Entrapment of air microbubbles by ice crystals during freezing exacerbates freeze-induced denaturation of proteins.

Freezing techniques are an essential part of biologics manufacturing processes, yet the formation of ice/water interfaces can impart detrimental effects on proteins. However, the absence of chemical and structural differences between ice and liquid water poses the question as to why ice can destabilize proteins. We hypothesize that the destabilizing stress of the ice-liquid water interface does not originate from the ice-water system itself but rather from the air microbubbles present during the freezing process. As the temperature decreases, the dissolved air is expelled from the ice crystal lattices in the form of microbubbles and is subsequently trapped by the advancing ice front. This newly formed air-water interface represents an additional interfacial area for the proteins to be adsorbed onto and denatured. The result showed that freezing at âˆ¼ 1 K/s led to the formation of small circular microbubbles with diameters ranging from 100 µm to 500 µm. In contrast, slower freezing resulted in the formation of larger, elongated millimeter-size bubbles. The reduction of the number of microbubbles was carried out by the deaeration process using agitation under reduced pressure at 20 kPa. The resulting deaerated (i.e., low dissolved air) protein samples were frozen and monitored for the formation of subvisible aggregates using micro-flow imaging (MFI). The results demonstrated that deaerating the samples prior to intermediate freezing (i.e., TFF) reduced the number of aggregates for both highly surface-active and low surface-active proteins (lactoferrin and bovine IgG, respectively). This reduction was more pronounced in spray freeze drying (SFD) than thin-film freezing (TFF), and less apparent in conventional lyophilization.

The applications of Machine learning (ML) in designing dry powder for inhalation by using thin-film-freezing technology.

Dry powder inhalers (DPIs) are one of the most widely used devices for treating respiratory diseases. Thin--film--freezing (TFF) is a particle engineering technology that has been demonstrated to prepare dry powder for inhalation with enhanced physicochemical properties. Aerosol performance, which is indicated by fine particle fraction (FPF) and mass median aerodynamic diameter (MMAD), is an important consideration during the product development process. However, the conventional approach for formulation development requires many trial-and-error experiments, which is both laborious and time consuming. As a state-of-the art technique, machine learning has gained more attention in pharmaceutical science and has been widely applied in different settings. In this study, we have successfully built a prediction model for aerosol performance by using both tabular data and scanning electron microscopy (SEM) images. TFF technology was used to prepare 134 dry powder formulations which were collected as a tabular dataset. After testing many machine learning models, we determined that the Random Forest (RF) model was best for FPF prediction with a mean absolute error of ± 7.251%, and artificial neural networks (ANNs) performed the best in estimating MMAD with a mean absolute error of ± 0.393 µm. In addition, a convolutional neural network was employed for SEM image classification and has demonstrated high accuracy (>83.86%) and adaptability in predicting 316 SEM images of three different drug formulations. In conclusion, the machine learning models using both tabular data and image classification were successfully established to evaluate the aerosol performance of dry powder for inhalation. These machine learning models facilitate the product development process of dry powder for inhalation manufactured by TFF technology and have the potential to significantly reduce the product development workload. The machine learning methodology can also be applied to other formulation design and development processes in the future.

Combining nonthermal plasma with perovskite-like catalyst for NOx storage and reduction.

A new NOx storage and reduction (NSR) system is developed for NOx removal by combining perovskite-like catalyst with nonthermal plasma technology. In this hybrid system, catalyst is mainly used for oxidizing NO to NO2 and storing them, while nonthermal plasma is applied as a desorption-reduction step for converting NOx into N2. An innovative catalyst with a high NOx storage capacity and good reduction performance is developed by successive impregnation. The catalysts prepared with various metal oxides were investigated for NOx storage capacity (NSC) and NOx conversion. Characterization of the catalysts prepared reveals that addition of cobalt (Co) and potassium (K) considerably increases the performance for NSC. Results also show that SrKMn0.8Co0.2O4 supported on BaO/Al2O3 has good NSC (209 µmol/gcatalyst) for the gas stream containing 500 ppm NO and 5 % O2 with N2 as carrier gas. For plasma reduction process, NOx conversion achieved with SrKMn0.8Co0.2O4/BaO/Al2O3 reaches 81 % with the applied voltage of 12 kV and frequency of 6 kHz in the absence of reducing agents. The results indicate that performance of plasma reduction process (81 %) is better than that of thermal reduction (64 %). Additionally, mixed gases including 1 % CO, 1 % H2 and 1 % CH4, and 2 % H2O(g) are simultaneously introduced into the system to investigate the effect on NSR with plasma system and results indicate that performance of NSR with plasma can be enhanced. Overall, the hybrid system is promising to be applied for removing NOx from gas streams. Graphical abstract ᅟ.