Impact of CYP2D6 Genetic Polymorphisms on the Vulnerability to Drug-drug Interactions With Tramadol

Interaction, Pain

Cytochrome, Polymorphism, Genotyping, Phenotyping, Tramadol, CYP2D6 inhibitor, Phenoconversion, Environment

Despite its poor abundance in the liver, CYP2D6 is the second most important CYP in drug metabolism, metabolizing 20% of drugs. The high inter-individual variability in CYP2D6 expression is explained by genetic variations, but also by drug-drug interactions (DDIs). Recent studies have pointed out the poor therapeutic predictable value of DDI. Indeed, the clinical outcomes of a DDI may involve several intrinsic factors affecting the vulnerability to and extent of DDI, such as genetic polymorphisms, comorbidities, age and sex. In this regard, the present research project aims to investigate the effect of genetic polymorphism on DDIs involving CYP2D6 (gene-environment interaction) and its implications for tramadol efficacy and safety in a clinical setting. In a previous study, we demonstrated differences in both the rate of phenoconversion and the magnitude of DDI in healthy volunteers, that were either heterozygote normal metabolizers (NMs) carrying a non-functional CYP2D6 allele (activity score (AS) 1) and homozygous NM carrying two fully-functional CYP2D6 alleles (AS 2). This prospective study will include patients scheduled for a general surgery of less than 3 hours and planned to be treated with oral tramadol as a routine post-operative pain management. Patients taking part in the study may receive diagnosis, therapeutic or other interventions but the groups of individuals (controls vs inhibited) are predefined based on the routine treatment of the patients. There will be no assigned specific interventions to the study participants and CYP2D6 phenotypes will be classified in five activity score groups (0.5, 1, 1.5, 2, \>2) in the absence or presence of a potent CYP2D6 inhibitor received as part of routine medical care. PK of tramadol and its active metabolite (M1), as well as its analgesic and PD effects and safety, will be compared between groups. Finally, the data generated will be used to build a physiologically-based PK (PBPK) model for tramadol in different sub-groups. The model will aim to predict the effect of CYP2D6 inhibition in virtual populations with different genetically-related CYP2D6 activities. This should allow prospective dose adjustment of tramadol (or appropriate drug selection) based on patients' genotype in the presence of a CYP2D6 inhibitor.

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