Tailoring amlodipine release from 3D printed tablets: Influence of infill patterns and wall thickness.

The aim of this study was to investigate the impact of infill patterns on the drug release of 3D-printed tablets and the possibility of tailoring drug release through the use of excipients. Furthermore, the influence of wall thickness was evaluated. Amlodipine was used as a model drug, polyvinyl alcohol (PVA) as a polymer and excipients including sodium starch glycolate (SSG) and hydroxypropyl methyl cellulose (HPMC) HME 4 M were used. Four different formulations were prepared. Firstly, the substances were mixed and then extruded by hot melt extrusion to form filaments. The obtained filaments were used to print amlodipine tablets by fused deposition modeling (FDM) 3D-printing technique. Each formulation was printed in four different infill patterns: zigzag, cubic, tri-hexagon and concentric, while infill density remained constant (20%). The mechanical properties of the obtained filaments were also evaluated using three-point bend test. Amlodipine tablets were printed with varying wall thickness (1 mm, 2 mm and 3 mm) and varying infill patterns. With regard to the infill patterns, higher drug release was achieved with zigzag infill pattern. The simultaneous effect of excipients and infill patterns on amlodipine release has been described and modeled through self - organizing maps (SOMs), which visualize the effect of these variables. Self-organizing maps confirmed the fastest drug release when the zigzag pattern and SSG were used, but also showed that the presence of HPMC HME 4 M was not decisive for drug release rate. As for the wall thickness, higher drug release was achieved with decreasing wall thickness. The results indicated that proper selection of excipients and/or adjusting the infill pattern and wall thickness are ways of tailoring drug release in FDM 3D printing. This study draws the attention to the importance of adjusting the settings of the printer and the usage of excipients to produce release-tailored medications.

The evaluation of the effect of different superdisintegrants on the drug release from FDM 3D printed tablets through different applied strategies: In vitro-in silico assessment.

Paracetamol-loaded tablets were printed by fused deposition modelling technique, using polyvinyl alcohol as a backbone polymer and Affinisol™ HPMC as a plasticizer in all formulations. Four different strategies were applied in order to accelerate the drug release from the tablets. First, different release enhancers were added: sodium starch glycolate, croscarmellose sodium, Kollidon CL and mannitol. Kollidon CL and mannitol showed the greatest influence on the drug dissolution rate. The second strategy included lowering the infill density, which did not make any significant changes in dissolution profiles, according to the calculated similarity factor. Then the best two release enhancers from the first strategy were combined (Kollidon CL and mannitol) and this proved to be the most effective in the drug release acceleration. The fourth strategy, increasing the percentage of the release enhancers in formulation, revealed the importance of their concentration limits. In summary, the drug release accelerated from 58% released after 5 h to reaching the plateau within 2 h. In silico physiologically-based biopharmaceutics modelling showed that formulations with mannitol and Kollidon CL, especially the formulation containing a combination of these release enhancers, can provide relatively fast drug release and extent of drug absorption that complies with an immediate release tablet.

Understanding the Effect of Energy Density and Formulation Factors on the Printability and Characteristics of SLS Irbesartan Tablets-Application of the Decision Tree Model.

Selective laser sintering (SLS) is a rapid prototyping technique for the production of three-dimensional objects through selectively sintering powder-based layer materials. The aim of the study was to investigate the effect of energy density (ED) and formulation factors on the printability and characteristics of SLS irbesartan tablets. The correlation between formulation factors, ED, and printability was obtained using a decision tree model with an accuracy of 80%. FT-IR results revealed that there was no interaction between irbesartan and the applied excipients. DSC results indicated that irbesartan was present in an amorphous form in printed tablets. ED had a significant influence on tablets' physical, mechanical, and morphological characteristics. Adding lactose monohydrate enabled faster drug release while reducing the possibility for printing with different laser speeds. However, formulations with crospovidone were printable with a wider range of laser speeds. The adjustment of formulation and process parameters enabled the production of SLS tablets with hydroxypropyl methylcellulose with complete release in less than 30 min. The results suggest that a decision tree could be a useful tool for predicting the printability of pharmaceutical formulations. Tailoring the characteristics of SLS irbesartan tablets by ED is possible; however, it needs to be governed by the composition of the whole formulation.

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.

Evaluation of exposure time and visible light irradiation in LCD 3D printing of ibuprofen extended release tablets.

Liquid crystal display (LCD) 3D printing technology is one of the three currently available photocuring three-dimensional printing technologies. LCD 3D printers usually use wavelengths in the ultraviolent (UV) range. However, recently introduced light-emitting diodes (LED) projectors enable visible light-induced photopolymerization, which would have an advantage in terms of safety in drug production. The aim of this work was to investigate the feasibility of printing ibuprofen extended release tablets under visible light irradiation and to evaluate characteristics of printed tablets. Influences of exposure time and wavelengths (UV versus visible light) on characteristics of tablets were evaluated. Tablets were printed using 405 nm and 450 nm LED light. Visible light enabled significantly faster printing as well as better dimensions accuracy of printed tablets. It was noticed that printing under 450 nm LED resulted in slightly softer tablets compared to tablets printing with 405 nm LED. Extended ibuprofen release was obtained from all formulations. Exposure time did not have influence on drug release in formulations with low water content. However, in a formulation with higher water content, the exposure time had a pronounced effect on drug release (in eight hours of testing, differences were from 27% to 95%). Wavelength affected the release rate of ibuprofen. Tablets prepared using 450 nm LEDs released ibuprofen faster than tablets prepared with 405 nm LEDs. The main mechanism of ibuprofen release was diffusion, regardless of exposure time and wavelength. Characteristics of obtained tablets indicate that further optimization of this process is necessary, but this new printing process approach opens the possibility for novel wavelength consideration in order to obtain the safe printing process of tablets.

Paracetamol extended release FDM 3D printlets: Evaluation of formulation variables on printability and drug release.

Paracetamol printlets were prepared via hot-melt extrusion process and fused deposition modelling, using two types of backbone polymers. Polycaprolactone (PCL) and Polyethylene oxides (PEO) 100 K and 200 K were used, while Arabic gum was used as a plasticizer to facilitate the material flow and Gelucire® 44/14 as an enhancer of drug release. Different drug/polymer ratios were prepared. Extrusion temperature was adjusted according to the mixture/polymer types. It was possible to produce filaments with maximum of 60% w/w of drug. Mechanical properties of filaments were evaluated using three-point bend test, while obtained parameters were modelled using decision tree as a data mining method. Correlation between maximum displacement, maximum force and printability was obtained with accuracy of 84.85% and can be a useful tool for predicting printability of filaments. This study briefly demonstrated that backbone polymer in formulation plays crucial role in obtaining FDM printlets with desired properties. PEO-based filaments were more prone to be clogged in printcore, but their printlets showed much faster drug release. Drug release from all printlets was prolonged: from 50% in 8 h (PCL), to complete release in 4 h (PEO). Paracetamol release kinetics was guided by anomalous transport, attributed to the diffusion and erosion process.

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

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.