M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines

Elbadawi, Moe; Castro, Brais Muñiz; Gavins, Francesca K.H.; Ong, Jun Jie; Gaisford, Simon; Pérez, Gilberto; Basit, Abdul W.; Cabalar, Pedro; Goyanes, Álvaro

doi:10.1016/j.ijpharm.2020.119837

Cited by 153 publications

(99 citation statements)

References 64 publications

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“…129,130 For example, ML has been used to accurately predict the printability of medicines; this could be well applied to printing of microbiome-targeted therapeutics to optimize formulation performance. 131 Toxicity of formulations can also be predicted by ML. For example, the skin and genital microbiomes are sensitive to the use of certain excipients.…”

Section: Entities Altering the Microbiome Microenvironmentmentioning

confidence: 99%

Harnessing machine learning for development of microbiome therapeutics

et al. 2021

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The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and predictionmaking, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field.

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Section: Entities Altering the Microbiome Microenvironmentmentioning

confidence: 99%

Harnessing machine learning for development of microbiome therapeutics

et al. 2021

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“…The manufacturing opportunities that 3DP could offer in preclinical research have not yet been fully exploited [46][47][48]. This highly flexible technology allows the on-demand manufacturing of devices containing the exact dosage of the drug and with sizes and geometries adapted to the animal model.…”

Section: Methylprednisolonementioning

confidence: 99%

3D Printed Tacrolimus Rectal Formulations Ameliorate Colitis in an Experimental Animal Model of Inflammatory Bowel Disease

et al. 2020

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The aim of this study was to fabricate novel self-supporting tacrolimus suppositories using semisolid extrusion 3-dimensional printing (3DP) and to investigate their efficacy in an experimental model of inflammatory bowel disease. Blends of Gelucire 44/14 and coconut oil were employed as lipid excipients to obtain suppository formulations with self-emulsifying properties, which were then tested in a TNBS (2,4,6-trinitrobenzenesulfonic acid) induced rat colitis model. Disease activity was monitored using PET/CT medical imaging; maximum standardized uptake values (SUVmax), a measure of tissue radiotracer accumulation rate, together with body weight changes and histological assessments, were used as inflammatory indices to monitor treatment efficacy. Following tacrolimus treatment, a significant reduction in SUVmax was observed on days 7 and 10 in the rat colon sections compared to non-treated animals. Histological analysis using Nancy index confirmed disease remission. Moreover, statistical analysis showed a positive correlation (R2 = 71.48%) between SUVmax values and weight changes over time. Overall, this study demonstrates the effectiveness of 3D printed tacrolimus suppositories to ameliorate colitis and highlights the utility of non-invasive PET/CT imaging to evaluate new therapies in the preclinical area.

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“…The correlation coefficient is an index for measuring the linearity of target and output values. The root mean square error calculates the deviation error of the output values compared to the target values [28]. These factors are considered the frequently used evaluation metrics in different modeling tasks [29].…”

Section: Input Layermentioning

confidence: 99%

Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Filament Fabrication Fused Deposition Modelling and Machine Learning

Moradi¹,

Meiabadi²,

Moghadam³

et al. 2020

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Polylactic Polylactic acid (PLA) is one of the high applicable material which is used in the 3D printers due to some significant features like its deformation property and affordable costacid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the quality of Fused Filament Fabrication (FFF)fused deposition modeling (FDM)-printed objects in PLA. The purpose of this investigation is to boost toughness and to reduce the production cost of the FDMFFF-printed tensile test samples with the desired part thickness. Due to prevent from many numerous and idle printing samples the response Surface Method (RSM) is used.To attain the research purpose number of experiments are designed and analyzed by the Response Surface Method (RSM). The statistical analysis is performed to deal with this concern considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The tensile test specimens are printed based on the designed experiments, and the tensile strength tests are conducted by SANTAM 150 universal testing machine based on ASTM D638. The pattern for filling is designed based on honeycomb which is applied to produce lightweight and high-strength specimens. The area under Force- Extension curve up to fracture is acquired as the toughness of the printed specimens. This study also developed a modeling process using artificial neural network (ANN) and artificial neural network- genetic algorithm (ANN-GA) techniques to develop an accurate estimation for toughness, part thickness, and production cost dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could could successfully improveenhances the accuracy of modeling about 7.5, 11.5 and 4.5 % for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other side, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects. The research is accomplished under the constraints of PLA compatibility with existing Fused Filament Fabrication fused deposition modeling installation, in the absence of the functional assistant of the machine in the absence of the functional assistant of the machine. Although the mechanical properties and dimensional accuracy of PLA have already been studied, there is little literature on the toughness of the printed PLA with honeycomb internal fill pattern.

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M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines

Cited by 153 publications

References 64 publications

Harnessing machine learning for development of microbiome therapeutics

Harnessing machine learning for development of microbiome therapeutics

3D Printed Tacrolimus Rectal Formulations Ameliorate Colitis in an Experimental Animal Model of Inflammatory Bowel Disease

Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Filament Fabrication Fused Deposition Modelling and Machine Learning

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