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Precision Medicine Using Pharmacogenomic Panel-Testing

Current Status and Future Perspectives
Open AccessPublished:September 16, 2020DOI:https://doi.org/10.1016/j.yamp.2020.07.012

      Keywords

      Key points

      • Logistics and cost-effectiveness of pharmacogenomics (PGx)-guided prescribing may be optimized when delivered in a preemptive panel approach.
      • Barriers impeding implementation of a preemptive PGx-panel approach include the lack of evidence of (cost-)effectiveness, the undetermined optimal target population and timing for delivering PGx, and the lack of tools supporting implementation.
      • Developments in sequencing and artificial intelligence will further improve the predictive utility of genetic variation to predict drug response.

      Introduction

      Although drug treatment is often successful, adverse drug reactions (ADRs) and lack of efficacy present a significant burden for individual patients and society as a whole. ADRs are an important cause of emergency department visits and hospital admissions. A study in 2 large UK hospitals showed that 6.5% of hospital admissions were attributable to ADRs [
      • Pirmohamed M.
      • James S.
      • Meakin S.
      • et al.
      Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients.
      ]. In the United States, ADR-related morbidity and mortality have been estimated at $30 billion to $136 billion annually [
      • Johnson J.A.
      • Bootman J.L.
      Drug-related morbidity and mortality. A cost-of-illness model.
      ]. In parallel, lack of efficacy also results in a significant burden. Its magnitude can be estimated by inspecting the number needed to treat of commonly used drugs [], which are commonly more than 10. As a result, most patients will not benefit from drug treatment and, in contrast, may experience harm from unsuccessfully treated disease. It has been estimated that $100 billion a year is wasted on ineffective drug treatment [
      • Harper A.R.
      • Topol E.J.
      Pharmacogenomics in clinical practice and drug development.
      ].
      Precision medicine aims to individualize or stratify application of pharmacotherapy, as opposed to the current population-based application, in an effort to optimize the benefit/risk ratio [
      • Jameson J.L.
      • Longo D.L.
      Precision medicine--personalized, problematic, and promising.
      ,
      • Peck R.W.
      Precision medicine is not just genomics: the right dose for every patient.
      ]. By enabling identification of individuals who are at higher risk for ADRs or lack of efficacy, before drug initiation and potential harm, an individualized dose and drug selection may be applied to reduce this risk. An individual’s germline genetic variation is a particularly promising predictive factor that can enable drug response prediction. This notion is supported by its pharmacologic plausibility and has been demonstrated in various studies [
      • Matthaei J.
      • Brockmoller J.
      • Tzvetkov M.V.
      • et al.
      Heritability of metoprolol and torsemide pharmacokinetics.
      ,
      • Alexanderson B.
      • Evans D.A.
      • Sjoqvist F.
      Steady-state plasma levels of nortriptyline in twins: influence of genetic factors and drug therapy.
      ,
      • Vesell E.S.
      • Page J.G.
      Genetic control of drug levels in man: phenylbutazone.
      ,
      • Stage T.B.
      • Damkier P.
      • Pedersen R.S.
      • et al.
      A twin study of the trough plasma steady-state concentration of metformin.
      ]. Drug-gene interactions (DGIs) can be categorized into 3 groups (Fig. 1A–C ): pharmacokinetic-dependent ADRs (see Fig. 1A), pharmacodynamic-dependent ADRs (see Fig. 1B), and idiosyncratic ADRs (see Fig. 1C).
      Figure thumbnail gr1
      Fig. 1Precision medicine using pharmacogenomic panel testing: current status and future perspectives. conc., concentration; PM, poor metabolizers; Rx, prescription.
      Pharmacogenomics (PGx) uses an individual’s germline genetic profile to identify those who are at higher risk for ADRs or lack of efficacy (see Fig. 1D) [
      • Relling M.V.
      • Evans W.E.
      Pharmacogenomics in the clinic.
      ,
      • Weinshilboum R.
      • Wang L.
      Pharmacogenomics: bench to bedside.
      ,
      • Roden D.M.
      • McLeod H.L.
      • Relling M.V.
      • et al.
      Pharmacogenomics.
      ]. This information can be used by health care professionals (HCPs) to guide dose and drug selection before drug initiation in an effort to optimize drug therapy [
      • Pirmohamed M.
      Personalized pharmacogenomics: predicting efficacy and adverse drug reactions.
      ]. Within germline PGx, the focus lies on inherited variation in genes, which play a role in drug absorption, distribution, metabolism, and elimination (ADME). To date, several randomized controlled trials (RCT) support the clinical utility of individual DGIs to either optimize dosing [
      • Pirmohamed M.
      • Burnside G.
      • Eriksson N.
      • et al.
      A randomized trial of genotype-guided dosing of warfarin.
      ,
      • Wu A.H.
      Pharmacogenomic testing and response to warfarin.
      ,
      • Verhoef T.I.
      • Ragia G.
      • de Boer A.
      • et al.
      A randomized trial of genotype-guided dosing of acenocoumarol and phenprocoumon.
      ,
      • Coenen M.J.
      • de Jong D.J.
      • van Marrewijk C.J.
      • et al.
      Identification of patients with variants in TPMT and dose reduction reduces hematologic events during thiopurine treatment of inflammatory bowel disease.
      ] or drug selection [
      • Mallal S.
      • Phillips E.
      • Carosi G.
      • et al.
      HLA-B∗5701 screening for hypersensitivity to abacavir.
      ,
      • Claassens D.M.F.
      • Vos G.J.A.
      • Bergmeijer T.O.
      • et al.
      A genotype-guided strategy for oral P2Y12 inhibitors in primary PCI.
      ]. Following the completion of the Human Genome Project, the Royal Dutch Pharmacists Association anticipated a proximate future where patients would present themselves in the pharmacy with their genetic information. In anticipation, the Dutch Pharmacogenetics Working Group (DPWG) was established in 2005 with the objective to develop clear guidelines for HCPs on how to interpret and apply PGx test results [
      • Swen J.J.
      • Nijenhuis M.
      • de Boer A.
      • et al.
      Pharmacogenetics: from bench to byte--an update of guidelines.
      ,
      • Swen J.J.
      • Wilting I.
      • de Goede A.L.
      • et al.
      Pharmacogenetics: from bench to byte.
      ]. In parallel, the Clinical Pharmacogenetics Implementation Consortium was initiated in 2008 and devises similar guidelines [
      • Relling M.V.
      • Klein T.E.
      CPIC: clinical pharmacogenetics implementation consortium of the pharmacogenomics research network.
      ].
      Significant debate persists regarding the optimal timing and methodology of testing for delivering PGx testing in clinical care [
      • Dunnenberger H.M.
      • Crews K.R.
      • Hoffman J.M.
      • et al.
      Preemptive clinical pharmacogenetics implementation: current programs in five US medical centers.
      ]. Some support a pretherapeutic single gene–drug approach, in which a PGx test of a single relevant gene is ordered once a target drug is prescribed, while others advocate for a preemptive panel-based strategy, in which multiple genes are tested simultaneously and saved for later use in preparation of future prescriptions throughout a patient’s lifetime [
      • Weitzel K.W.
      • Cavallari L.H.
      • Lesko L.J.
      Preemptive panel-based pharmacogenetic testing: the time is now.
      ]. When combined with a clinical decision support system (CDSS), the corresponding PGx guideline can be deployed by the CDSS at the point of care, thereby providing clinicians with the necessary information to optimize drug prescribing, when a target drug is prescribed. A CDSS is deemed useful because patients will receive multiple drug prescriptions with potential DGIs within their lifetime [
      • Dunnenberger H.M.
      • Crews K.R.
      • Hoffman J.M.
      • et al.
      Preemptive clinical pharmacogenetics implementation: current programs in five US medical centers.
      ,
      • Driest V.S.L.
      • Shi Y.
      • Bowton E.A.
      • et al.
      Clinically actionable genotypes among 10,000 patients with preemptive pharmacogenomic testing.
      ]. It has been estimated that half of the patients older than 65 years will use at least one of the drugs for which PGx guidelines are available during a 4-year period, and one-fourth to one-third will use 2 or more of these drugs [
      • Samwald M.
      • Xu H.
      • Blagec K.
      • et al.
      Incidence of exposure of patients in the United States to multiple drugs for which pharmacogenomic guidelines are available.
      ]. Logistics and cost-effectiveness are therefore optimized when delivered in a preemptive panel-based approach; pharmacotherapy does not have to be delayed, in awaiting single-gene testing results, and costs for genotyping are minimized, because marginal acquisition costs of testing and interpreting additional pharmacogenes is near zero [
      • Roden D.M.
      • Van Driest S.L.
      • Mosley J.D.
      • et al.
      Benefit of preemptive pharmacogenetic information on clinical outcome.
      ]. When PGx is adopted in such a model, it has been estimated that 23.6% of all indecent prescriptions will have a relevant DGI [
      • Bank P.C.D.
      • Swen J.J.
      • Guchelaar H.J.
      Estimated nationwide impact of implementing a preemptive pharmacogenetic panel approach to guide drug prescribing in primary care in The Netherlands.
      ]. To date, a small number of individual genes are tested pretherapeutically to guide pharmacotherapy of high-risk drugs. For example, DPYD-guided initial drug and dose selection of fluoropyridines to reduce risk of severe toxicity has been widely implemented in the Netherlands [
      • Lunenburg C.A.
      • van Staveren M.C.
      • Gelderblom H.
      • et al.
      Evaluation of clinical implementation of prospective DPYD genotyping in 5-fluorouracil- or capecitabine-treated patients.
      ]. Despite the progress in application of PGx in single-gene scenarios, a preemptive PGx-panel approach is still not routinely applied. As such, several barriers preventing the implementation of preemptive panel testing have been identified [
      • Abbasi J.
      Getting pharmacogenomics into the clinic.
      ,
      • Haga S.B.
      • Burke W.
      Pharmacogenetic testing: not as simple as it seems.
      ,
      • Swen J.J.
      • Huizinga T.W.
      • Gelderblom H.
      • et al.
      Translating pharmacogenomics: challenges on the road to the clinic.
      ]. Remaining barriers include the lack of evidence of (cost-)effectiveness supporting a PGx-panel approach, the undetermined optimal target population, and timing for delivering a PGx panel and the lack of tools supporting implementation. These remaining barriers and steps to overcome them are discussed in this review. Furthermore, the authors discuss future perspectives of these domains.

      The lack of evidence of (cost-) effectiveness supporting a pharmacogenomics-panel approach

      Several of the reported hurdles obstructing the implementation of PGx-panel testing are currently being addressed by various initiatives, in both the United States and the European Union. Overviews of these initiatives have previously been published [
      • Dunnenberger H.M.
      • Crews K.R.
      • Hoffman J.M.
      • et al.
      Preemptive clinical pharmacogenetics implementation: current programs in five US medical centers.
      ,
      • van der Wouden C.H.
      • Cambon-Thomsen A.
      • Cecchin E.
      • et al.
      Implementing pharmacogenomics in Europe: design and implementation strategy of the Ubiquitous Pharmacogenomics consortium.
      ]. Despite these initiatives, a major hurdle preventing implementation is the absence of evidence presenting the collective clinical utility of a panel of PGx markers for preemptive PGx testing. Although several RCT support the clinical utility of individual gene-drug pairs, delivered in a single-gene reactive approach [
      • Pirmohamed M.
      • Burnside G.
      • Eriksson N.
      • et al.
      A randomized trial of genotype-guided dosing of warfarin.
      ,
      • Wu A.H.
      Pharmacogenomic testing and response to warfarin.
      ,
      • Verhoef T.I.
      • Ragia G.
      • de Boer A.
      • et al.
      A randomized trial of genotype-guided dosing of acenocoumarol and phenprocoumon.
      ,
      • Coenen M.J.
      • de Jong D.J.
      • van Marrewijk C.J.
      • et al.
      Identification of patients with variants in TPMT and dose reduction reduces hematologic events during thiopurine treatment of inflammatory bowel disease.
      ,
      • Mallal S.
      • Phillips E.
      • Carosi G.
      • et al.
      HLA-B∗5701 screening for hypersensitivity to abacavir.
      ,
      • Claassens D.M.F.
      • Vos G.J.A.
      • Bergmeijer T.O.
      • et al.
      A genotype-guided strategy for oral P2Y12 inhibitors in primary PCI.
      ], evidence supporting clinical utility of the remaining DGIs for which recommendations are available when delivered in a preemptive panel approach is lacking. Significant debate persists regarding both the nature and the strength of evidence required for the clinical application of these remaining DGIs. Some argue an RCT is required for each individual DGI before clinical implementation is substantiated [
      • Janssens A.C.
      • Deverka P.A.
      Useless until proven effective: the clinical utility of preemptive pharmacogenetic testing.
      ]. Others argue that a mandatory requirement for prospective evidence to support the clinical validity for each PGx interaction is incongruous and excessive [
      • Altman R.B.
      Pharmacogenomics: "noninferiority" is sufficient for initial implementation.
      ,
      • van der Wouden C.H.
      • Swen J.J.
      • Schwab M.
      • et al.
      A brighter future for the implementation of pharmacogenomic testing.
      ,
      • Pirmohamed M.
      • Hughes D.A.
      Pharmacogenetic tests: the need for a level playing field.
      ,
      • Khoury M.J.
      Dealing with the evidence dilemma in genomics and personalized medicine.
      ]. Generating gold-standard evidence for each individual DGI for which PGx guidelines are available separately would require unrealistically large amounts of funds. On the other hand, extrapolating efficacy of all of these DGIs based on the conclusions of the previously mentioned RCTs, supporting clinical utility for a subset of individual DGIs, is also not substantiated.
      Regardless of the inconvenience, there is still a demand for evidence substantiating patient benefit and cost-effectiveness, to enable stakeholders to practice evidence-based medicine. The Ubiquitous Pharmacogenomics Consortium (U-PGx), a European Consortium funded by the Horizon 2020 program, aims to generate such evidence [
      • van der Wouden C.H.
      • Cambon-Thomsen A.
      • Cecchin E.
      • et al.
      Implementing pharmacogenomics in Europe: design and implementation strategy of the Ubiquitous Pharmacogenomics consortium.
      ]. The U-PGx consortium set out to quantify the collective clinical utility of a panel of PGx markers (50 variants in 13 pharmacogenes) within a single trial (the PREPARE study, ClinicalTrials.gov: NCT03093818) as a proof-of-concept across multiple potentially clinically relevant DGIs [
      • van der Wouden C.H.
      • Cambon-Thomsen A.
      • Cecchin E.
      • et al.
      Implementing pharmacogenomics in Europe: design and implementation strategy of the Ubiquitous Pharmacogenomics consortium.
      ,
      • Manson L.E.
      • van der Wouden C.H.
      • Swen J.J.
      • et al.
      The Ubiquitous Pharmacogenomics Consortium: making effective treatment optimization accessible to every European citizen.
      ]. It is a block RCT aiming to enroll 8100 patients across 7 European countries. Additional outcomes include cost-effectiveness, process indicators for implementation, and provider adoption of PGx.
      In the meantime, several smaller randomized and observational studies indicate the cost-effectiveness of PGx panel–based testing in psychiatry and polypharmacy patients [
      • Elliott L.S.
      • Henderson J.C.
      • Neradilek M.B.
      • et al.
      Clinical impact of pharmacogenetic profiling with a clinical decision support tool in polypharmacy home health patients: a prospective pilot randomized controlled trial.
      ,
      • Brixner D.
      • Biltaji E.
      • Bress A.
      • et al.
      The effect of pharmacogenetic profiling with a clinical decision support tool on healthcare resource utilization and estimated costs in the elderly exposed to polypharmacy.
      ,
      • Pérez V.
      • Salavert A.
      • Espadaler J.
      • et al.
      Efficacy of prospective pharmacogenetic testing in the treatment of major depressive disorder: results of a randomized, double-blind clinical trial.
      ,
      • Espadaler J.
      • Tuson M.
      • Lopez-Ibor J.M.
      • et al.
      Pharmacogenetic testing for the guidance of psychiatric treatment: a multicenter retrospective analysis.
      ]. Observed cost savings ranged from $218 [
      • Brixner D.
      • Biltaji E.
      • Bress A.
      • et al.
      The effect of pharmacogenetic profiling with a clinical decision support tool on healthcare resource utilization and estimated costs in the elderly exposed to polypharmacy.
      ] to $2778 [
      • Winner J.G.
      • Carhart J.M.
      • Altar C.A.
      • et al.
      Combinatorial pharmacogenomic guidance for psychiatric medications reduces overall pharmacy costs in a 1 year prospective evaluation.
      ] per patient. Others have modeled the cost-effectiveness of one-time genetic testing to minimize a lifetime of ADRs and concluded an incremental cost-effectiveness ratio (ICER) of $43,165 per additional life-year and $53,680 per additional quality-adjusted life-year, therefore considered cost-effective [
      • Alagoz O.
      • Durham D.
      • Kasirajan K.
      Cost-effectiveness of one-time genetic testing to minimize lifetime adverse drug reactions.
      ]. However, cost-effectiveness may vary across ethnic populations, as a result of differences in allele frequencies, differences in prescription patterns, and differences in health care costs and ICER cost-effectiveness thresholds. The study designed by the U-PGx consortium (the PREPARE Study) will enable the quantification of the cost-effectiveness over a 12-week time horizon.
      Clinical trials and prospective cohorts typically measure short-term benefits of PGx testing, whereas the time horizon for the benefits and risks of PGx testing is over a lifetime and therefore unable to be captured within regular trials. As such, the life-long cost-effectiveness of one-time preemptive panel-based testing to prevent ADRs is yet undetermined. Other methodologies, such as Markov models, can be deployed to simulate effectiveness over longer time horizons. The results of such models will be of interest to reimbursement policymakers, who require evidence that panel-based testing will yield downstream improved health outcomes at acceptable costs. Therefore, once the effectiveness of PGx-panel testing has been established, future research should model the cost-effectiveness of preemptive PGx testing to prevent a lifetime of ADRs. Optimally, such an analysis could be run on a longitudinal cohort of patients for which both prescription data and PGx results are available. Furthermore, such a data set could be used to explore the optimal timing and subgroup application of testing to optimize cost-effectiveness.

      Finding the optimal target population and timing for delivering pharmacogenomics

      The optimal target population and time at which panel-based testing should be performed remain to be determined. In the most progressive application of PGx panel-testing could be performed when no drug initiation is indicated, in anticipation of future drug prescriptions. However, if no drug is initiated in the near future, PGx testing would be a waste of resources. Alternatively, in a more efficient scenario, panel testing could be performed once a patient plans to initiate a drug for which PGx testing may be useful and reuse these results when future DGIs are encountered. Such a model was deployed in a pilot study [
      • van der Wouden C.H.
      • Bank P.C.D.
      • Ozokcu K.
      • et al.
      Pharmacist-initiated pre-emptive pharmacogenetic panel testing with clinical decision support in primary care: record of PGx results and real-world impact.
      ], whereby pharmacists requested a PGx-panel test when patients planned to initiate one of 10 drugs for which PGx guidelines are available. Here, 97% of patients (re)used PGx-panel results for at least one, and 33% for up to 4 newly initiated prescriptions with possible DGIs within a 2.5-year follow-up. In this case, 24% were actionable DGIs, requiring pharmacotherapy adjustment. This high rate of reuse indicates that such a model may be promising for delivering PGx panel-based testing. As an alternative model, another initiative at Vanderbilt University Medical Center has used a prediction model to select patients who may benefit from PGx testing in the near future algorithmically and using prescription data [
      • Pulley J.M.
      • Denny J.C.
      • Peterson J.F.
      • et al.
      Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project.
      ,
      • Grice G.R.
      • Seaton T.L.
      • Woodland A.M.
      • et al.
      Defining the opportunity for pharmacogenetic intervention in primary care.
      ].
      In addition to undetermined timing and methodology, the most optimal target group for testing is also yet undetermined. Current studies have identified potential patient subgroups for which preemptive PGx-panel testing may be most useful. Some initiatives have selected patients with particular indications in psychiatry [
      • Pérez V.
      • Salavert A.
      • Espadaler J.
      • et al.
      Efficacy of prospective pharmacogenetic testing in the treatment of major depressive disorder: results of a randomized, double-blind clinical trial.
      ,
      • Espadaler J.
      • Tuson M.
      • Lopez-Ibor J.M.
      • et al.
      Pharmacogenetic testing for the guidance of psychiatric treatment: a multicenter retrospective analysis.
      ,
      • Bradley P.
      • Shiekh M.
      • Mehra V.
      • et al.
      Improved efficacy with targeted pharmacogenetic-guided treatment of patients with depression and anxiety: a randomized clinical trial demonstrating clinical utility.
      ,
      • Walden L.M.
      • Brandl E.J.
      • Tiwari A.K.
      • et al.
      Genetic testing for CYP2D6 and CYP2C19 suggests improved outcome for antidepressant and antipsychotic medication.
      ]. Others have selected patients with particular characteristics, such as polypharmacy and elderly patients [
      • Elliott L.S.
      • Henderson J.C.
      • Neradilek M.B.
      • et al.
      Clinical impact of pharmacogenetic profiling with a clinical decision support tool in polypharmacy home health patients: a prospective pilot randomized controlled trial.
      ,
      • Brixner D.
      • Biltaji E.
      • Bress A.
      • et al.
      The effect of pharmacogenetic profiling with a clinical decision support tool on healthcare resource utilization and estimated costs in the elderly exposed to polypharmacy.
      ].
      Alternatively, consumers who have an interest in their PGx profile may also obtain their PGx test results outside the realm of health care and without the intervention of an HCP. In 2018, direct-to-consumer (DTC) PGx testing for specific DGIs was approved by the Food and Drug Administration (FDA). However, in contrast to DTC tests provided before 2013, the FDA has approved only a limited scope of 33 variants in 8 genes, and providers have mandated the need to retest. Concerns of DTC PGx testing have been reported to relate to patient actions (eg, to stop taking a prescribed medication or adjusting the regimen based on genotype without consultation with a health provider) [
      • Haga S.B.
      Managing increased accessibility to pharmacogenomic data.
      ]. However, a longitudinal study of DTC consumers showed that only 5.6% of consumers reported changing a medication they were taking or starting a new medication because of their PGx results. Of these, 45 (83.3%) reported consulting with an HCP regarding the change [
      • Carere D.A.
      • VanderWeele T.J.
      • Vassy J.L.
      • et al.
      Prescription medication changes following direct-to-consumer personal genomic testing: findings from the Impact of Personal Genomics (PGen) Study.
      ]. Nonetheless, the involvement of HCPs will optimize the use of PGx results when delivered in a DTC setting. In the same longitudinal study, the authors found that 63% of consumers planned to share their results with a primary care provider. However, at 6-month follow-up, only 27% reported having done so, and 8% reported sharing with another HCP. Among participants who discussed results with their PCP, 35% were very satisfied with the encounter, and 18% were not at all satisfied. These results indicate that PGx testing in a DTC model may be a safe model for obtaining PGx testing.

      The lack of tools supporting implementation of pharmacogenomics-panel testing

      Development of a Pharmacogenomics Panel to Facilitate Implementation

      Another important challenge hampering adoption of preemptive panel testing is the lack of standardization regarding variants included in such panels. Standardization would enable clinicians to understand PGx test results without extensive scrutiny of the alleles included in the panel. Despite the identification of standardization as a potential accelerator for PGx adoption, exchange, and continuity [
      • Caudle K.E.
      • Keeling N.J.
      • Klein T.E.
      • et al.
      Standardization can accelerate the adoption of pharmacogenomics: current status and the path forward.
      ], there are currently no standards defining which variants should be tested [
      • Pratt V.M.
      • Everts R.E.
      • Aggarwal P.
      • et al.
      Characterization of 137 genomic DNA reference materials for 28 pharmacogenetic genes: a GeT-RM collaborative project.
      ,
      • Pratt V.M.
      • Zehnbauer B.
      • Wilson J.
      • et al.
      Characterization of 107 genomic DNA reference materials for CYP2D6, CYP2C19, CYP2C9, VKORC1, and UGT1A1: a GeT-RM and Association for Molecular Pathology collaborative project.
      ]. Although some initiatives have developed standardized panels of relevant variants within individual genes [
      • Pratt V.M.
      • Del Tredici A.L.
      • Hachad H.
      • et al.
      Recommendations for clinical CYP2C19 genotyping allele selection: a report of the association for molecular pathology.
      ], and other initiatives across multiple genes [
      • Bush W.S.
      • Crosslin D.R.
      • Owusu-Obeng A.
      • et al.
      Genetic variation among 82 pharmacogenes: the PGRNseq data from the eMERGE network.
      ], a panel covering widely accepted genetic variants reflecting an entire set of guidelines is not yet available. Thus, in order to facilitate the clinical implementation of PGx testing, the U-PGx consortium set out to develop a pan-European panel based on actionable DPWG guidelines, called the “PGx-Passport” [
      • Van der Wouden C.H.
      • Van Rhenen M.H.
      • Jama W.
      • et al.
      Development of the PGx-passport: a panel of actionable germline genetic variants for pre-emptive pharmacogenetic testing.
      ]. Here, germline variant alleles were systematically selected using predefined criteria regarding allele population frequencies, effect on protein functionality, and association with drug response. A “PGx-Passport” of 58 germline variant alleles, located within 14 genes (CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5, DPYD, F5, HLA-A, HLA-B, NUDT15, SLCO1B1, TPMT, UGT1A1 and VKORC1), was composed. This “PGx-Passport” can be used in combination with the DPWG guidelines to optimize drug prescribing for 49 commonly prescribed drugs. An advantage of the approach is that the number of clinically interpretable results within their “PGx-Passport” is maximized, while costs remain reasonable.
      Importantly, the presented panel will not be able to fully identify those at risk for unwanted drug response. The overall ability of a panel to predict drug response is dependent on, first, the predictive utility of genetic variation to predict drug response and, second, the ability to adjust pharmacotherapy to reduce the risk of unwanted effects among high-risk individuals. In the following sections, the current limitations of both domains are further elaborated.

      Current predictive utility of genetic variation to predict drug response

      Even though multiple genetic variants have been discovered, the authors currently restrict testing to a subset of these variants. However, restricting testing to individual variants disregards untested or undiscovered variants that may also influence the functionality of the gene product. Therefore, the functionality of the gene product cannot be fully predicted (see Fig. 1E). Reasons for restriction of testing are twofold. First, technical limitations regarding the sequencing of complex loci prevent complete determination of both the gene of interest and other areas in the genome, which may have an effect on the gene product. Determining genetic variation is specifically difficult in highly polymorphic genes, such as the HLA genes, or genes located near pseudogenes, such as CYP2D6. Although sequencing of these loci is technically possible, it are costly and time-consuming. Second, even if one were to determine all genetic variation, the downstream effect on protein functionality may be unknown and therefore impossible to interpret clinically [
      • Drogemoller B.I.
      • Wright G.E.
      • Warnich L.
      Considerations for rare variants in drug metabolism genes and the clinical implications.
      ].
      However, progress in the interpretation of functional consequences of such uncharacterized variations may support future interpretation in silico [
      • Li B.
      • Seligman C.
      • Thusberg J.
      • et al.
      In silico comparative characterization of pharmacogenomic missense variants.
      ], in vitro, or in vivo [
      • Zhou Y.
      • Mkrtchian S.
      • Kumondai M.
      • et al.
      An optimized prediction framework to assess the functional impact of pharmacogenetic variants.
      ]. Importantly, a study has shown that 92.9% of genetic variation in ADME genes is rare, and an estimated 30% to 40% of functional variability in pharmacogenes can be attributed to these variants [
      • Kozyra M.
      • Ingelman-Sundberg M.
      • Lauschke V.M.
      Rare genetic variants in cellular transporters, metabolic enzymes, and nuclear receptors can be important determinants of interindividual differences in drug response.
      ]. In addition to the downstream functionality, the penetrance (ie, the potential of a variant to accurately predict the genetic component of drug response) is also unknown. The penetrance is a function of both the variant’s effect on protein functionality and the extent to which the protein functionality is associated with clinical outcome. Significant debate persists regarding both the nature and the strength of evidence required for the clinical application of variant alleles of unknown functionality. Because the strength of these functions differs across genes and DGIs, the authors do not foresee a one-size-fits-all consensus regarding and evidence threshold across all DGIs, but rather a different evidence threshold per individual DGI based on the genetics and pharmacology of the interaction. For example, in the case of the TPMT-thiopurine interaction, the effect of TPMT variation on protein functionality has been firmly established because it exhibits behavior similar to monogenetic codominant traits [
      • Weinshilboum R.M.
      • Sladek S.L.
      Mercaptopurine pharmacogenetics: monogenic inheritance of erythrocyte thiopurine methyltransferase activity.
      ]. Therefore, identified variants in TPMT (∗3A/∗3B/∗3C) are considered to have sufficient evidence to be applied in the clinic. The clinical interpretation has been clinically validated in a study specifically investigating clinical effects in patients carrying these variants [
      • Coenen M.J.
      • de Jong D.J.
      • van Marrewijk C.J.
      • et al.
      Identification of patients with variants in TPMT and dose reduction reduces hematologic events during thiopurine treatment of inflammatory bowel disease.
      ]. On the other hand, clinically relevant variant alleles in CYP2D6 are based on the pharmacology of the interaction. For example, the flecainide-CYP2D6 interaction is based on the associations between decreasing CYP2D6 activity leading to increasing flecainide plasma levels, which in turn leads to increased risk for flecainide intoxication. Therefore, all identified variants in CYP2D6, shown to have a significant effect on CYP2D6 enzyme activity, are considered clinically applicable. As such, both the functional effects and the penetrance of many rare variants are yet unknown. As an additional complication, these may also differ across substrates and drug responses. Even more fundamentally, variants may impact each other’s functionality, and therefore, individual variants may have different functionalities depending on the absence or presence of other variants.
      Another significant limitation, which is applicable to PGx testing and interpretation as it is performed today, is that predicted phenotypes are interpreted as categories rather than continuous scores, and it is assumed the sum of both alleles equals total metabolic capacity (see Fig. 1F). For example, for CYP2D6, patients are categorized into normal metabolizers, intermediate metabolizers, poor metabolizers, or ultrarapid metabolizers. However, the actual CYP2D6 phenotype is likely normally distributed [

      van der Lee M, Allard WG, Vossen RHAM, et al. A unifying model to predict variable drug response for personalised medicine. Biorxiv. 2020:2020.2003.2002.967554.

      ,
      • Hertz D.L.
      • Rae J.
      Pharmacogenetics of cancer drugs.
      ]. Imposing categorization, as opposed to the interpretation of the actual diplotype, therefore sacrifices information in order to simplify clinical interpretation. In the process, the functionality of each allele is interpreted individually, and it is assumed that the sum of these activity scores equals the total activity of the diplotype. Furthermore, these categorizations are currently substrate independent, even though the effects on metabolic capacity are known to differ between substrates [
      • Hicks J.K.
      • Swen J.J.
      • Gaedigk A.
      Challenges in CYP2D6 phenotype assignment from genotype data: a critical assessment and call for standardization.
      ].

      Current ability to adjust pharmacotherapy to optimize outcomes

      In addition to the ability of genetic variation to predict drug response, the second component determining the utility of PGx-guided pharmacotherapy is the ability to adjust pharmacotherapy to the specific genetic variants. Currently, there are 2 options to reduce the risk of ADRs and lack of efficacy: (1) selecting another drug and (2) adjusting the dose (see Fig. 1H).
      A successful example of choosing an alternative therapy to avoid an ADR is preemptive testing for HLA-B∗57:01 to guide drug selection for abacavir or another antiretroviral. Here, 0% of the prospectively screened group versus 2.7% of the control group experienced immunologically confirmed hypersensitivity [
      • Mallal S.
      • Phillips E.
      • Carosi G.
      • et al.
      HLA-B∗5701 screening for hypersensitivity to abacavir.
      ]. In this example, the PGx intervention and subsequent adjustment completely eliminated the risk of hypersensitivity.
      An example of adjusting the dose to reduce the risk of ADRs is preemptive testing for TPMT to guide dose selection of thiopurines to reduce the risk of severe hematologic ADRs [
      • Coenen M.J.
      • de Jong D.J.
      • van Marrewijk C.J.
      • et al.
      Identification of patients with variants in TPMT and dose reduction reduces hematologic events during thiopurine treatment of inflammatory bowel disease.
      ]. In contrast to the previously described abacavir/HLA-B∗57:01 example, this intervention has a smaller effect size. Here, severe hematologic ADRs still occurred in 2.6% of TPMT variant carriers who received an adjusted dose, compared with 22.9% of TPMT variant carriers treated with a normal dose. Although dose adjustment prevented ∼89% of severe hematologic ADRs, the remaining ∼11% could not be prevented by this intervention. Indeed, this could partially be a result of the sensitivity of TPMT testing not reaching 100%, but could also be due to the fact that dose reduction was not sufficient for avoiding this ADR. Furthermore, the incidence of severe hematological ADRs among noncarriers of TPMT variants was 7.3%, indicating that other (genetic) factors, such as NUDT15, may play a role in the risk of severe hematological ADRs.

      Enable Recording of Pharmacogenomics-Panel Results for Future Use

      To enable preemptive PGx testing, it is imperative that the PGx test results are recorded in the electronic medical records (EMRs) for future use (see Fig. 1G). Within a pilot study, the authors found that both pharmacists and general practitioners (GPs) are able to record PGx results in their EMRs as contraindications (96% and 33% of pharmacists and GPs, respectively), enabling the deployment of relevant guidelines by the CDSS when a DGI is encountered at both prescribing and dispensing [
      • van der Wouden C.H.
      • Bank P.C.D.
      • Ozokcu K.
      • et al.
      Pharmacist-initiated pre-emptive pharmacogenetic panel testing with clinical decision support in primary care: record of PGx results and real-world impact.
      ]. In contrast, a recent study showed that genotyping results were sparsely communicated and recorded correctly; only 3.1% and 5.9% of reported genotyping results were recorded by GPs and pharmacists, respectively, within a similar follow-up time of 2.36 years [
      • Simoons M.
      • Mulder H.
      • Schoevers R.A.
      • et al.
      Availability of CYP2D6 genotyping results in general practitioner and community pharmacy medical records.
      ].

      Future perspectives

      Generating Evidence for Effectiveness of Precision Medicine Approaches

      In an era where digitalization is driving data accumulation and a concomitant increase in stratification of patient groups and a more precise diagnosis, we are moving toward the utilization of real-world data to support precision medicine (see Fig. 1I). Several investigators have pointed out that precision medicine, and genomic medicine, in particular, would benefit from a convergence of implementation science and a learning health system to measure outcomes and generate evidence across a large population [
      • Chambers D.A.
      • Feero W.G.
      • Khoury M.J.
      Convergence of implementation science, precision medicine, and the learning health care system: a new model for biomedical research.
      ,
      • Lu C.Y.
      • Williams M.S.
      • Ginsburg G.S.
      • et al.
      A proposed approach to accelerate evidence generation for genomic-based technologies in the context of a learning health system.
      ]. However, this requires standardization of outcomes in EMRs to enable aggregation of phenotype data across large populations for both discovery and outcomes assessment within a genomic medicine implementation [
      • Peterson J.F.
      • Roden D.M.
      • Orlando L.A.
      • et al.
      Building evidence and measuring clinical outcomes for genomic medicine.
      ]. Many nationwide, large-scale initiatives are generating prospective longitudinal evidence supporting precision medicine approaches [
      • Turnbull C.
      • Scott R.H.
      • Thomas E.
      • et al.
      The 100 000 Genomes Project: bringing whole genome sequencing to the NHS.
      ,
      • Gottesman O.
      • Scott S.A.
      • Ellis S.B.
      • et al.
      The CLIPMERGE PGx Program: clinical implementation of personalized medicine through electronic health records and genomics-pharmacogenomics.
      ,
      • Leitsalu L.
      • Haller T.
      • Esko T.
      • et al.
      Cohort profile: estonian biobank of the Estonian Genome Center, University of Tartu.
      ]. For example, a landmark project specifically generating evidence for PGx is the All of Us project [
      • Collins F.S.
      • Varmus H.
      A new initiative on precision medicine.
      ]. Alternatively, pragmatic clinical trials offer researchers a means to study precision medicine interventions in real-world settings [
      • Khoury M.J.
      • Rich E.C.
      • Randhawa G.
      • et al.
      Comparative effectiveness research and genomic medicine: an evolving partnership for 21st century medicine.
      ,
      • Ford I.
      • Norrie J.
      Pragmatic trials.
      ]. In contrast to traditional clinical trials that are performed in ideal conditions, these pragmatic trials are conducted in the context of usual care [
      • Ford I.
      • Norrie J.
      Pragmatic trials.
      ]. Pragmatic clinical trials easily transition into existing health care infrastructures and therefore make them particularly appealing to comparative effectiveness research and the evidence-based mission of learning health care systems [
      • Fiore L.D.
      • Lavori P.W.
      Integrating randomized comparative effectiveness research with patient care.
      ,
      • Weinfurt K.P.
      • Hernandez A.F.
      • Coronado G.D.
      • et al.
      Pragmatic clinical trials embedded in healthcare systems: generalizable lessons from the NIH Collaboratory.
      ]. An example of such a pragmatic trial for generating evidence for preemptive PGx testing is the I-PICC study [
      • Brunette C.A.
      • Miller S.J.
      • Majahalme N.
      • et al.
      Pragmatic trials in genomic medicine: the Integrating Pharmacogenetics in Clinical Care (I-PICC) study.
      ].
      In parallel, evolving digital health technologies are driving data accumulation. Data collected by sensors (in smartphones, wearables, and ingestibles), mobile apps, and social media can be processed by machine learning to support medical decision making [
      • Sim I.
      Mobile devices and health.
      ]. Raw sensor data can also be processed into digital biomarkers and endpoints [
      • Coravos A.
      • Khozin S.
      • Mandl K.D.
      Developing and adopting safe and effective digital biomarkers to improve patient outcomes.
      ]. This development may be particularly useful for endpoint definition in disease areas where biological endpoints are lacking, such as in psychiatry and neurology, to enable quantification of disease progression and drug response. For example, novel digital endpoints are being developed to stratify mental health conditions and predict remission using passively collected smartphone data []. Another example is the development of a digital biomarker for Parkinson disease using motor active tests and passive monitoring through a smartphone [
      • Lipsmeier F.
      • Taylor K.I.
      • Kilchenmann T.
      • et al.
      Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial.
      ]. For precision medicine, in particular, we may also be more able to stratify patient groups into responders and nonresponders with improved endpoint development in these disease areas. Increased stratification of patient groups on the basis of genetic, (digital) biomarker, phenotypic, of psychosocial characteristics will drive more precise diagnoses and pharmacotherapy optimization [
      • Clay I.
      Impact of digital technologies on novel endpoint capture in clinical trials.
      ,
      • Haendel M.A.
      • Chute C.G.
      • Robinson P.N.
      Classification, ontology, and precision medicine.
      ]. This trend will drive demand for innovations for more efficient study designs because of increasing numbers of indications, whereas resources to fund these trials remain constant [
      • Miksad R.A.
      • Samant M.K.
      • Sarkar S.
      • et al.
      Small but mighty: the use of real-world evidence to inform precision medicine.
      ].

      Determining Optimal Timing and Target Group for Pharmacogenomics-Panel Testing

      Consensus regarding who should be tested, and when it is most cost-effective to perform preemptive panel-testing, remains undetermined [
      • Roden D.M.
      • Van Driest S.L.
      • Mosley J.D.
      • et al.
      Benefit of preemptive pharmacogenetic information on clinical outcome.
      ]. Moreover, the most cost-effective technique to determine the PGx profile is also undetermined. As novel DGIs are discovered, it may be more efficient to sequence whole genomes, to avoid testing of additional variants through genotyping over time. Clinically relevant PGx variants can successfully be extracted from sequencing data using bioinformatics pipelines [
      • Yang W.
      • Wu G.
      • Broeckel U.
      • et al.
      Comparison of genome sequencing and clinical genotyping for pharmacogenes.
      ,
      • van der Lee M.
      • Allard W.G.
      • Bollen S.
      • et al.
      Repurposing of diagnostic whole exome sequencing data of 1,583 individuals for clinical pharmacogenetics.
      ]. As the cost of sequencing techniques decrease, genotype-based testing will become obsolete. In this case, it may be more cost-effective to perform population-wide sequencing at birth, to ensure the maximization of instances in which a PGx result is available when a DGI is encountered. However, whole-exome sequencing and whole-genome sequencing are increasingly applied for other medical indications and objectives [
      • Holm I.A.
      • Agrawal P.B.
      • Ceyhan-Birsoy O.
      • et al.
      The BabySeq project: implementing genomic sequencing in newborns.
      ,
      • Kalia S.S.
      • Adelman K.
      • Bale S.J.
      • et al.
      Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics.
      ]. As this development expands, determining the cost-effectiveness of implementing PGx testing may become redundant, because the information on PGx variants becomes secondary findings, free of additional costs.

      Improving Predictive Utility of Genetic Variation to Predict Drug Response

      Recent advances have been made to improve the ability to determine an individual’s genetic variation. Technical limitations regarding the sequencing of complex loci may be overcome by advances in long-read sequencing technologies and synthetic long-read assembly [
      • Lauschke V.M.
      • Ingelman-Sundberg M.
      How to consider rare genetic variants in personalized drug therapy.
      ]. As a result, an increasing number of variants with unknown functionality will need to be interpreted. Because of the vast increasing number of rare variants, it is impossible to determine functionality in traditional expression systems. To overcome this challenge, advances have been made in the development of in silico methods to predict functionality. However, these methods are based on genes that are evolutionarily highly conserved. Because many ADME genes are only poorly conserved, steps have been taken to calibrate in silico models on data sets. For example, recently investigators developed a novel computational functionality prediction model optimized for pharmacogenetic assessments, which substantially outperformed standard algorithms [
      • Zhou Y.
      • Mkrtchian S.
      • Kumondai M.
      • et al.
      An optimized prediction framework to assess the functional impact of pharmacogenetic variants.
      ].
      Nonetheless, these models still do not enable prediction of the functionality of synonymous mutations, intronic variants, or variants in noncoding regions of the genome. Recent initiatives have provided an alternative method for the interpretation of variants with unknown functionality using machine learning [

      van der Lee M, Allard WG, Vossen RHAM, et al. A unifying model to predict variable drug response for personalised medicine. Biorxiv. 2020:2020.2003.2002.967554.

      ,

      McInnes G, Dalton R, Sangkuhl K, et al. Transfer learning enables prediction of CYP2D6 haplotype function. Biorxiv 2020:684357.

      ], one using an existing data set for model training and the other using a mock data set. In the first, the investigators trained a neural network model on the long-read sequencing profiles of CYP2D6 of 561 patients and used the metabolic ratio between tamoxifen and endoxifen as an outcome measure. The model explains 79% of the interindividual variability in CYP2D6 activity compared with 55% with the conventional categorization approach. In addition, this model is capable of assigning accurate enzyme activity to alleles containing previously uncharacterized combinations of variants. The suggested model has provided a method to determine predicted phenotype on a continuous scale. Indeed, enzyme activity may be expected to be normally distributed within a population and therefore better described by such a scale. A future is envisioned where phenotypes can be predicted more precisely by using all of an individual’s genetic variation, as opposed to limiting the view only to those variants included in a tested panel.
      Following a further understanding of the effects of individual variants to inform phenotype prediction on a continuous scale, one can imagine that this phenotype prediction will ultimately become substrate specific on top of gene specific. More fundamentally, in PGx, the view is currently limited to a single DGI, whereas multiple genes may be involved in the metabolism of drugs and their metabolites. If one were to expand their view to multiple genes involved to predict drug response, the predictive utility will further improve. To incorporate genetic variations of multiple genes, polygenic risk scores may prove useful [
      • Gibson G.
      On the utilization of polygenic risk scores for therapeutic targeting.
      ].
      Although genetics is considered the causal anchor of biological processes [
      • Watson J.D.
      • Crick F.H.
      Genetical implications of the structure of deoxyribonucleic acid.
      ], the biological mechanism underlying drug response may be downstream of a genetic variant. In these cases, genetics will have no predictive utility for drug response (see Fig. 1J, top left). Therefore, incorporating processes downstream of the genome, such as the epigenome [
      • Lauschke V.M.
      • Zhou Y.
      • Ingelman-Sundberg M.
      Novel genetic an epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity.
      ], transcriptome, microbiome [
      • Sun L.
      • Xie C.
      • Wang G.
      • et al.
      Gut microbiota and intestinal FXR mediate the clinical benefits of metformin.
      ], and metabolome [
      • Kaddurah-Daouk R.
      • Weinshilboum R.
      Metabolomic signatures for drug response phenotypes: pharmacometabolomics enables precision medicine.
      ], may further optimize the ability to predict drug response to enable more accurate stratification of patient populations. Combining these profiles in a systems medicine approach may have a synergistic effect.

      Improving Ability to Adjust Pharmacotherapy to Optimize Outcomes

      In the future, pharmacotherapy adjustment may be further improved by imminent technologies, such as 3-dimensional (3D) printing to enable personalized dosing and delivery [
      • Afsana
      • Jain V.
      • Haider N.
      • et al.
      3D printing in personalized drug delivery.
      ]. Currently, the DPWG calculates specific dose adjustments based on pharmacokinetic studies and rounds the recommended dose to the nearest corresponding marketed dose for clinical feasibility. The utilization of 3D-printing technologies may enable rapid compounding of tablets with a specific dose based on an individual’s genetic profile. In any case, adjustment of the pharmacotherapy will always be limited by the safety profile of available drugs. Opportunely, over the last decades, newly developed drugs have been shifting from unspecific small molecules to more targeted drugs in the form of humanized monoclonal antibodies [
      • Hansel T.T.
      • Kropshofer H.
      • Singer T.
      • et al.
      The safety and side effects of monoclonal antibodies.
      ], cell therapies [
      • Jackson H.J.
      • Rafiq S.
      • Brentjens R.J.
      Driving CAR T-cells forward.
      ], and gene therapies [
      • Naldini L.
      Gene therapy returns to centre stage.
      ] with fewer off-target ADRs.

      Recording Pharmacogenomics-Panel Results for Future Use

      Future initiatives should focus on the development of automated sharing of PGx results across EMRs. In the Netherlands, such an initiative has been launched but requires patient consent before it can be used. The National Exchange Point (“Landelijk Schakel Punt” [LSP]) is a nationwide secured EMR infrastructure to which nearly all HCPs can access [
      ]. Only when a patient has provided written consent for the LSP can a professional summary of the local pharmacy or GP EMR, including PGx results, be downloaded by another treating HCP in the same region, unless the patient chose to shield this information. Alternatively, providing the PGx results directly to patients may resolve the issue in terms of communicating and recording PGx results; for example, using the MSC safety-code card as used in the PREPARE study [
      • Samwald M.
      • Minarro-Giménez J.A.A.
      • Blagec K.
      • et al.
      Towards a global IT system for personalized medicine: the Medicine Safety Code initiative.
      ,
      • Blagec K.
      • Koopmann R.
      • Crommentuijn-van Rhenen M.
      • et al.
      Implementing pharmacogenomics decision support across seven European countries: the Ubiquitous Pharmacogenomics (U-PGx) project.
      ].

      Summary

      In conclusion, developments in evidence generation and in genetic sequencing and interpretation will revolutionize current stratified medicine to enable true precision medicine, whereby multiple -omics profiles of an individual are combined to predict drug response and optimize pharmacotherapy accordingly.

      Disclosure

      The authors have nothing to disclose.

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