Risk of CYP2C9 induction analyzed by a relative factor approach with human hepatocytes

General Framework to Quantitatively Predict Pharmacokinetic Induction Drug-Drug Interactions Using In Vitro Data

INTRODUCTION

Metabolic inducers can expose people with polypharmacy to adverse health outcomes. A limited fraction of potential drug-drug interactions (DDIs) have been or can ethically be studied in clinical trials, leaving the vast majority unexplored. In the present study, an algorithm has been developed to predict the induction DDI magnitude, integrating data related to drug-metabolising enzymes.

METHODS

The area under the curve ratio (AUC_ratio) resulting from the DDI with a victim drug in the presence and absence of an inducer (rifampicin, rifabutin, efavirenz, or carbamazepine) was predicted from various in vitro parameters and then correlated with the clinical AUC_ratio (N = 319). In vitro data including fraction unbound in plasma, substrate specificity and induction potential for cytochrome P450s, phase II enzymes and uptake, and efflux transporters were integrated. To represent the interaction potential, the in vitro metabolic metric (IVMM) was generated by combining the fraction of substrate metabolised by each hepatic enzyme of interest with the corresponding in vitro fold increase in enzyme activity (E) value for the inducer.

RESULTS

Two independent variables were deemed significant and included in the algorithm: IVMM and fraction unbound in plasma. The observed and predicted magnitudes of the DDIs were categorised accordingly: no induction, mild, moderate, and strong induction. DDIs were assumed to be well classified if the predictions were in the same category as the observations, or if the ratio between these two was < 1.5-fold. This algorithm correctly classified 70.5% of the DDIs.

CONCLUSION

This research presents a rapid screening tool to identify the magnitude of potential DDIs utilising in vitro data which can be highly advantageous in early drug development.

In vitro assessment of the potential for dolutegravir to affect hepatic clearance of levonorgestrel

Objectives:

The World Health Organization recommends that all countries adopt dolutegravir-based antiretroviral therapy as the preferred regimen for all individuals living with HIV. Levonorgestrel is a commonly used hormonal contraceptive, which undergoes drug–drug interactions with some antiretrovirals, but the potential interaction between dolutegravir and levonorgestrel has not been examined. We aimed to evaluate cytochrome P450 (CYP)-mediated levonorgestrel metabolism and quantify the effects of dolutegravir on levonorgestrel apparent intrinsic clearance (CL_int.app.) and CYP gene expression.

Methods:

In vitro CYP-mediated CL_int.app. of levonorgestrel was quantified using a recombinant human CYP (rhCYP) enzyme system. A primary human hepatocyte model of drug metabolism was used to assess the effects of dolutegravir on (1) levonorgestrel CL_int.app., using liquid chromatography-tandem mass spectrometry, and (2) the expression of specific CYP enzymes, using quantitative real-time polymerase chain reaction.

Results:

Levonorgestrel clearance was mediated by multiple rhCYPs, including rhCYP3A4. Under control conditions, levonorgestrel CL_int.app. was 22.4 ± 5.0 μL/min/10⁶ hepatocytes. Incubation with 43.1 nM of unbound dolutegravir elevated levonorgestrel CL_int.app. to 31.4 ± 7.8 μL/min/10⁶ hepatocytes (P = 0.168), while 142.23 nM increased levonorgestrel CL_int.app. to 37.0 ± 2.9 μL/min/10⁶ hepatocytes (P = 0.012). Unbound dolutegravir ≥ 431 nM induced expression of CYP3A4 (≥ two-fold) in a dose-dependent manner, while 1.44 μM of unbound dolutegravir induced CYP2B6 expression 2.2 ± 0.3-fold (P = 0.0004).

Conclusions:

In summary, this in vitro study suggests that dolutegravir has the potential to increase hepatic clearance of levonorgestrel by inducing both CYP3A and non-CYP3A enzymes. The observed in vitro dolutegravir–levonorgestrel drug–drug interaction should be further examined in clinical studies.