Niederquell A, Kuentz M. Biorelevant dissolution of poorly soluble weak acids studied by UV imaging reveals ranges of fractal-like kinetics.
Int J Pharm 2014;
463:38-49. [PMID:
24406675 DOI:
10.1016/j.ijpharm.2013.12.049]
[Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2013] [Revised: 12/20/2013] [Accepted: 12/28/2013] [Indexed: 11/19/2022]
Abstract
Much pharmaceutical research has been invested into drug dissolution testing and its mathematical modeling. Even today, there is no complete understanding of the dissolution process but novel imaging tools have been introduced into pharmaceutics that may spur further scientific advancement. We used UV imaging to study the intrinsic dissolution of various poorly soluble acidic model drugs to understand the effects of heterogeneity on early intrinsic drug dissolution using a biorelevant medium: celecoxib, ketoprofen, naproxen, and sulfathiazole. All compounds were characterized using X-ray powder diffraction and thermal analysis. Raman spectroscopy and scanning electron microscopy were employed before and after the initial dissolution phase. As a result, ranges of fractal-like dissolution behavior were found with all model compounds. Intrinsic dissolution rate exhibited a power law mainly at early time points. Subsequently, after several minutes, pseudo-equilibrium was reached with a nearly constant dissolution rate. Further research should investigate whether compounds other than acids demonstrate similar early dissolution kinetics in biorelevant media. The observed fractal-like intrinsic dissolution behavior has several pharmaceutical implications. This study primarily helps us to better understand in vitro dissolution testing, particularly on a miniaturized scale. This improved understanding of early dissolution events may advance future correlations with in vivo data. Therefore, fractal-like dissolution should be considered during biopharmaceutical modeling.
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