The study sought to gain insights into the application of multivariate data analysis by identifying variations among batches produced at different manufacturing scales for this technology. DoE and MVDA findings show that the granulation temperature, time, and Macrogol type significantly impact product performance. These factors, by influencing particle size distribution, become key predictors of product quality attributes such as resistance to crushing, disintegration time, and early-stage API dissolution in the profile. Software-aided data mining, with its multivariate and versatile nature, complements the empirical approach, which is reliant on trial and error during product scale-up.

Application of Multivariate Data Analysis on Historical Recombinant Adenovirus Zoster Vaccine Production Data for Upstream Process Improvements.

In recent years, multivariate data analysis (MVDA) has been widely used for process characterization and fault diagnosis in the biopharmaceutical industry. This study aims to investigate the feasibility of using MVDA for the development and scale-up of a perfusion process for HEK293 cell-based recombinant adenovirus zoster vaccine (Ad-HER) production. The Principal Component Analysis (PCA) results suggested comparable performance among the ATF, PATFP, and BFP perfusion systems in benchtop-scale stirred-tank bioreactor (STR). Then a Batch Evolution Model (BEM) was built using representative data from 10 L STR with a BFP system to assess the Ad-HER perfusion process performance at pilot-scale bioreactor (50 L STR and 50 L wave bioreactor). Furthermore, another BEM model and Batch Level Model (BLM) were built to monitor process parameters over time and predict the final adenovirus titer in 50 L wave bioreactor. The loading plot revealed that lactate dehydrogenase activity, viable cell diameter, and base-added during the virus production phase could be used as preliminary indicators of adenovirus yield. Finally, an adenovirus titer of 2.0±0.3×1010 IFU/mL was achieved in the 50 L wave bioreactor with BFP system, highlighting the robustness of the Ad-HER perfusion process at pilot-scale. Overall, this study emphasizes the effectiveness of MVDA as a tool for advancing the understanding of recombinant adenovirus vaccine perfusion production process development and scale-up.

Validation of a CFD model for cell culture bioreactors at large scale and its application in scale-up.

Among all the operating parameters that control the cell culture environment inside bioreactors, appropriate mixing and aeration are crucial to ensure sufficient oxygen supply, homogeneous mixing, and CO2 stripping. A model-based manufacturing facility fit approach was applied to define agitation and bottom air flow rates during the process scale-up from laboratory to manufacturing, of which computational fluid dynamics (CFD) was the core modeling tool. The realizable k-ε turbulent dispersed Eulerian gas-liquid flow model was established and validated using experimental values for the volumetric oxygen transfer coefficient (kLa). Model validation defined the process operating parameter ranges for application of the model, identified mixing issues (e.g., impeller flooding, dissolved oxygen gradients, etc.) and the impact of antifoam on kLa. Using the CFD simulation results as inputs to the models for oxygen demand, gas entrance velocity, and CO2 stripping aided in the design of the agitation and bottom air flow rates needed to meet cellular oxygen demand, control CO2 levels, mitigate risks for cell damage due to shear, foaming, as well as fire hazards due to high O2 levels in the bioreactor gas outlet. The recommended operating conditions led to the completion of five manufacturing runs with a 100% success rate. This model-based approach achieved a seamless scale-up and reduced the required number of at-scale development batches, resulting in cost and time savings of a cell culture commercialization process.

Development of a Semi-Mechanistic Modeling Framework for Wet Bead Milling of Pharmaceutical Nanosuspensions.

This study aimed to develop a practical semi-mechanistic modeling framework to predict particle size evolution during wet bead milling of pharmaceutical nanosuspensions over a wide range of process conditions and milling scales. The model incorporates process parameters, formulation parameters, and equipment-specific parameters such as rotor speed, bead type, bead size, bead loading, active pharmaceutical ingredient (API) mass, temperature, API loading, maximum bead volume, blade diameter, distance between blade and wall, and an efficiency parameter. The characteristic particle size quantiles, i.e., x10, x50, and x90, were transformed to obtain a linear relationship with time, while the general functional form of the apparent breakage rate constant of this relationship was derived based on three models with different complexity levels. Model A, the most complex and general model, was derived directly from microhydrodynamics. Model B is a simpler model based on a power-law function of process parameters. Model C is the simplest model, which is the pre-calibrated version of Model B based on data collected from different mills across scales, formulations, and drug products. Being simple and computationally convenient, Model C is expected to reduce the amount of experimentation needed to develop and optimize the wet bead milling process and streamline scale-up and/or scale-out.

Machine learning in bioprocess development: from promise to practice.

Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Alternative biological sources for extracellular vesicles production and purification strategies for process scale-up.

Extracellular vesicles (EVs) are phospholipidic bi-layer enclosed nanoparticles secreted naturally by all cell types. They are attracting increasing attention in the fields of nanomedicine, nutraceutics and cosmetics as biocompatible carriers for drug delivery, with intrinsic properties beneficial to human health. Scientific work now focuses on developing techniques for isolating EVs that can translate into industrial-scale production and meet rigorous clinical requirements. The science of EVs is ongoing, and many pitfalls must be addressed, such as the requirement for standard, reproducible, inexpensive, and Good Manufacturing Practices (GMP) adherent EV processing techniques. Researchers are exploring the use of alternative sources to EVs derived from mammalian cultures, such as plant EVs, as well as the use of bacteria, algae and milk. Regarding the downstream processing of EVs, many alternative techniques to the ultracentrifugation (UC) protocols most commonly used in the laboratory are emerging. In the context of process scale-up, membrane-based processes for isolation and purification of EVs are the most promising, either as stand-alone processes or in combination with chromatographic techniques. This review discusses current trends on EVs source selection and EVs downstream processing techniques, with a focus on plant-derived EVs and membrane-based techniques for EVs enrichment.

Scaling up self-sustained smouldering of sewage sludge for waste-to-energy.

Self-sustained smouldering combustion presents strong potential as a green waste-to-energy technique for a range of wastes, especially those with high moisture content like wastewater sewage sludge. While well-demonstrated in laboratory experiments, there is little known about scaling up this process to larger, commercial reactors. This paper addresses this knowledge gap by systematically conducting and analyzing experiments in a variety of reactors extending beyond the laboratory scale. This work reveals a robust treatment regime; however, it also identifies potential complications associated with perimeter heat losses at scale. Two key impacts, on the smouldering reactions and the air flow patterns, are shown to potentially degrade treatment if not properly understood and managed. Altogether, this study provides novel insight and guidance for scaling up smouldering science into practical, waste-to-energy systems.

Autotrophic nitrogen removal for decentralized treatment of ammonia-rich industrial textile wastewater: process assessment, stabilization and modelling.

Digital textile printing (DTP) is a game-changer technology that is rapidly expanding worldwide. On the other hand, process wastewater is rich in ammoniacal and organic nitrogen, resulting in relevant issues for discharge into sewer system and treatment in centralized plants. The present research is focused on the assessment of the partial nitritation/anammox process in a single-stage granular sequencing batch reactor for on-site decentralized treatment. The technical feasibility of the process was assessed by treating wastewater from five DTP industries in a laboratory-scale reactor, in one case investigating long-term process stabilization. While experimental results indicated nitrogen removal efficiencies up to about 70%, complying with regulations on discharge in sewer system, these data were used as input for process modelling, whose successful parameter calibration was carried out. The model was applied to the simulation of two scenarios: (i) the current situation of a DTP company, in which wastewater is discharged into the sewer system and treated in a centralized plant, (ii) the modified situation in which on-site decentralized treatment for DTP wastewater is implemented. The second scenario resulted in significant improvements, including reduced energy consumption (- 15%), reduced greenhouse gases emission, elimination of external carbon source for completing denitrification at centralized WWTP and reduced sludge production (- 25%).

High density bioprocessing of human pluripotent stem cells by metabolic control and in silico modeling.

To harness the full potential of human pluripotent stem cells (hPSCs) we combined instrumented stirred tank bioreactor (STBR) technology with the power of in silico process modeling to overcome substantial, hPSC-specific hurdles toward their mass production. Perfused suspension culture (3D) of matrix-free hPSC aggregates in STBRs was applied to identify and control process-limiting parameters including pH, dissolved oxygen, glucose and lactate levels, and the obviation of osmolality peaks provoked by high density culture. Media supplements promoted single cell-based process inoculation and hydrodynamic aggregate size control. Wet lab-derived process characteristics enabled predictive in silico modeling as a new rational for hPSC cultivation. Consequently, hPSC line-independent maintenance of exponential cell proliferation was achieved. The strategy yielded 70-fold cell expansion in 7 days achieving an unmatched density of 35 × 106 cells/mL equivalent to 5.25 billion hPSC in 150 mL scale while pluripotency, differentiation potential, and karyotype stability was maintained. In parallel, media requirements were reduced by 75% demonstrating the outstanding increase in efficiency. Minimal input to our in silico model accurately predicts all main process parameters; combined with calculation-controlled hPSC aggregation kinetics, linear process upscaling is also enabled and demonstrated for up to 500 mL scale in an independent bioreactor system. Thus, by merging applied stem cell research with recent knowhow from industrial cell fermentation, a new level of hPSC bioprocessing is revealed fueling their automated production for industrial and therapeutic applications.

Squalene Emulsion Manufacturing Process Scale-Up for Enhanced Global Pandemic Response.

Squalene emulsions are among the most widely employed vaccine adjuvant formulations. Among the demonstrated benefits of squalene emulsions is the ability to enable vaccine antigen dose sparing, an important consideration for pandemic response. In order to increase pandemic response capabilities, it is desirable to scale up adjuvant manufacturing processes. We describe innovative process enhancements that enabled the scale-up of bulk stable squalene emulsion (SE) manufacturing capacity from a 3000- to 5,000,000-dose batch size. Manufacture of concentrated bulk along with the accompanying viscosity change in the continuous phase resulted in a ≥25-fold process efficiency enhancement. Process streamlining and implementation of single-use biocontainers resulted in reduced space requirements, fewer unit operations, and minimization of cleaning requirements. Emulsion physicochemical characteristics were measured by dynamic light scattering, laser diffraction, and HPLC with charged aerosol detection. The newly developed full-scale process was demonstrated by producing two 5,000,000-dose batches of bulk concentrated SE. A scale-up of adjuvant manufacturing capacity through process innovation enables more efficient production capabilities for pandemic response.

Micro-structured copper and nickel metal foams for wastewater disinfection: proof-of-concept and scale-up.

It is necessary to disinfect treated wastewater prior to discharge to reduce exposure risks to humans and the environment. The currently practiced wastewater disinfection technologies are challenged by toxic by-products, chemicals and energy demand, a range of effectiveness limitations, among other concerns. An effective, eco-friendly, and energy-efficient alternative disinfection technique is desirable to modernize and enhance wastewater treatment operations. Copper and nickel micro-structured metal foams, and a conventional copper mesh, were evaluated as disinfecting surfaces for treating secondary-treated wastewater contaminated with coliform bacteria. The micro-structured copper foam was adopted for scale-up study, due to its stable and satisfactory bactericidal performance obtained over a wide range of bacterial concentrations and metal-to-liquid ratios. Three scales of experiments, using two types of reactor designs, were performed using municipal wastewater to determine the optimal scale-up factors: small lab-scale batch reactor, intermediate lab-scale batch reactor, and pilot-scale continuous tubular reactor experiments. The performance was evaluated with the aim of minimizing metal material requirement with respect to bactericidal efficiency and leaching risks at all scales. Copper foam, at or above optimal conditions, consistently inactivated over 95 % of total coliforms, fecal coliforms and E.coli in wastewater at various scales, and leachate copper concentrations were determined to be below Canadian guideline values for outfall. This study successfully implemented the "structure" strategy of process intensification, and opens up the possibility to apply micro-structured copper foam in a range of other water disinfection systems, from pre-treatment to point-of-use, and should thus become a topic of further research.

Aqueous Two-Phase Systems at Large Scale: Challenges and Opportunities.

Aqueous two-phase systems (ATPS) have proved to be an efficient and integrative operation to enhance recovery of industrially relevant bioproducts. After ATPS discovery, a variety of works have been published regarding their scaling from 10 to 1000 L. Although ATPS have achieved high recovery and purity yields, there is still a gap between their bench-scale use and potential industrial applications. In this context, this review paper critically analyzes ATPS scale-up strategies to enhance the potential industrial adoption. In particular, large-scale operation considerations, different phase separation procedures, the available optimization techniques (univariate, response surface methodology, and genetic algorithms) to maximize recovery and purity and economic modeling to predict large-scale costs, are discussed. ATPS intensification to increase the amount of sample to process at each system, developing recycling strategies and creating highly efficient predictive models, are still areas of great significance that can be further exploited with the use of high-throughput techniques. Moreover, the development of novel ATPS can maximize their specificity increasing the possibilities for the future industry adoption of ATPS. This review work attempts to present the areas of opportunity to increase ATPS attractiveness at industrial levels.

Large-scale microcarrier culture of HEK293T cells and Vero cells in single-use bioreactors.

Gene therapy and viral vaccine are becoming attractive therapeutic options for the treatment of different malignant diseases. Viral vector productions are often using static culture vessels and small volume stainless steel bioreactors (SSB). However, the yield of each vessel can be relatively low and multiple vessels often need to be operated simultaneously. This significantly increases labor intensity, production costs, contamination risks, and limits its ability to be scaled up, thus, creating challenges to meet the quantities required once the gene therapy or viral vaccine medicine goes into clinical phases or to market. Single-use bioreactor combining with microcarrier provides a good option for viral vector and vaccine production. The goal of the present studies was to develop the microcarrier bead-to-bead expansion and transfer process for HEK293T cells and Vero cells and scale-up the cultures to 50-200 l single-use bioreactors. Following microcarrier bead-to-bead transfer, the peak cell concentration of HEK293T cells reached 1.5 × 106 cells/ml in XDR-50 bioreactor, whereas Vero cells reached 3.1 × 106 cells/ml and 3.3 × 106 cells/ml in XDR-50 bioreactor and XDR-200 bioreactor, respectively. The average growth rates reached 0.61-0.68/day. The successful microcarrier-based scaleup of these two cell lines in single-use bioreactors demonstrates potential large-scale production capabilities of viral vaccine and vector for current and future vaccines and gene therapy.

Predictive models of lyophilization process for development, scale-up/tech transfer and manufacturing.

Scale-up and technology transfer of lyophilization processes remains a challenge that requires thorough characterization of the laboratory and larger scale lyophilizers. In this study, computational fluid dynamics (CFD) was employed to develop computer-based models of both laboratory and manufacturing scale lyophilizers in order to understand the differences in equipment performance arising from distinct designs. CFD coupled with steady state heat and mass transfer modeling of the vial were then utilized to study and predict independent variables such as shelf temperature and chamber pressure, and response variables such as product resistance, product temperature and primary drying time for a given formulation. The models were then verified experimentally for the different lyophilizers. Additionally, the models were applied to create and evaluate a design space for a lyophilized product in order to provide justification for the flexibility to operate within a certain range of process parameters without the need for validation.