Interplay between Pharmacokinetics and Pharmacogenomics

Abstract

Pharmacogenomics represents an attempt to optimize the efficacy of drugs, minimize adverse drug reactions, and facilitate drug discovery, development, and approval. Understanding an individual’s genetic makeup can be the key to creating personalized drugs with greater efficacy and safety, as pharmacogenetic testing can be used to identify individuals who may be more susceptible to adverse drug reactions. Interindividual variability in the pharmacokinetics of many medicinal products is prone to interindividual variability. Pharmacogenomics should be considered one of the factors affecting the pharmacokinetics of a drug. When a polymorphism in a metabolizing enzyme and/or transporter causes a difference in exposure, it may alter efficacy or safety.

1. Introduction

Pharmacogenetics is a component of precision medicine in which patient-specific genes are used to optimize the medical care of patients, which serves to achieve an optimal drug response in terms of efficacy and toxicity [1]. Observations related to the dependence of drug effects on the genetic constitution of the recipient can be traced back to the 1950s, when reports of primaquine-caused hemolysis were seen in individuals who were glucose-6-phosphate (G6PD)-deficient [2]. Researchers examined why different people had such diverse responses to the same medication. In 1957, Arno Motulsky, a pioneer of medical genetics, published a paper titled “Drug Reactions, Enzymes, and Biochemical Genetics.” Freidrich Vogel, a German geneticist, is credited with coining the term “pharmacogenetics” in 1959 to explain the influence of inherited genetic characteristics on clinical responses to xenobiotics. Werner Kalow, a German clinical pharmacologist, created the framework of pharmacogenetics in his book Pharmacogenetics: Heredity and Drug Response, which collected known research at the time, including his personal findings on genetic variation and ethnicity [3].

Between 1977 and 1988, an increasing number of different genetic variants and their corresponding enzymatic functions were reported [4]. In 1999, the National Institutes of Health (NIH) announced a mission “to enable the formation of a series of multi-disciplinary research groups funded to conduct studies addressing research problems in pharmacogenetics” [5]. The Pharmacogenetics Research Network (PGRN) was created in 2000, with a mission “to catalyze and lead research in precision medicine for the discovery and translation of genomic variation influencing therapeutic and adverse drug effects” [6]. In the same year, the Pharmacogenomics Knowledge Base (PharmGKB) was released, which is a searchable referenced database of clinically actionable gene-drug associations and the relationships between genotypes and their produced phenotypes [7].

Reports focused on specific genes have been implicated in medication response for many years; single-nucleotide polymorphisms (SNPs) are the most frequently encountered genetic variants. High-density maps of SNPs also make them the most technically accessible class of genetic variants. By correlating SNPs and drug response data, one will have gained an ability to predict drug efficacy or toxicity within reasonable limits for any individual [8]. With the completion of the human genome sequencing project in 2001, a cutting-edge information technology capable of processing tens of thousands of raw sequence data became accessible [9]. Several population-based programs were launched in subsequent years. The term “pharmacogenomics” has emerged to refer to a better knowledge of the influence genetic diversity has, in many genes, on medication pharmacology. Currently, research institutes, companies, and government laboratories are rapidly moving on toward acquiring pharmacogenomic database management systems (DBMS), which are meant to combine public and proprietary genomic databases, clinical data sets, and results from high-throughput screening technologies [10].

Drug safety is the evaluation and study of the pharmacological effects of a potential drug that are unrelated to the desired therapeutic effect [11]. It is an essential element throughout the spectrum of drug discovery and development. A focus on variation related to genes involved in a drug’s pharmacokinetics (PK), which is the complex interplay of absorption, distribution, metabolism, and excretion, can be helpful for the prevention and treatment of patients experiencing with adverse drug reaction (ADR) [12]. Consequently, it can assist in choosing the appropriate and safe drug and dosage for each patient [13]. The term “population pharmacokinetic” (PPK) was first used by Sheiner in 1977 for investigating the typical PK of a drug in a large target population using available data. The PPK approach aims to quantitate the effects of various physiologic factors on drug PK with the overall goal of explaining as much variability as possible [14]. Mathematical models are developed to estimate the population-specific pharmacokinetic model parameters for a given drug. For example, parameters can be used to quantify the relationship, e.g., of clearance to individual physiology, such as the function of the liver, kidney, or heart [15]. A dose regimen can then be adjusted to achieve a specific clinical goal, such as drug exposure, within the therapeutic concentration window in the whole population or, if necessary, for special subpopulations characterized by their individual physiology. It must be considered that the significant variability of genetic information is transmitted from one generation to the other and is not always unaltered, creating a further degree of variability with great potential clinical relevance [16]. Pharmacogenomics accounts for ≈80% of the variability in drug efficacy and safety. Over 400 genes are clinically relevant in drug metabolism, and ≈200 pharmagenes are associated ADRs. The main role of pharmacogenetics is to translate genetic information into everyday clinical practice and lower the impact of ADRs, both for patients and for the healthcare system. The Food and Drug Administration (FDA) recently approved a safety labeling change for multiple drugs, guiding clinicians to identify individuals who may be “more susceptible” to ADR [17]. This labeling change does not constitute a requirement for testing prior to drug use but represents a step toward the establishment of such testing as a standard of practice.

The development of diagnostic tests for clinically significant disorders should be the emphasis of pharmacogenomics research [18]. Not every association results in a potentially valuable pharmacogenetic test, and financial and technological resources may be squandered if the significance of more easily quantifiable values is not initially ruled out [19]. For example, 5-hydroxytryptamine-3 receptor antagonists, which are used to treat nausea and vomiting, are known to be metabolized by the cytochrome P450 2D6 (CYP2D6) enzyme. Kim et al. discovered genotype-dependent pharmacokinetics for tropisetron in healthy volunteers [20], indicating that cancer patients who are ultrarapid metabolizers (UM) are undertreated by a standard dose of tropisetron. This idea was tested in 270 cancer patients by Kaiser and colleagues. There were more nausea and vomiting episodes in patients with a significant number of functioning CYP2D6 alleles. Patients given 4 mg of ondansetron to prevent postoperative nausea and vomiting had a similar outcome [21]. The impact of the UM phenotype on the pharmacokinetics and therapeutic efficacy of 5-hydroxytryptamine 3 receptor antagonists is clearly demonstrated by these studies. The “number needed to genotype” (i.e., the number of individuals needed to genotype to prevent one patient from experiencing needless nausea and vomiting) appeared to be 50 due to the low frequency of the UM genotype in persons of northern European origin [22]. This number is likely too high to incorporate pharmacogenetic testing into normal clinical practice and, more significantly, simpler options for preventing nausea, such as dosage titration or the use of an alternate antiemetic regimen, are currently available [23].

The molecular biology background of pharmacogenetics describes variations seen in drugs’ pharmacokinetic phases and/or pharmacodynamics, where drug receptors and other targets may be different from one patient to another [24]. This chapter focuses on the interplay between pharmacokinetics and pharmacogenomics.

2. Single-gene pharmacokinetics disorders

2.1 Pseudocholinesterase deficiency

Pseudocholinesterase is a plasma enzyme produced in the liver that is responsible for the metabolism of common muscle relaxants, including succinylcholine and mivacurium [25]. The inherited form of the enzyme transfers in an autosomal recessive manner. Patients with defective inherited forms of pseudocholinesterase (heterozygotes and homozygotes) present with prolonged muscular paralysis. Acquired pseudocholinesterase deficiency can develop in a variety of diseases or as a side effect of certain medications [26]. Pseudocholinesterase deficiency can be induced by malnutrition, pregnancy and the postpartum period, burns, liver illness, renal disease, cancer, infections, and medications such as steroids and cytotoxic agents [27]. Both acquired and hereditary defects are considered uncommon. Caucasian males of European origin, as well as Alaskan Native tribes, have the highest frequency of pseudocholinesterase deficiency [28]. In pseudocholinesterase-deficient patients, there is no particular therapy for neuromuscular paralysis; nevertheless, respiratory assistance with mechanical ventilation can be provided until the neuromuscular blockade is resolved [29]. Nondepolarizing neuromuscular blockers, such as atracurium, rocuronium, and vecuronium, are indicated for those with pseudocholinesterase deficiency. In addition, relatives of those who have been diagnosed with pseudocholinesterase deficiency are advised to get tested for the condition [30].

2.2 Acute intermittent porphyria

Acute intermittent porphyria (AIP) is a pharmacogenetic disease caused by a porphyrin metabolic defect characterized by a lack of porphobilinogen deaminase and a rise in the activity of delta-aminolevulinic acid synthase—two essential enzymes required for heme production [31]. Patients may have stomach discomfort, vomiting, muscular weakness, constipation, and neuropsychiatric symptoms during an episode. Clinical episodes are produced by many drugs (including barbiturates, antiseizure drugs, and sulfonamide antibiotics), hormones, and dietary variables, all of which induce hepatic delta-aminolevulinic acid synthase [32].

The most accurate approach for confirming AIP in patients and their symptom-free relatives is DNA analysis. The hydroxymethylbilane synthase (HMBS) gene is directly sequenced to discover a mutation in the proband as well as asymptomatic gene carriers among family members [33]. The sensitivity of the mutation analysis ranges from 90–100%. So far, 391 mutations in the HMBS gene have been reported. Thus, DNA testing in a family’s index case may be more difficult and time-consuming, but mutation analysis thereafter may quickly identify numerous family members at risk [34].

Heme infusions are frequently used to reduce the intensity and frequency of recurring episodes. The goal of this treatment is to significantly lower the level of porphyrin precursors in the blood. Most individuals react effectively, although long-term therapy may lead to exogenic heme dependency. As a result, a patient’s heme need may grow from monthly to twice-weekly infusions, making treatment discontinuation difficult due to significant porphyric symptoms. The use of heme preparations on a regular basis might cause thrombotic problems in the superficial veins, necessitating the use of a permanent central venous catheter [35]. Furthermore, long-term heme therapy might result in iron excess and hemosiderosis, which can cause organ damage. Hepatopathy, heart failure and endocrinopathies may arise as a result of the disease progressing. Iron burden in organs is shown by computed tomography (CT) or magnetic resonance imaging (MRI) [36]. Venesections are generally unpopular, although iron chelates can help. Preventative actions can be taken if family members are tested to determine whether they are genetic carriers. Symptomatic treatment, a high carbohydrate diet, and intravenous hematin injection are all used to treat attacks [37].

2.3 Drug acetylation deficiency

N-acetyl transferase (NATS) activities in human hepatic drug metabolizing enzymes have previously been identified as a source of inter-individual heterogeneity in drug metabolism [38]. The liver’s cytochrome P450 enzymes are primarily responsible for phase I oxidation, whereas phase 2 conjugations include glucuronidation, sulfation, and acetylation [39]. Two genes (NAT 1) and (NAT 2) are now known to control N-acetyl transferase (NAT), with NAT 2 A and B accounting for clinically significant metabolic variance [40].

Caffeine, isoniazid, nitrazepam, and sulfonamides are among the many common medications that are acetylated. Aromatic and heterocyclic amines are also carcinogenic, which has led to the theory that NAT genotypes are linked to cancer risk. Individuals can be divided into two groups after receiving sulfamethazine, caffeine, or another marker drug and having plasma and urine drug concentrations measured after a standard time interval: fast acetylators with only low concentrations of the parent drug remaining in the blood and slow acetylators with much higher concentrations of the parent drug remaining in the blood [41]. The frequency of fast and slow acetylators varies by ethnicity; Caucasian and Negro populations have almost equal numbers of fast and slow acetylators, whereas Oriental races have over 90% quick acetylators. The slow acetylator phenotype predominates among Arab people in Asia (e.g., Saudi Arabia and the United Arab Emirate) [42, 43] and North Africa (e.g., Egypt and Morocco) [40, 44].

3. Genetic variants affecting pharmacokinetics

Genetic variation in drug-metabolizing enzymes and/or drug transporter genes might impact drug exposure in terms of PK key parameters, such as maximum drug concentration (Cmax) and area under the curve (AUC) [45]. These differences can affect a patient’s loading dose, maintenance dose, dosing interval, and as a result, medication response and safety [46, 47].

3.1 Pharmacogenomics of drug-metabolizing enzymes

In the last decade, technical advancements in gene scanning and gene variant identification have substantially increased our understanding of the function of pharmacogenetics in drug metabolism [48, 49]. The number of genetic variants identified for genes coding for drug-metabolizing enzymes (DMEs) considerably increased in the early 2000s and continues to increase. Variation in drug metabolism and drug response can be caused by temporary factors, such as transient enzyme inhibition and induction, or by permanent causes, such as genetic mutation, gene deletion, or amplification among people of the same body weight and on the same medicine dosage. However, not all variants result in significant changes in enzyme activity. Genetic polymorphism can be associated with three phenotype classes based on drug metabolizing ability: the extensive drug metabolizer phenotype (EM) is found in the general population; the poor drug metabolizer phenotype (PM) is caused by mutation and/or deletion of both alleles and is linked to the accumulation of specific drug substrates; and gene amplification causes the UM phenotype, which results in increased drug metabolism [50]. The cytochrome P450 enzymes in families 1–3 mediate 70–90% of all phase I-dependent metabolism of available drugs [51]. The polymorphic forms of P450s are responsible for the development of approximately 86% of the reported adverse drug reactions (ADRs) of substrate drugs. Polymorphic enzymes (in particular, CYP2C9, CYP2C19, and CYP2D6) mediate around 40% of P450-mediated drug metabolism [52]. The major CYP450 forms that are important in human drug metabolism are shown in Table 1, together with their main substrates and the clinical consequences of the polymorphism.

Metabolizing enzyme	Example substrates	Major allelic variants	Clinical consequence of the polymorphism	Ref
CYP2D6	Atomoxetine, propranolol, tramadol	CYP2D6^*4; CYP2D6^*10; CYP2D6^*17; CYP2D6^*41	Altered drug dosage	[47]
CYP1A2	Caffeine, duloxetine, melatonin, clozapine	CYP1A2^*1 K	Reduced enzyme inducibility	[48]
CYP2C9	Celecoxib, glimepiride, phenytoin, warfarin	CYP2C9^*2; CYP2C9^*3	Altered drug dosage	[49]
CYP2C19	Omeprazole, diazepam, rabeprazole, voriconazole	CYP2C19^*2; CYP2C19^*3	Altered drug dosage	[50]

Table 1.

CYP450 enzymes and related polymorphisms.

3.2 Pharmacogenomics of drug transporters

The distribution of drug transporters in tissues key to pharmacokinetics, such as the intestine (absorption), blood-brain barrier (distribution), liver (metabolism), and kidneys (excretion), strongly suggests that genetic variation associated with changes in protein expression or function of these transporters may have a substantial impact on systemic drug exposure and toxicity [53]. In the last decade, a greater focus has been given to the impact of genetic variation in membrane transporters on the pharmacokinetics and toxicity of numerous therapeutic drugs [54]. While most transporter-related pharmacogenetic research has been related to classic genes encoding the outward-directed ATP-binding cassette (ABC) transporters, such as ABCB1 (P-glycoprotein), ABCC2 (MRP2), and ABCG2 (BCRP), more studies have been conducted in recent years evaluating genes encoding solute carriers (SLC) that mediate the cellular uptake of drugs, such as SLCO1B1 (OATP1B1) and SLC22A1 (OCT1) [55]. The main drug transporters are shown in Table 2, together with their main substrates and the clinical consequences of the polymorphism(s).

Drug transporter	Example substrates	Polymorphism	Clinical consequence	Ref
OATP1B1	Atorvastatin, pravastatin, repaglinide, methotrexate	SLCO1B1^*5; Val174Ala; c.521 T > C	Increased susceptibility to drug induced adverse events	[54]
ABCG2	Sulfasalazine, atorvastatin	(rs2231142; Gln141Lys; c.421C > A	Increased susceptibility to drug induced adverse events	[55]
OCT1	Metformin, cisplatin	R61C; P160L; G401S; G465R	Altered drug dosage	[56]
ABCB1	Irinotecan, mycophenalic acid	3435C > T; 2677G/T/A	Increased susceptibility to drug induced adverse events	[57]
SLC6A4	Citalopram, respiridone	5-HTTLPR	Altered drug response	[58]

Table 2.

Drug transporters and related polymorphisms.

4. Utility of pharmacogenomics and clinically available tests

One of the earliest uses of sequencing the human genome was expected to be clinical testing to predict medication response. New pharmacogenetic tests have a positive or substantial impact on prescription practice, such as the adoption of a different medication or dosage regimen, which results in quantifiable improvements in patient outcomes [56]. Despite the availability of tests, several barriers have hindered their implementation in ordinary clinical practice, including poor knowledge among healthcare professionals, ethical concerns, and the cost-effectiveness of the clinical outcome [57]. Furthermore, determining medication responses in complex multifactorial characteristics is difficult due to the interaction of many genes and genetic variations with environmental variables, and the genetic element may only have a minor influence on the outcome of treatment [58]. Much of the early published research focused on single pathogenic mutations that are particularly conspicuous or that have an obvious or unambiguous “all or nothing” therapy [59]. In fact, the likelihood of medication benefit and risk is frequently a continuum with a broad range of variance among individuals in a community, and relying on a single predictive biomarker to guide the treatment of serious diseases may not be accurate or reliable enough. Confirming analytic validity (test accuracy and reliability) and determining clinical utility should be the first steps in assessing pharmacogenetic indicators in clinical treatment [60]. There are additional health-economic issues to address, such as whether the genetic markers are common enough in their patient population to warrant the screening expenses [61]. The practicality of applying the biomarker testing method in a way that does not delay patient care will then be examined by policymakers and funding agencies. When compared with the current availability of preemptive testing for many genetic markers, the previous practice of single gene as needed, or “one at a time’” testing, might appear inefficient and costly [60]. Several obstacles to pharmacogenomics implementation have been identified and reported in the literature. Many of these issues are comparable with those that arise when introducing any new therapeutic service in a variety of practice settings. Securing administrative and provider buy-in, building effective physician relationships, and integrating a new service into an established clinical workflow are just a few of the common obstacles [62]. Still, fast testing utilizing multi-gene panels is becoming more widely available; an individual’s genetic data from a single sample may now be used to advise them about a variety of treatment options that may occur later in their lives.

4.1 Warfarin and CYP2C9/VKCOR

The most commonly prescribed medication for which a pharmacogenetic test has been proposed is warfarin [63]. The adoption of such testing as a standard of care has had to overcome three major challenges: (i) the presence of an alternative biomarker, the international normalized ratio, which is widely used and trusted by the practice community [64]; (ii) the need for specific dosing guidelines resulting from testing; and (iii) the need to demonstrate improvements not only in short-term toxicity but also in long-term toxicity [65]. Several dosage algorithms have been presented, and an international collaboration of researchers led by the PharmGKB (Pharmacogenomics Knowledge Base) has developed a clear, generalizable dosing methodology. Only the VKORC1 (P = 6.2 10–13) and CYP2C9 (P = 5 10–4) polymorphisms were found to be significant in a genome-wide association investigation of about 550,000 polymorphisms, suggesting that extensive and expensive trials would be necessary to find other relevant genetic variations [66]. The United States Food and Drug Administration (FDA) has revised the labeling to include genetic information, recognizing the importance of the relationship between the CYP2C9 genotype and the risk of severe bleeding episodes as well as a number of effectiveness surrogates [67]. Additional studies on the specificity and sensitivity of testing for both effectiveness and toxicity will likely be required before existing practice guidelines are amended to incorporate CYP2C9 9 and VKORC1 testing as clinical recommendations [68].

4.2 Tamoxifen and CYP2D6

Numerous studies across the world have investigated correlations between genotype and clinical outcome since the Consortium on Breast Cancer Pharmacogenomics identified a substantial relationship between the active metabolite endoxifen concentrations and the CYP2D6 genotype [69, 70]. Although more than 10 studies have been published on the subject, they are all small, and none of the large prospective trials comparing tamoxifen to aromatase inhibitors have been opened for analysis [71]. This is important, since the publishing of small studies might lead to selection bias and underpowered results in the research. Given that complex CYP genotypes can now be determined from paraffin sections [72], these data should be critical to the long-term clinical utility of CYP2D6 testing for tamoxifen efficacy in the many practice settings where it is used.

4.3 (5-fluorouracil) and dihydropyrimidine dehydrogenase deficiency

5-Fluorouracil is a chemotherapeutic medication commonly used to treat solid tumors, but its inconsistent effectiveness is exacerbated by its equally variable and often severe mucocutaneous toxicity [73]. The main metabolic enzyme in the target of 5-fluorouracil, dihydropyrimidine dehydrogenase, has a significant number of functional genetic variations [74]. Because testing for these variations cannot identify all cases of toxicity, their specificity and sensitivity are limited. This might be due to contributions from unknown genetic variations or nongenetic variables, such as age and gender, which have been shown to impact 5-fluorouracil clearance and toxicity [75]. Genome-wide association studies and the development of predictive scores integrating both clinical and genetic variables may be useful in this regard.

4.4 Irinotecan and UGT1A1

Variable effectiveness and possibly life-threatening toxicity limit the use of irinotecan as an effective therapy for colorectal and lung cancer [76]. Irinotecan is metabolized to the active metabolite SN-38, which is then removed by uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1)-catalyzed phase II glucuronidation [77]. It was immediately realized that this enzyme is also the primary cause of Gilbert’s syndrome’s benign, congenital hyperbilirubinemia. UGT1A1 polymorphisms are also predictive of irinotecan pharmacokinetics and treatment results, according to a number of studies [78, 79]. Irinotecan’s FDA-approved label has been updated to contain a reference to UGT1A1 genetic testing but no precise dosage recommendations. This, along with evidence that UGT1A1 testing may not be predictive for all irinotecan dosage regimens, has limited the test’s utility [58]. FDA labeling has not resulted in widespread usage of this test. This might be due to the absence of well-established alternative therapy options for patients with certain UGT1A1 genotypes as well as the unknown effects of testing.

4.5 Azathioprine, 6-mercaptopurine, and thiopurine methyltransferase

Granulocytopenia is a rare but life-threatening complication caused by giving standard dosages of azathioprine or 6-mercaptopurine to those who have homozygous thiopurine methyltransferase gene variations (TPMT) [80]. Although these medicines are approved for the treatment of leukemia in children, most of their clinical usage is in the treatment of inflammatory bowel disease in adults, where there have been fewer studies evaluating their efficacy. The perception that alternative indicators, such as a simple measurement of white blood cell count, may be used in place of the genetic test has limited its usage [81]. Furthermore, the availability of an equally predictive alternative—the test for phenotypic enzyme activity—has led to the increased adoption of this phenotypic test in some situations. The TPMT genotyping test is now included on the FDA labels for the medicines in question, but neither the criteria for testing nor the ramifications of the test’s results are specified [82]. Overall, the rarity of the variant phenotype, the availability of alternative predictors, and an FDA label that is neither specific nor proscriptive restrict the widespread utility of testing for the TPMT genotype.

4.6 Abacavir and HLA*B5701

Abacavir’s usage in the treatment of HIV/AIDS is linked to severe skin sensitivity, which has severely restricted its use [83]. Fortunately, significant skin responses are strongly linked to a germline HLA variation, and HLA*B5701 testing has been routinely utilized to prevent these reactions all over the world [84]. As a result, the medication is now used more effectively, and genetic testing is now the standard of care when abacavir is administered. The release of large prospective clinical trials demonstrating decreases in the incidence of skin sensitivity responses with clinically acceptable specificity and sensitivity has contributed to this accomplishment [85]. These studies were linked to a significant rise in the usage of testing [86]. However, abacavir is not extensively utilized in all clinical settings, and a single pharmacoeconomic analysis found that HLA-B*5701 testing would remain the favored approach only if abacavir-based treatment was as effective as tenofovir-based treatment and if the cost was lower per month.

4.7 Carbamazepine and HLA-B*1502

Carbamazepine can induce severe skin responses, such as Stevens-Johnson syndrome and toxic epidermal necrolysis, similar to abacavir. These negative occurrences have been linked to a specific HLA allele, HLA-B*1502,18, which is found nearly exclusively in Asian people [87]. HLA-B*1502 genetic testing is available, and the FDA has determined that Asian individuals should be tested before commencing carbamazepine medication [88]. Unless the predicted benefit clearly justifies a higher risk of severe skin responses, carbamazepine should not be initiated if they test positive. Even if a pharmacogenetic impact is limited to a particular ethnic group, rapid FDA action is conceivable when the link is convincing and a therapeutic change may be achieved, as in the instance of carbamazepine. It is worth noting that the identical genetic variation has recently been linked to phenytoin hypersensitivity responses.

4.8 Clozapine and HLA-DQB1

Agranulocytosis, the most serious side effect of clozapine, has been linked to the HLA locus, limiting the use of this essential and effective medicine [89]. An association between the incidence of clozapine-related agranulocytosis and HLA-DQB1*0201 in schizophrenia patients has been reported [90]. This assumption is based on short trials with a small number of 50 patients, making it less powerful and specific than in the instances of abacavir and carbamazepine. As a result, the test has a lower acceptance rate, and recommendations have yet to be developed.

5. Conclusions

The discipline of pharmacogenomics (PGX) has rapidly developed, and its application to patient care is continuing to unfold. It is acknowledged that pharmacogenetics may not be equally important for every drug. Genetic factors influencing drugs’ pharmacokinetic phases are centered on drug metabolism and transportation. Pharmacogenetic testing investigations should be promoted in sectors where there is a high probability of a clinically meaningful impact on clinical practice.

Acknowledgments

The seed for this chapter was planted during the author’s participation in the Fulbright Program at University of Nebraska Medical Center (UNMC) in the summer of 2021. Special thanks to Dr. Amy Pick for her support, exchange of ideas, and the unique opportunity to wonder.

Conflict of interest

The author declares no conflicts of interest.