Several cytochrome P450 (CYP) CYP3A polymorphisms were associated with reduced enzyme function. We aimed to evaluate the influence of these alleles on the pharmacokinetic parameters (PK) of several CYP3A substrates. We included 251 healthy volunteers who received a single dose of ambrisentan, atorvastatin, imatinib, aripiprazole, fentanyl, amlodipine, donepezil, olanzapine, fesoterodine, or quetiapine. The volunteers were genotyped for CYP3A4 and CYP3A5 polymorphisms by qPCR. To compare the PK across studies, measurements were corrected by the mean of each parameter for every drug and were logarithmically transformed. Neither CYP3A phenotype nor individual CYP3A4 or CYP3A5 polymorphisms were significantly associated with differences in PK. However, regarding the substrates that are exclusively metabolized by CYP3A, we observed a higher normalized AUC (p = 0.099) and a tendency of lower normalized Cl (p = 0.069) in CYP3A4 mutated allele carriers what was associated with diminished drug metabolism capacity. CYP3A4 polymorphisms did not show a pronounced influence on PK of the analysed drugs. If so, their impact could be detectable in a very small percentage of subjects. Although there are few subjects carrying CYP3A4 double mutations, the effect in those might be relevant, especially due to the majority of subjects lacking the CYP3A5 enzyme. In heterozygous subjects, the consequence might be less noticeable due to the high inducible potential of the CYP3A4 enzyme.

Atorvastatin, prescribed for the treatment of hypercholesterolemia, demonstrated overwhelming benefits in reducing cardiovascular morbidity and mortality. However, many patients discontinue therapy due to adverse reactions, especially myopathy. The Dutch Pharmacogenetics Working Group (DPWG) recommends an alternative agent to atorvastatin and simvastatin or a dose adjustment depending on other risk factors for statin-induced myopathy in SLCO1B1 rs4149056 CC or TC carriers. In contrast, the Clinical Pharmacogenetics Implementation Consortium (CPIC) published their guideline on simvastatin, but not on atorvastatin. In this work, we aimed to demonstrate the effect of SLCO1B1 phenotype and other variants (e.g., in CYP3A4/5, UGT enzymes or SLC transporters) on atorvastatin pharmacokinetics. For this purpose, a candidate-gene pharmacogenetic study was proposed. The study population comprised 156 healthy volunteers enrolled in atorvastatin bioequivalence clinical trials. The genotyping strategy comprised a total of 60 variants in 15 genes. Women showed higher exposure to atorvastatin compared to men (p = 0.001), however this difference disappeared after dose/weight (DW) correction. The most relevant pharmacogenetic differences were the following: AUC/DW and C_max /DW based on (a) SLCO1B1 phenotype (p < 0.001 for both) and (b) CYP3A5*3 (p = 0.004 and 0.018, respectively). As secondary findings: SLC22A1 *2/*2 genotype was related to higher C_max/DW (ANOVA p = 0.030) and SLC22A1 *1/*5 genotype was associated with higher Vd/F (ANOVA p = 0.032) compared to SLC22A1 *1/*1, respectively. Finally, UGT2B7 rs7439366 *1/*1 genotype was associated with higher t_max as compared with the *1/*3 genotype (ANOVA p = 0.024). Based on our results, we suggest that SLCO1B1 is the best predictor for atorvastatin pharmacokinetic variability and that prescription should be adjusted based on it. We suggest that the CPIC should include atorvastatin in their statin-SLCO1B1 guidelines. Interesting and novel results were observed based on CYP3A5 genotype, which should be confirmed with further studies.

Aims: Pupillography is a noninvasive and cost‐effective method to determine autonomic nerve activity. Genetic variants in cytochrome P450 (CYP), dopamine receptor (DRD2, DRD3), serotonin receptor (HTR2A, HTR2C) and ATP‐binding cassette subfamily B (ABCB1) genes, among others, were previously associated with the pharmacokinetics and pharmacodynamics of antipsychotic drugs. Our aim was to evaluate the effects of aripiprazole and olanzapine on pupillary light reflex related to pharmacogenetics.Methods: Twenty‐four healthy volunteers receiving 5 oral doses of 10 mg aripiprazole and 5 mg olanzapine tablets were genotyped for 46 polymorphisms by quantitative polymerase chain reaction. Pupil examination was performed by automated pupillometry. Aripiprazole, dehydro‐aripiprazole and olanzapine plasma concentrations were measured by high‐performance liquid chromatography–tandem mass spectrometry.Results: Aripiprazole affected pupil contraction: it caused dilatation after the administration of the first dose, then caused constriction after each dosing. It induced changes in all pupillometric parameters (P < .05). Olanzapine only altered minimum pupil size (P = .046). Polymorphisms in CYP3A, HTR2A, UGT1A1, DRD2 and ABCB1 affected pupil size, the time of onset of constriction, pupil recovery and constriction velocity. Aripiprazole, dehydro‐aripiprazole and olanzapine pharmacokinetics were significantly affected by polymorphisms in CYP2D6, CYP3A, CYP1A2, ABCB1 and UGT1A1 genes.Conclusions: In conclusion, aripiprazole and its main metabolite, dehydro‐aripiprazole altered pupil contraction, but olanzapine did not have such an effect. Many polymorphisms may influence pupillometric parameters and several polymorphisms had an effect on aripiprazole, dehydro‐aripiprazole and olanzapine pharmacokinetics. Pupillography could be a useful tool for the determination of autonomic nerve activity during antipsychotic treatment.

For patients with type 2 diabetes, metformin is the most often recommended drug. However, there are substantial individual differences in the pharmacological response to metformin. To investigate the effect of transporter polymorphisms on metformin pharmacokinetics in an environment free of confounding variables, we conducted our study on healthy participants. This is the first investigation to consider demographic characteristics alongside all transporters involved in metformin distribution. Pharmacokinetic parameters of metformin were found to be affected by age, sex, ethnicity, and several polymorphisms. Age and SLC22A4 and SLC47A2 polymorphisms affected the area under the concentration-time curve (AUC). However, after adjusting for dose-to-weight ratio (dW), sex, age, and ethnicity, along with SLC22A3 and SLC22A4, influenced AUC. The maximum concentration was affected by age and SLC22A1, but after adjusting for dW, it was affected by sex, age, ethnicity, ABCG2, and SLC22A4. The time to reach the maximum concentration was influenced by sex, like half-life, which was also affected by SLC22A3. The volume of distribution and clearance was affected by sex, age, ethnicity and SLC22A3. Alternatively, the pharmacokinetics of metformin was unaffected by polymorphisms in ABCB1, SLC2A2, SLC22A2, or SLC47A1. Therefore, our study demonstrates that a multifactorial approach to all patient characteristics is necessary for better individualization.

Quetiapine is an atypical antipsychotic widely used for the treatment of schizophrenia and the depressive episodes of bipolar disorder. The aim of this work was to investigate the effect of variants in relevant pharmacogenes in the pharmacokinetics of quetiapine and to exploratorily evaluate adverse drug reaction (ADR) incidence based on genetic polymorphism. Specifically, 49 healthy volunteers enrolled in two bioequivalence clinical trials were included in this study. In addition, 80 variants in 19 relevant pharmacogenes were genotyped, including cytochrome P450 (CYP) genes, catechol-O-methyl transferase (COMT), other enzymes (e.g., UGT1A1 or UGT1A4), and transporters (e.g., SLCO1B1, ABCB1, or ABCG2). The COMT rs13306278 T allele was significantly related to quetiapine-increased exposure. We demonstrated the existence of quetiapine derivatives with a catechol-like structure (7,8-dihydroxi-quetiapine and 7,8-dihydroxi-N-desalkyl-quetiapine), which would be COMT metabolites and would explain quetiapine accumulation through CYP2D6 and CYP3A4 negative feedback. Moreover, CYP3A5 and CYP2B6 phenotypes were related to quetiapine exposure variability, which confirms (for CYP3A5) and suggests (for CYP2B6) that these enzymes play an important role in quetiapine’s metabolism. Finally, the ABCG2 rs2231142 T allele was related to quetiapine accumulation. Further studies are required to confirm the clinical relevance of our findings.