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.

中文翻译：

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基于优势的粗糙集方法在药丸压片工艺优化中的应用

对于制造商和患者而言，多单位药丸系统（MUPS）与常规固体剂型相比具有许多优势。包衣的小丸可以在经典的压片过程中有效地压制成MUPS，并能够控制释放活性药物成分（API）。对于患者而言，MUPS是可分割的，且不影响药物释放，并且易于吞咽。但是，在压缩过程中维护API发布配置文件可能是一个挑战。这项工作的目的是探索和发现描述以下数据之间的关系：组成，特性，过程参数（条件属性）和质量（决策属性，用相似因子f _2表示）），通过应用基于优势的粗略雷射方法（DRSA）数学数据挖掘技术，将含有MUPS的小丸（盐酸维拉帕米作为原料药）。DRSA生成的决策规则表示条件属性和决策属性之间的因果关系。从之前和压片后粒料类似的API的释放曲线可以通过适当的聚合物涂层来确保（尤特奇^® NE，不存在的乙基纤维素），超过6千牛顿压缩力越高，微晶纤维素（Avicel ^® 102）作为赋形剂和片剂硬度≥42.4Ñ DRSA对于分析复杂的技术数据很有用。具有高确认价值的决策规则可以帮助技术人员优化配方开发。