Artificial neural networks technology to model, understand, and optimize drug formulations

Landín, Mariana; Rowe, R.C.

doi:10.1533/9781908818508.7

Cited by 32 publications

(30 citation statements)

References 59 publications

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“…Additionally, their rheological properties were determined. Results obtained from confocal imaging and rheological measurements were used to create a predictive model based on neurofuzzy logic technology [39]. The generated model was used to identify an exemplary 3D printability window based on applied constraints, and 3D printed porous constructs were fabricated by an extrusion-based 3D printing procedure.…”

Section: Preparation Of Hydrogels and Experimental Designmentioning

confidence: 99%

“…It allows the definition of the design space with a relative small amount of data and the generation of complex non-linear models of easy and quick numerical solutions. Additionally, fuzzy logic technology enables generation of linguistic rules in order to explain the dependency of the outputs from the input parameters [39]. In this study, concentrations of PEG/methacrylated pHPMAlac copolymer and methacrylated hyaluronic acid were used as input parameters, and phase separation extent and rheological properties were used as outputs.…”

Section: Introductionmentioning

confidence: 99%

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Two-component thermosensitive hydrogels: Phase separation affecting rheological behavior

Abbadessa

Landín

Blenke

et al. 2017

European Polymer Journal

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Please cite this article as: Abbadessa, A., Landín, M., Oude Blenke, E., Hennink, W.E., Vermonden, T., Twocomponent thermosensitive hydrogels: Phase separation affecting rheological behavior, European Polymer Journal (2017), doi: http://dx. AbstractExtracellular matrices are mainly composed of a mixture of different biopolymers and therefore the use of two or more building blocks for the development of tissue-mimicking hydrogels is nowadays an attractive strategy in tissue-engineering. Multi-component hydrogel systems may undergo phase separation, which in turn can lead to new, unexpected material properties. The aim of this study was to understand the role of phase separation on the mechanical properties and 3D printability of hydrogels composed of triblock copolymers of polyethylene glycol (PEG) and methacrylated poly(N-(2-hydroxypropyl) methacrylamide-mono/dilactate) (pHPMAlac) blended with methacrylated hyaluronic acid (HAMA). To this end, hydrogels composed of different concentrations of PEG/pHPMAlac and HAMA, were analyzed for phase behavior and rheologicalproperties. Subsequently, phase separation and rheological behavior as function of the two polymer concentrations were mathematically processed to generate a predictive model. Results showed that PEG/pHPMAlac/HAMA hydrogels were characterized by hydrophilic, HAMA-richer internal domains dispersed in a more hydrophobic continuous phase, composed of PEG/pHPMAlac, and that the volume fraction of the dispersed phase increased by increasing HAMA concentration.Storage modulus, yield stress and viscosity increased with increasing HAMA concentration for low/medium HAMA contents (• 0.75% w/w), while a further increase of HAMA resulted in a 0.75% w/w), while a further increase of HAMA resulted in a decrease of the mentioned properties. On the other hand, by increasing the concentration of PEG/pHPMAlac these rheological properties were enhanced. The generated models showed a good fitting with experimental data, and were used to identify an exemplary 3D printability window for PEG/pHPMAlac/HAMA hydrogels, which was verified by rheological characterization and preparation of 3D printed scaffolds. In conclusion, a clear relationship between phase separation and rheological behavior in these two-component hydrogels can be described by complex functions of the two polymer concentrations. The predictive model generated in this study can be used as a valid tool for the identification of hydrogel compositions with desired, selected characteristics.3 Keywords: Thermosensitive polymers, hyaluronic acid, rheological properties, confocal laser scanning microscopy, neurofuzzy logic, 3D bioprinting. Highlights-Polymer ratios determine extent of phase separation in PEG/pHPMAlac/HAMA hydrogels.-Phase separation leads to hydrophilic domains dispersed in a more hydrophobic phase.-Phase separation affects rheological properties in a controllable fashion.-Mathematical models enabled a priori identification of hydrogels with desired properties.-Hydrogel 3D printing was achieved with a f...

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Section: Preparation Of Hydrogels and Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-component thermosensitive hydrogels: Phase separation affecting rheological behavior

Abbadessa

Landín

Blenke

et al. 2017

European Polymer Journal

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“…To find out the multifactorial dependency between formulation, process and quality attributes, the use of multivariate approaches, such as design of experiment (DoE), sensitivity analysis, response surface method and multivariate data analysis, is unavoidable. To find out these complex dependencies, the US Food and Drug Administration (FDA) introduced the quality by design (QbD) approach 4,5. Pharmaceutical QbD is a systematic approach toward formulation development that starts with predefined objectives and emphasizes product and process understanding 6.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have been performed by using traditional DoE technique to investigate how the roll compaction settings influence the properties of granules. Most of them used the DoE technique which involved response surface methodology (RSM) combined with multiple linear regression (MLR) 5. However, the behavior of most processes in the pharmaceutical industry is complex and nonlinear, which makes it difficult to model these systems precisely by using linear regression 8.…”

Section: Introductionmentioning

confidence: 99%

Effect of roll compaction on granule size distribution of microcrystalline cellulose–mannitol mixtures: computational intelligence modeling and parametric analysis

Kazemi¹,

Khalid²,

Gago³

et al. 2017

DDDT

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Dry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD) and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD. The understanding of how the roll compactor process parameters and material properties interact with each other will allow accurate control of the process, leading to the implementation of quality by design practices. Computational intelligence (CI) methods have a great potential for being used within the scope of quality by design approach. The main objective of this study was to show how the computational intelligence techniques can be useful to predict the GSD by using different process conditions of roll compaction and material properties. Different techniques such as multiple linear regression, artificial neural networks, random forest, Cubist and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized models for the prediction of GSD based on roll compaction process setting and material properties. The normalized root-mean-squared error and the coefficient of determination (R2) were used for model assessment. The best fit was obtained by Cubist model (normalized root-mean-squared error =3.22%, R2=0.95). Based on the results, it was confirmed that the material properties (true density) followed by compaction force have the most significant effect on GSD.

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“…11 Among the available artificial intelligence tools, artificial neural networks have been widely used for modeling several pharmaceutical process. 12,13 Despite its unquestionable utility to facilitate understanding of the processes and predict results without any mechanistic assumption, artificial neural networks have the disadvantage of generating blackbox models. Gene expression programing (GEP) introduced by Ferreira 14,15 has been proposed as a technology to overcome this limitation and to solve problems within the pharmaceutical field, as it is able to provide high predictive experimental equations relating the variables, and hence to generate transparent models.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Tools for Scaling Up of High Shear Wet Granulation Process

Landín

2017

Journal of Pharmaceutical Sciences

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The results presented in this article demonstrate the potential of artificial intelligence tools for predicting the endpoint of the granulation process in high-speed mixer granulators of different scales from 25L to 600L. The combination of neurofuzzy logic and gene expression programing technologies allowed the modeling of the impeller power as a function of operation conditions and wet granule properties, establishing the critical variables that affect the response and obtaining a unique experimental polynomial equation (transparent model) of high predictability (R > 86.78%) for all size equipment. Gene expression programing allowed the modeling of the granulation process for granulators of similar and dissimilar geometries and can be improved by implementing additional characteristics of the process, as composition variables or operation parameters (e.g., batch size, chopper speed). The principles and the methodology proposed here can be applied to understand and control manufacturing process, using any other granulation equipment, including continuous granulation processes.

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Artificial neural networks technology to model, understand, and optimize drug formulations

Cited by 32 publications

References 59 publications

Two-component thermosensitive hydrogels: Phase separation affecting rheological behavior

Two-component thermosensitive hydrogels: Phase separation affecting rheological behavior

Effect of roll compaction on granule size distribution of microcrystalline cellulose–mannitol mixtures: computational intelligence modeling and parametric analysis

Artificial Intelligence Tools for Scaling Up of High Shear Wet Granulation Process

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Artificial neural networks technology to model, understand, and optimize drug formulations

Cited by 32 publications

References 59 publications

Two-component thermosensitive hydrogels: Phase separation affecting rheological behavior

Two-component thermosensitive hydrogels: Phase separation affecting rheological behavior

Effect of roll compaction on granule size distribution of microcrystalline cellulose&ndash;mannitol mixtures: computational intelligence modeling and parametric analysis

Artificial Intelligence Tools for Scaling Up of High Shear Wet Granulation Process

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Product

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Effect of roll compaction on granule size distribution of microcrystalline cellulose–mannitol mixtures: computational intelligence modeling and parametric analysis