Improved Predictions of Drug–Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A

Abstract

Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug–drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro–in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min−1, the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min−1. These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

Refined Prediction of Pharmacokinetic Kratom-Drug Interactions: Time-Dependent Inhibition Considerations

“…Victim Drug Therapeutic drug class F g (Galetin et al, 2008) f m (Shou et al, 2008) AUCR (Mano et al, 2015) b (Yadav et al, 2018) c (Mao et al, 2011) f m , fraction of the object drug cleared by CYP3A; F g , fraction of the object drug escaping CYP3A-mediated metabolism in the gut; AUCR, ratio of area under the plasma concentrationtime curve in presence to absence of inhibitor This article has not been copyedited and formatted. The final version may differ from this version.…”

Mechanisms of Herb-Drug Interactions Involving Cinnamon and CYP2A6: Focus on Time-Dependent Inhibition by Cinnamaldehyde and 2-Methoxycinnamaldehyde

“…Atypical kinetics (i.e., non-Michaelis-Menten) are widely observed with P450s (Korzekwa et al, 1998;Atkins, 2005;Korzekwa, 2014;Leow and Chan, 2019), which have mechanistically been attributed to multiple binding (Korzekwa et al, 1998), protein heterogeneity (Davydov and Halpert, 2008), enzyme oligomerization, protein-protein interactions (Davydov et al, 2017;Davydova et al, 2019), or a combination. Atypical kinetics can profoundly impact TDI kinetics (Korzekwa et al, 2014;Nagar et al, 2014), and evidence suggests DDI predictions can be improved by accounting for mechanistic complexities (Yadav et al, 2018).…”

“…The concave upward curvature is indicative of either quasi-irreversible or partial inactivation as shown previously (Nagar et al, 2014) Using the numerical method (Korzekwa et al, 2014;Nagar et al, 2014), kinetic models were fit to the data, and kinetic parameters were estimated. The initial estimates of the rate constants were obtained from analyzing the data as detailed in previous publications (Korzekwa et al, 2014;Nagar et al, 2014;Barnaba et al, 2016;Yadav et al, 2018;Yadav et al, 2020). Association rate constants were fixed at 270 μM -1 min −1 (Barnaba et al, 2016).…”

“…Cotinine. Time-dependent inhibition of CYP2A6 was tested using a standard two-step approach using pooled (N = 50) human liver microsomes (Yadav et al, 2018) with slight modifications. Briefly, twelve concentrations of either CA (0-1000 μM) or MCA (0-250 μM) were incubated at 37°C with a 5 mg/mL suspension of HLM in 100 mM potassium phosphate buffer (pH = 7.4) as a primary incubation.…”

Impact of Lipid Partitioning on the Design, Analysis, and Interpretation of Microsomal Time-Dependent Inactivation

“…We have shown previously that using numerical methods to model TDI data has several advantages over the traditional replot method (Korzekwa et al, 2014;Nagar et al, 2014;Yadav et al, 2018): 1) Michaelis-Menten kinetics (MM) is not assumed and the frequently observed non-MM kinetics can be easily modeled; 2) parameter errors in all models (including MM) are significantly lower due to the lack of propagation of errors seen in the replot method; 3) other complexities such as quasi-irreversible inactivation, inhibitor loss, and sequential (Seq) metabolism can be modeled; and 4) the use of the numerical method has been shown to result in more accurate DDI predictions than the replot method. The replot method does have two advantages when MM kinetics applies.…”