Chiarentin Lucas, Moura Vera, Pais Alberto A C C, Vitorino Carla
Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal.
Laboratórios Basi Indústria Farmacêutica S.A., Parque Industrial Manuel Lourenço Ferreira, Lote 15, 3450-232 Mortágua, Portugal.
Pharmaceutics. 2025 Jun 26;17(7):835. doi: 10.3390/pharmaceutics17070835.
A key challenge in modern pharmaceutical research is developing predictive models for drug formulation behavior. Since permeability is closely linked to molecular properties, considering a broad of characteristics is essential for building reliable predictive tools. Near-infrared spectroscopy (NIR), a non-destructive, non-invasive, and chemically specific method, offers a powerful alternative to current gold-standard methods approved by regulatory agencies. This study aims to apply a partial analytical quality by design (AQbD) approach to enhance the understanding and development of NIR and RP-HPLC methodologies. The employment of NIR with multivariate data analysis enabled the establishment of chemometric models for the classification and quantification of bifonazole (BFZ) in cream formulations. An analytical target profile (ATP) was defined to guide the selection of critical method variables and support method design and development activities. Risk assessment was carried out using an Ishikawa diagram. For the RP-HPLC method, key performance parameters such as peak area, theoretical plates, tailing factor, and assay were evaluated, while NIR spectra and BFZ concentration were considered for method performance. The quantification models enabled the accurate determination of BFZ content, yielding results of 8.48 mg via NIR and 8.34 mg via RP-HPLC, with an RSD of 1.25%. : These findings demonstrate the robustness and reliability of the models, making them suitable for routine quality control of BFZ formulations. Future research should aim to explore its use for monitoring permeation dynamics in real time and integrating it into regulatory frameworks to standardize its application in pharmaceutical quality control and formulation development.
现代药物研究中的一个关键挑战是开发药物制剂行为的预测模型。由于渗透性与分子特性密切相关，考虑广泛的特征对于构建可靠的预测工具至关重要。近红外光谱（NIR）是一种无损、非侵入性且具有化学特异性的方法，为监管机构批准的当前金标准方法提供了有力的替代方案。本研究旨在应用部分设计质量分析（AQbD）方法，以增强对近红外光谱和反相高效液相色谱（RP-HPLC）方法的理解和开发。将近红外光谱与多变量数据分析相结合，能够建立化学计量学模型，用于乳膏制剂中联苯苄唑（BFZ）的分类和定量。定义了分析目标轮廓（ATP），以指导关键方法变量的选择，并支持方法设计和开发活动。使用石川图进行风险评估。对于RP-HPLC方法，评估了诸如峰面积、理论塔板数、拖尾因子和含量测定等关键性能参数，而对于方法性能则考虑了近红外光谱和BFZ浓度。定量模型能够准确测定BFZ含量，近红外光谱法测定结果为8.48 mg，RP-HPLC法测定结果为8.34 mg，相对标准偏差（RSD）为1.25%。这些发现证明了模型的稳健性和可靠性，使其适用于BFZ制剂的常规质量控制。未来的研究应旨在探索其用于实时监测渗透动力学，并将其纳入监管框架，以规范其在药物质量控制和制剂开发中的应用。
