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: 3.88). 01/2011; 71(1):72-87. DOI: 10.1111/j.1365-2125.2010.03799.x
Source: PubMed


• 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.
AIMS 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.
METHODS 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.
RESULTS 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%).
CONCLUSIONS 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.

Download full-text


Available from: Geoffrey T Tucker
  • Source
    • "It is challenging to understand the mechanistic basis for these differences, but it may reflect pharmacodynamic differences in that inactivation and induction result in downstream enzyme changes to achieve an altered steady-state, whereas competitive inhibition is a more direct interaction, requiring inhibitory drug present at the site of interaction. Current PBPK software packages such as Simcyp provide flexibility in terms of applying unbound liver inlet or outlet concentrations, although the latter is standard and considered more predictive of clinical outcome (Jamei et al., 2009a; Guest et al., 2011). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Prediction of human pharmacokinetics of new drugs, as well as other disposition attributes has become a routine practice in drug research and development. Prior to the 1990s, drug disposition science was used in a mostly descriptive manner in the drug development phase. With the advent of in vitro methods and availability of human-derived reagents for in vitro studies, drug disposition scientists became engaged in the compound design phase of drug discovery in order to optimize and predict human disposition properties prior to nomination of candidate compounds into the drug development phase. This has reaped benefits in that the attrition rate of new drug candidates in drug development for reasons of unacceptable pharmacokinetics has greatly decreased. Attributes that are predicted include clearance, volume of distribution, half-life, absorption, and drug-drug interactions. In this article, we offer our experience-based perspectives on the tools and methods of predicting human drug disposition using in vitro and animal data.
    Full-text · Article · Sep 2013 · Drug metabolism and disposition: the biological fate of chemicals
  • Source
    • "Wang (2010) compared the models for 54 interactions perpetrated by mechanism-based inhibitors of CYP3A4. Guest et al. (2011) used 35 DDIs to compare Simcyp V8's time-based model with its implementation of static models. All evaluations showed a comparable performance for the two models. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Simcyp, a population-based simulator, is widely used for evaluating drug-drug interaction (DDI) risks in healthy and disease populations. We compare the prediction performance of Simcyp with that of mechanistic static models using different types of inhibitor concentrations, with the aim of understanding their strengths/weaknesses and recommending the optimal use of tools in drug discovery/early development. The inclusion of an additional term in static equations to consider the contribution of hepatic first pass to DDIs (AUCR(hfp)) has also been examined. A second objective was to assess Simcyp's estimation of variability associated with DDIs. The data set used for the analysis comprises 19 clinical interactions from 11 proprietary compounds. Except for gut interaction parameters, all other input data were identical for Simcyp and static models. Static equations using an unbound average steady-state systemic inhibitor concentration (I(sys)) and a fixed fraction of gut extraction and neglecting gut extraction in the case of induction interactions performed better than Simcyp (84% compared with 58% of the interactions predicted within 2-fold). Differences in the prediction outcomes between the static and dynamic models are attributable to differences in first-pass contribution to DDI. The inclusion of AUCR(hfp) in static equations leads to systematic overprediction of interaction, suggesting a limited role for hepatic first pass in determining inhibition-based DDIs for our data set. Our analysis supports the use of static models when elimination routes of the victim compound and the role of gut extraction for the victim and/or inhibitor in humans are not well defined. A fixed variability of 40% of predicted mean area under the concentration-time curve ratio is recommended.
    Full-text · Article · May 2012 · Drug metabolism and disposition: the biological fate of chemicals
  • [Show abstract] [Hide abstract]
    ABSTRACT: Prediction of in vivo drug-drug interactions (DDIs) from in vitro and in vivo data, also named in vitro in vivo extrapolation (IVIVE), is of interest to scientists involved in the discovery and development of drugs. To avoid detrimental DDIs in humans, new drug candidates should be evaluated for their possible interaction with other drugs as soon as possible, not only as an inhibitor or inducer (perpetrator) but also as a substrate (victim). DDI risk assessment is addressed along the drug development program through an iterative process as the features of the new compound entity are revealed. Both in vitro and preclinical/clinical outcomes are taken into account to better understand the behavior of the developed compound and to refine DDI predictions. During the last decades, several equations have been proposed in the literature to predict DDIs, from a quantitative point of view, showing a substantial improvement in the ability to predict metabolism-based in vivo DDIs. Mechanistic and dynamic approaches have been proposed to predict the magnitude of metabolic-based DDIs. The purpose of this article is to provide an overview of the current equations and methods, the pros and cons of each method, the required input data for each of them, as well as the mechanisms (i.e., reversible inhibition, mechanism-based inhibition, induction) underlying metabolic-based DDIs. In particular, this review outlines how the methods (static and dynamic) can be used in a complementary manner during drug development. The discussion of the limitations and advantages associated with the various approaches, as well as regulatory requirements in that field, can give the reader a helpful overview of this growing area.
    No preview · Article · Dec 2011 · Drug metabolism and drug interactions
Show more