Inflammation-induced phenoconversion of polymorphic drug metabolizing enzymes: hypothesis with implications for personalized medicine

Abstract

Phenoconversion transiently converts genotypic extensive metabolizers (EMs) into phenotypic poor metabolizers (PMs) of drugs, potentially with corresponding changes in clinical response. This phenomenon, typically resulting from coadministration of medications that inhibit certain drug metabolizing enzymes (DMEs), is especially well documented for enzymes of the cytochrome P450 family. Nonclinical evidence gathered over the last two decades also strongly implicates elevated levels of some proinflammatory cytokines, released during inflammation, in down-regulation of drug metabolism, especially by certain DMEs of the P450 family, thereby potentially causing transient phenoconversion. Clinically, phenoconversion of NAT2, CYP2C19, and CYP2D6 has been documented in inflammatory conditions associated with elevated cytokines, such as human immunodeficiency virus infection, cancer, and liver disease. The potential of other inflammatory conditions to cause phenoconversion has not been studied but experimental and anecdotal clinical evidence supports infection-induced down-regulation of CYP1A2, CYP3A4, and CYP2C9 as well. Collectively, the evidence supports a hypothesis that certain inflammatory conditions associated with elevated proinflammatory cytokines may cause phenoconversion of certain DMEs. Since inflammatory conditions associated with elevated levels of proinflammatory cytokines are highly prevalent, phenoconversion of genotypic EM patients into transient phenotypic PMs may be more frequent than appreciated. Since drug pharmacokinetics, and therefore the clinical response, is influenced by DME phenotype rather than genotype per se, phenoconversion (whatever its cause) can have a significant impact on the analysis and interpretation of genotype-focused clinical outcome association studies. There is a risk that focusing on genotype alone may miss important associations between clinical outcomes and DME phenotypes, thus compromising future prospects of personalized medicine.