Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer.

Artificial neural networks (ANN) and data analysis (DA) are powerful tools for supporting decision-making. They are employed in diverse fields, and one of them is nanotechnology; for example, in predicting silver nanoparticles size. To our knowledge, we are the first to use ANN to predict liposome size (LZ). Liposomes are lipid nanoparticles used in different biomedical applications that can be produced in Dean-Forces-based microdevices such as the Periodic Disturbance Micromixer (PDM). In this work, ANN and DA techniques are used to build a LZ prediction model by using the most relevant variables in a PDM, the Flow Rate Radio (FRR), and the Total Flow Rate (TFR), and the temperature, solvents, and concentrations were kept constant. The ANN was designed in MATLAB and fed data from 60 experiments with 70% training, 15% validation, and 15% testing. For DA, a regression analysis was used. The model was evaluated; it showed a 0.98147 correlation coefficient for training and 0.97247 in total data compared with 0.882 obtained by DA.

Numerical and Experimental Validation of Mixing Efficiency in Periodic Disturbance Mixers.

The shape and dimensions of a micromixer are key elements in the mixing process. Accurately quantifying the mixing efficiency enables the evaluation of the performance of a micromixer and the selection of the most suitable one for specific applications. In this paper, two methods are investigated to evaluate the mixing efficiency: a numerical model and an experimental model with a software image processing technique. Using two methods to calculate the mixing efficiency, in addition to corroborating the results and increasing their reliability, creates various possible approaches that can be selected depending on the circumstances, resources, amount of data to be processed and processing time. Image processing is an easy-to-implement tool, is applicable to different programming languages, is flexible, and provides a quick response that allows the calculation of the mixing efficiency using a process of filtering of images and quantifying the intensity of the color, which is associated with the percentage of mixing. The results showed high similarity between the two methods, with a difference ranging between 0 and 6% in all the evaluated points.

Parametric Study of the Factors Influencing Liposome Physicochemical Characteristics in a Periodic Disturbance Mixer.

Liposomes encapsulate different substances ranging from drugs to genes. Control over the average size and size distribution of these nanoparticles is vital for biomedical applications since these characteristics determine to a high degree where liposomes will accumulate in the human body. Micromixers enable the continuous flow synthesis of liposomes, improving size control and reproducibility. Recently, Dean flow dynamics-based micromixers, such as the periodic disturbance mixer (PDM), have been shown to produce controlled-size liposomes in a scalable and reproducible way. However, contrary to micromixers based on molecular diffusion or chaotic advection, their production factors and their influence over liposome properties have not yet been addressed thoroughly. In this work, we present a comprehensive parametric study of the effects of flow conditions and molecular changing factors such as concentration, lipid type, and temperature on the physicochemical characteristics of liposomes. Numerical models and confocal images are used to quantitatively and qualitatively evaluate mixing performance under different liposome production conditions and their relationship with vesicle properties. The total flow rate (TFR) and, to a lesser extent, the flow rate ratio (FRR) control the liposome size and size distribution. Effects on liposome size are also observed by changing the molecular factors. Moreover, the liposome ζ potential is independent of the factors studied here. The micromixer presented in this work enables the production of liposomes as small as 24 nm, with monodispersed to low or close to low polydispersed liposome populations as well as a production rate as high as 41 mg/h.

The effect of different organic solvents in liposome properties produced in a periodic disturbance mixer: Transcutol®, a potential organic solvent replacement.

Liposomes are versatile particles used in the biomedical field as drug delivery systems (DDS). Liposome production using micromixers have shown to yield nanoparticles for DDS in a single step with a controllable size by changing flow conditions. Nonetheless, other factors such as the organic solvent, play a crucial role in the liposome formation process. Furthermore, drug solubility and toxicity are pivotal when deciding which organic solvent to choose. In this work, liposomes were produced in a periodic disturbance mixer (PDM). We investigated three conventional organic solvents: ethanol, methanol, and isopropanol as well as Transcutol®. We assessed the organic solvent influence on liposome characteristics (size, size distribution and zeta potential). Among the four organic solvents, Transcutol® yielded the smallest liposomes, which ranged from 80 nm to 160 nm. Moreover, a more in-depth investigation showed that Transcutol® produced smaller or similar-sized particles under different temperature and lipid concentration conditions, compared with ethanol. Furthermore, we proved that particles zeta potential was not influenced by the organic solvent, production temperature, or lipid concentration. This work results show that Transcutol® could replace the conventional alcohol-based solvents and can potentially avoid filtration steps due to its low toxicity. Therefore, the present approach is appealing for DDS development.

Surface Response Based Modeling of Liposome Characteristics in a Periodic Disturbance Mixer.

Liposomes nanoparticles (LNPs) are vesicles that encapsulate drugs, genes, and imaging labels for advanced delivery applications. Control and tuning liposome physicochemical characteristics such as size, size distribution, and zeta potential are crucial for their functionality. Liposome production using micromixers has shown better control over liposome characteristics compared with classical approaches. In this work, we used our own designed and fabricated Periodic Disturbance Micromixer (PDM). We used Design of Experiments (DoE) and Response Surface Methodology (RSM) to statistically model the relationship between the Total Flow Rate (TFR) and Flow Rate Ratio (FRR) and the resulting liposomes physicochemical characteristics. TFR and FRR effectively control liposome size in the range from 52 nm to 200 nm. In contrast, no significant effect was observed for the TFR on the liposomes Polydispersity Index (PDI); conversely, FRR around 2.6 was found to be a threshold between highly monodisperse and low polydispersed populations. Moreover, it was shown that the zeta potential is independent of TFR and FRR. The developed model presented on the paper enables to pre-establish the experimental conditions under which LNPs would likely be produced within a specified size range. Hence, the model utility was demonstrated by showing that LNPs were produced under such conditions.