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Titlebook: In Vitro-In Vivo Correlations; Book 1997

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樓主: CRUST
41#
發(fā)表于 2025-3-28 15:15:46 | 只看該作者
42#
發(fā)表于 2025-3-28 19:12:14 | 只看該作者
43#
發(fā)表于 2025-3-29 00:41:56 | 只看該作者
Approaches to IVIVR Modelling and Statistical Analysis,logy used is not based on the statistical properties of the data being analysed and consequently parameter estimates may be biased and the analysis may be inefficient. The second criticism is that a linear model is used and this is clearly very restrictive and limits the number of instances where we
44#
發(fā)表于 2025-3-29 07:08:13 | 只看該作者
45#
發(fā)表于 2025-3-29 10:43:39 | 只看該作者
The , and Drugs Administered in Extended Release (Er) Formulations,n arose from drug dissolution and absorption models which identified the key parameters controlling drug absorption as the dimensionless numbers; the Absorption number (A. ), the Dissolution number (D.) and the Dose number (D.). This led to a biopharmaceutic classification of drugs into four groups,
46#
發(fā)表于 2025-3-29 11:33:54 | 只看該作者
Investigating , Drug Release Mechanisms Inside Dosage Forms,lationships. Until relatively recently, it has been difficult to directly observe the processes inside dosage forms, and drug release mechanisms have been largely inferred from analyses of . release kinetics. It is still extremely difficult to obtain information about release mechanisms ., but metho
47#
發(fā)表于 2025-3-29 19:32:00 | 只看該作者
Artificial Neural Network Based , Correlations,re used to characterize impact of formulation variables on drug release and to optimize formulations to achieve a target drug release profile (assuming the . test is predictive of . drug release). Every manufactured batch must meet pre-set specifications prior to being released for distribution. Cer
48#
發(fā)表于 2025-3-29 20:41:29 | 只看該作者
49#
發(fā)表于 2025-3-30 01:46:52 | 只看該作者
50#
發(fā)表于 2025-3-30 07:59:07 | 只看該作者
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