A Multivariate Test for Similarity of Two Dissolution Profiles

Saranadasa, Hewa; Krishnamoorthy, K.

doi:10.1081/bip-200049832

Cited by 33 publications

(21 citation statements)

References 12 publications

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“…Statistical multivariate methods using multivariate ANOVA have also been used (60,61). These do take into account variability and correlation structure of cumulative percent-released-versus-time data.…”

Section: Tests For Similarity and Differencementioning

confidence: 99%

The Science of USP 1 and 2 Dissolution: Present Challenges and Future Relevance

Gray¹,

et al. 2009

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Since its inception, the dissolution test has come under increasing levels of scrutiny regarding its relevance, especially to the correlation of results to levels of drug in blood. The technique is discussed, limited to solid oral dosage forms, beginning with the scientific origins of the dissolution test, followed by a discussion of the roles of dissolution in product development, consistent batch manufacture (QC release), and stability testing. The ultimate role of dissolution testing, "to have the results correlated to in vivo results or in vivo in vitro correlation," is reviewed. The recent debate on mechanical calibration versus performance testing using USP calibrator tablets is presented, followed by a discussion of variability and hydrodynamics of USP Apparatus 1 and Apparatus 2. Finally, the future of dissolution testing is discussed in terms of new initiatives in the industry such as quality by design (QbD), process analytical technology (PAT), and design of experiments (DOE).

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Section: Tests For Similarity and Differencementioning

confidence: 99%

The Science of USP 1 and 2 Dissolution: Present Challenges and Future Relevance

Gray¹,

et al. 2009

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“…Seo et al 21 used a four metrics along with f 2 to compare dissolution profiles. Saranadasa et al 22 proposed a multivariate test to compare two dissolution profiles with the assumption of multivariate normality of the data. Interestingly, Maggio et al 23,24 proposed a method based on principal component analysis with the establishment of a confidence region (PCA-CR) after an outlier detection step using Hotelling's test to avoid high variability in the data.…”

Section: Current Approaches For Dissolution Profile Comparisonmentioning

confidence: 99%

Statistical comparison of dissolution profiles

Wang

Snee²,

Keyvan

et al. 2015

Drug Development and Industrial Pharmacy

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Statistical methods to assess similarity of dissolution profiles are introduced. Sixteen groups of dissolution profiles from a full factorial design were used to demonstrate implementation details. Variables in the design include drug strength, tablet stability time, and dissolution testing condition. The 16 groups were considered similar when compared using the similarity factor f2 (f2 > 50). However, multivariate ANOVA (MANOVA) repeated measures suggested statistical differences. A modified principal component analysis (PCA) was used to describe the dissolution curves in terms of level and shape. The advantage of the modified PCA approach is that the calculated shape principal components will not be confounded by level effect. Effect size test using omega-squared was also used for dissolution comparisons. Effects indicated by omega-squared are independent of sample size and are a necessary supplement to p value reported from the MANOVA table. Methods to compare multiple groups show that product strength and dissolution testing condition had significant effects on both level and shape. For pairwise analysis, a post-hoc analysis using Tukey's method categorized three similar groups, and was consistent with level-shape analysis. All these methods provide valuable information that is missed using f2 method alone to compare average profiles. The improved statistical analysis approach introduced here enables one to better ascertain both statistical significance and clinical relevance, supporting more objective regulatory decisions.

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“…Their stated measure of similarity (the pivotal MSD statistic) is independent of model parameters and thus seems to violate NR1. The implementation reported by Saranadasa and Krishnamoorthy (15) assumes that the curve shapes of test and reference are parallel, which is likely never exactly true and therefore cannot be a consistently tenable approximation.…”

Section: Multivariate Statistical Distance (Msd) Equivalence Testmentioning

confidence: 99%

In Vitro Dissolution Curve Comparisons: A Critique of Current Practice

LeBlond¹,

Altan²,

Novick³

et al. 2016

Dissolution Technol.

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Many pharmacologically active molecules are formulated as solid dosage form drug products. Following oral administration, the diffusion of an active molecule from the gastrointestinal tract into systemic distribution requires the disintegration of the dosage form followed by the dissolution of the molecule in the stomach lumen. Its dissolution properties may have a direct impact on its bioavailability and subsequent therapeutic effect. Consequently, dissolution (or in vitro release) testing has been the subject of intense scientific and regulatory interest over the past several decades. Much interest has focused on models describing in vitro release profiles over a time scale, and a number of methods have been proposed for testing similarity of profiles. In this article, we review previously published work on dissolution profile similarity testing and provide a detailed critique of current methods in order to set the stage for a Bayesian approach. KEY WORDS:Dissolution profile similarity; in vitro release; f 2 statistic; Bayesian model.

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A Multivariate Test for Similarity of Two Dissolution Profiles

Cited by 33 publications

References 12 publications

The Science of USP 1 and 2 Dissolution: Present Challenges and Future Relevance

The Science of USP 1 and 2 Dissolution: Present Challenges and Future Relevance

Statistical comparison of dissolution profiles

In Vitro Dissolution Curve Comparisons: A Critique of Current Practice

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