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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ropella, Glen E. P. Park, Sunwoo Yan, Li Sheikh-Bahaei, Shahab Hunt, C. Anthony |
| Abstract | The In Silico Liver (ISL) plugs together autonomous software objects that represent hepatic components at different scales and levels of details. ISL parameters sensitive to drug-specific physicochemical properties (PCPs) were tuned so that ISL outflow profiles from a single ISL matched in situ perfused rat liver outflow profiles of sucrose and six cationic drugs. Antipyrine and diltiazem have the greatest degree of separation in PCP space of all pairs of the six compounds. Data for the other four, more closely spaced compounds comprised a training set for a simple artificial neural network (ANN) that was used to predict the PCP-sensitive, ISL parameter values for antipyrine and diltiazem given their PCPs. Those predicted parameter values were combined with the already-validated, drug-insensitive ISL parameter values. Simulation of the resulting ISLs gave expected liver perfusion outflow profiles for antipyrine and diltiazem that were within two-fold of the observed profiles. |
| Starting Page | 162 |
| Ending Page | 169 |
| Page Count | 8 |
| File Format | |
| ISBN | 1565553195 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2008-04-14 |
| Publisher Place | San Diego |
| Access Restriction | Subscribed |
| Subject Keyword | Hepatic Drug disposition Systems biology Prediction Mechanistic Physiologically-based Agent oriented |
| Content Type | Text |
| Resource Type | Article |
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