A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending: Part II: Qualitative near-infrared models for prediction of blend homogeneity

El‐Hagrasy, Arwa S.; Delgado-Lopez, Miriam; Drennen, James K.

doi:10.1002/jps.20466

Cited by 90 publications

(36 citation statements)

References 18 publications

(22 reference statements)

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“…The powder discharge is also called "powder voiding," a term that is referred to in part I, and it is equivalent to emptying of a container and the ensuing discharge of the powder. It is shown that NIR may be used to obtain information on the flow properties of powders and increase process knowledge in addition to the already developed pharmaceutical applications that include blend uniformity analysis, drug concentration in tablets, and evaluation of moisture in drying granulations [23][24][25][26][27][28][29]. The NIR method complements previous efforts based on powder avalanching and NIR spectroscopy [30][31][32].…”

Section: Introductionmentioning

confidence: 89%

Near-Infrared Spectroscopy for the In-Line Characterization of Powder Voiding Part II: Quantification of Enhanced Flow Properties of Surface Modified Active Pharmaceutical Ingredients

et al. 2010

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In this work, dry-particle coating was used to modify the surface properties of active pharmaceutical ingredients (APIs) having extremely poor flow properties. Near-infrared (NIR) spectroscopy was utilized as a novel approach to characterize the improved flow behavior of APIs and their blends. Acetaminophen and ibuprofen were coated with nano-sized silica at two different coating levels (0.5% and 1% w/w of the API) in dry-particle coating devices viz. magnetically assisted impaction coater (MAIC) and Hybridizer. Surface modified (dry coated) APIs were then blended with excipient (spray dried lactose monohydrate) in a V-blender. As a baseline comparison to dry coating, the silica addition was also accomplished by two commonly used industry methods, i.e., passing a portion of API with silica through a sieve (sieve blending method) or blending a portion of API powder with silica in a V-blender (preblending method). Flow results showed that dry particle coated acetaminophen as well as ibuprofen blends performed significantly better than uncoated API blends at higher API concentrations. In addition, examination of the flow intensity from NIR spectra (inverse signal to noise ratio of spectra) and its standard deviation revealed that dry particle coated blends showed better uniformity of flow as compared to the other methods. Angle of repose measurements corroborated these results, showing that the majority of the blends prepared from coated APIs stayed in either passable or fair category.

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Section: Introductionmentioning

confidence: 89%

Near-Infrared Spectroscopy for the In-Line Characterization of Powder Voiding Part II: Quantification of Enhanced Flow Properties of Surface Modified Active Pharmaceutical Ingredients

et al. 2010

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“…Generally, a minimum of 10 blend samples of 1-3 times the dosage unit weight must be taken at different locations according to the blender used, and the mean of the blend samples must be between 90.0 and 110.0% of the theoretical drug content with a RSD ≤ 5% to have an acceptable degree of homogeneity [108]. In the last decade, methods based on the implementation of process analytical technology that determine blend uniformity in process (i.e., without removal of samples from the mixer) have been developed and are primarily based on near infrared spectroscopy [109][110][111][112]. These eliminate many of the problems associated with blend sampling error and bias.…”

Section: Drug Distribution Uniformitymentioning

confidence: 99%

Lactose characteristics and the generation of the aerosol

Pilcer¹,

Wauthoz²,

Amighi³

2012

Advanced Drug Delivery Reviews

178

113

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“…At time t ¼ 1, the initial probability of the states is p ¼ {p i }, where: In summary, a HMM can be described by the following notation l¼ ðA;B;pÞ (10) The HMM model describes the both the static and dynamic features of the process in terms of probability.…”

Section: Hidden Markov Models and Decision Making A Brief Review Of Hmentioning

confidence: 99%

“…(10). First, assuming there are N p blending conditions (e.g., not blended, partially blended, completely blended, etc.)…”

Section: Trainingmentioning

confidence: 99%

On-Line Monitoring of Pharmaceutical Production Processes Using Hidden Markov Model

Zhang

Jiang

et al. 2009

Journal of Pharmaceutical Sciences

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A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending: Part II: Qualitative near-infrared models for prediction of blend homogeneity

Cited by 90 publications

References 18 publications

Near-Infrared Spectroscopy for the In-Line Characterization of Powder Voiding Part II: Quantification of Enhanced Flow Properties of Surface Modified Active Pharmaceutical Ingredients

Near-Infrared Spectroscopy for the In-Line Characterization of Powder Voiding Part II: Quantification of Enhanced Flow Properties of Surface Modified Active Pharmaceutical Ingredients

Lactose characteristics and the generation of the aerosol

On-Line Monitoring of Pharmaceutical Production Processes Using Hidden Markov Model

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