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Dissolution Data Fitting in P harmaceutical Industry

Explore the impact of changing suppliers on dissolution profiles of pharmaceutical products using non-linear modeling. Study the Td50 and Td80 values to quantify differences post-supplier change.

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Dissolution Data Fitting in P harmaceutical Industry

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  1. Dissolution Data Fitting in Pharmaceutical Industry DiscoverySummit – Prague 2017 Noëlle Boussac-Marlière – Merial BI

  2. Context: Pharmaceutical Industry • Tablets dissolution Measure: Percentage of dissolvedproduct as a function of time • Fittingthiscurve  non-linear model This Weibull model : a Beta Td

  3. Context of the study • Change of supplier for the active compound • Weneedto study the impact of this change on the dissolution profile of the final product • Studydesign:  72 dissolution profiles to fit

  4. Question: Study the impact of this change on the dissolution profile • More precisely: Whatis the impact of thischange on the Td50, Td80 (time needed to obtain 50%-80% of dissolution) of the final product? • Td50, Td80 obtained by inverse prediction • Or solving : Time=Td x 50% Td50

  5. This challenge with JMP • Graph.. To see the data • Build the appropriate non linear model • Fit eachdissolution profile (x72) withWeibullmodel • Estimateof the Td50 (and Td80) for each dissolution profile using inverse prediction (72x2 Tds) • Buildthe Td tables • Comparaison of the Td50 of the 2 suppliers: graphs, quantification of the differences…

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