SeDeM Diagram: an expert system for preformation, characterization and optimization of tablets obtained by direct compression

Negre, Josep M. Suñé i; Carreras, Manuel Roiģ; García, Roser Fuster; Montoya, Encarna García; Lozano, Pilar Pérez; Aguilar, Johnny Edward; Carmona, Montserrat Miñarro; Grau, Josep R. Ticó

doi:10.1533/9781908818508.109

Cited by 16 publications

(10 citation statements)

References 6 publications

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“…SeDeM Method allows a faster design of formulations, avoiding unnecessary studies and reducing the time of development, providing a guide for taking the appropriate ingredients to design a formulation [10], facilitating the robust design of the formulation from a science based perspective, in agreement with the principles of Quality by Design (QbD), described in the guidelines of the International Conference on Harmonisation (ICH Q8) [11].…”

Section: Introductionmentioning

confidence: 99%

Characterisation of the Ability of Carbamazepine for Processing It through Direct Compression Applying the New Expert System SeDeM

Campiñez¹,

Casas²,

Caraballo³

2016

Int J Clin Pharmacol Pharmacother

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Background: SeDeM Expert System is an innovative tool which allows preformulation characterization of a powdered substance. This information is used to identify strengths and weaknesses for development of tablets by direct compression. Conclusion:Carbamazepine has an appropriate ability to be processed through direct compression. Its profile can be improved adding a small amount of suitable excipients, as Flow Aid materials and Direct Compression fillers. These results provide information to build a database of pharmaceutical ingredients which allows checking the powder properties and enables the design of new formulations or improvement of the already existing in the market.

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Section: Introductionmentioning

confidence: 99%

Characterisation of the Ability of Carbamazepine for Processing It through Direct Compression Applying the New Expert System SeDeM

Campiñez¹,

Casas²,

Caraballo³

2016

Int J Clin Pharmacol Pharmacother

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“…As is well described in the literature, SeDeM diagram is a pharmaceutical method useful in a pharmaceutical development [ 1 , 10 , 11 ]. In this point, the impact of the improved Icd characterization in a formula development is shown.…”

Section: Resultsmentioning

confidence: 99%

“…Otherwise, it can yield to a segregation during the compression. Therefore, it is great of interest in a pharmaceutical development to be able to identify and select which formulas gather these requirements, and the rejection of those that need previous processes, such as granulation [ 1 – 3 ]. The SeDeM methodology was designed aiming this goal.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Cohesion Index in the SeDeM Diagram Expert System and application of SeDeM Diagram: An improved methodology to determine the Cohesion Index

et al. 2018

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In this study, we suggest optimizing the methodology to determine the Cohesion Index (Icd) in order to avoid mistaken characterizations due to powder bulk density. For this purpose, five different excipients, with different bulk densities and of different chemical nature, were compressed at different heights. Their compression and their tablet characterization enable establishing a powder weight for compression in accordance with its bulk density. Therefore, the resulting tablet will have a height within a defined range of heights where it has no critical effects on its hardness. Then, the impact of this optimization is shown in a formula development, one of the main SeDeM’s applications. A mathematical equation was used to calculate the theoretical amount of excipient to formulate the API according to both methodologies. The compression results demonstrate that the characterization with the NM-Icd is more accurate than the previous one while preserving its simplicity.

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“…4-6, 8, 9 An ES is an intelligent program with the ability to accumulate and preserve the knowledge and experiences of the experts in a specific area (e.g., pharmaceutical formulations). 10 However, it is difficult to extract the vague experiences of pharmaceutical experts into the rules of ESs, and then to accurately predict the performance of a formulation. ANN was the most popular machine learning tool in pharmaceutical formulation prediction.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for in vitro prediction of pharmaceutical formulations

Yang

Ye²,

Su³

et al. 2019

Acta Pharmaceutica Sinica B

116

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Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. Results showed that the accuracies of both two deep neural networks were above 80% and higher than other machine learning models; the latter showed good prediction of pharmaceutical formulations. In summary, deep learning employing an automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was developed for the prediction of pharmaceutical formulations for the first time. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical research from experience-dependent studies to data-driven methodologies.

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SeDeM Diagram: an expert system for preformation, characterization and optimization of tablets obtained by direct compression

Cited by 16 publications

References 6 publications

Characterisation of the Ability of Carbamazepine for Processing It through Direct Compression Applying the New Expert System SeDeM

Characterisation of the Ability of Carbamazepine for Processing It through Direct Compression Applying the New Expert System SeDeM

Optimization of the Cohesion Index in the SeDeM Diagram Expert System and application of SeDeM Diagram: An improved methodology to determine the Cohesion Index

Deep learning for in vitro prediction of pharmaceutical formulations

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