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

Abstract

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.

Keywords: Additive manufacturing; Bespoke; Personalized medicine; Real-time release; Redistributed manufacturing.