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Cendrós et al. J Transl Genet Genom 2020;4:210-20 I http://dx.doi.org/10.20517/jtgg.2020.21 Page 211

of treatment. The effect of gene variants on plasma drug levels, treatment response and adverse effects were
examined by multinomial regression.

Results: CYP1A2 was found to be associated with psychic side effects (P = 0.02), with variants predicting higher
enzyme activity associated with lower adverse effects, and was the strongest predictor for this adverse effect
of all the studied factors. Functional variants in CYP genes were associated with plasma level differences, with
higher activity variants associated with lower plasma levels. No association with improvement of the condition, as
measured by the PANSS score, was found in this study.

Conclusion: The results suggest that increased CYP1A2 activity protects against psychic side effects. Few studies
have evaluated the impact of genetic factors on treatment response or side effects, and only in relation to a
selection of adverse reactions. These results are a step towards better understanding of the factors behind the
different aspects of clinical outcomes, such as various adverse effects.

Keywords: Pharmacogenetics, antipsychotics, CYP1A2, CYP2C19, CYP2D6, ABCB1, drug response, adverse drug
reactions

INTRODUCTION
Antipsychotic drugs are the mainstay of treatment for severe mental disorders such as schizophrenia and
bipolar disorders. The treatment response to antipsychotic medications has wide inter-individual variability.
[1]
About 30%-50% do not respond adequately and 50% develop side effects . Pharmacokinetics are expected
to play an important role in drug response. The effective antipsychotic dose varies among patients, and
[2]
drug response is highly related to plasma levels, as is drug safety . Plasma levels are a direct consequence
of the administered dose, but variations on pharmacokinetics, namely drug absorption and metabolism,
introduce an element of uncertainty in plasma levels. At the same time, plasma levels and all processes
capable of affecting them may modify the dose-dependent fraction of the drug’s response. These processes
include transport across biological membranes, such as absorption and excretion, and metabolism.
Individuals with greater metabolism may require higher doses, while those with lower metabolic rates
[3-7]
might be more prone to the drugs’ adverse effects and need dose reduction . With all these variables
potential predictors of drug response, which of them, or combination of, is the best predictor of clinical
endpoints, if at all.

Genetic variants are known to influence both drug transport and metabolism. Variants in certain genes
[i.e., members of the cytochrome (CYP) P450 superfamily, or CYPs] have attracted attention because
of their potential to influence treatment response. The CYP superfamily of enzymes is responsible for
the metabolism of most drugs used in medical practice, including psychiatric drugs. The activity of
the CYP enzymes, as predicted by the allelic variants of the genes encoding for them, can be classified
into one of five groups: poor, intermediate, normal, rapid or ultrarapid metabolizers. Most psychiatric
drugs are metabolized by the enzymes CYP2D6, CYP1A2 and CYP2C19. Another potentially important
gene is ABCB1, which encodes the transport protein MDR1. This protein is expressed in the luminal
membrane of enterocytes and it can influence the absorption and bioavailability of substrate drugs.
ABCB1 is also expressed in the blood-brain barrier and can affect penetration into the central nervous
[8,9]
system . Although CYP genetic variants have been widely investigated in relation to mental disorders
and their treatment, the contribution of ABCB1 to treatment response and its possible interactions with
CYP functional variants is scarcely known. The identification of factors with greater influence on clinical
doses and response to psychotropic drugs may have an important effect on the improvement of treatment
in psychiatry. CYP enzymes can have genetic variation ranging from loss-of-function alleles to full gene