Evaluation of the Use of Static and Dynamic Models to Predict Drug-Drug Interaction and Its Associated Variability: Impact on Drug Discovery and Early Development

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.

Optimization of Drug-Drug Interaction Study Design: Comparison of Minimal Physiologically Based Pharmacokinetic Models on Prediction of CYP3A Inhibition by Ketoconazole

“…To understand the relative merits of using static inhibitor concentrations versus dynamic PBPK models, several investigators have used the current Simcyp (version 11) ketoconazole model (Einolf, 2007;Guest et al, 2011;Peters et al, 2012). The general conclusion has been that dynamic PBPK models were not substantially better than static inhibitor concentration models.…”

“…Preliminary assessments of the ketoconazole-midazolam interaction using that model concluded that predictive performance was poor (Guest et al, 2011). Despite this conclusion, model 2 has been used to determine whether static or dynamic plasma concentration approaches are most appropriate for predicting clinically important DDIs (Einolf, 2007;Guest et al, 2011;Peters et al, 2012), to validate in vitro technologies (Youdim et al, 2008), and to estimate the fraction metabolized by CYP3A (f m ) for new drugs after in vivo studies (Rakhit et al, 2008;Yang et al, 2012). Of importance, model 2 was used by the Food and Drug Administration (FDA) to recommend specific clinical study designs (Zhao et al, 2009).…”

Evaluation of a New Molecular Entity as a Victim of Metabolic Drug-Drug Interactions--an Industry Perspective

“…Numerous methodologies based on both simplified and PBPK-based approaches have been well defined previously in the literature (Brown et al, 2006;Fahmi and Ripp, 2010;Peters et al, 2012). In all cases, model selection should be driven by specific development questions and the extent of data available at the time of model development.…”

Prediction of Drug-Drug Interactions with Crizotinib as the CYP3A Substrate Using a Physiologically Based Pharmacokinetic Model

“…The US Food and Drug Administration (FDA) and the European Medicines Agency have recently issued DDI guidances (CDER, 2012;CHMP, 2012), that emphasize the use of an integrated mechanistic approach, such as a physiologically based pharmacokinetic (PBPK) model, to quantitatively predict the magnitude of DDIs in the clinic. The dynamic modeling approach is being employed increasingly in all phases of drug discovery and development to evaluate potential DDI risks for NMEs (Boulenc and Barberan, 2011;Zhao et al, 2011;Huang and Rowland, 2012;Peters et al, 2012;Huang et al, 2013). Additionally, regulatory agencies express keen interest in the use of mechanistic dynamic models to provide a deeper understanding of complex DDIs, including simultaneous effects of two or more interacting drugs (e.g., inhibitors and inducers) on exposures of substrate drugs, as well as drug-disease interactions in patients with hepatic or renal impairment (Zhao et al, 2011;Huang and Rowland, 2012;Huang et al, 2013;Varma et al, 2015).…”