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制剂变量对通过流化热熔制粒法制备氯吡格雷片可制造性的影响——从实验室规模到中试规模

The Effect of Formulation Variables on the Manufacturability of Clopidogrel Tablets via Fluidized Hot-Melt Granulation-From the Lab Scale to the Pilot Scale.

作者信息

Kovács Béla, Tőkés Erzsébet-Orsolya, Kelemen Éva Katalin, Zöldi Katalin, Boda Francisc, Suba Edit, Kovács-Deák Boglárka, Casian Tibor

机构信息

Department F1, Biochemistry and Environmental Chemistry, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania.

Gedeon Richter Romania, 540306 Târgu Mureș, Romania.

出版信息

Pharmaceutics. 2024 Mar 13;16(3):391. doi: 10.3390/pharmaceutics16030391.

Abstract

Solid pharmaceutical formulations with class II active pharmaceutical ingredients (APIs) face dissolution challenges due to limited solubility, affecting in vivo behavior. Robust computational tools, via data mining, offer valuable insights into product performance, complementing traditional methods and aiding in scale-up decisions. This study utilizes the design of experiments (DoE) to understand fluidized hot-melt granulation manufacturing technology. Exploratory data analysis (MVDA) highlights similarities and differences in tablet manufacturability and dissolution profiles at both the lab and pilot scales. The study sought to gain insights into the application of multivariate data analysis by identifying variations among batches produced at different manufacturing scales for this technology. DoE and MVDA findings show that the granulation temperature, time, and Macrogol type significantly impact product performance. These factors, by influencing particle size distribution, become key predictors of product quality attributes such as resistance to crushing, disintegration time, and early-stage API dissolution in the profile. Software-aided data mining, with its multivariate and versatile nature, complements the empirical approach, which is reliant on trial and error during product scale-up.

摘要

含有II类活性药物成分(API)的固体药物制剂由于溶解度有限而面临溶出挑战,这会影响其体内行为。强大的计算工具通过数据挖掘,能为产品性能提供有价值的见解,补充传统方法并有助于扩大生产规模的决策。本研究利用实验设计(DoE)来了解流化热熔制粒制造技术。探索性数据分析(MVDA)突出了实验室和中试规模下片剂可制造性和溶出曲线的异同。该研究旨在通过识别该技术在不同生产规模下生产的批次之间的差异,深入了解多元数据分析的应用。DoE和MVDA的结果表明,制粒温度、时间和聚乙二醇类型对产品性能有显著影响。这些因素通过影响粒度分布,成为产品质量属性的关键预测指标,如抗碎性、崩解时间和溶出曲线中API的早期溶出。软件辅助的数据挖掘具有多元性和通用性,补充了在产品放大过程中依赖反复试验的经验方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b73e/10975944/cc2c85d97bc4/pharmaceutics-16-00391-g001.jpg

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