Muzi Lucas P, Antonio Marina, Maggio Rubén M
Área de Análisis de Medicamentos, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario and Instituto de Química Rosario (IQUIR, CONICET-UNR), Suipacha 531, Rosario S2002LRK, Argentina.
Pharmaceutics. 2024 Dec 13;16(12):1594. doi: 10.3390/pharmaceutics16121594.
Triclabendazole (TCB) is a well-established anthelmintic effective in treating fascioliasis, a neglected tropical disease. This study employs quality by design (QbD) to investigate the impact of TCB polymorphism and pharmacotechnical variables, from the development of immediate-release tablets to process optimization and green analysis. Critical process parameters (CPPs) and critical material attributes (CMAs), characterized by type of polymorph, composition of excipients (talc, lactose, cornstarch, and magnesium stearate), and compression force, were screened using a Plackett-Burman design (n = 24), identifying polymorphic purity and cornstarch as a CPP. To establish a mathematical model linking CPP to dissolution behaviour, a multiple linear regression (MLR) was applied to the training design (central composite design, n = 18). Simultaneously, a near-infrared spectroscopy coupled to partial least squares (NIR-PLSs) method was developed to analyze CPPs. An independent set of samples was prepared and analyzed using the NIR-PLSs model, and their dissolution profiles were also obtained. The PLSs model successfully predicted the CPPs in the new samples, yielding almost quantitative results (100 ± 3%), and MLR dissolution predictions mirrored the actual dissolution profiles (f2 = 85). In conclusion, the developed model could serve as a comprehensive tool for the development and control of pharmaceutical formulations, starting from the polymorphic composition and extending to achieve targeted dissolution outcomes.
三氯苯达唑（TCB）是一种成熟的驱虫药，对治疗被忽视的热带病肝片吸虫病有效。本研究采用质量源于设计（QbD）方法，从速释片的研发到工艺优化及绿色分析，考察TCB多晶型及制药工艺变量的影响。采用Plackett-Burman设计（n = 24）筛选关键工艺参数（CPPs）和关键物料属性（CMAs），其特征包括多晶型类型、辅料（滑石粉、乳糖、玉米淀粉和硬脂酸镁）组成及压片力，确定多晶型纯度和玉米淀粉为CPP。为建立CPP与溶出行为的数学模型，对训练设计（中心复合设计，n = 18）应用多元线性回归（MLR）。同时，开发了一种近红外光谱结合偏最小二乘法（NIR-PLSs）的方法来分析CPPs。制备了一组独立的样品，使用NIR-PLSs模型进行分析，并获得其溶出曲线。PLSs模型成功预测了新样品中的CPPs，得到了几乎定量的结果（100±3%），MLR溶出预测反映了实际溶出曲线（f2 = 85）。总之，所开发的模型可作为药物制剂研发和控制的综合工具，从多晶型组成开始，延伸至实现靶向溶出结果。
