Towards point-of-care manufacturing and analysis of immediate-release 3D printed hydrocortisone tablets for the treatment of congenital adrenal hyperplasia.

Hydrocortisone (HC) is the preferred drug in children with congenital adrenal hyperplasia due to its lower potency as well as fewer reports of side effects. Fused deposition modelling (FDM) 3D printing holds the potential to produce low-cost personalised doses for children at the point of care. However, the compatibility of the thermal process to produce immediate-release bespoke tablets for this thermally labile active is yet to be established. This work aims to develop immediate-release HC tablets using FDM 3D printing and assess drug contents as a critical quality attribute (CQA) using a compact, low-cost near-infrared (NIR) spectroscopy as a process analytical technology (PAT). The FDM 3D printing temperature (140 °C) and drug concentration in the filament (10%-15% w/w) were critical parameters to meet the compendial criteria for drug contents and impurities. Using a compact low-cost NIR spectral device over a wavelength of 900-1700 nm, the drug contents of 3D printed tablets were assessed. Partial least squares (PLS) regression was used to develop individual calibration models to detect HC content in 3D printed tablets of lower drug contents, small caplet design, and relatively complex formula. The models demonstrated the ability to predict HC concentrations over a wide concentration range (0-15% w/w), which was confirmed by HPLC as a reference method. Ultimately, the capability of the NIR model had preceding dose verification performance on HC tablets, with linearity (R2 = 0.981) and accuracy (RMSECV = 0.46%). In the future, the integration of 3DP technology with non-destructive PAT techniques will accelerate the adoption of on-demand, individualised dosing in a clinical setting.

The use of near-infrared as process analytical technology (PAT) during 3D printing tablets at the point-of-care.

Fused deposition modelling (FDM) is one of the most researched 3D printing technologies that holds great potential for low-cost manufacturing of personalised medicine. To achieve real-time release, timely quality control is a major challenge for applying 3D printing technologies as a point-of-care (PoC) manufacturing approach. This work proposes the use of a low-cost and compact near-infrared (NIR) spectroscopy modality as a process analytical technology (PAT) to monitor a critical quality attribute (drug content) during and after FDM 3D printing process. 3D printed caffeine tablets were used to manifest the feasibility of the NIR model as a quantitative analytical procedure and dose verification method. Caffeine tablets (0-40 % w/w) were fabricated using polyvinyl alcohol and FDM 3D printing. The predictive performance of the NIR model was demonstrated in linearity (correlation coefficient, R2) and accuracy (root mean square error of prediction, RMSEP). The actual drug content values were determined using the reference high-performance liquid chromatography (HPLC) method. The model of full-completion caffeine tablets demonstrated linearity (R2 = 0.985) and accuracy (RMSEP = 1.4 %), indicated to be an alternative dose quantitation method for 3D printed products. The ability of the models to assess caffeine contents during the 3D printing process could not be accurately achieved using the model built with complete tablets. Instead, by building a predictive model for each completion stage of 20 %, 40 %, 60 % and 80 %, the model of different completion caffeine tablets displayed linearity (R2 of 0.991, 0.99, 0.987, and 0.983) and accuracy (RMSEP of 2.22 %, 1.65 %, 1.41 %, 0.83 %), respectively. Overall, this study demonstrated the feasibility of a low-cost NIR model as a non-destructive, compact, and rapid analysis dose verification method enabling the real-time release to facilitate 3D printing medicine production in the clinic.