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Article

Application of Dominance-Based Rough Set Approach for Optimization of Pellets Tableting Process

1
Department of Pharmaceutical Technology, Collegium Medicum, Nicolaus Copernicus University, 85-089 Bydgoszcz, Poland
2
Adamed Pharma S.A., Pieńków, 05-152 Czosnów, Poland
3
Institute of Computing Science, Poznań University of Technology, 60-965 Poznań, Poland
4
Polpharma SA, 83-200 Starogard Gdański, Poland
5
Department of Physical Chemistry, Medical University of Gdańsk, 80-416 Gdańsk, Poland
6
Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Pharmaceutics 2020, 12(11), 1024; https://doi.org/10.3390/pharmaceutics12111024
Received: 25 September 2020 / Revised: 22 October 2020 / Accepted: 24 October 2020 / Published: 26 October 2020
Multiple-unit pellet systems (MUPS) offer many advantages over conventional solid dosage forms both for the manufacturers and patients. Coated pellets can be efficiently compressed into MUPS in classic tableting process and enable controlled release of active pharmaceutical ingredient (APIs). For patients MUPS are divisible without affecting drug release and convenient to swallow. However, maintaining API release profile during the compression process can be a challenge. The aim of this work was to explore and discover relationships between data describing: composition, properties, process parameters (condition attributes) and quality (decision attribute, expressed as similarity factor f2) of MUPS containing pellets with verapamil hydrochloride as API, by applying a dominance-based rough ret approach (DRSA) mathematical data mining technique. DRSA generated decision rules representing cause–effect relationships between condition attributes and decision attribute. Similar API release profiles from pellets before and after tableting can be ensured by proper polymer coating (Eudragit® NE, absence of ethyl cellulose), compression force higher than 6 kN, microcrystalline cellulose (Avicel® 102) as excipient and tablet hardness ≥42.4 N. DRSA can be useful for analysis of complex technological data. Decision rules with high values of confirmation measures can help technologist in optimal formulation development. View Full-Text
Keywords: tablets; multiple-unit pellet system; pharmaceutical technology; DRSA; data mining; knowledge discovery; machine learning tablets; multiple-unit pellet system; pharmaceutical technology; DRSA; data mining; knowledge discovery; machine learning
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MDPI and ACS Style

Karolak, M.; Pałkowski, Ł.; Kubiak, B.; Błaszczyński, J.; Łunio, R.; Sawicki, W.; Słowiński, R.; Krysiński, J. Application of Dominance-Based Rough Set Approach for Optimization of Pellets Tableting Process. Pharmaceutics 2020, 12, 1024. https://doi.org/10.3390/pharmaceutics12111024

AMA Style

Karolak M, Pałkowski Ł, Kubiak B, Błaszczyński J, Łunio R, Sawicki W, Słowiński R, Krysiński J. Application of Dominance-Based Rough Set Approach for Optimization of Pellets Tableting Process. Pharmaceutics. 2020; 12(11):1024. https://doi.org/10.3390/pharmaceutics12111024

Chicago/Turabian Style

Karolak, Maciej, Łukasz Pałkowski, Bartłomiej Kubiak, Jerzy Błaszczyński, Rafał Łunio, Wiesław Sawicki, Roman Słowiński, and Jerzy Krysiński. 2020. "Application of Dominance-Based Rough Set Approach for Optimization of Pellets Tableting Process" Pharmaceutics 12, no. 11: 1024. https://doi.org/10.3390/pharmaceutics12111024

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