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Prediction of metabolic drug-drug interactions
Unmanageable drug-drug interactions have led to the withdrawal of many drugs from the market. Many of these interactions involve inhibition and, to a lesser extent, induction of drug metabolising enzymes. Consequently, the ability to predict metabolically-based drug-drug interactions (mDDI) early in the drug development process is essential.
Simcyp provides an automated facility to predict such interactions in relevant patient populations with a high level of accuracy. This is done at two levels – the prediction of net change in exposure (as indicated by the area under the plasma drug concentration – time curve, AUC), and prediction of the change in the full plasma drug concentration – time profile.
A unique feature of the Simcyp algorithms is the ability to predict the distribution of outcome in populations, thereby allowing recognition of the often complex mix of individual patient characteristics that predispose to the greatest risk (Figure 9). These characteristics are often difficult to anticipate, and would not necessarily be picked up from relatively small studies in healthy subjects.
The Simcyp Population-based ADME Simulator allows you to indentify individuals at greatest risk at a very early stage in drug development.
Figure 9. Amplification of the extent of drug-drug interaction in individuals with specific characteristics. In this example, drug X is metabolised by CYP1A2 and CYP2D6 and is also renally excreted. The figure shows the fold change in AUC ratio in different individuals when drug X is co-administered with a CYP1A2 inhibitor, showing how simulation can be useful in identifying those most susceptible to an interaction.
