Continuous Flow Manufacturing Research Articles

AJICAP-M: Traceless Affinity Peptide Mediated Conjugation Technology for Site-Selective Antibody-Drug Conjugate Synthesis.

A traceless site-selective conjugation method, "AJICAP-M", was developed for native antibodies at sites using Fc-affinity peptides, focusing on Lys248 or Lys288. It produces antibody-drug conjugates (ADCs) with consistent drug-to-antibody ratios, enhanced stability, and simplified manufacturing. Comparative in vivo assessment demonstrated AJICAP-M's superior stability over traditional ADCs. This technology has been successfully applied to continuous-flow manufacturing, marking the first achievement in site-selective ADC production. This manuscript outlines AJICAP-M's methodology and its effectiveness in ADC production.

A hybrid design for jointly optimizing production scheduling, control chart design and maintenance in continuous flow manufacturing

Integrating the three crucial functions of quality control, economic production quantity (EPQ) and maintenance leads to cost savings and increased efficiency for manufacturing processes. While in the literature, they have typically been studied separately. Moreover, most of the existing integration models are designed for the discrete piece-part manufacturing environment. This study, nevertheless, focuses wholly on continuous flow manufacturing (CFM), which is distinctly different from piece-part manufacturing, as the sampling unit is described in light of laboratory requirements instead of the production unit. Based on this fact, this paper proposes an integrated EPQ scheme jointly designed with control-chart design and maintenance planning for the CFM process. A developed genetic algorithm is employed to minimize the expected total production cost under constraints on the economic-statistical quality. Of the costs considered, quality-related costs are quantified via an approximate formula derived from the quality loss curve depending on Taguchi’s quality loss function. A sensitivity analysis is performed on the expected total production cost and variations in the chosen system parameter values to offer insights into the issue. Finally, a comparative investigation of the suggested model and two similar models that contributed to this field is conducted.

Lignin oxidative depolymerization to phenolic compounds in a continuous-flow manufacturing platform

Effective oxidative depolymerization of lignin to phenolic monomers and oligomers is a crucial step in the economic feasibility of biorefinery. To date, most studies on the processing technical of lignin have focused on intermittent treatment, with little attention paid to microreactor. In this research, a novel and efficient process technical of oxidative depolymerization lignin via a continuous manufacturing platform was developed, in which the lignin alkaline solution is the disperse phase and the hydrogen peroxide is the continuous phase (oxidants, 8 wt%). The results show that different products can be obtained through varying the reaction conditions of lignin depolymerization. Under the optimized condition, 93.03 wt% conversion of raw lignin and 22.37 wt% yield of monomer was obtained. Besides, comparative characterizations on the samples of raw lignin and depolymerization products by GC-MS-FID and HSQC NMR analysis illustrated that 84.26 wt% of monomers (the sum of yield of 18.85 wt%) is phenolic compounds, with syringaldehyde (yield of 4.22 wt% and selectivity of 18.86 wt%) and vanillin (yield of 3.49 wt% and selectivity of 15.60 wt%) are the main products and the high yields attributed to the efficient cleavage of C-O linkages (mainly β-O-4 linkages) of lignin. Moreover, GPC and elemental analyses show that the light oil product with an oxygen content of 41.28% was mainly composed of dimers and trimers. This work demonstrating that continuous-flow oxidative depolymerization was an effective strategy on lignin processing technical and will be an interesting field of research on lignin comprehensive utilization.

Dynamic metastable polymersomes enable continuous flow manufacturing

Polymersomes are polymeric analogues of liposomes with exceptional physical and chemical properties. Despite being dubbed as next-generation vesicles since their inception nearly three decades ago, polymersomes have yet to experience translation into the clinical or industrial settings. This is due to a lack of reliable methods to upscale production without compromising control over polymersome properties. Herein we report a continuous flow methodology capable of producing near-monodisperse polymersomes at scale (≥3 g/h) with the possibility of performing downstream polymersome manipulation. Unlike conventional polymersomes, our polymersomes exhibit metastability under ambient conditions, persisting for a lifetime of ca. 7 days, during which polymersome growth occurs until a dynamic equilibrium state is reached. We demonstrate how this metastable state is key to the implementation of downstream processes to manipulate polymersome size and/or shape in the same continuous stream. The methodology operates in a plug-and-play fashion and is applicable to various block copolymers.

Customized Production of Holey Graphene Oxides via a Continuous Flow Process.

Continuous flow manufacturing is an innovative technology mainly applied in the chemical and pharmaceutical industries that is progressively being adapted to the manufacturing of nanomaterials to overcome the challenge of reproducing a product with consistent characteristics at a large scale. Here, a flow photochemical system is designed and prototyped for the synthesis of holey graphene oxides (hGOs). Compared to existing methods for the synthesis of hGO, the process is fast, highly scalable, and controllable. Through a combination of rigorous data analysis using machine learning algorithms on transmission electron microscope images and systematic studies of process parameters, it is demonstrated that characteristics of the produced hGO (i.e., porosity and pore size) are remarkably reproducible to the extent that it can be predicted by empirical models of processing-property correlations. Depending on the tailored nanopore structures, the synthesized hGOs out-performed GO in a range of applications that can benefit from the nanoporous two-dimensional (2D) sheets such as in supercapacitors, gas adsorption, and nanofiltration membranes. These results are significant in offering new perspectives on the low-cost industrialization of 2D nanomaterials.

Continuous-flow simulation of manufacturing systems with assembly/disassembly machines, multiple loops and general layout

Performance evaluation methods are important to design and control manufacturing systems. Approximate analytical methods are fast, but they may be limited by the restrictive assumptions on the system. On the contrary, simulation has not specific limitations in its applicability, but the time to model and analyse a manufacturing system can increase as the level of detail addressed by the model increases. The main contribution of this study is presenting a computationally efficient methodology to simulate single-part continuous-flow manufacturing systems with assembly/disassembly machines, multiple loops, general layout and general inter-event time distributions. By using graph theory, a new method is presented to identify the machines causing slowdown, blocking and starvation in a general layout and determine the time before the occurrence of a state transition for each machine and the time before the fulfilment or depletion of each buffer. By advancing the time clock to the next event-time accordingly, the number of discrete events needed to be simulated is decreased compared to a discrete-event simulation with discrete flow of parts. As a result, the proposed method is on average 15 times faster than DES methods in the analysis of discrete-flow systems, and 110 times faster on average in the analysis of continuous-flow systems. The low computational time of the proposed method allows to simulate systems under general assumptions and in a very short time.

Microwave-Induced Transient Heating Accelerates Protein PEGylation.

PEGylation is one of the most widely employed strategies to increase the circulatory half-life of proteins and to reduce immune responses. However, conventional PEGylation protocols often require excess reagents and extended reaction times because of their inefficiency. This study demonstrates that a microwave-induced transient heating phenomenon can be exploited to significantly accelerate protein PEGylation and even increase the degree of PEGylation achievable beyond what is possible at room temperature. This can be accomplished under conditions that do not compromise protein integrity. Several PEGylation chemistries and proteins are tested, and mechanistic insight is provided. Under certain conditions, extremely high levels of PEGylation were achieved in a matter of minutes. Moreover, considering the significantly reduced reaction times, the microwave-induced transient heating concept was adapted for continuous flow manufacturing of bioconjugates.

A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

In this paper, the growing significance of data analysis in manufacturing environments is exemplified through a review of relevant literature and a generic framework to aid the ease of adoption of regression-based supervised learning in manufacturing environments. To validate the practicality of the framework, several regression learning techniques are applied to an open-source multi-stage continuous-flow manufacturing process data set to typify inference-driven decision-making that informs the selection of regression learning methods for adoption in real-world manufacturing environments. The investigated regression learning techniques are evaluated in terms of their training time, prediction speed, predictive accuracy (R-squared value), and mean squared error. In terms of training time (TT), k-NN20 (k-Nearest Neighbour with 20 neighbors) ranks first with average and median values of 4.8 ms and 4.9 ms, and 4.2 ms and 4.3 ms, respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of prediction speed (PS), DTR (decision tree regressor) ranks first with average and median values of 5.6784×106 observations per second (ob/s) and 4.8691×106 observations per second (ob/s), and 4.9929×106 observations per second (ob/s) and 5.8806×106 observations per second (ob/s), respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of R-squared value (R2), BR (bagging regressor) ranks first with average and median values of 0.728 and 0.728, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 0.746 and 0.746, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. In terms of mean squared error (MSE), BR (bagging regressor) ranks first with average and median values of 2.7 and 2.7, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 3.5 and 3.5, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. All methods are further ranked inferentially using the statistics of their performance metrics to identify the best method(s) for the first and second stages of the predictive modeling of the multi-stage continuous-flow manufacturing process. A Wilcoxon rank sum test is then used to statistically verify the inference-based rankings. DTR and k-NN20 have been identified as the most suitable regression learning techniques given the multi-stage continuous-flow manufacturing process data used for experimentation.

A reliability-oriented integration model of production control, adaptive quality control policy and maintenance planning for continuous flow processes

Many studies reveal that integrating the concepts of production, maintenance and quality control helps reduce costs compared to studying them separately. But almost all the research works in this field are centred on piece part manufacturing. This paper presents an integrated model of the three concepts for the continuous flow process of manufacturing which is vastly different from piece-parts processes, as there is no explicit production unit. To quickly detect the assignable cause occurrence, an adaptive X̄ control chart with variable sampling interval designed for the continuous flow process is employed as the quality control tool. A mathematical definition is presented to describe the proposed model before a genetic algorithm is developed to find the optimal setting values of decision parameters that minimize the expected total cost per unit time. The numerical comparison indicates that the proposed model has reduced the cost by an average of 1.5% than the classical model with fixed sampling interval policy in continuous flow manufacturing. Finally, Taguchi’s design of experiments (DOE) is employed to investigate the main effects of cost and process parameters.

Liposome manufacturing under continuous flow conditions: towards a fully integrated set-up with in-line control of critical quality attributes.

Continuous flow manufacturing (CFM) has shown remarkable advantages in the industrial-scale production of drug-loaded nanomedicines, including mRNA-based COVID-19 vaccines. Thus far, CFM research in nanomedicine has mainly focused on the initial particle formation step, while post-formation production steps are hardly ever integrated. The opportunity to implement in-line quality control of critical quality attributes merits closer investigation. Here, we designed and tested a CFM setup for the manufacturing of liposomal nanomedicines that can potentially encompass all manufacturing steps in an end-to-end system. Our main aim was to elucidate the key composition and process parameters that affect the physicochemical characteristics of the liposomes. Total flow rate, lipid concentration and residence time of the liposomes in a high ethanol environment (i.e., above 20% v/v) emerged as critical parameters to tailor liposome size between 80 and 150 nm. After liposome formation, the pressure and the surface area of the filter in the ultrafiltration unit were critical parameters in the process of clearing the dispersion from residual ethanol. As a final step, we integrated in-line measurement of liposome size and residual ethanol content. Such in-line measurements allow for real-time monitoring and in-process adjustment of key composition and process parameters.

A perspective on automated advanced continuous flow manufacturing units for the upgrading of biobased chemicals toward pharmaceuticals.

Biomass is a renewable, almost infinite reservoir of a large diversity of highly functionalized chemicals. The conversion of biomass toward biobased platform molecules through biorefineries generally still lacks economic viability. Profitability could be enhanced through the development of new market opportunities for these biobased platform chemicals. The fine chemical industry, and more specifically the manufacturing of pharmaceuticals is one of the sectors bearing significant potential for these biobased building blocks to rapidly emerge and make a difference. There are, however, still many challenges to be dealt with before this market can thrive. Continuous flow technology and its integration for the upgrading of biobased platform molecules for the manufacturing of pharmaceuticals is foreseen as a game-changer. This perspective reflects on the main challenges relative to chemical, process, regulatory and supply chain-related burdens still to be addressed. The implementation of integrated continuous flow processes and their automation into modular units will help for tackling with these challenges.Graphical abstract

Analysis of a production line subject to degradation and preventive maintenance

This paper considers a continuous flow manufacturing system with two machines subjected to condition-based preventive maintenance, and separated by an intermediate buffer of finite capacity. Each machine can degrade into several discrete states characterized by different degradation levels and performance parameters, ranging from perfect functioning to complete failure. Unlike the existing literature, the proposed approach considers various side effect costs, including the cost related to quality deterioration. A methodology is developed to describe the degradation stochastic process, and to analyze the complex trade-off between the condition-based preventive maintenance and the buffer capacity. The obtained results show the importance of considering quality-related and other possible costs when selecting preventive maintenance policy to optimize the performance of the manufacturing system. It is also shown that the selection of preventive maintenance actions should be considered from the view of the whole system, rather than individual machines. The optimal solution depends on the different degradation levels of the upstream and downstream machines.

Enabling continuous flow manufacturing of magnetic nanoparticles with a millifluidic system

From a translational perspective, magnetic nanoparticles (MNP) require manufacturing processes that can be performed reliably and at scale. Continuous manufacturing processes are particularly well-suited for this purpose compared to batch processes, which have high technical variability and low throughput production. In this study, a modularly built millifluidic system for the continuous flow synthesis of MNP by co-precipitation is presented. The system prevents fouling and clogging of passages and enables a reproducible and highly scalable MNP synthesis. The modular assembly allows an independent variation of the input parameters of each module. Here, several input parameters are investigated such as different ratios of iron ions (Fe3+/Fe2+) and base to iron ions (OH–/(Fe3++Fe2+)) as well as flow rates of the used reactant solutions. The basic MNP properties, e.g. core and hydrodynamic size, magnetization values, were determined. Further, magnetic hyperthermia, magnetic resonance imaging (MRI) and magnetic particle imaging (MPI) studies were performed. MNP with core diameters of about 10 nm and low polydispersity index of up to 0.12 were successfully synthesized and show promising characteristics as MRI contrast agents (transverse relaxivity up to (465 ± 14) mM−1s−1), MPI tracers (amplitude ratio A5/A3 of the measured frequency components of up to 0.37) and heating agents (specific loss power of up to (236 ± 26)W/g).

Towards 4th industrial revolution efficient and sustainable continuous flow manufacturing of active pharmaceutical ingredients

The convergence of end-to-end continuous flow synthesis with downstream processing, process analytical technology (PAT), artificial intelligence (AI), machine learning and automation in ensuring improved accessibility of quality medicines on demand.

Adjustable variables multiple-dependent-state sampling plans based on a process capability index

Acceptance sampling plans are practical and economical methods of statistical quality control. In modern manufacturing processes, supplier’s products are produced from continuous-flow manufacturing processes; consequently, the quality of successive lots is expected to be homogeneous and dependent. The multiple dependent state (MDS) sampling plan considers the cumulative lot-sentencing results to prevent the primary disadvantage of past-lot exclusion from the single sampling plan (SSP). Unfortunately, the sampling efficiency of MDS plans declines as additional preceding-lot results are included. In this paper, an adjustable MDS (AMDS) sampling plan based on the most popular process capability index with bilateral specifications is developed. The proposed AMDS plans not only increase sampling performance as additional preceding lots are considered in the lot-sentencing decision but also garner economic gains from reducing the required sample size. After comparing the performance of different plans, our proposed AMDS plans demonstrate superior cost-effectiveness and discriminatory powers to that of the SSP and MDS plans; moreover, with adjustable mechanisms, they can be progressively implemented for developing a solid supplier-buyer partnership. Finally, the industrial applicability of the AMDS plans is illustrated in a case study.

Fully Continuous Flow Synthesis of 3-Chloro-4-oxopentyl Acetate: An Important Intermediate for Vitamin B1

A fully continuous flow synthesis of 3-chloro-4-oxopentyl acetate (2), an important intermediate for vitamin B1 (1), was developed. This continuous flow manufacturing included two chemical transformations and an inline extraction step without intermediate purification and solvent exchange. In this work, the traditional synthetic route for batch operation was efficiently simplified via a series of separated screening tests in flows under various conditions. We found that the chlorination reaction can be carried out in only 30 s at room temperature by flow. We also simplified the decarboxylation/acylation step by using a cross-mixer, so that acetic anhydride was no longer required in the acylation reaction. A computational fluid dynamics simulation was carried out to study the improved micromixing of liquid–liquid two-phase streams. Finally, 3-chloro-4-oxopentyl acetate (2) was obtained in a 90% isolated yield with a product purity of 96% and a total residence time of approximately 32 min. This fully continuous process was operated smoothly for 12 h, and approximately 19.1 g of the desired product was generated with a production rate of 1.79 g h–1.

One-step preparation of antimicrobial silicone materials based on PDMS and salicylic acid: insights from spatially and temporally resolved techniques

In this work, we introduce a one-step strategy that is suitable for continuous flow manufacturing of antimicrobial PDMS materials. The process is based on the intrinsic capacity of PDMS to react to certain organic solvents, which enables the incorporation of antimicrobial actives such as salicylic acid (SA), which has been approved for use in humans within pharmaceutical products. By combining different spectroscopic and imaging techniques, we show that the surface properties of PDMS remain unaffected while high doses of the SA are loaded inside the PDMS matrix. The SA can be subsequently released under physiological conditions, delivering a strong antibacterial activity. Furthermore, encapsulation of SA inside the PDMS matrix ensured a diffusion-controlled release that was tracked by spatially resolved Raman spectroscopy, Attenuated Total Reflectance IR (ATR-IR), and UV-Vis spectroscopy. The biological activity of the new material was evaluated directly at the surface and in the planktonic state against model pathogenic bacteria, combining confocal laser scanning microscopy, electron microscopy, and cell viability assays. The results showed complete planktonic inhibition for clinically relevant strains of Staphylococcus aureus and Escherichia coli, and a reduction of up to 4 orders of magnitude for viable sessile cells, demonstrating the efficacy of these surfaces in preventing the initial stages of biofilm formation. Our approach adds a new option to existing strategies for the antimicrobial functionalisation of a wide range of products such as catheters, wound dressings and in-dwelling medical devices based on PDMS.

A comprehensive review of flow chemistry techniques tailored to the flavours and fragrances industries.

Due to their intrinsic physical properties, which includes being able to perform as volatile liquids at room and biological temperatures, fragrance ingredients/intermediates make ideal candidates for continuous-flow manufacturing. This review highlights the potential crossover between a multibillion dollar industry and the flourishing sub-field of flow chemistry evolving within the discipline of organic synthesis. This is illustrated through selected examples of industrially important transformations specific to the fragrances and flavours industry and by highlighting the advantages of conducting these transformations by using a flow approach. This review is designed to be a compendium of techniques and apparatus already published in the chemical and engineering literature which would constitute a known solution or inspiration for commonly encountered procedures in the manufacture of fragrance and flavour chemicals.

Multiscale modeling and optimal operation of millifluidic synthesis of perovskite quantum dots: Towards size-controlled continuous manufacturing

Inorganic lead halide perovskite quantum dots (QDs) have emerged as a promising semiconducting nanomaterial candidate for widespread applications, including next-generation solar cells, displays, and photocatalysts. The optoelectronic properties of colloidal QDs are majorly dictated by their bandgap energy (related to their size). Thus, it is important to fine-tune the size while having fast and continuous production of QDs. However, the mass and heat transfer limitations of batch reactors with batch-to-batch variations have hindered precise control over the size-dependent optoelectronic properties of QDs. Thus, to address this knowledge gap, we propose a multiscale model for continuous flow manufacturing of colloidal perovskite QDs. Specifically, a first-principled kinetic Monte Carlo model is integrated with a continuum model to describe a plug-flow crystallizer (PFC). The PFC has two manipulated inputs, precursor concentration and superficial flow velocity, to fine-tune the size of QDs. Furthermore, a neural network based surrogate model is designed to identify an optimal input trajectory which will ensure that the desired QD size is achieved, thereby taking a step towards controlled and reliable nanomanufacturing of QDs.

Quality Prediction and Yield Improvement in Process Manufacturing Based on Data Analytics

Quality management is important for maximizing yield in continuous-flow manufacturing. However, it is more difficult to manage quality in continuous-flow manufacturing than in discrete manufacturing because partial defects can significantly affect the quality of an entire lot of final product. In this paper, a comprehensive framework that consists of three steps is proposed to predict defects and improve yield by using semi-supervised learning, time-series analysis, and classification model. In Step 1, semi-supervised learning using both labeled and unlabeled data is applied to generate quality values. In addition, feature values are predicted in time-series analysis in Step 2. Finally, in Step 3, we predict quality values based on the data obtained in Step 1 and Step 2 and calculate yield values with the use of the predicted value. Compared to a conventional production plan, the suggested plan increases yield by up to 8.7%. The production plan proposed in this study is expected to contribute to not only the continuous manufacturing process but the discrete manufacturing process. In addition, it can be used in early diagnosis of equipment failure.