Predicting drug release from diazepam FDM printed tablets using deep learning approach: Influence of process parameters and tablet surface/volume ratio

The aim of this study was to apply artificial neural networks as deep learning tools in establishing a model for understanding and prediction of diazepam release from fused deposition modeling (FDM) printed tablets. Diazepam printed tablets of various shapes were created by a computer-aided design (CAD) program and prepared by fused deposition modeling using previously prepared polyvinyl alcohol/diazepam filaments via hot-melt extrusion. The surface to volume ratio (SA/V) for each shape was calculated. Printing parameters were varied including infill density (20%, 70% and 100%) and infill pattern (line and zigzag). Influence of tablet SA/V ratio and printing parameters (infill density and infill pattern) on the release of diazepam from printed tablets were modeled using self-organizing maps (SOM) and multi-layer perceptron (MLP). SOM as an unsupervised neural network was used for visualizing interrelation among the data, whereas MLP was used for the prediction of drug release properties. MLP had three layers (with structure 2-3-5) and was trained using back propagation algorithm. Input parameters for the modeling were: infill density and SA/V ratio; while output parameters were percent of drug release in five time points. The data set for network training was divided into training, validation and test sets. The dissolution rate increased with higher SA/V ratio, lower infill density (less than 50%) and zigzag infill pattern. The established ANN model was tested; calculated f 2 factors for two tested formulations (70.24 and 77.44) showed similarity between experimentally observed and predicted drug release profiles. Trained MLP network was able to predict drug release behavior as a function of infill density and SA/Vol ratio, as established design space for formulated 3D printed diazepam tablets.

Digital Light Processing (DLP) 3D Printing of Atomoxetine Hydrochloride Tablets Using Photoreactive Suspensions

Three-dimensional (3D) printing technologies are based on successive material printing layer-by-layer and are considered suitable for the production of dosage forms customized for a patient’s needs. In this study, tablets of atomoxetine hydrochloride (ATH) have been successfully fabricated by a digital light processing (DLP) 3D printing technology. Initial materials were photoreactive suspensions, composed of poly(ethylene glycol) diacrylate 700 (PEGDA 700), poly(ethylene glycol) 400 (PEG 400), photoinitiator and suspended ATH. The amount of ATH was varied from 10.00 to 25.00% (w/w), and a range of doses from 12.21 to 40.07 mg has been achieved, indicating the possibility of personalized therapy. The rheological characteristics of all photoreactive suspensions were appropriate for the printing process, while the amount of the suspended particles in the photoreactive suspensions had an impact on the 3D printing process, as well as on mechanical and biopharmaceutical characteristics of tablets. Only the formulation with the highest content of ATH had significantly different tensile strength compared to other formulations. All tablets showed sustained drug release during at least the 8h. ATH crystals were observed with polarized light microscopy of photoreactive suspensions and the cross-sections of the tablets, while no interactions between ATH and polymers were detected by FT-IR spectroscopy.

Hydrophilic excipients in digital light processing (DLP) printing of sustained release tablets: Impact on internal structure and drug dissolution rate

Three-dimensional (3D) printing enables the production of different objects adjusted for the specific application, which has particular importance of providing personalized therapy, whereby the challenge is to apply pharmaceutical materials into 3D printing technology. In this study, effect of poly(ethylene glycol) 400 (PEG 400), sodium chloride (NaCl), and mannitol, as hydrophilic excipients, was investigated in order to overcome very slow and incomplete drug release from tablets (printlets) fabricated by photopolymerization using digital light processing (DLP) technology. Paracetamol (acetaminophen) was used as a model drug, while polyethylene glycol diacrylate (PEGDA) was used as a photopolymer and diphenyl (2,4,6-trimethylbenzoyl) phosphine oxide as a photoinitiator in photoreactive mixtures. Most of printlet formulations exhibit sustained release over 8 h, wherein drug release kinetics is the best described with Korsmeyer-Peppas kinetics. Variation in the content of photopolymer and excipients had an influence on the dissolution rate, mechanical characteristics, and internal structure of the investigated samples. The addition of hydrophilic polymers increased drug release rate, while PEGDA had the greatest influence on the tensile strength of printlets. The results indicate the possibility of implementation of traditional excipients into different formulations for photopolymerization based 3D printing for the production of small batches of tablets with tailored drug release.