Blending Validation and Content Uniformity of Low-Content, Noncohesive Powder Blends

Carstensen, J.T.; Dali, Mandar

doi:10.3109/03639049609041991

Cited by 18 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Dans l'analyse chimique des solutions, l'arsenal des validations vérifie la justesse et la précision de la mesure qui est le seul problème. Dans nombre d'analyses physiques d'un milieu complexe, tout compte, du choix de la mesure à la représentativité, mais : -les méthodes et les appareils dont on dispose sont souvent insuffisants [2][3][4][5]. Par exemple, les sondes biaisent, les préleveurs s'obstruent… -les recommandations officielles peuvent être ambiguës [2].…”

Section: Les Erreurs Systématiquesunclassified

See 1 more Smart Citation

Introduction au procédé d'échantillonnage

Deleuil¹

1999

Analusis

View full text Add to dashboard Cite

La victoire appartient à celui qui est le mieux informé. Les domaines militaire, bancaire ou médical en livrent chaque jour la preuve. La révolution informatique y puise sa justification.La chimie, en tant qu'activité de transformation de la matière, n'échappe pas à cette règle. Les informations dont elle a besoin sont de nature chimique, mais aussi biochimique, physicochimique et physique, que nous groupons ici sous l'appellation physique.Les informations chimiques sont qualitatives (identification des espèces présentes), ou quantitatives (dosage d'une concentration). Elles relèvent alors de l'échelle moléculaire (ou ionique), et utilisent assez librement des opérations intermédiaires, comme l'extraction, le broyage ou la dissolution.Les informations physiques portent sur les phases en pré-sence (polymorphisme), sur les interfaces (catalyse hétéro-gène, parois), et sur l'élaboration finale (transformation du produit chimique en produit d'usage) : poudre à laver, matière plastique, peinture, engrais, additif nutritionnel, médicament. Les informations relèvent alors de dimensions texturales, micro, méso ou macroscopiques, en milieux polyphasiques formulés, qui sont difficiles à échantillonner et à caractériser. De plus, l'adaptation à l'analyse est souvent délicate, une dissolution ou un broyage étant généralement prohibés.Tous les milieux sont concernés : les liquides homogènes, les liquides hétérogènes, les gaz, les solides divisés et les matériaux. Limiter la métrologie à l'analyse chimique de produits chimiques desservirait la notion de service rendu, et appauvrirait la compétitivité de cette industrie. Que les analyses soient chimiques ou physiques, en ligne ou au laboratoire, qu'elles visent à des mises au point ou à des contrôles, par des mesures ou par des tests, elles exigent simultanément ( Fig. 1) :

show abstract

Section: Les Erreurs Systématiquesunclassified

“…Par exemple, des sondes à logettes sont utilisées [2] pour vérifier l'homogénéité d'un mélange de poudres pour lequel les Autorités imposent RSD < 6. Les sondes provoquent un biais de plus de 6 %.…”

Section: La Pratique De L'échantillonnageunclassified

Introduction au procédé d'échantillonnage

Deleuil¹

1999

Analusis

View full text Add to dashboard Cite

show abstract

“…Also, as pointed out by previous authors (2,3), the best sampling device of the blending train (blender, The inadequacy of the 3ϫ sampling scheme dictated by Judge Wohlin in the Barr case has been reported conveyance, drumming, hoppering, and delivery to the feed frame) is the tablet itself. Finally, it is a goal of this several times in the past (1)(2)(3)(4)(5). Allegedly, the small cavity prevents representative sampling.…”

Section: Introductionmentioning

confidence: 99%

Sampling Bias in Blending Validation and a Different Approach to Homogeneity Assessment

Kræmer

Svensson²,

Melgaard³

1999

Drug Development and Industrial Pharmacy

View full text Add to dashboard Cite

Sampling of batches studied for validation is reported. A thief particularly suited for granules, rather than cohesive powders, was used in the study. It is shown, as has been demonstrated in the past, that traditional 1x to 3x thief sampling of a blend is biased, and that the bias decreases as the sample size increases. It is shown that taking 50 samples of tablets after blending and testing this subpopulation for normality is a discriminating manner of testing for homogeneity. As a criterion, it is better than sampling at mixer or drum stage would be even if an unbiased sampling device were available.

show abstract

“…The sampling protocol, which is defined by the number, size and position of the samples to be extracted, should be designed according to the type of blend (ordered or random, high or low potency) under analysis, the type of blender, the risks involved, etc. Establishing an adequate sampling method to validate the uniformity of a final blend has been the subject of much debate, and there are different approaches and acceptance criteria to ensure final product content uniformity [9][10][11][12][13][14]. Another important aspect is to identify the causes of sampling error for sampling tools and protocols, which has been the focus of several previous studies [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Method for Modeling Blend Composition Distributions in the Presence of Agglomerates

Llusa

Muzzio

2007

J Pharm Innov

View full text Add to dashboard Cite

Purpose This paper communicates a methodology that uses experimental data and a new statistical method that uses a simulation to reconstruct the composition distribution of a powder blend containing drug agglomerates. The reconstructed distribution can be used subsequently to optimize sampling protocols, compute operating characteristic curves, and estimate process capability for blends containing agglomerates. Methods A blend containing a cohesive API and an excipient is prepared in a blender. Although the API is sieved (mesh 100 μm) prior to its addition to the blender, API agglomerates are found in samples after blending. These agglomerates have either survived the mixing operation, or been created by the operation. The population of sample concentrations is used to estimate the parameters of a statistical model that combines multiple distribution functions to describe the binary mixture with the minor component partially agglomerated. The local concentration for the non-agglomerated portion of the minor component throughout the entire blend is estimated using a Gaussian distribution with parameters x (mode) and s (standard deviation) that are typically well established. The function and the parameters that describe the population of agglomerates of the minor component are estimated using (typically sparse) experimental data. The optimization of the parameters describing the agglomerate population is performed using a simulation of the powder bed in an iterative manner. The simulated blend, built using the statistical model, is extensively examined and a distribution of RSD estimates is obtained. The iterative process for seeking the parameters that describe the agglomerate population converges when the mode of the distribution of RSD estimates matches the experimental RSD value, which is the best available estimate of the blend homogeneity. Results This methodology is applied to populations of samples for different binary formulations prepared in different blenders. In all cases, after iterating to adjust the parameters of the model, there is an agreement between the experimental and the simulated population of samples with a confidence of, at least, 95%. Conclusions The methodology described here is an effective tool to create an accurate representation of agglomerated blends based on limited sampling results.

show abstract

Blending Validation and Content Uniformity of Low-Content, Noncohesive Powder Blends

Cited by 18 publications

References 4 publications

Introduction au procédé d'échantillonnage

Introduction au procédé d'échantillonnage

Sampling Bias in Blending Validation and a Different Approach to Homogeneity Assessment

A Quantitative Method for Modeling Blend Composition Distributions in the Presence of Agglomerates

Contact Info

Product

Resources

About