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首页> 外文期刊>Journal of Pharmaceutical and Biomedical Analysis: An International Journal on All Drug-Related Topics in Pharmaceutical, Biomedical and Clinical Analysis >Development of near infrared spectroscopic calibration models for in-line determination of low drug concentration, bulk density, and relative specific void volume within a feed frame
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Development of near infrared spectroscopic calibration models for in-line determination of low drug concentration, bulk density, and relative specific void volume within a feed frame

机译:开发近红外光谱校准模型,用于在线测定进给框架内的低药物浓度,堆积密度和相对特定的空隙体积

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摘要

This study describes the development of a near infrared (NIR) calibration model for real time determination of drug concentration, powder density, and porosity or relative specific void volume (RSVV) of 3.00%w/w acetaminophen blends within a feed frame. The NIR calibration model was developed from 1.50 to 4.50%w/w of acetaminophen, using a high variability of major excipients (from 12.92 to 81.95%w/w) which facilitates the prediction of powder density and RSVV based on near infrared calibration spectra. The model using second derivative as spectral preprocessing explained the changes related to acetaminophen concentration in the first latent variable. The second latent variable was related to changes in concentration of microcrystalline cellulose and lactose in the powder blends. NIR calibrations were also developed based on the bulk density and RSVV of the powder blends using the same design as the API model, due to the physical properties of the particles and their effects on the NIR spectra. The RSVV was predicted for the independent set blends with an RSEP(%) below 4% with a significantly low bias (0.04 cm(3)/g) from reference values of 1.33 to 1.58 cm(3)/g. The bulk density model also exhibited excellent predictions with RSEP(%) below 2.6% and significantly low bias (0.01 g/cm(3)) from reference values of 0.45 to 0.51 g/cm(3). The excellent results obtained show the potential of near infrared spectroscopic measurements within the feed frame for a Process Analytical Technology method to control the critical properties such as tablet mass, hardness and dissolution in batch and continuous manufacturing processes. (C) 2018 Elsevier B.V. All rights reserved.
机译:本研究描述了用于实时测定药物浓度,粉末密度和孔隙率或相对特异性空隙体积(RSVV)的近红外(NIR)校准模型的开发,其在进料框架内的3.00%w / w的乙酰氨基酚混合物。利用主要赋形剂的高可变性(从12.92至81.95%w / w),NIR校准模型从1.50〜4.50%的乙酰氨基酚开发,这有助于将粉末密度和RSVV的预测基于近红外校准光谱预测。使用第二衍生物作为光谱预处理的模型解释了与第一潜在变量中的乙酰氨基酚浓度相关的变化。第二潜变量与粉末共混物中微晶纤维素和乳糖的浓度的变化有关。由于颗粒的物理性质及其对NIR光谱的影响,还基于使用与API模型相同的设计的粉末混合物的堆积密度和RSVV而开发的NIR校准。 RSVV预测与低于4%的rESEP(%)的独立集合,其具有明显低的偏差(0.04cm(3)/ g),从参考值为1.33-1.58cm(3)/ g。堆积密度模型还具有低于2.6%的RSEP(%)的优异预测,从参考值0.45至0.51g / cm(3)的参考值,偏差明显低偏差(0.01g / cm(3))。获得的优异结果显示了用于加入框架内的近红外光谱测量的电位,用于控制分批和批次的平板药物,硬度和溶解等关键性质和连续的制造方法。 (c)2018年elestvier b.v.保留所有权利。

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