Application of Dominance-Based Rough Set Approach for Optimization of Pellets Tableting Process
Autor: | Łukasz Pałkowski, Rafał Łunio, Jerzy Błaszczyński, Jerzy Krysiński, Maciej Karolak, Roman Słowiński, Bartłomiej Kubiak, Wiesław Sawicki |
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Rok vydání: | 2020 |
Předmět: |
knowledge discovery
Pellets lcsh:RS1-441 Pharmaceutical Science Excipient Article lcsh:Pharmacy and materia medica chemistry.chemical_compound Tableting Ethyl cellulose DRSA medicine Process engineering Mathematics Active ingredient business.industry tablets Dominance-based rough set approach multiple-unit pellet system data mining Controlled release Microcrystalline cellulose machine learning chemistry pharmaceutical technology business medicine.drug |
Zdroj: | Pharmaceutics, Vol 12, Iss 1024, p 1024 (2020) Pharmaceutics Volume 12 Issue 11 |
ISSN: | 1999-4923 |
DOI: | 10.3390/pharmaceutics12111024 |
Popis: | 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&ndash 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 &ge 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. |
Databáze: | OpenAIRE |
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