Impact of Pharmacogenomics in Clinical Practice

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1		Nicola Principi	--	2878	2024-01-18 09:42:42	\|
2	Reference format revised.	Lindsay Dong	Meta information modification	2878	2024-01-19 03:45:51	\|

This entry is adapted from the peer-reviewed paper 10.3390/ph16111596

Polymorphisms of genes encoding drug metabolizing enzymes and transporters can significantly modify pharmacokinetics, and this can be associated with significant differences in drug efficacy, safety, and tolerability. Moreover, genetic variants of some components of the immune system can explain clinically relevant drug-related adverse events. Knowledge of how genetic variations can modify the effectiveness, safety, and tolerability of a drug can lead to the adjustment of usually recommended drug dosages, improve effectiveness, and reduce drug-related adverse events.

drug prescription drug-related adverse events genetic variants pharmacogenomics pharmacokinetics

1. Introduction

For many years, it has been established that in a great number of patients the expected efficacy, tolerability, and safety of most medicines could be achieved only when drug prescribing could be individualized. It is well known that several factors, such as age, sex, disease characteristics, environmental factors, and diet, can significantly influence drug pharmacokinetics and pharmacodynamics and that, when the relevance of one or more of these factors in each patient is not carefully considered and traditionally recommended dosages are not individually adjusted, the impact of drug therapy can be different from that which is desired ^[1]. However, clinical effectiveness can be lower and the risk of severe drug-related adverse events significantly higher than expected. Traditionally reported examples in this regard are the need to adjust the dosage of some drugs in patients with reduced renal function ^[2], in those with severe liver disease ^[3], and in neonates and younger infants ^[4]^[5]. More recently, the importance of personalized drug dosage has been further highlighted by the evidence that the impact of drug administration could be strictly dependent on genetic factors and that genetic variants could contribute up to 95% to determining the interindividual variability in drug responses ^[6].

Several studies have shown that polymorphisms of genes encoding drug metabolizing enzymes and transporters can significantly modify the absorption, distribution, metabolism, and elimination of medicines, and this can be associated with significant differences in drug efficacy, safety, and tolerability ^[7]. Moreover, genetic variants of some components of the immune system, mainly human leukocyte antigens (HLAs) and T-cell receptors (TCRs), can explain clinically relevant drug-related adverse events ^[8]. All these findings have strongly increased the interest in pharmacogenomics, and several drug regulatory agencies, including the European Medicines Agency (EMA) ^[9] and the U.S. Food and Drug Administration (FDA) ^[10], consider studies of genetic factors that cause variability in drug response an essential part of the process of developing and authorizing drugs. Furthermore, it has definitively established that correlations between genetic variants and clinical effects should be systematically included in the package leaflet of all the drugs for which this information is known. In the USA, it occurs in more than 100 commercially available drugs ^[11]. Finally, to translate pharmacogenomics into clinical practice, several pharmacogenomics consortia, including the Clinical Pharmacogenetics Implementation Consortium (CPIC), have been created ^[12]^[13]^[14]^[15]. These institutions publish genotype-based drug guidelines to help clinicians understand how available genetic test results could be used to optimize drug therapy in each patient, according to the characteristics and frequency of genetic polymorphisms in the treated population. With this information, a few hospitals have included pharmacogenomic tests in routine clinical practice to promote genetic-guided precision medicine at least in some selected patients ^[16]^[17]. However, the implementation of drug dose individualization based on pharmacogenomics remains scarce, although it has been evidenced that about 10% of children receive one drug for which a change in prescribing due to genetic variants could be recommended ^[18].

2. Genetic Variations and Impact on Drug Transportation and Metabolism

2.1. Normal Mechanisms of Drug Transportation and Metabolism

The activity of transporters and that of metabolizing enzymes play an essential role in conditioning the pharmacokinetics of most drugs. Transporters are proteins that regulate the movement of drugs into and out of the various tissues and fluid compartments, maintaining homeostasis and controlling drug access to metabolizing enzymes and excretory pathways ^[19]. Among transporters, the most important are the Solute Carrier Superfamily (SLC) and the ATP-Binding Cassette superfamily (ABC) ^[20]. Among these, those with common polymorphisms are SLC22A1, ABCB1, ABCC2, ABCG2, and SLCO1B1, SLCO1B3, ABCB1, and ABCC2 ^[21].

To be detoxified and more easily excreted, most drugs undergo chemical modifications that occur in various organs and body systems, mainly the liver, through the activity of several enzymes ^[22]^[23]. These metabolic processes are categorized as phase I and phase II drug metabolism. Phase 1 reactions convert a parent drug to more water-soluble active metabolites by unmasking or inserting a polar functional group (−OH, −SH, −NH2). Among the phase 1 metabolizing enzymes, those included in the cytochrome P (CYP) 450 family, particularly CYP2C9, CYP2C19, and CYP2D6, are the most important. They are responsible for the metabolism of about 80% of commonly prescribed drugs ^[22], and variations in these genes influence the metabolism of 60% of these drugs ^[23]. Phase 2 reactions result in the conjugation of the drug with an endogenous substance by acetylation, glucuronidation, sulfation, and methylation with the formation of an inactive metabolite. Uridine diphosphate glucuronosyltransferase (UGT), glutathione S-transferase (GST), sulfotransferase, N-acetyltransferase (NAT), and thiopurine methyltransferase (TPMT) are the most common phase 2 metabolizing enzymes and those with more frequent genetic polymorphisms.

2.2. Impact of Genetic Variants on Pharmacokinetics

2.2.1. Polymorphisms of the Most Important Phase I Metabolism Enzymes

CYP2C9 metabolizes approximately 25% of clinically administered drugs. The prevalence of PMs ranges from 3 to 4% in Southern Europe and the Eastern Mediterranean coast to <1% in Asian and African populations, except for Emiratis (11.1%) ^[24]. Among the drugs metabolized by CYP2C9, there are the anticoagulant S-warfarin, the anticonvulsant phenytoin, some nonsteroidal anti-inflammatory drugs (NSAIDs), and some hypoglycemic agents such as glipizide and tolbutamide. In some cases, poor metabolization leads to severe clinical problems. When usual doses of warfarin are used, in PMs, the risk of internal bleeding is greatly increased ^[25]. To reduce this problem, a number of dosing algorithms incorporating point-of-care genotyping information leading in most cases to an improved anticoagulation control were developed ^[26]^[27]. One of these was tested, with favorable results in children ^[28]. Similarly, dose adjustments are needed in adult patients receiving phenytoin ^[29].

Regarding NSAIDs that are metabolized by CYP2C9 (i.e., ibuprofen, celecoxib meloxicam, flurbiprofen, piroxicam), data indicate that in PMs, the drug’s half-life is significantly increased, with an increase in the risk of drug-related adverse events. In these subjects, it is recommended to initiate treatment at 25–50% of the traditional dose or use NSAIDs not metabolized by CYP2C9 (i.e., acetylsalicylic acid, ketorolac, naproxen, sulindac) ^[30].

The prevalence of CYP2C19 PM metabolism phenotype is 2–5% among Caucasians and Africans and ~15% in Asians. On the contrary, URs can be identified in 18–28% of European populations, in 17–18% of African populations, and in 0.3–4% of Asian populations ^[31]. Diazepam, proton pump inhibitors, voriconazole, and clopidrogel are included among drugs whose levels are influenced by CYP2C19 genetic polymorphisms. Systemic drug exposure to diazepam can vary by more than sixfold between individuals. Standard doses may be poorly effective. In PMs, on the contrary, recommended diazepam doses can lead to higher-than-expected drug levels with extensive sedative effects ^[32]. However, at the moment, this adverse event is not reported on the drug’s label unless the drug is given with other medicines such as cimetidine, ketoconazole, fluvoxamine, fluoxetine, and omeprazole that inhibit CYP2C19 expression ^[33]. The metabolization of omeprazole to 5-hydroxy omeprazole and omeprazole sulphone can vary significantly. In Ems, increased metabolization rapidly reduces drug concentrations and leads to poor clinical response, as evidenced in patients with Helicobacter pylori infection ^[34]^[35]. The antiplatelet activity of clopidogrel is significant influenced by CYP2C19 activity, as the enzyme converts the prodrug into an active drug.

CYP2D6 actively metabolizes approximately 20–25% of all administered drugs ^[36], including drugs for pain management, cancer, mental health disorders, antiarrhythmics, and β-blockers ^[37]. The prevalence of CYP2D6 gene polymorphisms varies significantly between populations. PMs have been identified in 0.4–6.5% of individuals, with the highest values in European and American Caucasians and the lowest in East Asian, Oceanian, and Middle Eastern populations. The UM phenotype occurs in 1–2% of patients, although studies have reported that it is present in up to 28% of North Africans, Ethiopians, and Arabs; up to 10% in Caucasians; 3% in African Americans; and no more than 1% in Hispanic, Chinese, and Japanese populations ^[38].

A good example of the clinical impact of CYP2D6 gene polymorphisms is given by the studies regarding psychiatric drugs ^[39]. It has been shown that in subjects with gene mutations, risperidone and aripiprazole metabolism was significantly changed. In PMs and IMs, exposure to active drugs after recommended doses was increased, and a substantial reduction in dosage was required to maintain normal blood levels. On the contrary, in UMs, drug levels were inadequate to obtain favorable clinical results ^[40]. However, the most clinically relevant example of the impact of CYP2D6 genetic variations on drug metabolism in children is given by codeine. This opioid is converted by CYP2D6 into its active metabolite, morphine, which is truly responsible for the clinical efficacy, safety, and tolerability of the drug. PMs convert only 10% of codeine to morphine, whereas this occurs in 40% and 51% of EMs and UMs, respectively.

2.2.2. Polymorphisms of the Most Important Phase II Metabolism Enzymes

Several studies have shown that conjugation with glucuronic acid trough UGT enzyme activity is essential not only for the clearance and detoxification of several endogenous compounds (bilirubin, steroids, thyroid hormones, neurotransmitters, fatty acids) but is relevant also for the metabolization of a great number of commonly used drugs, such as paracetamol, some nonsteroidal anti-inflammatory drugs (naproxen, flurbiprofen, indomethacin, diclofenac), several neurologic drugs (anticonvulsants, antipsychotics, and benzodiazepine), and some anticancer drugs ^[41]. Old studies carried out on subjects with UGT gene polymorphisms have shown that these gene variations, despite being very common ^[42] and the cause of clinical syndromes with high unconjugated hyperbilirubinemia levels (i.e., Gilbert’s disease and Crigler–Najjar syndrome) ^[43], do not play a relevant role as a cause of drug clearance modification. Mutations were not associated with significant alterations in valproate ^[44], zidovudine, morphine, or codeine metabolism ^[45]. A substantial reduction in benzodiazepine clearance initially reported in individuals carrying the UGT2B15*2 variation ^[46] was not confirmed. However, the results of recent studies seem to suggest that the metabolism of some anticancer drugs is significantly affected by some UGT polymorphisms. In PMs, the administration of irinotecan has been found to be associated with higher systemic active metabolite concentrations with a higher risk of severe adverse events, such as profuse diarrhea and severe or life-threatening neutropenia ^[47]. Similar problems were found in PMs with the UGT1A1 *28/*28 genotype receiving sacituzumab govitecan-hziy ^[48].

2.2.3. Polymorphisms of the Most Important Transporters

A limited, if any, role of transporter gene variations on drug disposition has been shown. Data regarding ABCB1 variants are inconsistent. Moreover, although the polymorphism of ABCG2 has been associated with modifications in statin bioavailability, and ABCC2 variants have been shown to be the cause of reduced methotrexate and statin disposition ^[49], none of these biomarkers are currently used for drug dosage optimization. Significant evidence that polymorphisms of ABC efflux transporters can have severe clinical consequences is lacking ^[50]. Similar conclusions can be drawn regarding SLC gene polymorphisms. Studies regarding their impact on drug disposition are conflicting, as evidenced by the results of studies concerning metformin.

3. Genetic Variants That Affect Immune Response to Drugs

Immune-mediated adverse drug reactions account for about 20% of all adverse drug reactions. Most of them depend on HLA polymorphism ^[51]. Polymorphic HLA produces >10,000 HLA class I genetic variants and >4500 HLA class II chain genetic variations. Variants may modify specific immune responses with the development of abnormal reactions, such as autoimmune diseases ^[51]. Practically, drugs interact with certain HLA variants forming an immunogenic complex that is recognized by the immune system and evokes an immune reaction, leading to the development of drug-related adverse events. As the number of possible HLA–drug combinations is very high, HLA-mediated adverse events can only rarely be predicted. Prediction is further complicated by the evidence that, in the same subject, more than one HLA polymorphism influencing the safety of a single drug can be present and that, in some cases, these polymorphisms can be protective.

Most HLA polymorphism-mediated adverse events involve the liver and the skin ^[52]. Fortunately, in most of the cases, they have poor clinical relevance, and manifestations are generally resolved in a few days after drug therapy has been suspended. However, repeated administration can lead to more severe disease, suggesting that a careful medication history may reveal important information regarding the safety of a given drug. However, in some cases, particularly those that are very severe, immune-mediated drug-related diseases can occur without any previous history. This is the case of acute liver failure ^[53] and the most severe drug reactions, such as SJS, TEN, DRESS, and maculopapular exanthema (MPE) ^[54]. A great number of drugs have been associated with immune-mediated adverse events ^[55]^[56].

4. Implementation of Pharmacogenomics

Knowledge of the genetic characteristics of a patient allows us to define whether an indicated drug can be efficacious, whether the patient is at an increased risk of developing severe drug-related adverse events, and finally, what the optimal drug dosage is. To help clinicians understand how available genetic test results should be used to optimize drug use, several guidelines concerning drugs whose disposition and safety are influenced by pharmacogenomics have been prepared ^[57]. Moreover, several methods for developing and applying pharmacogenomics and personalizing drug therapy have been proposed ^[58]. Despite this, the implementation of pharmacogenomics in routine clinical practice has been sparse, and very few centers currently include pharmacogenetic tests in routine clinical care ^[59]^[60]^[61]^[62]^[63]^[64]. Several factors can explain this finding. A role can be played by the lack of precise information on the real frequency of genetic variations involved in drug disposition or adverse event determination in different populations, particularly those with less advanced health systems. The lack of a shared definition of the level of evidence that is necessary to implement pharmacogenetics-based information into clinical care also seems to be important. Organizations that curate pharmacogenetic evidence, including the CPIC and FDA, differ significantly in their interpretation of the available pharmacogenetic data, and this explains, at least in part, why pharmacogenomics recommendations to personalize therapy from medical societies are different and controversial ^[65].

Some limits of RCTs could be overcome by performing a very large initial screening in order to evaluate the importance of pharmacogenomic testing only in patients with a known genetic variation. But this method is also debatable as it raises important ethical limitations. If the variant under study is associated with an increased risk of life-threatening adverse events, as in the case of carbamazepine-induced severe cutaneous reactions in patients with an HLA*15:02 allele, the inclusion of patients at risk in the control group receiving standard therapy is deemed to be unethical ^[66]^[67]. In any case, whatever the method used, there is no doubt that verifying the benefits of the introduction of pharmacogenomics in clinical practice can be very expensive and discourage research, especially when it concerns rarely used drugs and relatively uncommon genetic variants.

Another consideration regarding the poor implementation of pharmacogenomics in clinical practice regards the poor knowledge about this method for medicine personalization by health care providers. A recent survey of the inclusion of pharmacogenomics in medical and pharmacy study programs showed that in only about 10% of cases pharmacogenomics was considered a mandatory subject ^[68].

The implementation of drug dose individualization programs and the prediction of effective and safe drug dosages is further complicated in pediatrics by the relative expression of some genes in the early developmental stages. The differentiation of drug metabolism between subjects with genetic variants conditioning poor or no metabolic function and those with the wild-type genotype can be very difficult or totally impossible when gene activity is poorly expressed. Only later, when enzyme activity is completely matured, the effect of polymorphism can be identified. Data collected in term and preterm infants receiving pantoprazole are a good example of the impact of ontogeny on genotype–phenotype discordance. Pantoprazole, used to treat gastroesophageal reflux, is a substrate for the CYP2C19 enzyme. In adult PMs, the systemic exposure to pantoprazole increases up to fivefold in the presence of a nonfunctional enzyme, as in this case drug clearance is reduced ^[69].

5. Conclusions

Several examples indicate that personalized medicine can significantly improve therapy, disease prevention, and health maintenance in a great number of individuals. Knowledge of how genetic variations can modify the effectiveness, safety, and tolerability of drugs can lead to an adjustment in usually recommended drug dosages, an improvement in effectiveness, and a reduction in drug-related adverse events. Despite some efforts to introduce pharmacogenomics in clinical practice, presently very few centers routinely use genetic tests as a guide for drug prescription. This is because several factors, among which the most important seem to be the poor knowledge of the frequency of genetic variations in a given population, the clinical impact of the use of pharmacogenomics, the complexity of the pharmacogenomics implementation, and the relevance of costs, may discourage local health authorities from personalizing medicine using genetic information. The education of health care professionals seems to be critical to keep pace with the rapidly evolving field of pharmacogenomics. The gap between geneticists and clinicians should be reduced. Clinicians should understand that pharmacogenomics is only one of the variables that should be considered when personalizing drug prescriptions. Clinicians usually take into account age and body system functions when they prescribe drugs and must also learn to use genetic information for this purpose. Multimodal algorithms incorporating both clinical and genetic factors could significantly help in this regard. Obviously, further studies definitively establishing which genetic variations play a role in conditioning drug effectiveness and safety are needed.

References

Thummel, K.E.; Lin, Y.S. Sources of interindividual variability. In Enzyme Kinetics in Drug Metabolism; Humana Press: Totowa, NJ, USA, 2014; Volume 1113, pp. 363–415.
Joy, M.S. Impact of glomerular kidney diseases on the clearance of drugs. J. Clin. Pharmacol. 2012, 52 (Suppl. S1), 23S–34S.
Weersink, R.A.; Bouma, M.; Burger, D.M.; Drenth, J.P.; Hunfeld, N.G.; Kranenborg, M.; Monster-Simons, M.H.; van Putten, S.A.; Metselaar, H.J.; Taxis, K.; et al. Evaluating the safety and dosing of drugs in patients with liver cirrhosis by literature review and expert opinion. BMJ Open 2016, 6, e012991.
Ruggiero, A.; Ariano, A.; Triarico, S.; Capozza, M.A.; Ferrara, P.; Attinà, G. Neonatal pharmacology and clinical implications. Drugs Context 2019, 8, 212608.
Porta, A.; Esposito, S.; Menson, E.; Spyridis, N.; Tsolia, M.; Sharland, M.; Principi, N. Off-label antibiotic use in children in three European countries. Eur. J. Clin. Pharmacol. 2010, 66, 919–927.
Kalow, W.; Tang, B.K.; Endrenyi, L. Hypothesis: Comparisons of inter- and intra-individual variations can substitute for twin studies in drug research. Pharmacogenetics 1998, 8, 283–289.
Relling, M.; Evans, W. Pharmacogenomics in the clinic. Nature 2015, 526, 343–350.
Wang, C.-W.; Preclaro, I.A.C.; Lin, W.-H.; Chung, W.-H. An Updated Review of Genetic Associations with Severe Adverse Drug Reactions: Translation and Implementation of Pharmacogenomic Testing in Clinical Practice. Front. Pharmacol. 2022, 13, 886377.
European Medicines Agency. Use of Pharmacogenetic Methodologies in the Pharmacokinetic Evaluation of Medicinal Products—Scientific Guideline. Available online: https://www.ema.europa.eu/en/use-pharmacogenetic-methodologies-pharmacokinetic-evaluation-medicinal-products-scientific-guideline (accessed on 1 September 2023).
U.S Food and Drug Administration. Administration Clinical Pharmacogenomics: Premarket Evaluation in Early-Phase Clinical Studies and Recommendations for Labeling. Available online: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-pharmacogenomics-premarket-evaluation-early-phase-clinical-studies-and-recommendations (accessed on 1 September 2023).
U.S. Food and Drug Administration. Table of Pharmacogenetic Associations. Available online: www.fda.gov/medical-devices/precision-medicine/table-pharmacogenetic-associations (accessed on 1 September 2023).
Relling, M.V.; Klein, T.E. CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network. Clin. Pharmacol. Ther. 2011, 89, 464–467.
Relling, M.V.; Klein, T.E.; Gammal, R.S.; Whirl-Carrillo, M.; Hoffman, J.M.; Caudle, K.E. The Clinical Pharmacogenetics Implementation Consortium: 10 years later. Clin. Pharmacol. Ther. 2020, 107, 171–175.
Swen, J.J.; Wilting, I.; Goede, A.L. Pharmacogenetics: From benchto byte. Clin. Pharmacol. Ther. 2008, 83, 781–787.
Thorn, C.F.; Klein, T.E.; Altman, R.B. PharmGKB: The pharmacogenomics knowledge base. Pharmacogenomics Methods Protoc. 2013, 1015, 311–320.
Borobia, A.M.; Dapia, I.; Tong, H.Y.; Arias, P.; Muñoz, M.; Tenorio, J.; Hernández, R.; García García, I.; Gordo, G.; Ramírez, E.; et al. Clinical Implementation of Pharmacogenetic Testing in a Hospital of the Spanish National Health System: Strategy and Experience over 3 Years. Clin. Transl. Sci. 2018, 11, 189–199.
Chen, T.; O’Donnell, P.H.; Middlestadt, M.; Ruhnke, G.W.; Danahey, K.; van Wijk, X.M.; Choksi, A.; Knoebel, R.; Hartman, S.; Yeo, K.T.J.; et al. Implementation of pharmacogenomics into inpatient general medicine. Pharmacogenetics Genom. 2023, 33, 19–23.
Ramsey, L.B.; Ong, H.H.; Schildcrout, J.S.; Shi, Y.; Tang, L.A.; Hicks, J.K.; El Rouby, N.; Cavallari, L.H.; Tuteja, S.; Aquilante, C.L.; et al. Prescribing Prevalence of Medications with Potential Genotype-Guided Dosing in Pediatric Patients. JAMA Netw. Open 2020, 3, e2029411.
Nigam, S.K. What do drug transporters really do? Nat. Rev. Drug Discov. 2015, 14, 29–44.
Yee, S.W.; Brackman, D.J.; Ennis, E.A.; Sugiyama, Y.; Kamdem, L.K.; Blanchard, R.; Galetin, A.; Zhang, L.; Giacomini, K.M. Influence of Transporter Polymorphisms on Drug Disposition and Response: A Perspective from the International Transporter Consortium. Clin. Pharmacol. Ther. 2018, 104, 803–817.
Iversen, D.B.; Andersen, N.E.; Dalgård Dunvald, A.C.; Pottegård, A.; Stage, T.B. Drug metabolism and drug transport of the 100 most prescribed oral drugs. Basic Clin. Pharmacol. Toxicol. 2022, 131, 311–324.
Ingelman-Sundberg, M. Human drug metabolising cytochrome P450 enzymes: Properties and polymorphisms. Naunyn-Schmiedeberg’s Arch. Pharmacol. 2004, 369, 89–104.
Ahmed, S.; Zhou, Z.; Zhou, J.; Chen, S.Q. Pharmacogenomics of Drug Metabolizing Enzymes and Transporters: Relevance to Precision Medicine. Genom. Proteom. Bioinform. 2016, 14, 298–313.
Zhou, Y.; Nevosadová, L.; Eliasson, E.; Lauschke, V.M. Global distribution of functionally important CYP2C9 alleles and their inferred metabolic consequences. Hum. Genom. 2023, 17, 15.
Aithal, G.P.; Day, C.P.; Kesteven, P.J.; Daly, A.K. Association of polymorphisms in the cytochrome P450 CYP2C9 with warfarin dose requirement and risk of bleeding complications. Lancet 1999, 353, 717–719.
Jorgensen, A.L.; Prince, C.; Fitzgerald, G.; Hanson, A.; Downing, J.; Reynolds, J.; Zhang, J.E.; Alfirevic, A.; Pirmohamed, M. Implementation of genotype-guided dosing of warfarin with point-of-care genetic testing in three UK clinics: A matched cohort study. BMC Med. 2019, 17, 76.
Lee, K.E.; Yee, J.; Lee, G.Y.; Chung, J.E.; Seong, J.M.; Chang, B.C.; Gwak, H.S. Genotype-guided warfarin dosing may benefit patients with mechanical aortic valve replacements: Randomized controlled study. Sci. Rep. 2020, 10, 6988.
Al-Metwali, B.Z.; Rivers, P.; Goodyer, L.; O’Hare, L.; Young, S.; Mulla, H. Personalised Warfarin Dosing in Children Post-cardiac Surgery. Pediatr. Cardiol. 2019, 40, 1735–1744.
Karnes, J.H.; Rettie, A.E.; Somogyi, A.A.; Huddart, R.; Fohner, A.E.; Formea, C.M.; Ta Michael Lee, M.; Llerena, A.; Whirl-Carrillo, M.; Klein, T.E.; et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2C9 and HLA-B Genotypes and Phenytoin Dosing: 2020 Update. Clin. Pharmacol. Ther. 2021, 109, 302–309.
Theken, K.N.; Lee, C.R.; Gong, L.; Caudle, K.E.; Formea, C.M.; Gaedigk, A.; Klein, T.E.; Agúndez, J.A.; Grosser, T. Clinical Pharmacogenetics Implementation Consortium Guideline (CPIC) for CYP2C9 and Nonsteroidal Anti-Inflammatory Drugs. Clin. Pharmacol. Ther. 2020, 108, 191–200.
Dehbozorgi, M.; Kamalidehghan, B.; Hosseini, I.; Dehghanfard, Z.; Sangtarash, M.H.; Firoozi, M.; Ahmadipour, F.; Meng, G.Y.; Houshmand, M. Prevalence of the CYP2C19*2 (681 G>A), *3 (636 G>A) and *17 (-806 C>T) alleles among an Iranian population of different ethnicities. Mol. Med. Rep. 2018, 17, 4195–4202.
Jung, F.; Richardson, T.H.; Raucy, J.L.; Johnson, E.F. Diazepam metabolism by cDNA-expressed human 2C P450s: Identification of P4502C18 and P4502C19 as low K(M) diazepam N-demethylases. Drug Metab. Dispos. 1997, 25, 133–139.
Dean, L. Diazepam Therapy and CYP2C19 Genotype. In Medical Genetics Summaries ; Pratt, V.M., Scott, S.A., Pirmohamed, M., Esquivel, B., Kattman, B.L., Malheiro, A.J., Eds.; National Center for Biotechnology Information (US): Bethesda, MD, USA, 2012.
Baldwin, R.M.; Ohlsson, S.; Pedersen, R.S.; Mwinyi, J.; Ingelman-Sundberg, M.; Eliasson, E.; Bertilsson, L. Increased omeprazole metabolism in carriers of the CYP2C19*17 allele; a pharmacokinetic study in healthy volunteers. Br. J. Clin. Pharmacol. 2008, 65, 767–774.
Schwab, M.; Schaeffeler, E.; Klotz, U.; Treiber, G. CYP2C19 polymorphism is a major predictor of treatment failure in white patients by use of lansoprazole-based quadruple therapy for eradication of Helicobacter pylori. Clin. Pharmacol. Ther. 2004, 76, 201–209.
Ingelman-Sundberg, M. Genetic polymorphisms of cytochrome P450 2D6 (CYP2D6): Clinical consequences, evolutionary aspects and functional diversity. Pharmacogenom. J. 2005, 5, 6–13.
Zhou, S.-F.; Di, Y.M.; Chan, E.; Du, Y.-M.; Chow, V.D.-W.; Xue, C.C.; Lai, X.; Wang, J.-C.; Li, C.G.; Tian, M.; et al. Clinical pharmacogenetics and potential application in personalized medicine. Curr. Drug Metab. 2008, 9, 738–784.
Dean, L.; Kane, M. Codeine Therapy and CYP2D6 Genotype. In Medical Genetics Summaries ; National Center for Biotechnology Information (US): Bethesda, MD, USA, 2012. Available online: https://www.ncbi.nlm.nih.gov/books/NBK100662/ (accessed on 1 September 2023).
Molden, E.; Jukić, M.M. CYP2D6 Reduced Function Variants and Genotype/Phenotype Translations of CYP2D6 Intermediate Metabolizers: Implications for Personalized Drug Dosing in Psychiatry. Front. Pharmacol. 2021, 12, 650750.
Jukic, M.M.; Smith, R.L.; Haslemo, T.; Molden, E.; Ingelman-Sundberg, M. Effect of CYP2D6 genotype on exposure and efficacy of risperidone and aripiprazole: A retrospective, cohort study. Lancet Psychiatry 2019, 6, 418–426.
Rowland, A.; Miners, J.O.; Mackenzie, P.I. The UDP-glucuronosyltransferases: Their role in drug metabolism and detoxification. Int. J. Biochem. Cell. Biol. 2013, 45, 1121–1132.
Holthe, M.; Rakvåg, T.N.; Klepstad, P.; Idle, J.R.; Kaasa, S.; Krokan, H.E.; Skorpen, F. Sequence variations in the UDPglucuronosyltransferase 2B7 (UGT2B7) gene: Identification of 10 novel single nucleotide polymorphisms (SNPs) and analysis of their relevance to morphine glucuronidation in cancer patients. Pharmacogenom. J. 2003, 3, 17–26.
Udomuksorn, W.; Elliot, D.J.; Lewis, B.C.; Mackenzie, P.I.; Yoovathaworn, K.; Miners, J.O. Influence of mutations associated with Gilbert and Crigler–Najjar type II syndromes on the glucuronidation kinetics of bilirubin and other UDP-glucuronosyltransferase 1A substrates. Pharmacogenet Genom. 2007, 17, 1017–1029.
Chung, J.-Y.; Cho, J.-Y.; Yu, K.-S.; Kim, J.-R.; Lim, K.; Sohn, D.-R.; Shin, S.-G.; Jang, I.-J. Pharmacokinetic and pharmacodynamic interaction of lorazepam and valproic acid in relation to UGT2B7 genetic polymorphism in healthy subjects. Clin. Pharmacol. Ther. 2008, 83, 595–600.
Krishnaswamy, S.; Hao, Q.; Duan, S.X.; Patten, C.J.; Von Moltke, L.L.; Greenblatt, D.J. Evaluation of 3-azido-3-deoxythymidine, morphine, and codeine as probe substrates for udp-glucuronosyltransferase 2B7 (UGT2B7) in human liver microsomes: Specificity and influence of the UGT2B7*2 polymorphism. Drug Metab. Disp. 2003, 31, 1125–1133.
He, X.; Hesse, L.M.; Hazarika, S.; Masse, G.; Harmatz, J.S.; Greenblatt, D.J.; Court, M.H. Evidence for oxazepam as an in vivo probe of UGT2B15: Oxazepam clearance is reduced by UGT2B15 D85Y polymorphism but unaffected by UGT2B17 deletion. Br. J. Clin. Pharmacol. 2009, 68, 721–730.
Hulshof, E.C.; de With, M.; de Man, F.M.; Creemers, G.J.; Deiman, B.A.; Swen, J.J.; Houterman, S.; Koolen, S.L.; Bins, S.; Thijs, A.M.; et al. UGT1A1 genotype-guided dosing of irinotecan: A prospective safety and cost analysis in poor metaboliser patients. Eur. J. Cancer 2022, 162, 148–157.
Wong, M.; Behrendt, C.E.; Yu, W.; Bosserman, L.D.; Lavasani, S.M.; Patel, N.; Sedrak, M.S.; Stewart, D.B.; Waisman, J.R.; Yuan, Y.; et al. UGT1A1 *28/*28 genotype and risk of toxicity and disease progression in breast cancer patients treated with sacituzumab govitecan-hziy. J. Clin. Oncol. 2023, 41 (Suppl. S16), 1033–1036.
Bruhn, O.; Cascorbi, I. Polymorphisms of the drug transporters ABCB1, ABCG2, ABCC2 and ABCC3 and their impact on drug bioavailability and clinical relevance. Expert Opin. Drug Metab. Toxicol. 2014, 10, 1337–1354.
Bruckmueller, H.; Cascorbi, I. ABCB1, ABCG2, ABCC1, ABCC2, and ABCC3 drug transporter polymorphisms and their impact on drug bioavailability: What is our current understanding? Expert Opin. Drug Metab. Toxicol. 2021, 17, 369–396.
Van Den Driessche, G.; Fourches, D. Adverse drug reactions triggered by the common HLA-B*57:01 variant: A molecular docking study. J. Cheminform. 2017, 9, 13.
Kloypan, C.; Koomdee, N.; Satapornpong, P.; Tempark, T.; Biswas, M.; Sukasem, C. A Comprehensive Review of HLA and Severe Cutaneous Adverse Drug Reactions: Implication for Clinical Pharmacogenomics and Precision Medicine. Pharmaceuticals 2021, 14, 1077.
Gerussi, A.; Natalini, A.; Antonangeli, F.; Mancuso, C.; Agostinetto, E.; Barisani, D.; Di Rosa, F.; Andrade, R.; Invernizzi, P. Immune-Mediated Drug-Induced Liver Injury: Immunogenetics and Experimental Models. Int. J. Mol. Sci. 2021, 22, 4557.
Redwood, A.J.; Pavlos, R.K.; White, K.D.; Phillips, E.J. HLAs: Key regulators of T-cell-mediated drug hypersensitivity. HLA 2018, 91, 3–16.
Jaruthamsophon, K.; Thomson, P.J.; Sukasem, C.; Naisbitt, D.J.; Pirmohamed, M. HLA Allele-Restricted Immune-Mediated Adverse Drug Reactions: Framework for Genetic Prediction. Annu. Rev. Pharmacol. Toxicol. 2022, 62, 509–529.
Vakrinou, A.; Bellampalli, R.; Gulcebi, M.I.; Martins Custodio, H.; Research Consortium, G.E.; Balestrini, S.; Sisodiya, S.M. Risk-conferring HLA variants in an epilepsy cohort: Benefits of multifaceted use of whole genome sequencing in clinical practice. J Neurol Neurosurg Psychiatry. 2023, 94, 887–892.
Caudle, K.E.; Klein, T.E.; Hoffman, J.M.; Muller, D.J.; Whirl-Carrillo, M.; Gong, L.; McDonagh, E.M.; Sangkuhl, K.; Thorn, C.F.; Schwab, M.; et al. Incorporation of pharmacogenomics into routine clinical practice: The Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline development process. Curr. Drug Metab. 2014, 15, 209–217.
McDermott, J.H.; Newman, W. Introduction to pharmacogenetics. Drug Ther. Bull. 2023, 61, 168–172.
Dunnenberger, H.M.; Crews, K.R.; Hoffman, J.M.; Caudle, K.E.; Broeckel, U.; Howard, S.C.; Hunkler, R.J.; Klein, T.E.; Evans, W.E.; Relling, M.V. Preemptive clinical pharmacogenetics implementation: Current programs in five US medical centers. Annu. Rev. Pharmacol. Toxicol. 2015, 55, 89–106.
Hicks, J.K.; Stowe, D.; Willner, M.A.; Wai, M.; Daly, T.; Gordon, S.M.; Lashner, B.A.; Parikh, S.; White, R.; Teng, K.; et al. Implementation of Clinical Pharmacogenomics within a Large Health System: From Electronic Health Record Decision Support to Consultation Services. Pharmacotherapy 2016, 36, 940–948.
Cavallari, L.H.; Beitelshees, A.L.; Blake, K.V.; Dressler, L.G.; Duarte, J.D.; Elsey, A.; Eichmeyer, J.N.; Empey, P.E.; Franciosi, J.P.; Hicks, J.K.; et al. The IGNITE Pharmacogenetics Working Group: An Opportunity for Building Evidence with Pharmacogenetic Implementation in a Real-World Setting. Clin. Transl. Sci. 2017, 10, 143–146.
van der Wouden, C.H.; Cambon-Thomsen, A.; Cecchin, E.; Cheung, K.C.; Dávila-Fajardo, C.L.; Deneer, V.H.; Dolžan, V.; Ingelman-Sundberg, M.; Jönsson, S.; Karlsson, M.O.; et al. Implementing Pharmacogenomics in Europe: Design and Implementation Strategy of the Ubiquitous Pharmacogenomics Consortium. Clin. Pharmacol. Ther. 2017, 101, 341–358.
Petry, N.; Baye, J.; Aifaoui, A.; Wilke, R.A.; Lupu, R.A.; Savageau, J.; Gapp, B.; Massmann, A.; Hahn, D.; Hajek, C.; et al. Implementation of wide-scale pharmacogenetic c testing in primary care. Pharmacogenomics 2019, 20, 903–913.
Luzum, J.A.; Pakyz, R.E.; Elsey, A.R.; Haidar, C.E.; Peterson, J.F.; Whirl-Carrillo, M.; Handelman, S.K.; Palmer, K.; Pulley, J.M.; Beller, M.; et al. The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Outcomes and Metrics of Pharmacogenetic Implementations across Diverse Healthcare Systems. Clin. Pharmacol. Ther. 2017, 102, 502–510.
Huebner, T.; Steffens, M.; Scholl, C. Current status of the analytical validation of next generation sequencing applications for pharmacogenetic profiling. Mol. Biol. Rep. 2023, 50, 9587–9599.
Relling, M.V.; Altman, R.B.; Goetz, M.P.; Evans, W.E. Clinical implementation of pharmacogenomics: Overcoming genetic exceptionalism. Lancet Oncol. 2010, 11, 507–509.
Zineh, I.; Lesko, L.J. Pharmacogenetics in medicine: Barriers, critical factors and a framework for dialogue. Pers. Med. 2009, 6, 359–361.
Karas Kuželički, N.; Prodan Žitnik, I.; Gurwitz, D.; Llerena, A.; Cascorbi, I.; Siest, S.; Simmaco, M.; Ansari, M.; Pazzagli, M.; Di Resta, C.; et al. Pharmacogenomics education in medical and pharmacy schools: Conclusions of a global survey. Pharmacogenomics 2019, 20, 643–657.
El Rouby, N.; Lima, J.J.; Johnson, J.A. Proton pump inhibitors: From CYP2C19 pharmacogenetics to precision medicine. Expert Opin. Drug Metab. Toxicol. 2018, 14, 447–460.

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