Optimal design of micromixer for preparation of nanoliposomes

Qi, Xianjun; Wang, Jianchun; Chen, Chengmin; Wang, Luyuan

doi:10.1016/j.cep.2024.109677

Cited by 3 publications

(4 citation statements)

References 28 publications

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“…These variables have relevant influences on the process and uniquely contribute to the formation and characteristics of liposomes. 22,24,52,53 Lipid molecular descriptors provide insights into the physicochemical properties of the lipids, influencing their behaviour in the microfluidic process. While formulation parameters have a direct impact on circulation and drug retention, chip geometry affects the mixing and assembly of lipids, influencing liposome size and uniformity.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Chips with different channel geometry were 3D printed and utilized in the production offering flexibility and independence at low cost (Supporting information Fig S1 ). 24 Immediately following liposome production, the sample underwent analysis via dynamic light scattering (DLS) 52 and the hydrodynamic diameter and the polydispersity index (PDI) of the liposomes were chosen as the primary response for optimization in this study. 6 The data set obtained from the lipid screening was analysed using the Tree map (TMAP) dimensionality reduction method (Fig.…”

Section: Liposomal Formulation Screeningmentioning

confidence: 99%

“…22,24 Previous experiments have shown that total flow rate (TFR) becomes less important above a certain high turbulent flow, 27 where its influence on liposome formation is minimal which is supported by simulations elsewhere. 27,52 Therefore, the TFR was excluded from the initial feature vector after ensuring to be above the threshold of the used chips (>8 mL/min). A well-constructed feature vector (Fig.…”

Section: Feature Selectionmentioning

confidence: 99%

“…15 Furthermore, variations in microfluidic chip design complicate the standardization of production processes. 52 Robustly 3D-printing these chips might offer a pathway to standardization, facilitating broader application in pharmaceutical manufacturing. 24 Additionally, microfluidic production depends on many controllable parameters (Formulation, Chip, FRR, etc.…”

Section: Models To Predict Microfluidic Process Parametersmentioning

confidence: 99%

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Leveraging machine learning to streamline the development of liposomal drug delivery systems

Eugster,

Orsi,

Buttitta

et al. 2024

Preprint

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Drug delivery systems efficiently and safely administer therapeutic agents to specific body sites. Liposomes, spherical vesicles made of phospholipid bilayers, have become a powerful tool in this field, especially with the rise of microfluidic manufacturing during the COVID-19 pandemic. Despite its efficiency, microfluidic liposomal production poses challenges, often requiring laborious, optimization on a case-by-case basis. This is due to a lack of comprehensive understanding and robust methodologies, compounded by limited data on microfluidic production with varying lipids. Artificial intelligence offers promise in predicting lipid behaviour during microfluidic production, with the still unexploited potential of streamlining development. Herein we employ machine learning to predict critical quality attributes and process parameters for microfluidic-based liposome production. Validated models predict liposome formation, size, and production parameters, significantly advancing our understanding of lipid behaviour. Extensive model analysis enhanced interpretability and investigated underlying mechanisms, supporting the transition to microfluidic production. Unlocking the potential of machine learning in drug development can accelerate pharmaceutical innovation, making drug delivery systems more adaptable and accessible.

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Section: Feature Selectionmentioning

confidence: 99%

Section: Liposomal Formulation Screeningmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

Section: Models To Predict Microfluidic Process Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging machine learning to streamline the development of liposomal drug delivery systems

Eugster,

Orsi,

Buttitta

et al. 2024

Preprint

View full text Add to dashboard Cite

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Optimized design of macro‐microreactor for scale‐up of liquid–liquid chemical processes

Zhao,

Hu,

Wang

et al. 2024

AIChE Journal

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The scale‐up of microreactor‐based flow chemical process represents a grand challenge in chemical engineering. The small characteristic size (<1000 μm) of a typical microreactor leads to not only microscale effect (process intensification) but also low throughput. Here, we report macro‐microreactor to achieve scale‐up of liquid–liquid chemical operation with process intensification. By incorporating the designed internals based on computational fluid dynamics, the characteristic size of the macro‐microreactor is expanded into 3000–4000 μm. The optimized design of macro‐microreactor with helical‐shaped internal exhibits both similar or even stronger microscale effect and high throughput in contrast to the typical microreactor. For the liquid–liquid chemical process, seven times higher mass transfer coefficient and about half reduction of the pressure drop are realized. These macro‐microreactors would find further applications in industrial chemical manufacturing.

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Characteristics, Applications and Prospects of Nanobiomaterials

Huang

2024

E3S Web of Conf.

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The research on nanobiomaterials is of great significance for the intersection and development of physics, chemistry and biology, and has wide applications in biology, medicine, environmental science and other fields. This article classifies nanobiomaterials into metal-based, inorganic non-metallic and composite nanobiomaterials according to their composition, summarizes several typical preparation methods for different types of nanobiomaterials, such as ball milling, liquid phase reduction, template method, ultrasonic dispersion, etc., analyzes the characteristics of these preparation methods, summarizes the applications of nanobiomaterials in different fields, and makes prospects for their future development. Nanobiomaterials have diverse types and unique properties. The research on their properties is developing towards informatization and diversification. The preparation methods are numerous and moving towards high efficiency and green development. The breadth and depth of application fields continue to expand. This article makes a systematic summary of nanobiomaterials and prospects their development direction.

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Optimal design of micromixer for preparation of nanoliposomes

Cited by 3 publications

References 28 publications

Leveraging machine learning to streamline the development of liposomal drug delivery systems

Leveraging machine learning to streamline the development of liposomal drug delivery systems

Optimized design of macro‐microreactor for scale‐up of liquid–liquid chemical processes

Characteristics, Applications and Prospects of Nanobiomaterials

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