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Purpose Spray drying plays an important role in the pharmaceutical industry for product development of sensitive bio-pharmaceutical formulations. Process design, implementation and optimisation require in-depth knowledge of process-product interactions. Here, an integrated approach for the rapid, early-stage spray drying process development of trehalose and glucagon on lab-scale is presented. Methods Single droplet drying experiments were used to investigate the particle formation process. Process implementation was supported using in-line process analytical technology within a data acquisition framework recording temperature, humidity, pressure and feed rate. During process implementation, off-line product characterisation provided additional information on key product properties related to residual moisture, solid state structure, particle size/morphology and peptide fibrillation/degradation. Results A psychrometric process model allowed the identification of feasible operating conditions for spray drying trehalose, achieving high yields of up to 84.67%, and significantly reduced levels of residual moisture and particle agglomeration compared to product obtained during non-optimal drying. The process was further translated to produce powders of glucagon and glucagon-trehalose formulations with yields of >83.24%. Extensive peptide aggregation or degradation was not observed. Conclusions The presented data-driven process development concept can be applied to address future isolation problems on lab-scale and facilitate a systematic implementation of spray drying for the manufacturing of sensitive bio-pharmaceutical formulations.

A statistical model enables auto-calibration of second harmonic generation (SHG) images for quantifying trace crystallinity within amorphous solid dispersions (ASDs) over a wide dynamic range of crystallinity. In this paper, we demonstrate particle-counting approaches for quantifying trace crystallinity, combined with analytical expressions correcting for particle overlap bias in higher crystallinity regimes to extend the continuous dynamic range of standard particle-counting algorithms through to the signal averaging regime. The reliability of the values recovered by these expressions was demonstrated with simulated data as well as experimental data obtained for an amorphous solid dispersion formulation containing evacetrapib, an Eli Lilly and Company compound. Since particle counting independently recovers the crystalline volume and the SHG intensity, the average SHG intensity per unit volume can be used as an internal calibrant for quantifying crystallinity at higher volume fractions, for which particle counting is no longer applicable.

For oral solid dosage forms, disintegration and dissolution properties are closely related to the powders and particles used in their formulation. However, there remains a strong need to characterize the impact of particle structures on tablet compaction and performance. Three-dimensional non-invasive tomographic imaging plays an increasingly essential role in the characterization of drug substances, drug product intermediates, and drug products. It can reveal information hidden at the micro-scale which traditional characterization approaches fail to divulge due to a lack of resolution. In this study, two batches of spray-dried particles (SDP) and two corresponding tablets of an amorphous product, merestinib (LY2801653), were analyzed with 3D X-Ray Microscopy. Artificial intelligence-based image analytics were used to quantify physical properties, which were then correlated with dissolution behavior. The correlation derived from the image-based characterization was validated with conventional laboratory physical property measurements. Quantitative insights obtained from image-analysis including porosity, pore size distribution, surface area and pore connectivity helped to explain the differences in dissolution behavior between the two tablets, with root causes traceable to the microstructure differences in their corresponding SDPs.