Optimization and prediction of ibuprofen release from 3D DLP printlets using artificial neural networks

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. 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

Prediction of possible CaMnO3 modifications using an ab initio minimization data-mining approach

We have performed a crystal structure prediction study of CaMnO3 focusing on structures generated by octahedral tilting according to group-subgroup relations from the ideal perovskite type (Pm (3) over barm), which is the aristotype of the experimentally known CaMnO3 compound in the Pnma space group. Furthermore, additional structure candidates have been obtained using data mining. For each of the structure candidates, a local optimization on the ab initio level using density-functional theory (LDA, hybrid B3LYP) and the Hartree--Fock (HF) method was performed, and we find that several of the modifications may be experimentally accessible. In the high-pressure regime, we identify a post-perovskite phase in the CaIrO3 type, not previously observed in CaMnO3. Similarly, calculations at effective negative pressure predict a phase transition from the orthorhombic perovskite to an ilmenite-type (FeTiO3) modification of CaMnO3

Spatial Characterization of Hot Melt Extruded Dispersion Systems Using Thermal Atomic Force Microscopy Methods:The effects of processing parameters on phase separation

In this study we explore the use of nano-scale localized thermal analysis (LTA) and transition temperature microcopy (TTM) as a novel combined approach to studying phase separation in HME dispersions of cyclosporine A in Eudragit EPO