Impact of polymorphisms in CYP and UGT enzymes and ABC and SLCO1B1 transporters on the pharmacokinetics and safety of desvenlafaxine

PBPK modeling to predict the pharmacokinetics of venlafaxine and its active metabolite in different CYP2D6 genotypes and drug-drug interactions with clarithromycin and paroxetine

Venlafaxine, a serotonin-norepinephrine reuptake inhibitor (SNRI), is indicated for the treatment of major depressive disorder, social anxiety disorder, generalized anxiety disorder, and panic disorder. Venlafaxine is metabolized to the active metabolite desvenlafaxine mainly by CYP2D6. Genetic polymorphism of CYP2D6 and coadministration with other medications can significantly affect the pharmacokinetics and/or pharmacodynamics of venlafaxine and its active metabolite. This study aimed to establish the PBPK models of venlafaxine and its active metabolite related to CYP2D6 genetic polymorphism and to predict drug-drug interactions (DDIs) with clarithromycin and paroxetine in different CYP2D6 genotypes. Clinical pharmacogenomic data for venlafaxine and desvenlafaxine were collected to build the PBPK model. Physicochemical and absorption, distribution, metabolism, and excretion (ADME) characteristics of respective compounds were obtained from previously reported data, predicted by the PK-Sim® software, or optimized to capture the plasma concentration-time profiles. Model evaluation was performed by comparing the predicted pharmacokinetic parameters and plasma concentration-time profiles to the observed data. Predicted plasma concentration-time profiles of venlafaxine and its active metabolite were visually similar to the observed profiles and all predicted AUC and Cmax values for respective compounds were included in the twofold error range of observed values in non-genotyped populations and different CYP2D6 genotypes. When clarithromycin or clarithromycin plus paroxetine was concomitantly administered, predicted plasma concentration-time profiles of venlafaxine properly captured the observed profiles in two different CYP2D6 genotypes and all predicted DDI ratios for AUC and Cmax were included within the acceptance range. Consequently, the present model successfully captured the pharmacokinetic alterations of venlafaxine and its active metabolite according to CYP2D6 genetic polymorphism as well as the DDIs between venlafaxine and two CYP inhibitors. The present model can be used to predict the pharmacokinetics of venlafaxine and its active metabolite considering different races, ages, coadministered drugs, and CYP2D6 activity of individuals and it can contribute to individualized pharmacotherapy of venlafaxine.

Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms

BACKGROUND

Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug.

PURPOSE

A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK).

METHODS

The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model's performance was evaluated by comparing predicted and observed values of plasma concentration-time (PCT) curves and PK parameters values.

RESULTS

In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96 h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups.

CONCLUSIONS

In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.

Use of Exposure Data to Establish Causality in Drug-Adverse Event Relationships: An Example with Desvenlafaxine

Causality algorithms help establish relationships between drug use and adverse event (AE) occurrence. High drug exposure leads to a higher likelihood of an AE being classified as an adverse drug reaction (ADR). However, there is a knowledge gap regarding what concentrations are predictive of ADRs, as this has not been systematically studied. In this work, the Spanish Pharmacovigilance System (SEFV) algorithm was used to define the relationship between the AE occurrence and drug administration in 178 healthy volunteers participating in five desvenlafaxine single-dose clinical trials, a selective serotonin and norepinephrine reuptake inhibitor that may cause dizziness, headache, nausea, dry mouth, constipation and hyperhidrosis. Eighty-three subjects presented 172 AEs that were classified as possible (101), conditional (31), unrelated (24) and probable (16). AUC∞ and Cmax were significantly higher in volunteers with vs. without ADRs (5981.24 ng·h/mL and 239.06 ng/mL and 4770.84 ng·h/mL and 200.69 ng/mL, respectively). Six of 19 subjects with conditional AEs with an SEFV score of 3 points presented an AUC∞ ≥ 6500 ng·h/mL or a Cmax ≥ 300 ng/mL (i.e., above percentile 75) and were summed one point on their SEFV score and classified as "possible" (4 points), improving the capacity of ADR detection.