Optimization and Prediction of Ibuprofen Release from 3D DLP Printlets Using Artificial Neural Networks

Abstract

The aim of this work was to investigate effects of the formulation factors on tablet printability as well as to optimize and predict extended drug release from cross-linked polymeric ibuprofen printlets using an artificial neural network (ANN). Printlets were printed using digital light processing (DLP) technology from formulations containing polyethylene glycol diacrylate, polyethylene glycol, and water in concentrations according to D-optimal mixture design and 0.1% w/w riboflavin and 5% w/w ibuprofen. It was observed that with higher water content longer exposure time was required for successful printing. For understanding the effects of excipients and printing parameters on drug dissolution rate in DLP printlets two different neural networks were developed with using two commercially available softwares. After comparison of experimental and predicted values of in vitro dissolution at the corresponding time points for optimized formulation, the R2 experimental vs. predicted value was 0.9811 (neural network 1) and 0.9960 (neural network 2). According to difference f1 and similarity factor f2 (f1 = 14.30 and f2 = 52.15) neural network 1 with supervised multilayer perceptron, backpropagation algorithm, and linear activation function gave a similar dissolution profile to obtained experimental results, indicating that adequate ANN is able to set out an input–output relationship in DLP printing of pharmaceutics.

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

“…So far, PEGDA 575, without the addition of any excipients or with addition of PEG 300, was used for printing polypills containing: paracetamol, naproxen, caffeine, aspirin, prednisolone, and chloramphenicol [ 15 ], while PEGDA 400 was used for printing tablets containing theophylline as active substance. Paracetamol [ 11 , 16 ], 4-aminosalicylic acid [ 11 ] and ibuprofen [ 35 , 47 ], were used as active substances in photopolymerization based printing processes, combined with a mixture of PEGDA and PEGs of different molecular weights. In our study, tablets containing four different amounts of ATH suspended in PEGDA/PEG 400 mixture were successfully produced by the DLP printer.…”

“…The benefits of using the DLP printer here demonstrated are the possibility of customization of the printing parameters depending on the formulation factors, fast printing of pharmaceutical dosage forms, as well as printing at room temperature. Previous studies based on DLP 3D printing have also shown these possibilities as potential advantages of this technology [ 12 , 16 , 35 ]. In comparison with other 3D printing technologies investigated for tablet production, DLP has a limited number of photopolymers suitable for pharmaceutical use.…”

“…For formulations A1–A4, it was examined which printing parameters allow the adhesion of the tablets to the building plate and do not lead to over curing of the photoreactive suspension. Previous studies [ 15 , 35 ] have shown that it is necessary to adjust the printing parameters for each formulation separately, because there are no predefined optimal conditions as is the case with commercially available photoreactive mixtures used in other industries. Photopolymerization was conducted for layers thickness of 0.1 mm, to prevent problems with light penetration which can occur due to the interaction of light with suspended particles.…”

Two-Photon Polymerisation 3D Printing of Microneedle Array Templates with Versatile Designs: Application in the Development of Polymeric Drug Delivery Systems

“…In general, a digitally controlled printing head emits light to selective points of a building platform immersed in uncured resin, forming consecutive layers of newly cured solid polymer (10). The two most common forms of photopolymerisation are stereolithography (SLA) and digital light processing (DLP), with various examples of their applications available in the literature (13)(14)(15)(16)(17)(18).…”

3D Printing of Pharmaceuticals and Drug Delivery Devices

“…The drug release predicted with STATISTICA 7.0 was quite similar to the one obtained experimentally. This study described that suitable ANN allows to recognise the input-output relationship in DLP printing of pharmaceutics [16].…”