International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Research Paper | Biological Engineering | Iraq | Volume 6 Issue 8, August 2017 | Popularity: 6.5 / 10


     

Mathematical Modeling of Delayed Release Dosage Form Dissolution Test and Bioavailability

Auns Hashem, Fadhil Abid, Khawla Rasheed


Abstract: In this study in-vitro dissolution of modified release diclofenac sodium (DS) dosage form were studied, a100 mg DS tablets were tested under simulated gastric and intestinal medium according to united states pharmacopoeia (USP), the data were modeled mathematically to identify to which model the drug release is fitted using linear regression analysis. In-vivo the pharmacokinetic parameters were evaluated by administrating the drug to seven healthy volunteers, DS concentrations in plasma was measured using revers phase high performance liquid chromatography (HPLC). Primary kinetic parameters AUC_ (0), C_max, t_max, and other kinetic parameters where obtained using non compartmental analysis. In-vivo study accomplished using compartmental analysis, the data analyzed as single compartment model with single time variant input and single time variant output. This analysis allows the data to represented in three differential equations, in-vitro the data fitted to Higuchi release model, in-vivo the data fitted to single compartment model, non-compartmental data were in accepted range.


Keywords: Dissolution, Mathematical modeling, Bioavailability, HPLC


Edition: Volume 6 Issue 8, August 2017


Pages: 18 - 23



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Auns Hashem, Fadhil Abid, Khawla Rasheed, "Mathematical Modeling of Delayed Release Dosage Form Dissolution Test and Bioavailability", International Journal of Science and Research (IJSR), Volume 6 Issue 8, August 2017, pp. 18-23, URL: https://www.ijsr.net/getabstract.php?paperid=ART20175882, DOI: https://www.doi.org/10.21275/ART20175882



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A Comprehensive Study of Machine Learning Models in Radiogenomics

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