The piper at the gates of brain: A systematic review of surface modification strategies on lipid nanoparticles to overcome the Blood-Brain-Barrier.

The Blood-Brain Barrier (BBB) significantly impedes drug delivery to the central nervous system. Nanotechnology, especially surface-functionalized lipid nanoparticles, offers innovative approaches to overcome this barrier. However, choosing an effective functionalization strategy is challenging due to the lack of detailed comparative analysis in current literature. Our systematic review examined various functionalization strategies and their impact on BBB permeability from 2041 identified articles, of which 80 were included for data extraction. Peptides were the most common modification (18) followed by mixed strategies (12) proteins (9), antibodies (7), and other strategies (8). Interestingly, 26 studies showed BBB penetration with unmodified or modified nanoparticles using commonly applied strategies such as PEGylation or surfactant addition. Statistical analysis across 42 studies showed correlation between higher in vivo permeation improvements and nanoparticle type, size, and functionalization category. The highest ratios were found for nanostructured lipid carriers or biomimetic systems, in studies with particle sizes under 150 nm, and in those applying mixed functionalization strategies. The interstudy heterogeneity we observed highlights the importance of adopting standardized evaluation protocols to enhance comparability. Our systematic review aims to provide a comparative insight and identify future research directions in the development of more effective lipid nanoparticle systems for drug delivery to the brain to help improve the treatment of neurological and psychiatric disorders and brain tumours.

Energy Consumption Optimization of a Fluid Bed Dryer in Pharmaceutical Manufacturing Using EDA (Exploratory Data Analysis).

In this paper, a data preprocessing methodology, EDA (Exploratory Data Analysis), is used for performing an exploration of the data captured from the sensors of a fluid bed dryer to reduce the energy consumption during the preheating phase. The objective of this process is the extraction of liquids such as water through the injection of dry and hot air. The time taken to dry a pharmaceutical product is typically uniform, independent of the product weight (Kg) or the type of product. However, the time it takes to heat up the equipment before drying can vary depending on different factors, such as the skill level of the person operating the machine. EDA (Exploratory Data Analysis) is a method of evaluating or comprehending sensor data to derive insights and key characteristics. EDA is a critical component of any data science or machine learning process. The exploration and analysis of the sensor data from experimental trials has facilitated the identification of an optimal configuration, with an average reduction in preheating time of one hour. For each processed batch of 150 kg in the fluid bed dryer, this translates into an energy saving of around 18.5 kWh, giving an annual energy saving of over 3.700 kWh.