Predicting powder feedability: A workflow for assessing the risk of flow stagnation and defining the operating space for different powder-feeder combinations.

Powder feeding is of critical importance for continuous manufacturing (CM) since next to in-process segregation it is the phenomenon primarily responsible for fluctuations in content uniformity and for content deviations in the final drug product. So far, feeding studies have focused on the characterization of specific feeders and the prediction of their performance for various materials. This work presents a more holistic approach, an early general assessment of the "feedability" of raw materials. With that regard, we established a workflow to: i) predict potential feeding issues, such as the flow stagnation in the hopper based on both the material attributes and the feeder's geometry; and ii) predict the feed rate space using various feeder/screw combinations for powders with an acceptable risk of hopper flow stagnation. Statistical models were developed for this twofold approach using a dataset comprising nine powders and four different feeders. In order to include different feeding equipment into the statistical models, novel equipment descriptors (capturing the effect of different geometries) and performance indicators (the end fill level as indicator for the risk of powder flow stagnation) were introduced. The application of the workflow was demonstrated for a simple formulation, and model validation was successfully performed for an additional powder that was not contained in the original dataset. Finally, the most relevant material attributes were identified, and reduced material characterization data sets were investigated in terms of effects on the model's prediction performance. The workflow presents a promising tool for initial process assessment in early-phase development.

Linked experimental and modelling approaches for tablet property predictions.

Recent years have seen the advent of Quality-by-Design (QbD) as a philosophy to ensure the quality, safety, and efficiency of pharmaceutical production. The key pharmaceutical processing methodology of Direct Compression to produce tablets is also the focus of some research. The traditional Design-of-Experiments and purely experimental approach to achieve such quality and process development goals can have significant time and resource requirements. The present work evaluates potential for using combined modelling and experimental approach, which may reduce this burden by predicting the properties of multicomponent tablets from pure component compression and compaction model parameters. Additionally, it evaluates the use of extrapolation from binary tablet data to determine theoretical pure component model parameters for materials that cannot be compacted in the pure form. It was found that extrapolation using binary tablet data - where one known component can be compacted in pure form and the other is a challenging material which cannot be - is possible. Various mixing rules have been evaluated to assess which are suitable for multicomponent tablet property prediction, and in the present work linear averaging using pre-compression volume fractions has been found to be the most suitable for compression model parameters, while for compaction it has been found that averaging using a power law equation form produced the best agreement with experimental data. Different approaches for estimating component volume fractions have also been evaluated, and using estimations based on theoretical relative rates of compression of the pure components has been found to perform slightly better than using constant volume fractions (that assume a fully compressed mixture). The approach presented in this work (extrapolation of, where necessary, binary tablet data combined with mixing rules using volume fractions) provides a framework and path for predictions for multicomponent tablets without the need for any additional fitting based on the multicomponent formulation composition. It allows the knowledge space of the tablet to be rapidly evaluated, and key regions of interest to be identified for follow-up, targeted experiments that that could lead to an establishment of a design and control space and forgo a laborious initial Design-of-Experiments.

Computer-aided molecular design of solvents for accelerated reaction kinetics.

Solvents can significantly alter the rates and selectivity of liquid-phase organic reactions, often hindering the development of new synthetic routes or, if chosen wisely, facilitating routes by improving rates and selectivities. To address this challenge, a systematic methodology is proposed that quickly identifies improved reaction solvents by combining quantum mechanical computations of the reaction rate constant in a few solvents with a computer-aided molecular design (CAMD) procedure. The approach allows the identification of a high-performance solvent within a very large set of possible molecules. The validity of our CAMD approach is demonstrated through application to a classical nucleophilic substitution reaction for the study of solvent effects, the Menschutkin reaction. The results were validated successfully by in situ kinetic experiments. A space of 1,341 solvents was explored in silico, but required quantum-mechanical calculations of the rate constant in only nine solvents, and uncovered a solvent that increases the rate constant by 40%.

Shape and temperature memory of nanocomposites with broadened glass transition.

Shape-memory polymers can revert to their original shape when they are reheated. The stress generated by shape recovery is a growing function of the energy absorbed during deformation at a high temperature; thus, high energy to failure is a necessary condition for strong shape-memory materials. We report on the properties of composite nanotube fibers that exhibit this particular feature. We observed that these composites can generate a stress upon shape recovery up to two orders of magnitude greater than that generated by conventional polymers. In addition, the nanoparticles induce a broadening of the glass transition and a temperature memory with a peak of recovery stress at the temperature of their initial deformation.