Step-By-Step Standardization of the Bottom-Up Semi-Automated Nanocrystallization of Pharmaceuticals: A Quality By Design and Design of Experiments Joint Approach.

Nanosized drug crystals have been reported with enhanced apparent solubility, bioavailability, and therapeutic efficacy compared to microcrystal materials, which are not suitable for parenteral administration. However, nanocrystal design and development by bottom-up approaches are challenging, especially considering the non-standardized process parameters in the injection step. This work aims to present a systematic step-by-step approach through Quality-by-Design (QbD) and Design of Experiments (DoE) for synthesizing drug nanocrystals by a semi-automated nanoprecipitation method. Curcumin is used as a drug model due to its well-known poor water solubility (0.6 µg mL-1 , 25 °C). Formal and informal risk assessment tools allow identifying the critical factors. A fractional factorial 24-1 screening design evaluates their impact on the average size and polydispersity of nanocrystals. The optimization of significant factors is done by a Central Composite Design. This response surface methodology supports the rational design of the nanocrystals, identifying and exploring the design space. The proposed joint approach leads to a reproducible, robust, and stable nanocrystalline preparation of 316 nm with a PdI of 0.217 in compliance with the quality profile. An orthogonal approach for particle size and polydispersity characterization allows discarding the formation of aggregates. Overall, the synergy between advanced data analysis and semi-automated standardized nanocrystallization of drugs is highlighted.

Deep Eutectic Solvents: An Eco-friendly Design for Drug Engineering.

Invited for this month's cover, the researchers from UTCBS and CiTCoM from Université Paris Cité (Paris, France), as well as Materia Nova (Mons, Belgium). The image emphasizes the deep eutectic solvent preparation thanks to hydrogen bond acceptor and donor interactions for drugs formulation and therapeutic applications. The Review itself is available at 10.1002/cssc.202300669.

Deep Eutectic Solvents: An Eco-friendly Design for Drug Engineering.

In the spirit of circular economy and sustainable chemistry, the use of environmentally friendly chemical products in pharmacy has become a hot topic. In recent years, organic solvents have been the subject of a great range of restriction policies due to their harmful effects on the environment and toxicity to human health. In parallel, deep eutectic solvents (DESs) have emerged as suitable greener solvents with beneficial environmental impacts and a rich palette of physicochemical advantages related to their low cost and biocompatibility. Additionally, DESs can enable remarkable solubilizing effect for several active pharmaceutical ingredients (APIs), thus forming therapeutic DESs (TheDESs). In this work, special attention is paid to DESs, presenting a precise definition, classification, methods of preparation, and characterization. A description of natural DESs (NaDESs), i. e., eutectic solvents present in natural sources, is also reported. Moreover, the present review article is the first one to detail the different approaches for judiciously selecting the constituents of DESs in order to minimize the number of experiments. The role of DESs in the biomedical and pharmaceutical sectors and their impact on the development of successful therapies are also discussed.

Microfluidic Manufacturing of Liposomes: Development and Optimization by Design of Experiment and Machine Learning.

Liposomes constitute the most exploited drug-nanocarrier with several liposomal drugs on the market. Microfluidic-based preparation methods stand up as a promising approach with high reproducibility and the ability to scale up. In this study, liposomes composed of DOPC, cholesterol, and DSPE-PEG 2000 with different molar ratios were fabricated using a microfluidic system. Process and conditions were optimized by applying design of experiments (DoE) principles. Furthermore, data were used to build an artificial neural network (ANN) model, to predict size and polydispersity index (PDI). Sets of runs were designed by DoE and performed on a micromixer microfluidic chip. Lipids' molar ratio and the process parameters, i.e. total flow rate (TFR) and flow rate ratio (FRR), were found to be the most influential factors on the formation of vesicles with target size and PDI under 100 nm and lower than 0.2, respectively. Size and PDI were predicted by the ANN model for 3 preparations with defined experimental conditions. The results showed no significant difference in size and PDI between the preparations and their values calculated with the ANN. In conclusion, production of optimized liposomes with high reproducibility was achieved by the application of microfluidic manufacturing processes, DoE, and Artificial Intelligence (AI). Microfluidic-based preparation methods assisted by computational tools would enable a faster development and clinical transfer of nanobased medications.