Tailoring Atomoxetine Release Rate from DLP 3D-Printed Tablets Using Artificial Neural Networks: Influence of Tablet Thickness and Drug Loading

Stanojević, Gordana; Medarević, Djordje; Adamov, Ivana; Pešić, Nikola; Kovačević, Jovana; Ibrić, Svetlana

doi:10.3390/molecules26010111

Cited by 41 publications

(15 citation statements)

References 39 publications

(25 reference statements)

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“…Additionally, atomoxetine-containing tablets using DLP were developed in two different research articles. One utilised the tablets to develop artificial neural networks (ANN) predictive models where the release rates were studied for tablets with different thicknesses and drug loading [ 90 ], whereas the other study evaluated how the formulation composition affected atomoxetine release and kinetics, as well as the mechanical properties of the tablets [ 91 ]. Likewise, paracetamol was incorporated in tablets, and its release, tensile strength of tablets, dissolution rate, and internal structure were assessed upon varying tablet ingredients [ 92 ].…”

Section: Resultsmentioning

confidence: 99%

3D Printing of Thermo-Sensitive Drugs

et al. 2021

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Three-dimensional (3D) printing is among the rapidly evolving technologies with applications in many sectors. The pharmaceutical industry is no exception, and the approval of the first 3D-printed tablet (Spiratam®) marked a revolution in the field. Several studies reported the fabrication of different dosage forms using a range of 3D printing techniques. Thermosensitive drugs compose a considerable segment of available medications in the market requiring strict temperature control during processing to ensure their efficacy and safety. Heating involved in some of the 3D printing technologies raises concerns regarding the feasibility of the techniques for printing thermolabile drugs. Studies reported that semi-solid extrusion (SSE) is the commonly used printing technique to fabricate thermosensitive drugs. Digital light processing (DLP), binder jetting (BJ), and stereolithography (SLA) can also be used for the fabrication of thermosensitive drugs as they do not involve heating elements. Nonetheless, degradation of some drugs by light source used in the techniques was reported. Interestingly, fused deposition modelling (FDM) coupled with filling techniques offered protection against thermal degradation. Concepts such as selection of low melting point polymers, adjustment of printing parameters, and coupling of more than one printing technique were exploited in printing thermosensitive drugs. This systematic review presents challenges, 3DP procedures, and future directions of 3D printing of thermo-sensitive formulations.

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Section: Resultsmentioning

confidence: 99%

3D Printing of Thermo-Sensitive Drugs

et al. 2021

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“…It is possible to predict drug release based on GRNNs. In a study by Stanojević et al [38], they predicted the dissolution curve of 3D printed atomoxetine tablets using two types of ANN. A self-organizing map was used to visualize the effect of the inputs on atomoxetine release and a GRNN was then used to predict the atomoxetine release.…”

Section: Generalized Regression Neural Networkmentioning

confidence: 99%

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

Wang¹,

Di²,

Wang

et al. 2022

Pharmaceutics

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During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.

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“…1) General regression neural network 44,45 (GRNN) -It is used for estimation of drug behavior in vivo and compensation of dissimilarities in the drug release kinetics under various conditions. Stanojevi'c et al 46 used two types of ANN to predict the dissolution curve of 3D printed atomoxetine tablets in their study. The effect of the inputs on atomoxetine release was visualized using a self-organizing map, and the atomoxetine release was predicted using a GRNN.…”

Section: Ai Algorithms Employed In Product Development and Their Appl...mentioning

confidence: 99%

Impact of Artificial Intelligence (AI) on Drug Discovery and Product Development

Narayanan¹,

Durga²,

Nagalakshmi³

2022

Ind. J. Pharm. Edu. Res

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Artificial Intelligence (AI) had transfigured different sectors in society, where the pharmaceutical sector is not an exceptional case. Pharmaceutical sectors have reached new heights with the emergence of these sophisticated technologies. The evolution of artificial intelligence in the pharmaceutical industry is in a growth phase opening the possibilities of discovering many new drugs. The diseases affecting humans are increasing tremendously whereas the drugs which are available to treat or cure are very much minimal. But this kind of scenario will not be present in the future because of the combination of artificial intelligence and the pharmaceutical industry which results in faster discovery of drugs with increased clinical outcomes. There was a shift in the paradigm of various stages in drug discovery because of the utilization of artificial intelligence. Each stage of drug discovery involves a certain timeline that can be cut down with the help of artificial intelligence. Many pharma companies are engaged with AI-based drug discovery approaches for treating various diseases like Parkinson's disease diabetes, Alzheimer's, Obsessive Compulsive Disorder, etc., AI is also being employed in product development for the fabrication of nanomedicines and nanorobots. Few AI-based drugs are already in the phase of clinical trials which indicates the growth of AI-driven drug discovery. In this review, we have highlighted the application of AI in drug discovery and product development of pharmaceuticals.

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Tailoring Atomoxetine Release Rate from DLP 3D-Printed Tablets Using Artificial Neural Networks: Influence of Tablet Thickness and Drug Loading

Cited by 41 publications

References 39 publications

3D Printing of Thermo-Sensitive Drugs

3D Printing of Thermo-Sensitive Drugs

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

Impact of Artificial Intelligence (AI) on Drug Discovery and Product Development

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