---------------------------------------------------------------------- = README = ---------------------------------------------------------------------- *Author: Frederik J. S. Doerr CMAC Future Manufacturing Research Hub University of Strathclyde Technology and Innovation Centre 99 George Street, Glasgow, G1 1RD, U.K. orcid.org/0000-0001-5245-0503 https://github.com/frederik-d %%% --- --- --- --- --- --- --- --- --- --- --- --- == # Overview: == This is a collection of data and methodologies used in the following publication. Title: Peptide Isolation via Spray Drying: Particle Formation, Process Design and Implementation for the Production of Spray Dried Glucagon Authors: Frederik J. S. Doerr, Lee J. Burns, Becky Lee, Jeremy Hinds, Rebecca L. Davis-Harrison, Scott A. Frank, Alastair J. Florence Journal: Pharmaceutical Research Accepted Date: 28/09/2020 DOI: Abstract: 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. Keywords: Pharmaceutical Formulation, Micro-XRT Particle Analysis, Watershed Image Segmentation, Sensitivity Analysis, Feature Selection, Machine Learning, Classification Model %%% --- --- --- --- --- --- --- --- --- --- --- --- == # 1) Single droplet drying curves (SDD_Data) == CSV files with experimenta data from SDD experiments (Fig S4). %%% --- --- --- --- --- --- --- --- --- --- --- --- == # 2) Spray drying data (ExpProtocol_SP_Data) == Each Excel file contains information for one spray drying experiment (format: xlsx). File Sheets: > Main: Spray dryer configuration and process overview. > RS232: Data from B290 serial interface (setpoints, temperature T_P3 and T_P5). > XS_RS232: Recorded feed weight (P11). > BME680: Recorded exhaust gas temperature, relative humidity, pressure and VOC (P9). %%% --- --- --- --- --- --- --- --- --- --- --- --- == # 3) Powder Characterisation (PrChar_Data) == Files: > PrChar_Data_PSD - CSV files with laser diffraction PSD data for TRE and GLUC (Fig 10). > PrChar_Data_DSC - CSV files with DSC data TRE (Fig S13). > PrChar_Data_TGMS - CSV files with TGMS data TRE and GLUC (Fig S10, Fig S11). > PrChar_Data_XRPD - CSV files with XRPD data TRE and GLUC (Fig S12). %%% --- --- --- --- --- --- --- --- --- --- --- --- == # 4) BME680 Exhaust Gas Sensor (BME680_SensorMount) == BME680_SensorMount_S1.mp4 - video file showing the design. BME680_SensorMount_S1.STL - design file for 3D printing.