NoteApplication of artificial neural networks (ANNs) and genetic programming (GP) for prediction of drug release from solid lipid matrices
Graphical abstract
A model based on artificial neural networks and genetic programming was derived, which is able to predict the release profiles of solid lipid extrudates of different dimensions.
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2021, International Journal of PharmaceuticsCitation Excerpt :Several AI applications have been adopted in the study and property prediction for extrudates. Güres et al. developed a mathematical model to predict the diprophylline release profiles of a solid lipid extrudate based on ANNs and genetic programming, and successfully used it in release profile calculations (Güres et al., 2012). Recently, Manda et al. investigated the influence of formulation and process variables on the drug release behavior from a multiple-unit pellet system (produced by the extrusion-spheronization method) using ANNs, and revealed that the in vitro release profile was significantly impacted by two of the investigated factors, microcrystalline cellulose concentration and sodium starch glycolate concentration, and negligibly influenced by the other two parameters, spheronization time and extrusion speed (Manda et al., 2019).
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2020, Release and Bioavailability of Nanoencapsulated Food IngredientsComparison of multi-linear regression, particle swarm optimization artificial neural networks and genetic programming in the development of mini-tablets
2018, International Journal of PharmaceuticsCitation Excerpt :Additionally, another AI methodology used successfully in multivariate regression problems is the implementation of an evolutionary algorithm known as GP. In GP, computer programs (or mathematical operations) are encoded as a set of genes that are then modified (evolved) based on Darwin’s Natural Selection theory (Güres et al., 2012; Koza, 1994). The use of such AI regression approaches in combination with DoE may show enhanced fitting performance (i.e. superiority) compared to MLR or logistic regression techniques (and hence, a more precise/safe design space or control strategy can be constructed during QbD-based product development) due to the fact that they: 1) require less formal statistical training to develop, 2) can implicitly model more complex nonlinear relationships between independent and dependent variables, 3) can detect “hidden” (to MLR) interactions between input and output variables (i.e. factors and responses), and 4) can show increased adaptability by embedding multiple training approaches (such as swarm-based intelligence with ANNs) (Tu, 1996).
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2019, Advanced Drug Delivery ReviewsCitation Excerpt :System inputs included the diameter and length of the extrudates and dissolution times and the amount of released diprophylline was considered as output. Using the modified Weibull equation, it has been revealed that enhancement of the extrudate diameter results in reduced release rate [213]. ANNs have also been used to design the optimal formulations of sustained release dosage forms and predict their dissolution profiles [214].
Application of Artificial Neural Networks for modeling drug release from a bicomponent hydrogel system
2016, 2016 20th International Conference on System Theory, Control and Computing, ICSTCC 2016 - Joint Conference of SINTES 20, SACCS 16, SIMSIS 20 - Proceedings