Impact statement
Pharmacogenomics, the study of the effect of inherited or acquired genetic variation on differences in drug response or adverse effects. Around 95% of the population carry one or more actionable pharmacogenetic variants and over 75% of adults over 50 years old are on a prescription with a known PGx association. Pharmacogenomic evidence for cardiovascular drugs is growing along with emerging evidence for efficacy and cost-effectiveness. Successful pharmacogenomic implementation in healthcare requires strong scientific evidence, comprehensive and updated clinical guidelines, clinician champions and stakeholder engagement.
Introduction
An average one-size-fits-all approach is the foundation of the existing general healthcare paradigm of therapeutic, and preventative interventions. Whilst this is a very practical and effective strategy, only 40–50% of patients respond to treatment in this all-comers approach prescribed as per current practice, indicating a large proportion of the population may be facing a deficit in addressing their medical needs (Collins and Varmus, Reference Collins and Varmus2015). This requirement for a transformation in the current paradigm of healthcare has motivated the emergence of precision medicine as a more targeted approach to treatment (Goldberger and Buxton, Reference Goldberger and Buxton2013; Schork, Reference Schork2015). Precision medicine envisages an integration of an individual’s clinical and biological features obtained from laboratory tests, imaging, high-throughput omics and health records, to drive a personalised approach to diagnosis and treatment with a higher chance of success (Collins and Varmus, Reference Collins and Varmus2015). The anticipated benefits of the precision medicine approach for patients are quicker diagnosis and targeted treatment leading to higher treatment success with minimal to no adverse drug reactions (ADRs), with wider benefits in terms of decreased healthcare costs and increased economic productivity.
One of the routes to precision medicine is pharmacogenomics (PGx), the study of the effect of inherited or acquired genetic variation on drug absorption, distribution, metabolism and excretion (pharmacokinetics) or modification of drug target or biological pathways (pharmacodynamics) resulting in variations in drug response or adverse effects. Around 95% of the population carry one or more actionable pharmacogenetic variants and over 75% of adults over 50 years old are on a prescription with a known PGx association (Chanfreau-Coffinier et al., Reference Chanfreau-Coffinier, Hull, Lynch, DuVall, Damrauer, Cunningham, Voight, Matheny, Oslin, Icardi and Tuteja2019; Heise et al., Reference Heise, Gallo, Curry and Woosley2020; Zhou and Lauschke, Reference Zhou and Lauschke2022; Zhou et al., Reference Zhou, Nevosadova, Eliasson and Lauschke2023). The U.S. Food and Drug Administration (FDA) lists around 499 drugs which have PGx biomarkers in the labelling, with around a 100 of them linked to data supporting PGx-guided therapeutic recommendations (FDA, 2023a, 2023b). PharmGKB (PharmGKB, 2023a, 2023b) and the Clinical Pharmacogenetics Implementation Consortium (CPIC) (Relling et al., Reference Relling, Klein, Gammal, Whirl-Carrillo, Hoffman and Caudle2020) publish evidence-based, peer-reviewed guidelines on applying PGx test results into actionable prescribing decisions. PharmGKB Level 1 genes or gene–drug combinations are considered pharmacogenomically significant and are linked to specific prescribing guidance. Similarly, CPIC Levels A and B indicate that genetic information should be considered before prescribing.
CPIC currently reports around 480 gene–drug interactions, including 93 gene–drug pairs (24 genes with 75 drugs) that are annotated with Level A evidence and prescription guidelines (Crews et al., Reference Crews, Gaedigk, Dunnenberger, Leeder, Klein, Caudle, Haidar, Shen, Callaghan, Sadhasivam, Prows, Kharasch, Skaar and Implementation2014; Ramsey et al., Reference Ramsey, Johnson, Caudle, Haidar, Voora, Wilke, Maxwell, McLeod, Krauss, Roden, Feng, Cooper-DeHoff, Gong, Klein, Wadelius and Niemi2014; Hicks et al., Reference Hicks, Bishop, Sangkuhl, Muller, Ji, Leckband, Leeder, Graham, Chiulli, LLerena, Skaar, Scott, Stingl, Klein, Caudle and Gaedigk2015; Bell et al., Reference Bell, Caudle, Whirl-Carrillo, Gordon, Hikino, Prows, Gaedigk, Agundez, Sadhasivam, Klein and Schwab2017; Johnson et al., Reference Johnson, Caudle, Gong, Whirl-Carrillo, Stein, Scott, Lee, Gage, Kimmel, Perera, Anderson, Pirmohamed, Klein, Limdi, Cavallari and Wadelius2017; Amstutz et al., Reference Amstutz, Henricks, Offer, Barbarino, Schellens, Swen, Klein, McLeod, Caudle, Diasio and Schwab2018; Relling et al., Reference Relling, Schwab, Whirl-Carrillo, Suarez-Kurtz, Pui, Stein, Moyer, Evans, Klein, Antillon-Klussmann, Caudle, Kato, Yeoh, Schmiegelow and Yang2019; CPIC, 2022). Although integration of PGx into routine clinical practice is not widespread, the recent PREPARE trial demonstrated both efficacy and feasibility of implementation of a 12 gene pharmacogenomic panel across diverse European health-care system organisations and settings (Swen et al., Reference Swen, van der Wouden, Manson, Abdullah-Koolmees, Blagec, Blagus, Bohringer, Cambon-Thomsen, Cecchin, Cheung, Deneer, Dupui, Ingelman-Sundberg, Jonsson, Joefield-Roka, Just, Karlsson, Konta, Koopmann, Kriek, Lehr, Mitropoulou, Rial-Sebbag, Rollinson, Roncato, Samwald, Schaeffeler, Skokou, Schwab, Steinberger, Stingl, Tremmel, Turner, van Rhenen, Davila Fajardo, Dolzan, Patrinos, Pirmohamed, Sunder-Plassmann, Toffoli and Guchelaar2023) even if only limited to current CPIC Level A drugs (Chanfreau-Coffinier et al., Reference Chanfreau-Coffinier, Hull, Lynch, DuVall, Damrauer, Cunningham, Voight, Matheny, Oslin, Icardi and Tuteja2019; Heise et al., Reference Heise, Gallo, Curry and Woosley2020; Relling et al., Reference Relling, Klein, Gammal, Whirl-Carrillo, Hoffman and Caudle2020; Hicks et al., Reference Hicks, El Rouby, Ong, Schildcrout, Ramsey, Shi, Anne Tang, Aquilante, Beitelshees, Blake, Cimino, Davis, Empey, Kao, Lemkin, Limdi, PL, Rosenman, Skaar, Teal, Tuteja, Wiley, Williams, Winterstein, Van Driest, Cavallari and Peterson2021; Pritchard et al., Reference Pritchard, Patel, Stephens and Mc Leod2022). Only a small subset of the roughly 15% of medications that cite PGx information on their labels have actionable pharmacogenes (Ehmann et al., Reference Ehmann, Caneva, Prasad, Paulmichl, Maliepaard, Llerena, Ingelman-Sundberg and Papaluca-Amati2015; Mehta et al., Reference Mehta, Uber, Ingle, Li, Liu, Thakkar, Ning, Wu, Yang, Harris, Zhou, Xu, Tong, Lesko and Fang2020). Of the approximately 20,000 human genes, only 34 of them are considered clinically actionable with PGx (PharmGKB level 1) (PharmGKB, 2023a, 2023b). The majority of PGx-labelled agents are cancer therapies targeted for somatic mutations, rather than germline variants. Actionable germline PGx variants are present for around 7% of medications with CPIC Level A or B recommendations directing prescribing changes based on genotype (Relling et al., Reference Relling, Klein, Gammal, Whirl-Carrillo, Hoffman and Caudle2020).
Pharmacogenomics
The broad clinical relevance of PGx is evident across the medical spectrum from improving treatment efficacy to avoiding ADRs. CYP2D6 genotype guided optimisation of opioid analgesia resulted in a 30% reduction in pain intensity among 24% of patients (Smith et al., Reference Smith, Weitzel, Elsey, Langaee, Gong, Wake, Duong, Hagen, Harle, Mercado, Nagoshi, Newsom, Wright, Rosenberg, Starostik, Clare-Salzler, Schmidt, Fillingim, Johnson and Cavallari2019). Antidepressant prescribing guided by PGx variants across eight genes (CYP1A2, CYP2C9, CYP2C19, CYP3A4, CYP2B6, CYP2D6, HTR2A, SLC6A4) in the Genomics Used to Improve DEpression Decisions (GUIDED) trial (Greden et al., Reference Greden, Parikh, Rothschild, Thase, Dunlop, DeBattista, Conway, Forester, Mondimore, Shelton, Macaluso, Li, Brown, Gilbert, Burns, Jablonski and Dechairo2019) showed improved response and remission rates in difficult-to-treat depression, but no difference between the study arms for symptom improvement (primary outcome). A trial in a predominantly white human immunodeficiency virus type 1 infected population showed 100% elimination of immunologically confirmed abacavir hypersensitivity syndrome in those randomised to pre-emptive HLA-B*57:01-guided abacavir initiation (Mallal et al., Reference Mallal, Phillips, Carosi, Molina, Workman, Tomazic, Jagel-Guedes, Rugina, Kozyrev, Cid, Hay, Nolan, Hughes, Hughes, Ryan, Fitch, Thorborn and Benbow2008). Similarly, pre-emptive DPYD genotype guided dosing reduced from 73% to 28% the risk of fluoropyrimidine toxicity and completely abolished fluoropyrimidine-related mortality (Deenen et al., Reference Deenen, Meulendijks, Cats, Sechterberger, Severens, Boot, Smits, Rosing, Mandigers, Soesan, Beijnen and Schellens2016). Whilst these examples are compelling, the case for cardiovascular PGx is still evolving. In this review, we shall summarise the current status of PGx in cardiovascular diseases (CVDs) and look at the key enablers and barriers to PGx implementation in clinical practice.
Warfarin
The coumarin derivatives (warfarin, acenocoumarol and phenprocoumon) are a mainstay of CVD therapy due to their crucial role in preventing or treating thromboembolism.
Coumarins inhibit vitamin K epoxide reductase complex subunit 1 (VKORC1) and thence clotting factors II, VII, IX and X to yield its pharmacological anticoagulant effect (Verhoef et al., Reference Verhoef, Redekop, Daly, van Schie, de Boer and Maitland-van der Zee2014). Coumarins are racemic mixtures with one dominant pharmacological enantiomer. For warfarin, S-warfarin is 3–5 times more potent than R-warfarin and is preferentially metabolised by CYP2C9 (Kaminsky and Zhang, Reference Kaminsky and Zhang1997). Warfarin is unique in that, unlike most other drugs, its dose titration is based on coagulation levels in response to treatment. Warfarin has a narrow therapeutic index and exceeding optimal anticoagulation (measured by the international normalised ratio, INR) increases the risk of bleeding, necessitating frequent monitoring and dose titration (Landefeld and Beyth, Reference Landefeld and Beyth1993). One study found hospitalisation due to bleeding and supra-therapeutic INRs was seen in 6–7% of patients prescribed warfarin (Hylek et al., Reference Hylek, Evans-Molina, Shea, Henault and Regan2007; Lau et al., Reference Lau, Li, Wong, Man, Lip, Leung, Siu and Chan2017), while conversely, decreased time in the therapeutic INR range (TTR) was associated with increased ischaemic stroke, other thromboembolic events and mortality (Jones et al., Reference Jones, McEwan, Morgan, Peters, Goodfellow and Currie2005; Cancino et al., Reference Cancino, Hylek, Reisman and Rose2014).
There is substantial interpatient variability in warfarin response, with warfarin doses necessary to attain target INR ranging from <1 mg/day to >10 mg/day (stable dosing after loading dose) (Pokorney et al., Reference Pokorney, Simon, Thomas, Fonarow, Kowey, Chang, Singer, Ansell, Blanco, Gersh, Mahaffey, Hylek, Go, Piccini and Peterson2015). Genetic variation accounts for 55–60% of this dose variability: VKORC1 (∼25%), CYP2C9 (∼15%), CYP4F2*3 (∼1–7%) (Zhou et al., Reference Zhou, Nevosadova, Eliasson and Lauschke2023). Non-genetic factors collectively account for <20%: age, body mass index (BMI), smoking and drug interactions (Rost et al., Reference Rost, Fregin, Ivaskevicius, Conzelmann, Hortnagel, Pelz, Lappegard, Seifried, Scharrer, Tuddenham, Muller, Strom and Oldenburg2004; Wadelius et al., Reference Wadelius, Chen, Lindh, Eriksson, Ghori, Bumpstead, Holm, McGinnis, Rane and Deloukas2009; Verhoef et al., Reference Verhoef, Redekop, Daly, van Schie, de Boer and Maitland-van der Zee2014; Bourgeois et al., Reference Bourgeois, Jorgensen, Zhang, Hanson, Gillman, Bumpstead, Toh, Williamson, Daly, Kamali, Deloukas and Pirmohamed2016).
The CYP2C9*2, *3, *5, *6, *8 and *11 alleles reduce clearance of the more active S-warfarin, thus decreasing dose requirements by 5–7 mg/week in those carrying *2, *8 and *11 alleles, and 14 mg/week reported for the *3 and *5 alleles. Consequently, these variants are also associated with increased risk of over-anticoagulation. The *2 and *3 alleles are common among Europeans, while the *5, *6, *8 and *11 alleles occur almost exclusively in African ancestry populations (Johnson et al., Reference Johnson, Caudle, Gong, Whirl-Carrillo, Stein, Scott, Lee, Gage, Kimmel, Perera, Anderson, Pirmohamed, Klein, Limdi, Cavallari and Wadelius2017; Zhou et al., Reference Zhou, Nevosadova, Eliasson and Lauschke2023).
VKORC1 regulatory variant c.−1639G>A (rs9923231) is associated with reduced VKORC1 expression and lower warfarin dose requirements, with the −1,639 AA (high sensitivity) genotype more common among Asians and the −1,639 GG (reduced sensitivity) genotype more common among Africans (Limdi et al., Reference Limdi, Wadelius, Cavallari, Eriksson, Crawford, Lee, Chen, Motsinger-Reif, Sagreiya, Liu, Wu, Gage, Jorgensen, Pirmohamed, Shin, Suarez-Kurtz, Kimmel, Johnson, Klein and Wagner2010; Johnson et al., Reference Johnson, Caudle, Gong, Whirl-Carrillo, Stein, Scott, Lee, Gage, Kimmel, Perera, Anderson, Pirmohamed, Klein, Limdi, Cavallari and Wadelius2017; Zhou and Lauschke, Reference Zhou and Lauschke2022). Consequently, warfarin dose requirements are, respectively, lower and higher in Asian and African ancestry patients, respectively, as compared to Europeans (Limdi et al., Reference Limdi, Wadelius, Cavallari, Eriksson, Crawford, Lee, Chen, Motsinger-Reif, Sagreiya, Liu, Wu, Gage, Jorgensen, Pirmohamed, Shin, Suarez-Kurtz, Kimmel, Johnson, Klein and Wagner2010).
The CYP4F2 enzyme contributes to the variation in warfarin dose requirements not by metabolising warfarin, but rather by metabolising 75–90% of all vitamin K consumed by humans. Vitamin K1 reduction to vitamin K hydroquinone is critical to clotting factor activation. The *3 allele (rs2108622) is associated with reduced CYP4F2 activity resulting in higher concentrations of vitamin K1 and, consequently, higher warfarin dose requirements compared to the *1 allele, but this affects only European and Asian populations, with no impact on African ancestry individuals (Danese et al., Reference Danese, Raimondi, Montagnana, Tagetti, Langaee, Borgiani, Ciccacci, Carcas, Borobia, Tong, Davila-Fajardo, Rodrigues Botton, Bourgeois, Deloukas, Caldwell, Burmester, Berg, Cavallari, Drozda, Huang, Zhao, Cen, Gonzalez-Conejero, Roldan, Nakamura, Mushiroda, Gong, Kim, Hirai, Itoh, Isaza, Beltran, Jimenez-Varo, Canadas-Garre, Giontella, Kringen, Haug, Gwak, Lee, Minuz, Lee, Lubitz, Scott, Mazzaccara, Sacchetti, Genc, Ozer, Pathare, Krishnamoorthy, Paldi, Siguret, Loriot, Kutala, Suarez-Kurtz, Perini, Denny, Ramirez, Mittal, Rathore, Sagreiya, Altman, Shahin, Khalifa, Limdi, Rivers, Shendre, Dillon, Suriapranata, Zhou, Tan, Tatarunas, Lesauskaite, Zhang, Maitland-van der Zee, Verhoef, de Boer, Taljaard, Zambon, Pengo, Zhang, Pirmohamed, Johnson and Fava2019; Zhou and Lauschke, Reference Zhou and Lauschke2022).
While VKORC1 and CYP2C9 variants have emerged as the main genetic contributors to warfarin dose requirements in European and Asian ancestry populations (Cooper et al., Reference Cooper, Johnson, Langaee, Feng, Stanaway, Schwarz, Ritchie, Stein, Roden, Smith, Veenstra, Rettie and Rieder2008), the associations in African ancestry populations include single nucleotide polymorphisms (SNPs) in the chromosome 10 CYP2C cluster and in chromosome 6 upstream of EPHA7 (Perera et al., Reference Perera, Cavallari, Limdi, Gamazon, Konkashbaev, Daneshjou, Pluzhnikov, Crawford, Wang, Liu, Tatonetti, Bourgeois, Takahashi, Bradford, Burkley, Desnick, Halperin, Khalifa, Langaee, Lubitz, Nutescu, Oetjens, Shahin, Patel, Sagreiya, Tector, Weck, Rieder, Scott, Wu, Burmester, Wadelius, Deloukas, Wagner, Mushiroda, Kubo, Roden, Cox, Altman, Klein, Nakamura and Johnson2013; De et al., Reference De, Alarcon, Hernandez, Liko, Cavallari, Duarte and Perera2018; Zhou and Lauschke, Reference Zhou and Lauschke2022).
Validation of PGx-based warfarin dosing
The complexity of estimating initial warfarin dosing has been significantly diminished by the development of dosing algorithms, which take into account not only an individual’s clinical features (e.g., age, BMI and use of CYP2C9 inhibiting drugs), but also their genotype (VKORC1 −1639G>A, CYP2C9*2 and CYP2C9*3 alleles) (Gage et al., Reference Gage, Eby, Johnson, Deych, Rieder, Ridker, Milligan, Grice, Lenzini, Rettie, Aquilante, Grosso, Marsh, Langaee, Farnett, Voora, Veenstra, Glynn, Barrett and McLeod2008; International Warfarin Pharmacogenetics et al., Reference Klein, Altman, Eriksson, Gage, Kimmel, Lee, Limdi, Page, Roden, Wagner, Caldwell and Johnson2009). However, CYP2C9*5, *6, *8, *11 and rs12777823 are not represented in the algorithms significantly reducing their utility in patients of African ancestry. The Gage algorithm incorporates CYP2C9*5, *6 and CYP4F2*3 allele (Gage et al., Reference Gage, Eby, Johnson, Deych, Rieder, Ridker, Milligan, Grice, Lenzini, Rettie, Aquilante, Grosso, Marsh, Langaee, Farnett, Voora, Veenstra, Glynn, Barrett and McLeod2008; International Warfarin Pharmacogenetics et al., Reference Klein, Altman, Eriksson, Gage, Kimmel, Lee, Limdi, Page, Roden, Wagner, Caldwell and Johnson2009).
Three large multi-site RCTs (EU-PACT, COAG and GIFT) have evaluated the efficacy of genotype-guided warfarin dosing (Kimmel et al., Reference Kimmel, French, Kasner, Johnson, Anderson, Gage, Rosenberg, Eby, Madigan, McBane, Abdel-Rahman, Stevens, Yale, Mohler, Fang, Shah, Horenstein, Limdi, Muldowney, Gujral, Delafontaine, Desnick, Ortel, Billett, Pendleton, Geller, Halperin, Goldhaber, Caldwell, Califf, Ellenberg and Investigators2013; Pirmohamed et al., Reference Pirmohamed, Burnside, Eriksson, Jorgensen, Toh, Nicholson, Kesteven, Christersson, Wahlstrom, Stafberg, Zhang, Leathart, Kohnke, Maitland-van der Zee, Williamson, Daly, Avery, Kamali and Wadelius2013; Gage et al., Reference Gage, Bass, Lin, Woller, Stevens, Al-Hammadi, Li, Rodriguez, Miller, McMillin, Pendleton, Jaffer, King, Whipple, Porche-Sorbet, Napoli, Merritt, Thompson, Hyun, Anderson, Hollomon, Barrack, Nunley, Moskowitz, Davila-Roman and Eby2017) incorporating VKORC1 −1639G>A and CYP2C9*2 and *3 variants in a PGx algorithm, with CYP4F2 additionally included in the GIFT trial (Gage et al., Reference Gage, Bass, Lin, Woller, Stevens, Al-Hammadi, Li, Rodriguez, Miller, McMillin, Pendleton, Jaffer, King, Whipple, Porche-Sorbet, Napoli, Merritt, Thompson, Hyun, Anderson, Hollomon, Barrack, Nunley, Moskowitz, Davila-Roman and Eby2017). The primary endpoint was TTR for the EU-PACT and COAG trials (Kimmel et al., Reference Kimmel, French, Kasner, Johnson, Anderson, Gage, Rosenberg, Eby, Madigan, McBane, Abdel-Rahman, Stevens, Yale, Mohler, Fang, Shah, Horenstein, Limdi, Muldowney, Gujral, Delafontaine, Desnick, Ortel, Billett, Pendleton, Geller, Halperin, Goldhaber, Caldwell, Califf, Ellenberg and Investigators2013; Pirmohamed et al., Reference Pirmohamed, Burnside, Eriksson, Jorgensen, Toh, Nicholson, Kesteven, Christersson, Wahlstrom, Stafberg, Zhang, Leathart, Kohnke, Maitland-van der Zee, Williamson, Daly, Avery, Kamali and Wadelius2013) and clinical outcomes for the GIFT trial (Gage et al., Reference Gage, Bass, Lin, Woller, Stevens, Al-Hammadi, Li, Rodriguez, Miller, McMillin, Pendleton, Jaffer, King, Whipple, Porche-Sorbet, Napoli, Merritt, Thompson, Hyun, Anderson, Hollomon, Barrack, Nunley, Moskowitz, Davila-Roman and Eby2017). PGx-guided dosing showed significant improvement in the primary endpoints for EU-PACT and GIFT, but not COAG trials. EU-PACT (Pirmohamed et al., Reference Pirmohamed, Burnside, Eriksson, Jorgensen, Toh, Nicholson, Kesteven, Christersson, Wahlstrom, Stafberg, Zhang, Leathart, Kohnke, Maitland-van der Zee, Williamson, Daly, Avery, Kamali and Wadelius2013) compared genotype-guided warfarin dosing on days 1–5 followed by routine practice to routine practice. At 12 weeks, TTR was 7% higher in the genotype-guided arm (67.4% vs. 60.3%, P < 0.001). Conversely, TTR was similar in both the genotype-guided and clinically guided dosing arms of the COAG trial (4-week TTR 45.2% vs. 45.4%) (Kimmel et al., Reference Kimmel, French, Kasner, Johnson, Anderson, Gage, Rosenberg, Eby, Madigan, McBane, Abdel-Rahman, Stevens, Yale, Mohler, Fang, Shah, Horenstein, Limdi, Muldowney, Gujral, Delafontaine, Desnick, Ortel, Billett, Pendleton, Geller, Halperin, Goldhaber, Caldwell, Califf, Ellenberg and Investigators2013). In the GIFT trial (Gage et al., Reference Gage, Bass, Lin, Woller, Stevens, Al-Hammadi, Li, Rodriguez, Miller, McMillin, Pendleton, Jaffer, King, Whipple, Porche-Sorbet, Napoli, Merritt, Thompson, Hyun, Anderson, Hollomon, Barrack, Nunley, Moskowitz, Davila-Roman and Eby2017), the primary composite endpoint (INR ≥ 4, 30-day major bleeding, 30-day mortality death, 60-day incident venous thromboembolism) was lower in the genotype-guided group (10.8% vs. 14.7%, P = 0.02). Participants included in both the EU-PACT and GIFT trials were predominantly European. Although 27% of the COAG trial participants were African American, only the CYP2C9 alleles common in Caucasians (*2 and *3) were genotyped. Thus, all the three trials were blind to African ancestry-specific variants, and failure to account for these variants resulted in substantial warfarin overdosing in African American participants in the genotype-guided arm of COAG (Kimmel et al., Reference Kimmel, French, Kasner, Johnson, Anderson, Gage, Rosenberg, Eby, Madigan, McBane, Abdel-Rahman, Stevens, Yale, Mohler, Fang, Shah, Horenstein, Limdi, Muldowney, Gujral, Delafontaine, Desnick, Ortel, Billett, Pendleton, Geller, Halperin, Goldhaber, Caldwell, Califf, Ellenberg and Investigators2013). The reason is that CYP2C9*5, *6, *8 or *11 allele (present in ~15% of patients of African ancestry) or rs12777823 A allele (>40% of patients) may be misclassified as normal metabolisers (e.g., *1/*1) and dosed accordingly (Drozda et al., Reference Drozda, Wong, Patel, Bress, Nutescu, Kittles and Cavallari2015).
Patients with two or more CYP2C9 or VKORC1 variants are more prone to rapid INR surges and supratherapeutic anticoagulation at warfarin initiation. This may explain the differences between EU-PACT which used a loading dose and COAG which did not (Arwood et al., Reference Arwood, Deng, Drozda, Pugach, Nutescu, Schmidt, Duarte and Cavallari2017).
Clinical implementation of warfarin PGx
CYP2C9*2, *3, *5, *6, *8, *11, and VKORC1 −1639G>A alleles (Pratt et al., Reference Pratt, Cavallari, Del Tredici, Hachad, Ji, Kalman, Ly, Moyer, Scott, Whirl-Carrillo and Weck2020) are the minimum set of panel variants supported by cost-effectiveness data on the implementation of multigene genotype-guided warfarin dosing (Zhu et al., Reference Zhu, Swanson, Rojas, Wang, St Sauver, Visscher, Prokop, Bielinski, Wang, Weinshilboum and Borah2020). Both the FDA and Dutch Pharmacogenetics Working Group (DPWG) genotype-guided dosing recommendations are limited to just VKORC1 −1639G>A and CYP2C9*2 and *3 alleles. CPIC, in contrast, provides African and non-African specific guidance, with the former requiring CYP2C9*5, *6, *8 and *11 genotypes, and the latter requiring on CYP2C9*2 and *3 and VKORC1 genotypes (Johnson et al., Reference Johnson, Caudle, Gong, Whirl-Carrillo, Stein, Scott, Lee, Gage, Kimmel, Perera, Anderson, Pirmohamed, Klein, Limdi, Cavallari and Wadelius2017). Presence of CYP4F2*3 allele in non-African individuals results in a 5–10% dose increase. For those of African ancestry, rs12777823 variant, if available, results in an additional 15–30% dose reduction (Johnson et al., Reference Johnson, Caudle, Gong, Whirl-Carrillo, Stein, Scott, Lee, Gage, Kimmel, Perera, Anderson, Pirmohamed, Klein, Limdi, Cavallari and Wadelius2017).
Clopidogrel
Antiplatelet therapy is a cornerstone of atherosclerotic CVD management involving aspirin or a P2Y12 receptor antagonist (clopidogrel, prasugrel and ticagrelor), either as single agent therapy for secondary prevention or dual agents after percutaneous coronary intervention (PCI) (Roffi et al., Reference Roffi, Patrono, Collet, Mueller, Valgimigli, Andreotti, Bax, Borger, Brotons, Chew, Gencer, Hasenfuss, Kjeldsen, Lancellotti, Landmesser, Mehilli, Mukherjee, Storey and Windecker2016; Ibanez et al., Reference Ibanez, James, Agewall, Antunes, Bucciarelli-Ducci, Bueno, ALP, Crea, Goudevenos, Halvorsen, Hindricks, Kastrati, Lenzen, Prescott, Roffi, Valgimigli, Varenhorst, Vranckx and Widimsky2018). Prasugrel and ticagrelor are more potent P2Y12 receptor antagonists with an increased bleeding risk but are preferred over clopidogrel in high-risk cases (Wallentin et al., Reference Wallentin, Becker, Budaj, Cannon, Emanuelsson, Held, Horrow, Husted, James, Katus, Mahaffey, Scirica, Skene, Steg, Storey, Harrington, Investigators, Freij and Thorsen2009). Genetic variation is partly responsible for the observed variability in effectiveness of antiplatelet therapy (Angiolillo et al., Reference Angiolillo, Rollini, Storey, Bhatt, James, Schneider, Sibbing, So, Trenk, Alexopoulos, Gurbel, Hochholzer, De Luca, Bonello, Aradi, Cuisset, Tantry, Wang, Valgimigli, Waksman, Mehran, Montalescot, Franchi and Price2017). Assessment of platelet function status is time-consuming, lacks standard reference values and is hence not clinically feasible for tailoring antiplatelet therapy. The prospect of a genotype profile providing a measure of antiplatelet efficacy and thus predicting adverse cardiovascular outcomes makes a compelling case for the use of PGx to personalise treatment.
Clopidogrel, the most commonly prescribed antiplatelet drug, is a prodrug that undergoes a two-step transformation to its active metabolite which irreversibly inhibits platelet activation (Kazui et al., Reference Kazui, Nishiya, Ishizuka, Hagihara, Farid, Okazaki, Ikeda and Kurihara2010). CYP2C19 is involved in both activation steps, and thus, plays a crucial role in the bioactivation process of clopidogrel (Sangkuhl et al., Reference Sangkuhl, Klein and Altman2010). CYP2C19 is highly polymorphic with alleles representing a range of metaboliser phenotypes (summarised in Table 1; Kazui et al., Reference Kazui, Nishiya, Ishizuka, Hagihara, Farid, Okazaki, Ikeda and Kurihara2010; Sangkuhl et al., Reference Sangkuhl, Klein and Altman2010; Scott et al., Reference Scott, Sangkuhl, Stein, Hulot, Mega, Roden, Klein, Sabatine, Johnson, Shuldiner and Implementation2013; Pratt et al., Reference Pratt, Del Tredici, Hachad, Ji, Kalman, Scott and Weck2018; Zhou and Lauschke, Reference Zhou and Lauschke2022; Zhou et al., Reference Zhou, Nevosadova, Eliasson and Lauschke2023).
Table 1. CYP2C19 allele dependent enzyme activity
Abbreviations: EM, extensive metaboliser; IM, intermediate metaboliser; PM, poor metaboliser; UM, ultrarapid metaboliser.
The CYP2C19 poor metaboliser (PM) and intermediate metaboliser (IM) phenotypes have higher on-treatment platelet reactivity and an increased risk of ischaemic events compared to the normal metaboliser (NM) phenotype (*1/*1 genotype) (Varenhorst et al., Reference Varenhorst, James, Erlinge, Brandt, Braun, Man, Siegbahn, Walker, Wallentin, Winters and Close2009; Mega et al., Reference Mega, Close, Wiviott, Shen, Hockett, Brandt, Walker, Antman, Macias, Braunwald and Sabatine2009a). The equivalent of a 75 mg dose of clopidogrel in NMs is 225 mg in IMs, but 300 mg is insufficient in PMs (Mega et al., Reference Mega, Hochholzer, Frelinger, Kluk, Angiolillo, Kereiakes, Isserman, Rogers, Ruff, Contant, Pencina, Scirica, Longtine, Michelson and Sabatine2011; Price et al., Reference Price, Murray, Angiolillo, Lillie, Smith, Tisch, Schork, Teirstein, Topol and Investigators2012; Carreras et al., Reference Carreras, Hochholzer, Frelinger, Nordio, O’Donoghue, Wiviott, Angiolillo, Michelson, Sabatine and Mega2016). The antiplatelet drugs prasugrel and ticagrelor are not affected by the CYP2C19 genotype, offering the option for switching IMs and PMs to these drugs in preference to clopidogrel dose escalation in the absence of contraindications (Varenhorst et al., Reference Varenhorst, James, Erlinge, Brandt, Braun, Man, Siegbahn, Walker, Wallentin, Winters and Close2009; Mega et al., Reference Mega, Close, Wiviott, Shen, Hockett, Brandt, Walker, Antman, Macias, Braunwald and Sabatine2009b; Wallentin et al., Reference Wallentin, James, Storey, Armstrong, Barratt, Horrow, Husted, Katus, Steg, Shah, Becker and investigators2010).
Several real-world studies showed a significantly higher risk of major adverse cardiovascular events (MACE) in CYP2C19 PMs and IMs compared to NMs (Hulot et al., Reference Hulot, Collet, Silvain, Pena, Bellemain-Appaix, Barthelemy, Cayla, Beygui and Montalescot2010; Mega et al., Reference Mega, Simon, Collet, Anderson, Antman, Bliden, Cannon, Danchin, Giusti, Gurbel, Horne, Hulot, Kastrati, Montalescot, Neumann, Shen, Sibbing, Steg, Trenk, Wiviott and Sabatine2010; Holmes et al., Reference Holmes, Perel, Shah, Hingorani and Casas2011; Zabalza et al., Reference Zabalza, Subirana, Sala, Lluis-Ganella, Lucas, Tomas, Masia, Marrugat, Brugada and Elosua2012; Sorich et al., Reference Sorich, Rowland, McKinnon and Wiese2014; Cavallari et al., Reference Cavallari, Lee, Beitelshees, Cooper-DeHoff, Duarte, Voora, Kimmel, McDonough, Gong, Dave, Pratt, Alestock, Anderson, Alsip, Ardati, Brott, Brown, Chumnumwat, Clare-Salzler, Coons, Denny, Dillon, Elsey, Hamadeh, Harada, Hillegass, Hines, Horenstein, Howell, Jeng, Kelemen, Lee, Magvanjav, Montasser, Nelson, Nutescu, Nwaba, Pakyz, Palmer, Peterson, Pollin, Quinn, Robinson, Schub, Skaar, Smith, Sriramoju, Starostik, Stys, Stevenson, Varunok, Vesely, Wake, Weck, Weitzel, Wilke, Willig, Zhao, Kreutz, Stouffer, Empey, Limdi, Shuldiner, Winterstein, Johnson and Network2018; Kheiri et al., Reference Kheiri, Simpson, Osman, Kumar, Przybylowicz, Merrill, Golwala, Rahmouni, Cigarroa and Zahr2020). However, the higher risk of MACE in clopidogrel-treated PMs and IMs was less evident in lower-risk populations, such as atrial fibrillation or medically managed acute coronary syndrome (ACS) cases (Bauer et al., Reference Bauer, Bouman, van Werkum, Ford, ten Berg and Taubert2011; Holmes et al., Reference Holmes, Perel, Shah, Hingorani and Casas2011). Two prospective trials, POPular Genetics (Claassens et al., Reference Claassens, Vos, Hermanides, van’t Hof, van der Harst, Barbato, Morisco, Tjon Joe Gin, Asselbergs, Mosterd, Herrman, WJM, PWA, Kelder, Postma, de Boer, Boersma, VHM and Ten Berg2019) and TAILOR-PCI (Pereira et al., Reference Pereira, Farkouh, So, Lennon, Geller, Mathew, Bell, Bae, Jeong, Chavez, Gordon, Abbott, Cagin, Baudhuin, Fu, Goodman, Hasan, Iturriaga, Lerman, Sidhu, Tanguay, Wang, Weinshilboum, Welsh, Rosenberg, Bailey and Rihal2020) trials stratified IMs and PMs to prasugrel or ticagrelor while NMs received clopidogrel. The CYP2C19-guided approach reduced bleeding risk and was non-inferior to treatment with prasugrel or ticagrelor in preventing atherothrombotic events in the POPular Genetics study that enrolled post ST-segment elevation MI patients undergoing PCI (Claassens et al., Reference Claassens, Vos, Hermanides, van’t Hof, van der Harst, Barbato, Morisco, Tjon Joe Gin, Asselbergs, Mosterd, Herrman, WJM, PWA, Kelder, Postma, de Boer, Boersma, VHM and Ten Berg2019). In the TAILOR-PCI trial (Pereira et al., Reference Pereira, Farkouh, So, Lennon, Geller, Mathew, Bell, Bae, Jeong, Chavez, Gordon, Abbott, Cagin, Baudhuin, Fu, Goodman, Hasan, Iturriaga, Lerman, Sidhu, Tanguay, Wang, Weinshilboum, Welsh, Rosenberg, Bailey and Rihal2020), patients with either stable coronary disease or ACS undergoing PCI showed lower rates of the composite cardiovascular primary endpoint in the genotype-guided group compared to the non-genotype-guided cohort at 1-year follow-up, but this did not reach statistical significance (HR 0.66; 95% CI 0.43–1.02; P = 0.06) (Pereira et al., Reference Pereira, Farkouh, So, Lennon, Geller, Mathew, Bell, Bae, Jeong, Chavez, Gordon, Abbott, Cagin, Baudhuin, Fu, Goodman, Hasan, Iturriaga, Lerman, Sidhu, Tanguay, Wang, Weinshilboum, Welsh, Rosenberg, Bailey and Rihal2020). A post hoc analysis indicated benefit in the genotype-directed group during the first 3 months after PCI (HR 0.21; 95% CI 0.08–0.54; P = 0.001) (Pereira et al., Reference Pereira, Farkouh, So, Lennon, Geller, Mathew, Bell, Bae, Jeong, Chavez, Gordon, Abbott, Cagin, Baudhuin, Fu, Goodman, Hasan, Iturriaga, Lerman, Sidhu, Tanguay, Wang, Weinshilboum, Welsh, Rosenberg, Bailey and Rihal2020). Other indications for clopidogrel include stroke prevention and peripheral arterial disease. PMs and IMs show reduced rates of stent patency after endovascular treatment for peripheral arterial disease (Guo et al., Reference Guo, Tan, Guo, Shi, Zhang and Guo2014; Diaz-Villamarin et al., Reference Diaz-Villamarin, Davila-Fajardo, Martinez-Gonzalez, Carmona-Saez, Sanchez-Ramos, Alvarez Cubero, Salmeron-Febres, Cabeza Barrera and Fernandez-Quesada2016). For stroke, a large randomised controlled trial (RCT) showed that absence of the CYP2C19 no-function allele in patients with a minor ischaemic stroke or transient ischaemic attack (TIA) predicted better effectiveness of clopidogrel plus aspirin over aspirin alone (Wang et al., Reference Wang, Zhao, Lin, Li, Johnston, Lin, Pan, Liu, Wang, Wang, Meng, Xu, Wang and investigators2016). A meta-analysis including nearly 5,000 clopidogrel-treated patients with ischaemic stroke or TIA confirmed higher risk of new stroke in PMs and IMs (Pan et al., Reference Pan, Chen, Xu, Yi, Han, Yang, Li, Huang, Johnston, Zhao, Liu, Zhang, Wang, Wang and Wang2017).
Clinical implementation of clopidogrel PGx
Since 2010, the FDA, European Medicine Agency (EMA) and other regulatory bodies recommend alternative P2Y12 inhibitors to clopidogrel in PMs (but not IMs) in their labels (Holmes et al., Reference Holmes, Dehmer, Kaul, Leifer, O’Gara and Stein2010). The FDA table of gene–drug pairs includes therapeutic management recommendations for IMs and PMs (FDA, 2023a, 2023b), which is echoed by CPIC guidelines citing ‘strong’ evidence for IMs and PMs with ACS or undergoing PCI, and ‘moderate’ evidence PMs for all indications. In all of the above cases, alternative antiplatelet agents are recommended (Lee et al., Reference Lee, Luzum, Sangkuhl, Gammal, Sabatine, Stein, Kisor, Limdi, Lee, Scott, Hulot, Roden, Gaedigk, Caudle, Klein, Johnson and Shuldiner2022).
Joint PCI guidelines from 2016 by the American College of Cardiology (ACC) and the American Heart Association (AHA) recommend against routine genotyping for all patients undergoing PCI, but to consider testing high-risk patients and use either prasugrel or ticagrelor for patients with the no-function allele. The 2020 European Society of Cardiology (ESC) guidelines were influenced by the POPular Genetics trial to recommend genotype-guided de-escalation for post-PCI patients deemed to be at high bleeding risk (Claassens and Sibbing, Reference Claassens and Sibbing2020; Collet et al., Reference Collet, Thiele, Barbato, Barthelemy, Bauersachs, Bhatt, Dendale, Dorobantu, Edvardsen, Folliguet, Gale, Gilard, Jobs, Juni, Lambrinou, Lewis, Mehilli, Meliga, Merkely, Mueller, Roffi, Rutten, Sibbing and GCM2021).
CYP2C19-guided antiplatelet therapy after PCI is one of the most common PGx tests in clinical practice (Empey et al., Reference Empey, Stevenson, Tuteja, Weitzel, Angiolillo, Beitelshees, Coons, Duarte, Franchi, Jeng, Johnson, Kreutz, Limdi, Maloney, Owusu Obeng, Peterson, Petry, Pratt, Rollini, Scott, Skaar, Vesely, Stouffer, Wilke, Cavallari, Lee and Network2018) conducted either for patients at high risk of MACE in line with ACC/AHA guidelines or for all-comers (Empey et al., Reference Empey, Stevenson, Tuteja, Weitzel, Angiolillo, Beitelshees, Coons, Duarte, Franchi, Jeng, Johnson, Kreutz, Limdi, Maloney, Owusu Obeng, Peterson, Petry, Pratt, Rollini, Scott, Skaar, Vesely, Stouffer, Wilke, Cavallari, Lee and Network2018). If point-of-care genotyping is not available, a de-escalation approach is proposed where patients are commenced on prasugrel or ticagrelor initially pending genotype results and then switched to clopidogrel if the genotype results indicate the NM phenotype. This approach maximises benefit given the high risk of atherothrombotic events early after ACS and PCI, while reducing the high risk of bleeding with prasugrel and ticagrelor during long-term therapy (Becker et al., Reference Becker, Bassand, Budaj, Wojdyla, James, Cornel, French, Held, Horrow, Husted, Lopez-Sendon, Lassila, Mahaffey, Storey, Harrington and Wallentin2011; Rollini et al., Reference Rollini, Franchi and Angiolillo2016; Angiolillo et al., Reference Angiolillo, Rollini, Storey, Bhatt, James, Schneider, Sibbing, So, Trenk, Alexopoulos, Gurbel, Hochholzer, De Luca, Bonello, Aradi, Cuisset, Tantry, Wang, Valgimigli, Waksman, Mehran, Montalescot, Franchi and Price2017). The case for implementing pre-emptive CYP2C19 genotyping (Peterson et al., Reference Peterson, Field, Unertl, Schildcrout, Johnson, Shi, Danciu, Cleator, Pulley, McPherson, Denny, Laposata, Roden and Johnson2016) is evident due to the impact of CYP2C19 genotype on other drugs in addition to clopidogrel, such as proton pump inhibitors (Lima et al., Reference Lima, Thomas, Barbarino, Desta, Van Driest, El Rouby, Johnson, Cavallari, Shakhnovich, Thacker, Scott, Schwab, Uppugunduri, Formea, Franciosi, Sangkuhl, Gaedigk, Klein, Gammal and Furuta2021) and selective serotonin reuptake inhibitors (SSRIs) (Hicks et al., Reference Hicks, Bishop, Sangkuhl, Muller, Ji, Leckband, Leeder, Graham, Chiulli, LLerena, Skaar, Scott, Stingl, Klein, Caudle and Gaedigk2015).
Direct-acting oral anti-coagulants
Apixaban, dabigatran, edoxaban and rivaroxaban are direct-acting oral anticoagulants (DOACs) with several advantages compared to warfarin – wider therapeutic index, regular monitoring not required, lower risk of intracranial haemorrhage, stroke or systemic embolic events (Proietti et al., Reference Proietti, Romanazzi, Romiti, Farcomeni and Lip2018). Despite the favourable profile of DOACs, their higher cost, lower adherence rates, limited indications, and the high cost of reversal agents has limited uptake of DOAC compared to warfarin (Zhu et al., Reference Zhu, Alexander, Nazarian, Segal and Wu2018; Ho et al., Reference Ho, van Hove and Leng2020). Pharmacokinetic variation related to genetic variation is indicated but there is no data on clinical outcomes yet.
In a sub-study of the ENGAGE AF TIMI-48 trial (which compared warfarin and edoxaban in atrial fibrillation patients; Mega et al., Reference Mega, Walker, Ruff, Vandell, Nordio, Deenadayalu, Murphy, Lee, Mercuri, Giugliano, Antman, Braunwald and Sabatine2015) warfarin-treated participants with a sensitive or highly sensitive genotype (e.g., VKORC1 −1639AA or CYP2C9*1/*3) spent a greater proportion of time within the supratherapeutic INR range (i.e., INR >4) and had higher rates of bleeding in the initial 90 days of treatment, as compared to those with non-sensitive genotypes. In a genetic sub-study of the RE-LY trial (dabigatran versus warfarin in atrial fibrillation), carriers of the CES1 rs2244613 minor allele had a reduced risk of bleeding with dabigatran than with warfarin (Shi et al., Reference Shi, Wang, Nguyen, Bleske, Liang, Liu and Zhu2016).
Statins
Lipid lowering treatment by statins (HMG-CoA reductase inhibitors) are used in the prevention of CVD (Catapano et al., Reference Catapano, Graham, De Backer, Wiklund, Chapman, Drexel, Hoes, Jennings, Landmesser, Pedersen, Reiner, Riccardi, Taskinen, Tokgozoglu, WMM, Vlachopoulos, Wood, Zamorano and Cooney2016). Statin-associated muscle symptoms (SAMS) (range from mild myalgia without an elevation in creatine kinase to life-threatening rhabdomyolysis or autoimmune-necrotizing myositis) are the commonest reasons for treatment discontinuation (Alfirevic et al., Reference Alfirevic, Neely, Armitage, Chinoy, Cooper, Laaksonen, Carr, Bloch, Fahy, Hanson, Yue, Wadelius, Maitland-van Der Zee, Voora, Psaty, Palmer and Pirmohamed2014). A number of enzymes and transporters are responsible for intracellular skeletal myocyte entry that underlie disruption of muscle function leading to SAMS (Turner and Pirmohamed, Reference Turner and Pirmohamed2019). Hepatic uptake and elimination of statins are mainly carried out by the solute carrier anion transporter family 1B1 gene (SLCO1B1) encoding the organic anion transporting polypeptide 1B1 (OATP1B1) (Shitara, Reference Shitara2011). The rs4149056 SNP in the SLCO1B1 gene (SLCO1B1*5) is linked to OATP1B1 function (Tirona et al., Reference Tirona, Leake, Merino and Kim2001) with the C allele being associated with decreased OATP1B1 transporter function with greatest reduction in homozygous patients resulting in significantly increased plasma concentrations of all statins, except fluvastatin (Tirona et al., Reference Tirona, Leake, Merino and Kim2001). Additionally, the risk of myopathy increases by 2.6 and 4.3 per copy of SLCO1B1*5 in patients, respectively, on simvastatin 40 mg and 80 mg daily (Tirona et al., Reference Tirona, Leake, Merino and Kim2001). The mechanism of SLCO1B1*5 variant causing statin-related myopathy is through the accumulation of circulating simvastatin acid (the active form of simvastatin) reflecting liver transport (Choi et al., Reference Choi, Bae, Cho, Ghim, Choe, Jung, Jin, Kim and Lim2015). This effect is most prominent for simvastatin followed by pitavastatin, lovastatin and atorvastatin (Ramsey et al., Reference Ramsey, Johnson, Caudle, Haidar, Voora, Wilke, Maxwell, McLeod, Krauss, Roden, Feng, Cooper-DeHoff, Gong, Klein, Wadelius and Niemi2014). Each copy of the C allele of rs4149056 increases the risk of statin-induced myopathy threefold in genome-wide association studies (GWAS) (Carr et al., Reference Carr, Francis, Jorgensen, Zhang, Chinoy, Heckbert, Bis, Brody, Floyd, Psaty, Molokhia, Lapeyre-Mestre, Conforti, Alfirevic, van Staa and Pirmohamed2019). Atorvastatin is partially metabolised by the CYP3A and UDP-glucuronosyltransferase 1A1 (UGT1A) enzyme families. One study showed the SNP rs45446698 just upstream of CYP3A7 and another, rs887829, located in multiple overlapping UGT1A genes, to be associated with atorvastatin-to-metabolite ratios in patients with ACS (Turner et al., Reference Turner, Fontana, Zhang, Carr, Yin, FitzGerald, Morris and Pirmohamed2020). Inconsistent associations with SAMS have been reported for polymorphisms in CYP3A4, ABCB1, COQ2 (involved in coenzyme Q10 synthesis) and GATM (involved in creatine synthesis) (Fiegenbaum et al., Reference Fiegenbaum, da Silveira, Van der Sand, Van der Sand, Ferreira, Pires and Hutz2005; Hoenig et al., Reference Hoenig, Walker, Gurnsey, Beadle and Johnson2011; Mangravite et al., Reference Mangravite, Engelhardt, Medina, Smith, Brown, Chasman, Mecham, Howie, Shim, Naidoo, Feng, Rieder, Chen, Rotter, Ridker, Hopewell, Parish, Armitage, Collins, Wilke, Nickerson, Stephens and Krauss2013; Carr et al., Reference Carr, Francis, Jorgensen, Zhang, Chinoy, Heckbert, Bis, Brody, Floyd, Psaty, Molokhia, Lapeyre-Mestre, Conforti, Alfirevic, van Staa and Pirmohamed2019).
Validation of PGx-based statin dosing
The pragmatic SLCO1B1 genotype-informed statin therapy (GIST) trial randomised patients who had discontinued any statins due to myalgia to SLCO1B1 genotype guided therapy (rosuvastatin, pravastatin, or fluvastatin for SLCO1B1*5 carriers and any statin for non-carriers) or standard care (Peyser et al., Reference Peyser, Perry, Singh, Gill, Mehan, Haga, Musty, Milazzo, Savard, Li, Trujilio and Voora2018). At the end of 8-month follow-up, increased statin re-initiation, reduced LDL-C levels, and no change in self-reported medication adherence were seen in those randomised to genotype guided (Peyser et al., Reference Peyser, Perry, Singh, Gill, Mehan, Haga, Musty, Milazzo, Savard, Li, Trujilio and Voora2018). Whilst these results are interesting, the inclusion of patients who developed myopathy from any statins in the trial limits the translational potential of the results. This is because the impact of SLCO1B1 variation is highest for simvastatin and variable for other statins, hence the results of the trial do not present a clear case for genotype-guided simvastatin therapy.
Clinical implementation of statin PGx
The SLCO1B1*5 variant (rs4149056) shows wide population differences (1%, 8% and 16% in African, Asian and European populations, respectively). CPIC recommends not exceeding a dose of simvastatin 20 mg/day or, prescribing another statin (rosuvastatin or pravastatin) in patients who carry at least one rs4149056 C allele (Voora et al., Reference Voora, Shah, Spasojevic, Ali, Reed, Salisbury and Ginsburg2009; Danik et al., Reference Danik, Chasman, MacFadyen, Nyberg, Barratt and Ridker2013; Ramsey et al., Reference Ramsey, Johnson, Caudle, Haidar, Voora, Wilke, Maxwell, McLeod, Krauss, Roden, Feng, Cooper-DeHoff, Gong, Klein, Wadelius and Niemi2014; Lamoureux et al., Reference Lamoureux and Duflot2017). The French National Network of Pharmacogenetics recommends commencing statins in patients with risk factors for myopathy only after rs4149056 genotyping (Lamoureux et al., Reference Lamoureux and Duflot2017). The DPWG recommends that homozygotes avoid simvastatin entirely and individuals with other clinical risk factors for SAMS avoid atorvastatin (de Keyser et al., Reference de Keyser, Peters, Becker, Visser, Uitterlinden, Klungel, Verstuyft, Hofman, Maitland-van der Zee and Stricker2014; Bank et al., Reference Swen and Guchelaar2019; Linskey et al., Reference Linskey, English, Perry, Ochs-Balcom, Ma, Isackson, Vladutiu and Luzum2020; Turner et al., Reference Turner, Fontana, Zhang, Carr, Yin, FitzGerald, Morris and Pirmohamed2020).
Beta blockers
β-Adrenergic receptor antagonists, or beta blockers, are indicated for treatment of heart failure, hypertension, and secondary prevention of myocardial infarction. CYP2D6 is responsible for biotransformation of 70–80% of an oral dose of metoprolol and has negligible effects on other beta blockers (Ingelman-Sundberg et al., Reference Ingelman-Sundberg, Sim, Gomez and Rodriguez-Antona2007; Baudhuin et al., Reference Baudhuin, Miller, Train, Bryant, Hartman, Phelps, Larock and Jaffe2010; Blake et al., Reference Blake, Kharasch, Schwab and Nagele2013; Zisaki et al., Reference Zisaki, Miskovic and Hatzimanikatis2015; Vieira et al., Reference Vieira, Neves, Coelho and Lanchote2018). There is only weak evidence for PGx-guided prescribing of beta blockers (PharmGKB level 2–3, CPIC level B/C). Compared to EMs, IMs and PMs are associated with a decreased heart rate (Bijl et al., Reference Bijl, Visser, van Schaik, Kors, Witteman, Hofman, Vulto, van Gelder and Stricker2009; Batty et al., Reference Batty, Hall, White, Wikstrand, de Boer, van Veldhuisen, van der Harst, Waagstein, Hjalmarson, Kjekshus and Balmforth2014; Anstensrud et al., Reference Anstensrud, Molden, Haug, Qazi, Muriq, Fosshaug, Spigset and Oie2020) and lower diastolic BP (Bijl et al., Reference Bijl, Visser, van Schaik, Kors, Witteman, Hofman, Vulto, van Gelder and Stricker2009; Batty et al., Reference Batty, Hall, White, Wikstrand, de Boer, van Veldhuisen, van der Harst, Waagstein, Hjalmarson, Kjekshus and Balmforth2014; Hamadeh et al., Reference Hamadeh, Langaee, Dwivedi, Garcia, Burkley, Skaar, Chapman, Gums, Turner, Gong, Cooper-DeHoff and Johnson2014; Anstensrud et al., Reference Anstensrud, Molden, Haug, Qazi, Muriq, Fosshaug, Spigset and Oie2020). These studies have not studied the entire spectrum of major variations in CYP2D6 and have not been independently validated.
Three other genes (ADRB1, ADRB2 and GRK5) have been associated with the beta blocker pharmacodynamics rather than pharmacokinetics, but there is no evidence of clinical utility for using these variants to guide prescribing (White et al., Reference White, de Boer, Maqbool, Greenwood, van Veldhuisen, Cuthbert, Ball, Hall and Balmforth2003; Pacanowski et al., Reference Pacanowski, Gong, Cooper-Dehoff, Schork, Shriver, Langaee, Pepine, Johnson and Investigators2008; Magvanjav et al., Reference Magvanjav, McDonough, Gong, McClure, Talbert, Horenstein, Shuldiner, Benavente, Mitchell, Johnson and SiGN2017; Huang et al., Reference Huang, Li, Song, Fan, You, Tan, Xiao, Li, Ruan, Hu, Cui, Li, Ni, Chen, Woo, Xiao and Wang2018).
FDA and DPWG have slightly different recommendations on metoprolol dosing. The FDA recommends caution with co-administration of strong CYP2D6 inhibitors (SSRIs, antipsychotics) or substrates. The DPWG recommend cautious dose titration and reduced maximal doses in CYP2D6 IMs and PMs supramaximal metoprolol dose or an alternative beta blocker in UMs (Brouwer et al., Reference Brouwer, Nijenhuis, Soree, Guchelaar, Swen, van Schaik, Weide, Rongen, Buunk, de Boer-Veger, Houwink, van Westrhenen, Wilffert, VHM and Mulder2022).
Hydralazine
Hydralazine is a direct vasodilator seldom used in the treatment of hypertension (Whelton et al., Reference Whelton, Carey, Aronow, Casey, Collins, Dennison Himmelfarb, DePalma, Gidding, Jamerson, Jones, MacLaughlin, Muntner, Ovbiagele, Smith, Spencer, Stafford, Taler, Thomas, Williams, Williamson and Wright2018). Hydralazine is metabolised primarily by hepatic N-acetyltransferase type 2 (NAT2) acetylation. The common NAT2*4 genetic variant defines a ‘rapid acetylator’ phenotype with decreased hydralazine levels after drug administration (Gonzalez-Fierro et al., Reference Gonzalez-Fierro, Vasquez-Bahena, Taja-Chayeb, Vidal, Trejo-Becerril, Perez-Cardenas, de la Cruz-Hernandez, Chavez-Blanco, Gutierrez, Rodriguez, Fernandez and Duenas-Gonzalez2011; Han et al., Reference Han, Ryu, Cusumano, Easterling, Phillips, Risler, Shen and Hebert2019). Homozygous NAT2*5, *6, and *7 indicate a ‘slow acetylator’ phenotype, while heterozygous individuals (e.g., *4/*5) are ‘intermediate acetylators’. One study of resistant hypertension patients demonstrated that only those with the slow acetylator phenotype showed notable blood pressure reductions in response to hydralazine (Spinasse et al., Reference Spinasse, Santos, Suffys, Muxfeldt and Salles2014).
One of the rare side effects of hydralazine is the occurrence of lupus-like symptoms, with indirect evidence suggesting slow acetylators are more prone to developing this ADR (Weber and Hein, Reference Weber and Hein1985; Mazari et al., Reference Mazari, Ouarzane and Zouali2007; Schoonen et al., Reference Schoonen, Thomas, Somers, Smeeth, Kim, Evans and Hall2010). However, clinical utility and cost-effectiveness data are lacking.
Antiarrhythmic drugs
Inhibition of the rapid component of the delayed rectifier potassium current, I kr, encoded by KCNH2 is the commonest cause of drug induced long QT syndrome (LQTS) and torsades des pointes (TdP; ventricular tachycardia (Roden and Viswanathan, Reference Roden and Viswanathan2005; Wada et al., Reference Wada, Yang, Shaffer, Daniel, Glazer, Davogustto, Lowery, Farber-Eger, Wells and Roden2022).
Similar to beta blockers, the class 1 antiarrhythmic drugs flecainide and propafenone are metabolised by CYP2D6 (PharmGKB level 2A, CPIC level B/C; Doki et al., Reference Doki, Sekiguchi, Kuga, Aonuma and Homma2015; Rouini and Afshar, Reference Rouini and Afshar2017) with CYP2D6 genotype-related differences in QTc interval (Lim et al., Reference Lim, Jang, Kim, Kim, Jeon, Tae, Yi, Eum, Cho, Shin and Yu2010). The FDA recommends caution in the use of propafenone in patients with CYP2D6 deficiency when combined with CYP3A4 inhibition. The DPWG recommends a dose reduction of 50% and 30%, respectively, for flecainide and propafenone in CYP2D6 PMs.
Quinidine- or dofetilide-induced QT prolongation and drug-induced TdP was significantly associated with a polygenic risk score constructed from 61 SNPs excluding the CYP2D6 locus (Arking et al., Reference Arking, Pulit, Crotti, van der Harst, Munroe, Koopmann, Sotoodehnia, Rossin, Morley, Wang, Johnson, Lundby, Gudbjartsson, Noseworthy, Eijgelsheim, Bradford, Tarasov, Dorr, Muller-Nurasyid and Lahtinen2014; Strauss et al., Reference Strauss, Vicente, Johannesen, Blinova, Mason, Weeke, Behr, Roden, Woosley, Kosova, Rosenberg and Newton-Cheh2017). Though not validated, this highlights the potential for using polygenic risk scores in predicting drug-induced arrhythmias.
PGx implementation
Successful pharmacogenomic implementation in healthcare require strong scientific evidence, comprehensive and updated clinical guidelines, clinician champions and stakeholder engagement (Manolio et al., Reference Manolio, Chisholm, Ozenberger, Roden, Williams, Wilson, Bick, Bottinger, Brilliant, Eng, Frazer, Korf, Ledbetter, Lupski, Marsh, Mrazek, Murray, O’Donnell, Rader, Relling, Shuldiner, Valle, Weinshilboum, Green and Ginsburg2013).
Laboratory
Characterisation of pharmacogenomic variants in patients requires a certified molecular pathology laboratory to ensure analytical accuracy, precision, sensitivity and specificity of the results (Tayeh et al., Reference Tayeh, Gaedigk, Goetz, Klein, Lyon, McMillin, Rentas, Shinawi, Pratt and Scott2022). Most clinical PGx tests based on selected panel of clinically relevant variants (single gene or multigene) are more cost-effective than sequencing panels. It is likely that the decreasing cost of sequencing will make sequencing cost-competitive over multi-gene panels in the future (Figure 1).
Figure 1. Pharmacogenomic implementation. The top panel shows the range of stakeholders, technology, knowledge and evidence that need to be harnessed to realise the value of PGx. The middle panel depicts the uses of PGx in the clinical prescribing pathway. The bottom panel presents the applications of PGx. CPIC, the Clinical Pharmacogenetics Implementation Consortium; DPWG, Dutch Pharmacogenetics Working Groups; PharmGKB, the Pharmacogenomics Knowledge Base.
Guidelines and clinical decision support systems
Effective pharmacogenomic guided prescribing requires evidence from multiple sources to be distilled into guidelines and made available through clinical decision support systems (CDSS) that distil information on drug–gene interactions from published guidelines or prescribing labels. Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Groups (DPWG) have published guidelines covering 66 medications across several drug classes. However, the major PGx guideline and recommendation sources are not completely concordant in terms of their advice. A recent study found inconsistencies in clinical PGx recommendations (48.1%) and in 93.3% of recommendations from CPIC, FDA and clinical practice guidelines (Shugg et al., Reference Shugg, Pasternak, London and Luzum2020). These inconsistencies were spread across a range of domains – recommendation category (29.8%), the patient group (35.4%) and routine screening (15.2%), suggesting a potential barrier to rapid PGx implementation until this is resolved.
CDSS is an effective tool to guide clinicians with limited PGx knowledge (van der Wouden et al., Reference van der Wouden, Cambon-Thomsen, Cecchin, Cheung, Davila-Fajardo, Deneer, Dolzan, Ingelman-Sundberg, Jonsson, Karlsson, Kriek, Mitropoulou, Patrinos, Pirmohamed, Samwald, Schaeffeler, Schwab, Steinberger, Stingl, Sunder-Plassmann, Toffoli, Turner, van Rhenen, Swen and Guchelaar2017). In pre-emptive PGx, patient-specific CDSS alerts prompt and guide clinicians to use genetic information when prescribing drugs with known genetically-determined ADRs (Overby et al., Reference Overby, Erwin, Abul-Husn, Ellis, Scott, Obeng, Kannry, Hripcsak, Bottinger and Gottesman2014; Dunnenberger et al., Reference Dunnenberger, Crews, Hoffman, Caudle, Broeckel, Howard, Hunkler, Klein, Evans and Relling2015).
PGx may be implemented either reactively on a gene-by-gene basis at the time of prescribing a drug, or pre-emptively where a single sample is assessed for several pharmacogenes simultaneously with the results stored for future prescribing encounters. Reactive implementation is expensive and has a slow turnaround time and is unsuitable in situations where rapid drug initiation is required. In contrast, pre-emptive screening of multiple pharmacogenes is likely to be more cost-effective and provides the patient with a lifetime’s worth of test results readily available whenever a drug is prescribed, especially when integrated into electronic health records (EHRs) and drug prescription systems (Relling and Evans, Reference Relling and Evans2015). This further underlines the importance of efficient interoperability between different healthcare systems. A patient may be screened for CYP2C19 prior to being prescribed clopidogrel. These results could inform the prescription of an SSRI or proton pump inhibitor in the future – but only if the results have been stored in an EHR in an accessible format and trigger a CDSS alert at the point of prescription.
Health informatics
PGx implementation in healthcare can be developed in-house if there is availability of capabilities in laboratory and informatics infrastructure and expertise or outsourced to commercial partners. Due to the considerable diversity in commercial PGx products, it is essential to ensure the clinical, IT integration and interoperability requirements along with robust and continuous updating of evidence are rigorously assessed before deciding on the PGx service provider. The significant costs associated with the use of PGx in clinical practice are now in the domain of decision support, IT integration and interoperability, rather than in laboratory genetic testing (Dunnenberger et al., Reference Dunnenberger, Crews, Hoffman, Caudle, Broeckel, Howard, Hunkler, Klein, Evans and Relling2015; Relling and Evans, Reference Relling and Evans2015; van der Wouden et al., Reference van der Wouden, Cambon-Thomsen, Cecchin, Cheung, Davila-Fajardo, Deneer, Dolzan, Ingelman-Sundberg, Jonsson, Karlsson, Kriek, Mitropoulou, Patrinos, Pirmohamed, Samwald, Schaeffeler, Schwab, Steinberger, Stingl, Sunder-Plassmann, Toffoli, Turner, van Rhenen, Swen and Guchelaar2017). Informatics builds within the EHR are easier for a single gene–drug pair as opposed to the multiple pairs and networks that form as drug interactions and clinical factors are also considered. However, the cost-effectiveness data on the pre-emptive panel approach must be assessed, particularly when considering implementation early in life.
Patient and provider acceptability
Patient and healthcare professional acceptability is critical for effective and successful PGx implementation. This requires early and continuous engagement with both clinicians and patients, preferably with champions who are committed (Dressler et al., Reference Dressler, Bell, Ruch, Retamal, Krug and Paulus2018; McDermott et al., Reference McDermott, Wright, Sharma, Newman, Payne and Wilson2022). The main barriers to be tackled in the route to implementation are demonstrating that the system will not overburden the physicians, seamlessly integrate into hospital cornerstone systems, provide sufficient support for the users of the system to navigate the pharmacogenetic evidence base through education and decision support systems, demonstrate utility and cost-effectiveness (Stanek et al., Reference Stanek, Sanders, Taber, Khalid, Patel, Verbrugge, Agatep, Aubert, Epstein and Frueh2012; Just et al., Reference Just, Turner, Dolzan, Cecchin, Swen, Gurwitz and Stingl2019; Bagautdinova et al., Reference Bagautdinova, Lteif, Eddy, Terrell, Fisher and Duarte2022; Scheuner et al., Reference Scheuner, Sales, Hoggatt, Zhang, Whooley and Kelley2023).
Pharmacists are crucial in the PGx service for evaluating appropriate patient eligibility, providing informative post-test counselling, or leading a PGx consult service (Crews et al., Reference Crews, Cross, McCormick, Baker, Molinelli, Mullins, Relling and Hoffman2011; Brown et al., Reference Brown, MacDonald, Yapel, Luczak, Hanson and Stenehjem2021; Bagautdinova et al., Reference Bagautdinova, Lteif, Eddy, Terrell, Fisher and Duarte2022; Krause and Dowd, Reference Krause and Dowd2022).
Health economics
Implementation of PGx in clinical practice requires demonstration of its value and cost-effectiveness to key decision makers and a lack of RCTs that compare genotype-guided prescribing with conventional therapy has not helped. Conducting RCTs for each single drug–gene pair across different ethnicities is not a viable option. Big data analysis of EHRs has the advantage of being able to study diverse populations, limiting concerns about external validity of data and health equality (as is exemplified by the warfarin dosing algorithms that fail to serve patients of African descent). There is limited data on cost-effectiveness multiplexed pre-emptive strategies which are likely to be the preferred solution and the majority of existing cost-effectiveness PGx data are from single gene–drug pair studies (Roden et al., Reference Roden, Van Driest, Mosley, Wells, Robinson, Denny and Peterson2018). Most of the cost-effectiveness studies have been conducted separate from implementation initiatives and they indicate that PGx testing results in a reduction in per-patient treatment cost (Winner et al., Reference Winner, Carhart, Altar, Goldfarb, Allen, Lavezzari, Parsons, Marshak, Garavaglia and Dechairo2015; Deenen et al., Reference Deenen, Meulendijks, Cats, Sechterberger, Severens, Boot, Smits, Rosing, Mandigers, Soesan, Beijnen and Schellens2016), lower cost-per-QALY (Mitropoulou et al., Reference Mitropoulou, Fragoulakis, Bozina, Vozikis, Supe, Bozina, Poljakovic, van Schaik and Patrinos2015) and cost savings in long-term care (Saldivar et al., Reference Saldivar, Taylor, Sugarman, Cullors, Garces, Oades and Centeno2016). A recent systematic appraisal of economic evaluations of PGx testing to prevent ADRs found a number of deficiencies in the quality of data used in cost-effectiveness and cost-utility analyses (Turongkaravee et al., Reference Turongkaravee, Jittikoon, Rochanathimoke, Boyd, Wu and Chaikledkaew2021). Of the 14 economic evaluation studies of CYP2C9 and VKORC1 testing, 10 studies showed that CYP2C9 and VKORC1 testing would be a variably cost-effective and four studies suggested otherwise (Turongkaravee et al., Reference Turongkaravee, Jittikoon, Rochanathimoke, Boyd, Wu and Chaikledkaew2021). In contrast, all nine economic evaluation studies of CYP2C19 testing before prescription of clopidogrel ACS patients undergoing PCI showed that CYP2C19 testing would be a potentially cost-effective treatment strategy for avoiding MACE.
The clopidogrel–CYP2C19 implementation successes need to be contrasted with the difficulties faced in the implementation of warfarin–CYP2C9/CYP4F2/VKORC1 PGx. The key enablers for clopidogrel–CYP2C19 implementation include a discrete patient population (post-PCI), single-gene testing, a high frequency of actionable results, clinically well-established alternative therapies, and a focused group of providers (interventional cardiologists) (Crisamore et al., Reference Crisamore, Nolin, Coons and Empey2019).
Implementation in diverse health care systems
Whilst the above discussion related to healthcare systems in high-income countries, the specific challenges in implementing PGx low- and middle-income countries need to be recognised – lack of clinical efficacy and effectiveness data, under-resourced clinical settings, socio-cultural issues and the identification of population specific pharmacogenomic markers (Tata et al., Reference Tata, Ambele and Pepper2020; Magavern et al., Reference Magavern, Gurdasani, Ng and Lee2022; Sukri et al., Reference Sukri, Salleh, Masimirembwa and Teh2022). The lack of consistent and widely accepted definitions of race, ethnicity and ancestry in genomics and clinical research has resulted in erroneous, inconclusive or absent data on non-European ancestry populations (Popejoy et al., Reference Popejoy, Crooks, Fullerton, Hindorff, Hooker, Koenig, Pino, Ramos, Ritter, Wand, Wright, Yudell, Zou, Plon, Bustamante and Ormond2020). Initiatives such as Human Heredity and Health in Africa (H3Africa) Consortium and the African Pharmacogenomics Research Consortium attempt to increase the representativeness of pharmacogenomic panels (Matimba et al., Reference Matimba, Dhoro and Dandara2016). It is imperative that progress in pharmacogenomic research and implementation occurs at pace in diverse populations so that health disparities are not amplified when PGx becomes more mainstream in clinical practice.
Author contribution
S.P., C.T. and A.F.D. made substantial contributions to the conception drafting, and revision of the manuscript, it critically for important intellectual content. S.P., C.T. and A.F.D. provided final approval of the version to be published.
Abbreviations
- ABCB1
-
ATP-binding cassette sub-family B member 1
- ACC
-
American College of Cardiology
- ACS
-
acute coronary syndrome
- ADR
-
adverse drug reaction
- ADRB1
-
β1-adrenergic receptor
- ADRB2
-
β2-adrenergic receptor
- AHA
-
American Heart Association
- BMI
-
body mass index
- CDSS
-
clinical decision support systems
- CES1
-
liver carboxylesterase 1
- CPIC
-
the Clinical Pharmacogenetics Implementation Consortium
- CVD
-
cardiovascular disease
- CYP
-
cytochrome P450
- DOAC
-
direct-acting oral anticoagulants
- DPYD
-
dihydropyrimidine dehydrogenase
- DPWG
-
Dutch Pharmacogenetics Working Groups
- EHR
-
electronic health records
- EMA
-
European Medicines Agency
- EPHA7
-
ephrin type A receptor 7
- ESC
-
European Society of Cardiology
- FDA
-
U.S. Food and Drug Administration
- GATM
-
Glycine amidinotransferase, mitochondrial
- GIST
-
genotype-informed statin therapy
- GRK5
-
G protein-coupled receptor kinase 5
- GWAS
-
genome-wide association studies
- HLA-B
-
human leukocyte antigen B
- IM
-
intermediate metaboliser
- INR
-
international normalised ratio
- KCNH2/hERG
-
human Ether-à-go-go-related gene
- LQTS
-
long QT syndrome
- MACE
-
major adverse cardiovascular event
- NAT2
-
N-acetyltransferase 2
- NM
-
normal metaboliser
- OATP1B1
-
organic anion transporting polypeptide 1B1
- PCI
-
percutaneous coronary intervention
- PM
-
poor metaboliser
- PGx
-
pharmacogenomics
- QALY
-
quality-adjusted life year
- RCT
-
randomised controlled trial
- SAMS
-
statin-associated muscle symptoms
- SLCO1A2
-
organic anion transporter family member 1A2
- SLCO1B1
-
solute carrier anion transporter family 1B1
- SNP
-
single nucleotide polymorphism
- SSRI
-
selective serotonin reuptake inhibitors
- STEMI
-
ST-segment elevation myocardial infarction
- TdP
-
torsades des pointes
- TIA
-
transient ischaemic attack
- TTR
-
time in the therapeutic INR range
- UGT1A
-
UDP-glucuronosyltransferase 1A1
- UM
-
ultrarapid metaboliser
- VKORC1
-
vitamin K epoxide reductase complex subunit 1
Open peer review
To view the open peer review materials for this article, please visit http://doi.org/10.1017/pcm.2023.17.
Financial support
S.P. and A.F.D. are supported by the British Heart Foundation Centre of Excellence Award (RE/18/6/34217) and the UKRI Strength in Places Fund (SIPF00007/1).
Competing interest
The authors have no competing interest to disclose.
Open access
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