Phenoconversion is the mismatch between the individual’s genotype-based prediction of drug metabolism and the true capacity to metabolize drugs due to non genetic factors.1 For example, changes due to drugs inhibiting or inducing metabolizing enzymes can severely impact an individual’s ability to respond to a medication. Because of this, phenoconversion is an important factor to consider when making prescribing decisions. Let’s take a closer look at a few examples of phenoconversion.
Examples of phenoconversion
Yasui-Furukori et al. observed that the CYP2C19 inhibitor fluvoxamine can mimic the effects of being a CYP2C19 poor metabolizer (PMs) on omeprazole drug exposure in healthy volunteers (n=18). In figure 1, the drug exposure of omeprazole between the CYP2C19 PMs represented by the unfilled circles (far right graph) and phenoconverted PMs in filled circles (all three graphs) were not significantly different.
Yasui-Furukori N, Takahata T, Nakagami T, et al. Different inhibitory effect of fluvoxamine on omeprazole metabolism between CYP2C19 genotypes. Br J Clin Pharmacol. 2004;57(4):487-494. doi:10.1111/j.1365-2125.2003.02047.x
Mean plasma concentration-time curves of omeprazole during placebo and fluvoxamine treatment in homozygous extensive metabolizers (EMs) (n = 6), heterozygous EMs (n = 6) and poor metabolizers (PMs) (n = 6) for CYP2C19. Data are shown as mean and bars are SD. Data during control (○); data during fluvoxamine treatment (•)
Not only can someone taking a strong inhibitor mimic the effects of being born a poor metabolizer on drug exposure, but genetic changes in enzyme function can exacerbate or attenuate these changes in drug exposure. To illustrate the point that genetic polymorphisms may exacerbate drug-induced increases in drug exposure, we’ll evaluate research conducted by Azuma et al. on 27 healthy male patients taking aripiprazole.
These investigators found that renal clearance of aripiprazole was decreased in patients administered paroxetine and fluvoxamine (2D6 and 3A4 inhibitors) and that genetic polymorphisms within CYP2D6 further decreased drug clearance (figure 2).
Azuma J, Hasunuma T, Kubo M, et al. The relationship between clinical pharmacokinetics of aripiprazole and CYP2D6 genetic polymorphism: effects of CYP enzyme inhibition by coadministration of paroxetine or fluvoxamine. Eur J Clin Pharmacol. 2012;68(1):29-37. doi:10.1007/s00228-011-1094-4
Change in the estimated systemic clearance (CL/F) or aripiprazole (APZ) by CYP enzyme inhibition after coadministration with SSRIs. The error bars represent the standard deviation of CYP2D6 (lower), CYP3A4 (middle) and the total (upper). EM Extensive Metabolizer, IM intermediate metabolizer.
Phenoconversion and pharmacogenetics
In summary, we can be born poor metabolizers or we can be turned into poor metabolizers, and this concept of phenoconversion is one of the common criticisms of pharmacogenetics. Critics will say, “genetic testing is limited, because it doesn’t take drug-drug interactions into account.” An article by Shah and Smith (2015) states that phenoconversion is “the Achilles’ heel of personalized medicine”.
This is a valid criticism of pharmacogenetic testing, which is why Genomind launched the Precision Medicine Software. We designed the software to address the phenomenon of phenoconversion. The software estimates CYP450 drug-drug interactions, while also evaluating how your genetic profile may mimic, exacerbate or attenuate those changes in drug exposure.
You can see in the figures taken from the software below that whether you are born a 2D6 PM (Figure 3) or you are a normal metabolizer taking a 2D6 inhibitor like fluoxetine (Figure 4), both scenarios produce similarly weighted warnings on increases in aripiprazole drug exposure.
These results are in strong concordance with the FDA label for aripiprazole, which recommends halving the starting dose in both these scenarios (Figure 5), as well as quartering the dose in CYP2D6 poor metabolizer who are also taking strong CYP3A4 inhibitors.
In short, your genes AND knowledge of the effect of other medications on metabolizing enzymes are both important for predicting drug exposure.
Phenoconversion can present a challenge when it comes to using pharmacogenetic testing to inform prescribing decisions. The process of phenoconversion means drug-drug interactions can mimic the effects of genetically being a poor metabolizer.
While this is a common criticism of PGx testing, having the right tools can ensure you are making the most informed treatment decisions possible. That’s why Genomind equips all providers with Precision Medicine Software to track both gene-drug and drug-drug interactions as well as environmental factors.
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- J. Clin. Med. 2020, 9(9), 2890; https://doi.org/10.3390/jcm9092890