The DDI Module in GastroPlus® allows you to predict mechanistic and static drug-drug interactions (DDIs) among drugs and metabolites. The ability to accurately estimate potential DDIs in silico has several benefits for pharmaceutical companies:
Explore possible effects on the pharmacology and toxicology of drugs
Identify species-specific changes to estimate how a drug behaves in animals vs. humans
Investigate the safety profile of drugs that are co-administered prior to filing regulatory submissions with the FDA, EMA, and other agencies
With the DDI Module, calculating either mechanistic steady-state and/or dynamic drug interactions is managed through our easy-to-use interface. We provide a database of validated compound model files (>30) for which all relevant parameters (including reported Kis and full compartmental PK & PBPK models) are defined. Of course, you may predict DDIs among any drugs by simply entering the required inputs. As with other GastroPlus modules, there is no equation or code writing required.
NEW! Population Simulator™ linked with DDI predictions! Now incorporate variability between subjects in your dynamic simulations and see the impact on victim, perpetrator, and metabolite(s) concentrations and AUC ratios. Define your population (American, Asian, pediatric, etc…) and number of subjects (up to 2500) in your trial to start.
| Pathway | Substrate | Inhibitor | Inducer |
|---|---|---|---|
| CYP3A | alfentanil, midazolam, triazolam* | Strong: itraconazole, ketoconazole, voriconazole* Moderate: diltiazem, fluconazole* Weak: ranitidine |
Strong: rifampin, phenytoin, carbamazepine Moderate: efavirenz, rifabutin |
| CYP2C8 | repaglinide*, rosiglitazone | Strong: gemfibrozil* | |
| CYP2C9 | warfarin | No known strong clinical index inhibitors Moderate: fluconazole* |
|
| CYP2C19 | Strong: fluconazole* | ||
| CYP2B6 | bupropion | No known strong clinical index inhibitors | |
| CYP2D6 | dextromethorphan* | Strong: quinidine* | No known clinical inducers |
| UGT1A1 | dolutegravir* |
Transporter-mediated DDIs are increasingly recognized as critical determinants of drug disposition and therapeutic outcome. This table showcases validated index substrates and perpetrators (inhibitors/inducers) for major transporter pathways including P-gp, OATP, OCT, and MATE. Inclusion of these transporters in drug development strategies—alongside enzyme interactions—ensures a more holistic understanding of a compound’s ADME profile. By simulating interactions using these transporter models, researchers can anticipate complex interplay between drug molecules, supporting both early decision-making and regulatory compliance. The strategic application of these models enables smarter trial designs and provides compelling evidence for regulatory filings.
| Pathway | Substrate | Inhibitor | Inducer |
|---|---|---|---|
| P-gp | digoxin, edoxaban, fexofenadine | rifampin, quinidine | rifampin |
| OATP1B1 / 3 | atorvastatin*, pravastatin, rosuvastatin | rifampin | No known clinical inducer |
| OCT2/MATE | metformin | dolutegravir* | No known clinical inducer |
