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1.
Ther Innov Regul Sci ; 50(2): 204-212, 2016 Mar.
Article in English | MEDLINE | ID: mdl-30227008

ABSTRACT

BACKGROUND: The differentiation of tablets by their physical appearance is a contributing factor to the safe use of medications. In this study, a "score card" was developed to assess how well one tablet is differentiated from another tablet on the basis of the physical attributes of color, size, and shape. METHODS: The score card was derived from a "2-out-of-5" difference test, in which participants were presented with groups of 5 tablets with varying color, size, and shape, and were asked to identify the 2 tablets that were different from the other 3 tablets. RESULTS: Based on the study results (ie, recognition rate of the differences in the tablets, and confidence in such recognition), simplified metrics were derived to "score" a comparison of 2 tablets differing in color, size, and/or shape. The higher the score, the better the 2 tablets could be visually distinguished from each other. The scores were ranked as representing "strong," "moderate," or "weak" differentiation, with a corresponding stoplight color code, to create the final score card. The score card was internally verified by applying it to the tablets used in the study, then to the multiple strengths of Gilotrif® (afatinib) tablets, a Boehringer Ingelheim approved drug product. CONCLUSION: The score card is a first step in the assessment of adequate differentiation of tablets and can be used for the design of tablets that promote safe use of medication.

2.
J Biopharm Stat ; 25(3): 417-37, 2015.
Article in English | MEDLINE | ID: mdl-24896319

ABSTRACT

Statistical equivalence analyses are well-established parts of many studies in the biomedical sciences. Also in pharmaceutical development and manufacturing equivalence testing methods are required in order to statistically establish similarities between machines, process components, or complete processes. This article presents a choice of multivariate equivalence testing procedures for normally distributed data as generalizations of existing univariate methods. In all derived methods, variability is interpreted as nuisance parameter. The use of the proposed methods in pharmaceutical development is demonstrated with a comparative analysis of dissolution profiles.


Subject(s)
Data Interpretation, Statistical , Drug Industry/statistics & numerical data , Pharmaceutical Preparations/standards , Research Design/statistics & numerical data , Drug Industry/methods , Drug Industry/standards , Multivariate Analysis , Normal Distribution , Pharmaceutical Preparations/analysis , Pharmaceutical Preparations/chemistry , Solubility
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