‘Personalized medicine’ considers the concept of extensive use of both genetic and phenotypic data to tailor drug therapy to the individual patient, in order to maximize drug response and minimize the risk of adverse drug reactions. Besides factors such as age, organ function, concomitant therapy, drug–drug interactions and the nature of the disease, genetic factors have been recognized as important determinants of interindividual variability of drug response Citation[1]. The particular aim of pharmacogenomics (PGx) is to elucidate the influence of genetic variation on drug response by the implementation of genome-wide data, using a variety of novel innovative genomic technologies currently available, such as genome-wide association studies (GWAS) and next-generation sequencing (NGS). In this context, pharmacokinetics is of special interest since it is involved in all processes of drug absorption, distribution, metabolism and excretion (ADME) and, in part, is highly variable among individuals. While the field of PGx started with focus on drug-metabolizing enzymes Citation[2], over the last 10 years, it has been extended to focus on membrane transporters (uptake and efflux transporters) that also influence ADME processes Citation[3]. Multiple genetic variants and haplotypes have been described in efflux and uptake transporters, and have been investigated with respect to potential implications for drug disposition and to the apparent intersubject variability in drug response Citation[4,5].
Interindividual variability of the expression and function of membrane transporters is not only affected by genetic factors, but could also be explained by drug–drug interactions (DDIs), especially through the inhibition or induction of transporter proteins (e.g., P-glycoprotein [P-gp]) by a coadministered drug. It is obvious that the presence of genetic variants may also affect DDIs, demonstrating the complexity of predicting drug response Citation[6]. Genetic variation in transporters or in ligand-activated nuclear receptors mediating the induction (e.g., pregnane X receptor and constitutive androstane receptor) may modify systemic availability of an inducer subsequently resulting, for example, in an induction-related failure of coadministered drugs. Of note, the consequences of genetic variants in drug-metabolizing enzymes and transporters on drug exposure have been studied extensively, but little attention has been given to whether genetic variability alters the degree of induction or inhibition of enzymes as well as transport proteins.
Organic cation transporters of the SLC22 family
Approximately 40% of all orally administered drugs are cations or weak bases at physiological pH Citation[7]. Several families of membrane transporters have been recognized to play a role in the transport of organic cations across the plasma membrane, including members of the solute carrier (SLC) family 22 (organic cation transporters [OCTs]) Citation[8]. Within the SLC22 family, OCT1, OCT2 and OCT3 were identified to translocate organic cations and weak bases in an electrogenic manner. The genes encoding human OCT1 (gene symbol: SLC22A1), OCT2 (SLC22A2), and OCT3 (SLC22A3) are located in a cluster on chromosome 6q26–q27 and have a common structure of 11 coding exons and ten introns.
In humans, OCT1 mRNA is most prominently expressed in the liver Citation[9,10], where the OCT1 protein is present in the sinusoidal (basolateral) hepatocyte membrane Citation[10]. There, it mediates the uptake of substrates from the blood, which is the first step in hepatic excretion of many cationic drugs. Other reported locations of human OCT1 include the lateral membrane of intestinal epithelial cells Citation[11] and the luminal (apical) membrane of ciliated cells in the lung Citation[12]. Human OCT2 mRNA is most strongly expressed in kidney Citation[9] and the OCT2 protein has been localized in the basolateral membrane of proximal tubule epithelial cells Citation[13,14]. Similar to OCT1 in hepatocytes, OCT2 plays an important role in the secretion of organic cations in the kidney by mediating the first step, that is, the uptake of organic cations across the basolateral membrane. At variance to OCT1 and OCT2, OCT3 has a broad tissue distribution with transcripts being detectable in the placenta, adrenal gland, liver, kidney, heart, lung, brain and intestine Citation[8,10,15]. In liver, the OCT3 protein is present in the sinusoidal hepatocyte membrane Citation[10].
Drugs transported by OCTs
The substrate specificities of human OCT1, OCT2 and OCT3 are largely overlapping. Substrates are typically organic cations with one or two positive charges or weak bases positively charged at physiological pH Citation[8,16]. Non-charged compounds, such as cimetidine at alkaline pH, may be transported as well Citation[17]. Transported endogenous substrates of human OCTs include monoamine neurotransmitters, neuromodulators and other compounds, such as choline, creatinine and guanidine Citation[8,16]. Among the more than 120 clinically used drugs that interact with human OCTs, at least 20 were identified as being transported. These include antineoplastic platinum compounds, the histamine H2 receptor antagonist cimetidine, the antiviral drugs acyclovir, ganciclovir, lamivudine and zalcitabine, the antidiabetic drug metformin, and the antiarrhythmic drug quinidine Citation[10,17–24].
Pharmacogenomics of OCTs
More than 1600 single-nucleotide polymorphisms (SNPs) are at present listed for the SLC22A1-A3 genes in the NCBI-SNP database Citation[101], including many rare genetic variants discovered by the currently ongoing 1000 Genomes project Citation[102]. The potential functional effects of single amino acid substitutions can be predicted in silico by algorithms such as Polymorphism phenotyping (PolyPhen) Citation[103] or Sorting Intolerant from Tolerant (SIFT) Citation[104]. However, these in silico predictions cannot substitute for the experimental analysis of each amino acid variant to proof functional changes of the respective OCT transporter. Differences between in silico predictions and in vitro experiments may partly be due to the fact that several variants are not properly incorporated into the plasma membrane, but are rather retained intracellularly Citation[25]. Moreover, genetic variants may alter transport function in a substrate-dependent manner (e.g., OCT1-Met420del; Citation[25–27]), illustrating the difficulty to predict complex effects of mutagenesis on functions of polyspecific transporters.
Currently, data on tissue expression of OCTs correlated to genetic variants are limited. In a recent systematic analysis a total of 36 variants in the SLC22A1 gene were tested, including some SNPs, which showed reduced function in vitro. The Arg61Cys polymorphism (rs12208357) is the only one so far that affects OCT1 expression in human liver on mRNA and protein levels Citation[10], even after correction for nongenetic factors (such as cholestasis) and additional SLC22A1 variants. A key publication of OCT pharmacogenomics showed that the AUC and Cmax of the antidiabetic drug metformin are significantly higher in OCT1-variant healthy subjects compared with individuals with OCT1 reference gene sequence Citation[28]. In line with OCT1 knockout mice experiments, OCT1 variant human subjects revealed poor response to metformin measured by the oral glucose tolerance test Citation[25] predicting better response to metformin. However this association is currently under debate Citation[16,29], thus genetic testing for OCT1 variants in clinical practice cannot be recommended so far.
The OCT2 variant Ala270Ser (rs316019) showed discrepant results in PK studies of metformin Citation[30–33]. However, cisplatin-induced nephrotoxicity was reduced in OCT2-Ala270Ser variant patients, which corresponds to the fact that cisplatin is an OCT2 substrate and OCT2 is highly expressed in human kidney Citation[34]. This association is corroborated by a recent study demonstrating no signs of ototoxicity and only mild nephrotoxicity after treatment with cisplatin in OCT1/2 double-knock-out versus wild-type mice Citation[35]. Thus, competing OCT2-mediated cisplatin uptake in renal proximal tubular and cochlear hair cells may be a promising approach to overcome oto- and nephro-toxicity in platinum drug-containing chemotherapeutical protocols in cancer patients.
Although the physiological role of OCTs is not completely understood, genotype-dependent OCT expression may also contribute to disease susceptibility. Of interest, the SLC22A3 locus was identified as a susceptibility gene for various diseases, whereas convincing data for both, SLC22A1 and SLC22A2, are lacking. The SLC22A3 gene was identified as potential risk factor for prostate cancer as well as coronary artery disease by GWAS including thousands of index cases and confirmed by independent control groups Citation[36,37].
The frequency of allele and genotype distributions of genetic variants significantly differs among ethnic groups Citation[38]. Regarding the SLC22A family, the allele frequency of the SLC22A1-Arg61Cys polymorphism is approximately 8% in European–Americans and Caucasians, whereas no variant subject was identified in African–Americans and Asian–Americans Citation[10,26,27]. By contrast, for the SLC22A1-Pro341Leu variant a significant higher allele frequency was found in African–Americans and Asian–Americans (8 and 17%) compared with Caucasians (up to 2%) Citation[10,27]. The Met408Val polymorphism was detected with a similar frequency distribution in Caucasians, Africans and Asians Citation[10,26,27]. Whether these differences in allele frequencies render individuals more susceptible for the development of certain diseases is currently unclear. Since, however, aflatoxin B1 is a substrate of OCT1 Citation[39] and the incidence of hepatocellular carcinoma is significantly higher in Asians than in Caucasians, one may assume that the higher frequency distribution of genetic variants of OCT1 may be an important contributing factor for susceptibility of hepatocellular carcinoma in Asians.
Drug–drug interactions
Various clinically used drugs have been identified as inhibitors of OCT-mediated transport by inhibition of in vitro uptake of transported cations, such as metformin or cimetidine. For example, OCT2-mediated cimetidine transport is inhibited by ranitidine Citation[40]. OCT1- and OCT2-mediated metformin transport is inhibited by the oral antidiabetics repaglinide and rosiglitazone Citation[41] and sodium channel blockers Citation[42], respectively. Clinical studies suggest that DDIs involving OCTs may mainly affect cationic drugs that are predominantly eliminated by renal secretion Citation[43,44]. The inhibition of tubular secretion of metformin by cimetidine was first described more than 20 years ago Citation[45], and recently this DDI was attributed to OCT2 Citation[31]. Other in vivo DDIs were reported between lamivudine and trimethoprim and between cisplatin and cimetidine or imatinib. For instance, renal lamivudine clearance was decreased after coadministration of trimethoprim Citation[46] and concomitant administration of imatinib, an OCT2 inhibitor, showed a protective effect against cisplatin-induced nephrotoxicity Citation[47].
Conclusion
The understanding of genetic factors on transport is critical to the benefit and risk assessment of a drug. From today’s perspective, it is challenging to predict if pharmacotherapy will substantially benefit from OCT pharmacogenomics, although several clinically relevant OCT drugs have been already identified. Compared with other transporters (e.g., P-gp Citation[48] and OATP1B1 Citation[49]) the research on the impact of OCT variants is only just beginning. Comprehensive genotype–phenotype correlation studies including different human tissues (e.g., kidney and intestine) as well as clinical response data are limited. In this context, the contribution of nongenetic factors and DDIs are also important for interindividual variability of drug response for OCT substrates. Of note, such information may be particularly helpful for novel treatment strategies, for instance by using an OCT-inhibiting agent to avoid drug toxicity of an OCT substrate (e.g., platinum drugs) in cells (e.g., renal tubular cells), which are not the targets of therapy. Whether genetic variability may also define the individual susceptibility for or the degree of clinically relevant DDIs by influencing the basal level of the expression/function of OCTs in different tissues needs to be elucidated. By improving our knowledge of OCT pharmacogenomics, pharmacokinetic–pharmacodynamic (response) processes will be better understood in the future.
Financial & competing interests disclosure
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in this manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
No writing assistance was utilized in the production of this manuscript.
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Websites
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