Article

Assessment of algorithms for predicting drug-drug interactions via inhibition mechanisms: comparison of dynamic and static models.

School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Oxford Road, Manchester, UK.
British Journal of Clinical Pharmacology (Impact Factor: 3.69). 01/2011; 71(1):72-87. DOI: 10.1111/j.1365-2125.2010.03799.x
Source: PubMed

ABSTRACT The prediction of drug-drug interactions (DDIs) from in vitro data usually utilizes an average dosing interval estimate of inhibitor concentration in an equation-based static model. Simcyp®, a population-based ADME simulator, is becoming widely used for the prediction of DDIs and has the ability to incorporate the time-course of inhibitor concentration and hence generate a temporal profile of the inhibition process within a dynamic model.
Prediction of DDIs for 35 clinical studies incorporating a representative range of drug-drug interactions, with multiple studies across different inhibitors and victim drugs. Assessment of whether the inclusion of the time course of inhibition in the dynamic model improves prediction in comparison with the static model. Investigation of the impact of different inhibitor and victim drug parameters on DDI prediction accuracy including dosing time and the inclusion of active metabolites. Assessment of ability of the dynamic model to predict inter-individual variability in the DDI magnitude.
Static and dynamic models (incorporating the time course of the inhibitor) were assessed for their ability to predict drug-drug interactions (DDIs) using a population-based ADME simulator (Simcyp®V8). The impact of active metabolites, dosing time and the ability to predict inter-individual variability in DDI magnitude were investigated using the dynamic model.
Thirty-five in vivo DDIs involving azole inhibitors and benzodiazepines were predicted using the static and dynamic model; both models were employed within Simcyp for consistency in parameters. Simulations comprised of 10 trials with matching population demographics and dosage regimen to the in vivo studies. Predictive utility of the static and dynamic model was assessed relative to the inhibitor or victim drug investigated.
Use of the dynamic and static models resulted in comparable prediction success, with 71 and 77% of DDIs predicted within two-fold, respectively. Over 40% of strong DDIs (>five-fold AUC increase) were under-predicted by both models. Incorporation of the itraconazole metabolite into the dynamic model resulted in increased prediction accuracy of strong DDIs (80% within two-fold). Bias and imprecision in prediction of triazolam DDIs were higher in comparison with midazolam and alprazolam; >50% of triazolam DDIs were under-predicted regardless of the model used. Predicted inter-individual variability in the AUC ratio (coefficient of variation of 45%) was consistent with the observed variability (50%).
High prediction accuracy was observed using both the Simcyp dynamic and static models. The differences observed with the dose staggering and the incorporation of active metabolite highlight the importance of these variables in DDI prediction.

0 Bookmarks
 · 
90 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: An approach was proposed in 2007 for quantitative predictions of cytochrome P450 (CYP)3A4-mediated drug-drug interactions. It is based on two characteristic parameters: the contribution ratio (CR; i.e., the fraction of victim drug clearance by CYP) and the inhibition ratio (IR) of the inhibitor. Knowledge of these parameters allows forecasting of the ratio between the area under the plasma concentration-time curve (AUC) of the victim drug when given with the inhibitor and the AUC of the victim drug when it is given alone. So far, these parameters were established for 21 substrates and 17 inhibitors. The goals of our study were to test the assumption of substrate independence of the potency of inhibitors in vivo and to estimate the CR and IR for an extended list of substrates and inhibitors of CYP3A4. The assumption of independence of IRs from the substrate was evaluated on a set of eight victim drugs and eight inhibitors. Forty-four AUC ratios were available. This assumption was rejected in four cases, but it did not result in more than a twofold error in AUC ratio predictions. The extended list of substrates and inhibitors was defined by a thorough literature search. Fifty-nine AUC ratios were available for the global analysis. Final estimates of CRs and IRs were obtained for 37 substrates and 25 inhibitors, respectively. The mean prediction error of the ratios was 0.02, while the mean absolute prediction error was 0.58. Predictive distributions for 917 possible interactions were obtained, giving detailed information on some drugs or inhibitors that have been poorly studied so far.
    The AAPS Journal 10/2014; · 3.91 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: To examine whether initiation of fibrates or statins in sulfonylurea users is associated with hypoglycemia, and examine in vitro inhibition of cytochrome P450 (CYP) enzymes by statins, fenofibrate and glipizide. We used healthcare data to conduct nested case-control studies of serious hypoglycemia (i.e., resulting in hospital admission or emergency department treatment) in persons taking glipizide or glyburide, and calculated adjusted overall and time-stratified odds ratios (ORs) and 95% confidence intervals [CIs]. We also characterized the in vitro inhibition of CYP enzymes by statins, fenofibrate, and glipizide using fluorometric CYP450 inhibition assays, and estimated area under the concentration-time curve ratios (AUCRs) for drug pairs. We found elevated adjusted overall ORs for glyburide-fenofibrate (OR 1.84, 95% CI 1.37-2.47) and glyburide-gemfibrozil (OR 1.57, 95% CI 1.25-1.96). The apparent risk did decline over time as might be expected from a pharmacokinetic mechanism. Fenofibrate was a potent in vitro inhibitor of CYP2C19 (IC50 = 0.2 μM) and CYP2B6 (IC50 = 0.7 μM), and a moderate inhibitor of CYP2C9 (IC50 = 9.7 μM). The predicted CYP-based AUCRs for fenofibrate-glyburide and gemfibrozil-glyburide interactions were only 1.09 and 1.04, suggesting that CYP inhibition is unlikely to explain such an interaction. Use of fenofibrate or gemfibrozil together with glyburide was associated with elevated overall risks for serious hypoglycemia. CYP inhibition seems unlikely to explain this observation. We speculate that a pharmacodynamic effect of fibrates (e.g., activate peroxisome proliferator-activator receptor alpha) may contribute to these apparent interactions.
    British Journal of Clinical Pharmacology 02/2014; · 3.69 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Static and dynamic (PBPK) prediction models were applied to estimate the drug-drug interaction (DDI) risk of AZD2066. The predictions were compared to the results of an in vivo cocktail study. Various in vivo measures for tolbutamide as a probe agent for cytochrome P450 2C9 (CYP2C9) were also compared. In vitro inhibition data for AZD2066 were obtained using human liver microsomes and CYP-specific probe substrates. DDI prediction was performed using PBPK modelling with the SimCYP simulator™ or static model. The cocktail study was an open label, baseline, controlled interaction study with 15 healthy volunteers receiving multiple doses of AD2066 for 12 days. A cocktail of single doses of 100 mg caffeine (CYP1A2 probe), 500 mg tolbutamide (CYP2C9 probe), 20 mg omeprazole (CYP2C19 probe) and 7.5 mg midazolam (CYP3A probe) was simultaneously applied at baseline and during the administration of AZD2066. Bupropion as a CYP2B6 probe (150 mg) and 100 mg metoprolol (CYP2D6 probe) were administered on separate days. The pharmacokinetic parameters for the probe drugs and their metabolites in plasma and urinary recovery were determined. In vitro AZD2066 inhibited CYP1A2, CYP2B6, CYP2C9, CYP2C19 and CYP2D6. The static model predicted in vivo interaction with predicted AUC ratio values of >1.1 for all CYP (except CYP3A4). The PBPK simulations predicted no risk for clinical relevant interactions. The cocktail study showed no interaction for the CYP2B6 and CYP2C19 enzymes, a possible weak inhibition of CYP1A2, CYP2C9 and CYP3A4 activities and a slight inhibition (29 %) of CYP2D6 activity. The tolbutamide phenotyping metrics indicated that there were significant correlations between CLform and AUCTOL, CL, Aemet and LnTOL24h. The MRAe in urine showed no correlation to CLform. CONCLUSIONS: DDI prediction using the static approach based on total concentration indicated that AZD20066 has a potential risk for inhibition. However, no DDI risk could be predicted when a more in vivo-like dynamic prediction method with the PBPK with SimCYP™ software based on early human PK data was used and more parameters (i.e. free fraction in plasma, no DDI risk) were taken into account. The clinical cocktail study showed no or low risks for clinical relevant DDI interactions. Our findings are in line with the hypothesis that the dynamic prediction method predicts DDI in vivo in humans better than the static model based on total plasma concentrations.
    European Journal of Clinical Pharmacology 11/2013; · 2.70 Impact Factor

Full-text (2 Sources)

Download
26 Downloads
Available from
May 15, 2014