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Fig. 27
Example of dissolution profile of an optimal delivery
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Thus an optimal delivery system can be built (Fig. 27). The requirements are that A = 0, B = 0.272, and C = 0.1. We would then go back to the excipients that we use to develop the delivery system and, with a minimum of experimentation, find the ones that will give the proper values (or conversely, find the ones that we thought we could use will not, under any manipulation, give us the proper values). We used the Makoid-Banakar function to find our optimal delivery system's parameters in the above example. That is not the only function that could be used, nor is it necessarily the best one for all release profiles. It is simply one that fits the data that we observe. |
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The process is the important consideration: |
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1. Pick/create an equation that fits the data, |
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2. Make sure it has meaning/understanding for you, |
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3. Relate the parameters of the equation to the variables (excipients or processes), |
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4. Design your optimal system, and |
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5. Use a feed back loopwill your variables give you the optimal system? Yes, go with it; no, change variables. |
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IV. In Vitro-in Vivo Correlation |
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In vitro dissolution tests seem to be the most sensitive and reliable predictors of in vivo availability. Attempts to estimate physiologic availability, until recently, of a drug from a solid dosage form by in vitro methods have been con- |
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