Value of Supportive Care Pharmacogenomics in Oncology Practice
JAI N. PATEL ,aLAUREN A. WIEBE,
bHENRY M. DUNNENBERGER,
bHOWARD L. MCLEOD
c
aLevine Cancer Institute, Carolinas HealthCare System, Charlotte, North Carolina, USA; bNorthShore University Health System, Evanston,Illinois, USA; cThe DeBartolo Family Personalized Medicine Institute, Moffitt Cancer Center, Tampa, Florida, USADisclosures of potential conflicts of interest may be found at the end of this article.
Key Words. Supportive care • Pharmacogenomics • Pharmacogenetics • Cancer • Oncology • Symptom management
ABSTRACT
Genomicmedicine provides opportunities to personalize cancertherapy for an individual patient. Although novel targetedtherapies prolong survival, most patients with cancer continueto suffer from burdensome symptoms including pain, depres-sion, neuropathy, nausea and vomiting, and infections, whichsignificantly impair quality of life. Suboptimal management ofthese symptoms can negatively affect response to cancer treat-ment and overall prognosis. The effect of genetic variation ondrug response—otherwise known as pharmacogenomics—is
well documented and directly influences an individual patient’sresponse to antiemetics, opioids, neuromodulators, antidepres-sants, antifungals, and more. The growing body of pharmacoge-nomic data can now guide clinicians to select the safest andmost effective supportive medications for an individual patientwith cancer from the very first prescription.This review outlinesa theoretical patient case and the implications of using pharma-cogenetic test results to personalize supportive care throughoutthe cancer care continuum.The Oncologist 2018;23:1–9
Implications for Practice: Integration of palliative medicine into the cancer care continuum has resulted in increased quality of lifeand survival for patients with many cancer types. However, suboptimal management of symptoms such as pain, neuropathy,depression, and nausea and vomiting continues to place a heavy burden on patients with cancer. As demonstrated in thistheoretical case, pharmacogenomics can have a major effect on clinical response to medications used to treat these conditions.Recognizing the value of supportive care pharmacogenomics in oncology and application into routine practice offers an objectivechoice for the safest andmost effective treatment compared with the traditional trial and error method.
INTRODUCTION
Personalization of medicines and careful attention to quality oflife (QOL) are increasingly part of expectations for patients withcancer throughout the care trajectory. With the growing com-plexity of both antineoplastic and supportive care, a practicingoncologist has diminishing time to manage each patient’s myr-iad supportive care concerns by trial and error. Suboptimalmanagement of these symptoms compromises potential bene-fits from cancer therapy, disrupts clinic workflow, increasesemergency room visits, and affects both patient satisfactionand reimbursement [1–5]. Better tools are needed to makeindividual, tailored choices easier for busy clinicians every day.
Genetic variation is well documented across the humangenome and ultimately affects a patient’s response to medica-tions with regard to efficacy and toxicity. The genome is quicklybecoming a pragmatic tool that can assist medical oncologistsand palliative medicine providers in the selection of the bestsupportive care treatments for patients with cancer. Notably,knowledge of pharmacogenetic variants associated with drugresponse is rapidly evolving. To aid in the use of pharmacoge-netic data, the Clinical Pharmacogenetics Implementation
Consortium (CPIC) develops peer-reviewed guidelines on howto best apply genetic data to modify drug therapy [6, 7]; how-ever, there is also an emerging category of relevant genes notcurrently covered by CPIC guidelines. CPIC categorizes patientsinto metabolizer phenotypes based on their genotype (Table 1)and provides specific dosing or therapy selection recommenda-tions for each category. Increasingly in this era of personalizedmedicine, patients with cancer are expecting their oncologist touse their unique genomes to choose therapy correctly the firsttime and minimize drug-related toxicities [8].
THE CASE: BARB G.
TheOncologist 2018;23:1–9 www.TheOncologist.com Oc AlphaMed Press 2018
Symptom Management and Supportive Care
Correspondence: Jai N. Patel, Pharm.D., Levine Cancer Institute, Carolinas HealthCare System, 1021 Morehead Medical Drive, Charlotte,North Carolina 28204, USA. Telephone: 980-442-4113; e-mail: Jai.Patel@carolinashealthcare.org Received November 14, 2017; acceptedfor publication February 21, 2018; published Online First on April 6, 2018. http://dx.doi.org/10.1634/theoncologist.2017-0599
Barb G., a 60-year-old woman, is a new patient in clinic with abreast mass found to be adenocarcinoma. Many of her relativeshad extreme reactions to prescription medications, so sheresearched extensively and wants to do a full pharmacoge-nomic profile, as she heard this kind of testing could informdrug choice and dosing throughout her cancer journey. Shehands you her results that show she is a CYP2D6 poor metabo-lizer (PM) and a CYP2C19 ultrarapid metabolizer (UM).
The Oncologist 2018;23:956–964 www.TheOncologist.com ©AlphaMed Press 2018
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The plan is neoadjuvant chemotherapy with doxorubicin
and cyclophosphamide. Barb is terrified of nausea and asks you
if the pharmacogenomic test results will direct your decisions
about antiemetic selection. She wants to be sure she is getting
the best supportive care possible.
ANTIEMETIC SELECTIONChemotherapy-induced nausea and vomiting (CINV) is one ofthe most notorious and debilitating adverse drug effects experi-enced by patients treated with cytotoxic chemotherapy agents[9]. Ineffective control of CINV can lead to patient distress,unacceptable QOL, and treatment noncompliance [10]. Sincetheir advent, serotonin receptor antagonists (5HT3-RA) havebeen the backbone of CINV prophylaxis and treatment. CYP2D6is a key metabolic pathway for inactivation of most 5HT3-RAs—particularly ondansetron and palonosetron, the two mostwidely used 5HT3-RAs. For example, CYP2D6 UMs, who arefound in approximately 5% of the white population, degradeondansetron too rapidly, resulting in ineffective blood levelsand thus weak control of CINV [10–13]. Studies showmore epi-sodes of vomiting and higher reported nausea for CYP2D6 UMsreceiving ondansetron on equivalent chemotherapy regimens[13, 14].
CPIC guidelines support a change in therapy for patientswith known CYP2D6 UM status and planned ondansetron [15].Granisetron is the only 5HT3-RA that does not involve CYP2D6in its metabolism; thus, it might be the most reasonable optionin a suspected UM [10]. If switching 5HT3-RAs does not havean effect on the poorly controlled nausea and vomiting, mostguidelines support the addition of a neurokinin 1-receptorantagonist. The pharmacogenomic test results could be submit-ted to insurance in order to justify nonformulary coverage in acase such as this. Although many polymorphisms exist thatmight explain patient variability in 5HT3-RA efficacy for acuteCINV, only CYP2D6 appears to be clinically actionable. Currentlyin clinical practice, CYP2D6 genetic testing is readily availableand may be used to guide future 5HT3-RA regimen choicesbecause of its consistent clinical data, relatively low cost, andhigh patient benefit. (See Fig. 1.)
Barb is a CYP2D6 poor metabolizer and is likely to have the
appropriate benefit from ondansetron, which is a mainstay of
your practice. Given that she will have slowed inactivation of
the ondansetron, she might be at a slightly higher risk for side
Table 1. Definition of phenotypes and potential clinical implication on drug response
Phenotypes Definition
Clinical implication
Active drug Prodrug
Ultrarapidmetabolizer (UM)
Increased enzyme activitycompared with rapid metabolizers
Significantly increased inactivation andreduced response
Significantly increasedactivation and increasedresponse and side effects
Rapid metabolizer(RM)
Increased enzyme activitycompared with normal metabolizersbut less than ultrarapidmetabolizers
Increased inactivation and reduceresponse
Increased activation andincreased response and sideeffects
Normal metabolizer(NM)
Fully functional enzyme activity Normal or expected clinical response Normal or expected clinicalresponse
Intermediatemetabolizer (IM)
Decreased enzyme activitycompared with normal metabolizersbut more than poor metabolizers
Reduced inactivation and increasedresponse and side effects
Reduced activation andreduced response
Poor metabolizer(PM)
Little to no enzyme activity Significantly reduced inactivation andincreased response and side effects
Significantly reduced activationand reduced response
Clinical implications noted in the table are generally true, but may differ based on the specific gene and drug (e.g. CYP3A5 NMs may require highertacrolimus doses than PMs since PM is the predominant phenotype and NMs may have sub-therapeutic concentrations).
Figure 1. Pharmacogenetic-driven treatment pathway forchemotherapy-induced nausea and vomiting. CYP2D6 UMs receivingmoderate to high emetogenic chemotherapeutic regimens are rec-ommended to receive granisetron as the first-line 5HT3-RA becauseof increased metabolism or inactivation of other 5HT3-RAs. PMsmay require closer and more frequent monitoring for side effects(malaise, constipation, headache, QTprolongation) because of possi-ble supratherapeutic serum levels. Clinical risk factors (emesis withprior chemotherapy, female gender, younger age, lack of a signifi-cant history of alcohol consumption, history of motion sickness, con-current radiation treatment, history of hyperemesis gravidarum, andhigh dose or highly emetogenic combination chemotherapy regi-mens) should be considered when deciding whether or not toadminister a neurokinin 1 receptor antagonist in patients receivingmoderate emetogenic chemotherapy or a 5HT3-RA in patientsreceiving low emetogenic chemotherapy.1, Monitor closely for 5HT3-RA side effects such as constipation,
low-grade headache, QT prolongation, or malaise because ofpotentially increased blood levels.2, If patient is unable to take granisetron or if granisetron is
unavailable, then may consider using high-dose ondansetron.Abbreviations: 5HT3-RA, serotonin receptor antagonist; CYP2D6,
cytochrome P450 2D6; IM, intermediate metabolizer; PM, poormetabolizer; NM, normal metabolizer; UM, ultrarapid metabolizer.
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effects such as headache or constipation. There is no current
recommendation to reduce the dose of the 5HT3-RA in this set-
ting, but it may be considered in the case of intolerable side
effects, for which she should be closely monitored. If her CINV
requires the addition of intravenous palonosetron, she would
be expected to respond favorably to that as well. Her CYP2D6PM phenotype suggests that appropriate, effective drug levels
will be present in the serum.Barb tolerates her chemotherapy generally well and has a
favorable response with desired downsizing of the tumor. Next,
she undergoes surgery for removal of the shrinking mass and
calls your nurse the day after discharge from the surgery. She
was given a prescription for Tylenol #3 (acetaminophen con-
taining codeine; Johnson & Johnson, New Brunswick, NJ) and
was instructed to take one tablet every 6 hours maximum. She
mentioned that Tylenol #3 did not help her after an oral surgerya few years ago, so the breast surgeon decided to instead try
tramadol 50 mg every 4 hours because it is not a schedule II
medication and the patient was more comfortable trying this
first. Barb administers tramadol around the clock for 1 week
but tells your nurse that the pain medicine did “absolutely
nothing” and asks her to please help.
OPIOID SELECTIONAny practicing oncologist knows that pain is one of the mostpersistent and burdensome symptoms in patients with cancer,affecting approximately 50% of those with curable cancer andup to 75% with advanced disease. Only one third of patientswith cancer in the U.S. achieve significant pain improvementwith standard strategies [16]. Known factors associated withineffective analgesia include geriatric age, minority race, andinadequate clinician assessment [17]; however, there is a grow-ing realization that a patient’s unique genetic makeup couldaffect clinical response to opioids and thus could be used fordrug and/or dose selection. (See Fig. 2.) CYP2D6 is responsiblefor the activation of codeine, tramadol, oxycodone, and hydro-codone into stronger opioids: morphine, o-desmethyltramadol,oxymorphone, and hydromorphone, respectively [18]. Morethan 100 CYP2D6 alleles have been identified that may alterenzyme function. Even within an ethnic group, the frequencyof the common alleles that result in either reduced function orloss of function are highly variable (15%–41%), thus makinggeneralization of pharmacogenomic phenotype by race highlyunreliable in clinical practice [19].
Codeine
The analgesic effect of codeine is mainly attributed to its con-version to morphine mediated by CYP2D6. Morphine has a 200times higher affinity and 50 times higher intrinsic activity at them-opioid receptor than codeine itself. Codeine-related deathshave been reported in patients known to be CYP2D6 UMs, nowa black-box warning [20–26]. Alternatively, CYP2D6 PMs willfind codeine to be an ineffective analgesic given that they haveno conversion of codeine to the more active morphine. CPICguidelines strongly recommend that CYP2D6 UMs and PMsshould avoid codeine because of the increased risk of toxicitiesand lack of analgesic effects, respectively [27].Without pharma-cogenomic testing, astute clinicians might avoid codeine ifpatients report inefficacy; however, the issue of codeine inCYP2D6 UMs is a real risk of harm without the benefit of formalpharmacogenomic testing.
Oxycodone and Hydrocodone
Although the drugs oxycodone and hydrocodone have someanalgesic activity, they are metabolized by CYP2D6 to the muchmore potent metabolites of oxymorphone and hydromor-phone, respectively. A study of 450 patients with cancer receiv-ing oxycodone demonstrated that plasma concentrations ofthe more active oxymorphone were up to 11 times higher inpatients with rapid metabolism than in those with poor metab-olism at CYP2D6 (p< .0001) [28]. In another study, dependingon CYP2D6 metabolism, patients required either 16 (UMs) or25 (PMs) mg of oxycodone to achieve equal analgesic effect(p5 .005) [29]. Studies have shown that a similar phenomenonoccurs when patients are given hydrocodone. CYP2D6 UMs had
Figure 2. Pharmacogenetic-driven treatment pathway for painmanagement. CYP2D6 UMs and PMs should avoid tramadol,codeine, hydrocodone, and oxycodone. PMs may be at risk fortreatment failure because of their inability to convert the parentdrug into its more active metabolite. UMs may be at risk fortreatment-related side effects because of supratherapeutic con-centrations of active metabolites. Patients with GG genotypes forCOMT and/or OPRM1 may require higher morphine equivalentsfor analgesia. Oxycodone and hydrocodone are also inactivatedvia CYP3A4; therefore, drugs that inhibit or induce the CYP3A4pathway should be avoided, when possible.1, If patient is on a strong CYP2D6 inhibitor, then classify as a
poor metabolizer.2, If APAP or an NSAID is ineffective for pain, may consider
either increasing dose or progressing to selection from moderatecategory.
3, If COMT and/or OPRM1 GG genotype, patient may requirehigher doses or rapid titration for pain relief.4, May consider methadone in patients unresponsive to stand-
ard pain therapy; refer to pain specialist if necessary. Polymor-phisms in CYP2B6 may alter methadone exposure.Abbreviations: APAP, acetaminophen; CYP2D6, cytochrome
P450 2D6; IM, intermediate metabolizer; NM, normal metabolizer;NSAID, nonsteroidal anti-inflammatory drug; PM, poor metabo-lizer; UM, ultrarapid metabolizer.
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a 10-fold increase in plasma concentrations of hydromorphonecompared with patients at the other end of the spectrum(p5 .023), which correlated with pain relief [30]. Finally, inCYP2D6 PMs, opioids that are activated more slowly have lesspredictable clearance and can result in safety concerns, as thedrug and its metabolites are present longer than expected.
Tramadol
Like codeine, tramadol is a prodrug and requires CYP2D6-mediated activation for analgesic activity. Depending on geno-type, the area under the curve of the active metabolite canrange from 0 to 235 ng3 hr/mL [31], thus leading to wildly dif-ferent perceptions of clinical efficacy [31–33]. In a prospectivestudy of approximately 300 patients recovering from abdominalsurgery, the percent of nonresponders was significantly higherin the PM group (46.7%) compared with the normal metabo-lizer group (21.6%; p5 .005) [33]. Most concerning, tramadol-induced respiratory depression was reported in a CYP2D6 UMpatient who also had renal impairment [32]. These data suggestthat CYP2D6 is highly informative for consideration of tramadoltherapy, similar to the guidelines set forth for codeine by CPIC.
For patients with either ultrarapid or poor CYP2D6 metabo-lism who are prescribed codeine, CPIC guidelines recommendalternative drugs that are not affected by CYP2D6, such as mor-phine. Specifically, tramadol, hydrocodone, and oxycodone arenot ideal choices given that they are metabolized by CYP2D6.
A patient like Barb, who is a CYP2D6 PM and previously
failed codeine therapy, will also likely not activate the tramadol
to its active metabolite and will thus miss most of the intended
analgesic effect. A prescription for either morphine or hydro-
morphone would bypass any need for activation and would be
the most appropriate selection in this case. If a practitioner
wished to prescribe either hydrocodone or oxycodone, Barb’s
CYP2D6 PM status predicts that she may require higher doses
than usual for appropriate analgesic effect.
You let the surgeon know that you feel comfortable pre-
scribing morphine based on her pharmacogenomic profile. You
call Barb back and let her know that a prescription for morphine
15 mg immediate release every 4 hours as needed is waiting for
her at the pharmacy, which should be a more effective analge-
sic in her case. Barb ultimately experiences significant pain relief
with morphine.With regard to analgesia, pharmacogenomic testing is guid-
ing drug choice and dose recommendations in an increasinglydata-driven way. Beyond the above data on CYP2D6, there areadditional ways in which pharmacogenomic testing may affectopioid prescribing in patients with cancer.
Emerging Genes: OPRM1 and COMT
The gene responsible for coding the mu-opioid receptor isOPRM1. Mu receptor activation leads to analgesia and knownopioid side effects, including respiratory depression, sedation,euphoria, and decreased gastrointestinal motility [34]. Multiplestudies have shown that variation in alleles at this gene resultin different clinical responses to opioids. Given altered receptorfunction, a simple base-pair substitution can lead a patient torequire 60%–100% more morphine for equal analgesia than inthe average population [9, 35–37]. At the bedside, it mayappear that the patient has poor or almost no response toopioids even if they are titrated. These patients are at a real riskof uncontrolled pain, as clinicians may be appropriately hesitant
to escalate opioid doses rapidly without objective genotype-directed information to support an aggressive titration.
Opioid analgesia can also be enhanced by the presence ofcatecholamines, which are involved in the modulation of pain.Catechol-O-methyltransferase (COMT) is responsible for themetabolism and inactivation of native catecholamines such asdopamine, epinephrine, and norepinephrine. One relativelycommon base-pair substitution in the coding of COMT reducesthe enzyme’s activity by three- to fourfold. This increase inendogenous catecholamines sensitizes patients to opioid ago-nists, lowering the morphine equivalents required for analgesiacompared with patients with higher COMT activity, who mayrequire at least doubling of the dose [35, 38–40]. Although themajority of research has studied morphine in this context, it isclear that the mu binding and thus dosing of any opioid will bealtered [41–44]. The combined presence of genotypic variationsat OPRM1 and COMT result in further complexities in opioiddose selection, which are partially described but undergoingfurther research at this time [45].
OPRM1 and COMT appear to be promising genotypicmarkers for determining opioid sensitivity and the dose requiredfor analgesic response. Given the recent institution of manda-tory ceilings on opioid prescription quantities and doses, insur-ers are now less likely to fill the appropriate opioid prescriptionfor patients with severe cancer pain in the setting of theseknown polymorphisms. Although opioid dose selection andtitration should be driven by patient-reported clinical response,these test results may offer an objective measurement to rein-force rapid or slow dose titration and improve clinical care.
Barb now has painful neuropathy from her chemotherapy,
so she is started on gabapentin by a nurse practitioner. Accord-
ing to her known pharmacogenomic profile, there is no altered
metabolism predicted based on her results, so the gabapentin is
escalated to 3,600 mg daily per usual practice. At full dose, there
is no perceivable benefit in her neuropathy, and she begins to
develop mental status changes, so you taper the gabapentin and
consider another medication. Barb’s insurance company states
that she must next try either nortriptyline or amitriptyline for
painful chemotherapy-induced neuropathy. If the tricyclic antide-
pressant fails, only then will her insurance cover duloxetine.
Painful Neuropathy
Approximately 40% of patients treated withmore than one formof chemotherapy will have some form of peripheral neuropathy[46]. The neuropathy can have long-term effects on QOL [47].The practice guideline by the American Society of Clinical Oncol-ogy (ASCO) for the management of chemotherapy-inducedperipheral neuropathy suggests the use of duloxetine, tricyclicantidepressants (TCAs), or gabapentin [48]. Gabapentin metabo-lism is not significantly affected by known pharmacogenetic var-iations. However, duloxetine is inactivated by two liver enzymes,CYP2D6 and CYP1A2, whereas the TCAs have more complexpharmacogenomic considerations with CYP2D6 and CYP2C19.
Amitriptyline is metabolized by CYP2C19 into nortriptyline,whereas both agents require CYP2D6 for metabolism into lessactive compounds [49]. In a large study, CYP2D6 PMs givenTCAs were substantially more likely than patients in the controlgroup to stop the drug because of adverse effects such asdry mouth, dizziness, and cardiac concerns [50]. Alternatively,CYP2D6 UMs have an increased risk of therapeutic failure and
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discontinuation when treated with amitriptyline [51], likelybecause they cannot hold sufficient amitriptyline or nortripty-line in the bloodstream to be effective. CPIC recommendsavoiding TCAs completely in both PMs and UMs at CYP2D6, aswell as avoiding amitriptyline and imipramine in CYP2C19 UMsand PMs [49].
The fact that Barb is a CYP2D6 PM and has ultrarapid
metabolism by CYP2C19 is concerning for the use of amitripty-
line or nortriptyline. Amitriptyline is metabolized to nortriptyline
very quickly by CYP2C19 in a patient like Barb. However, given
that her metabolism at CYP2D6 is slow, the nortriptyline is likely
to reach very high blood levels because of poor removal from
the system. You decide to avoid amitriptyline altogether and try
extremely low doses of nortriptyline, warning her to stop the
medicine at the first sign of any labeled side effects. She toler-
ates the 5 mg of nortriptyline but with no effect on her neurop-
athy. You increase the dose to 10 mg, and 3 days later she stops
the drug with complaints of dry mouth and severe headache.
With Barb’s pharmacogenomic test results in hand, you petition
the insurance company successfully to cover duloxetine. You
know that duloxetine requires some CYP2D6 for inactivation,
and Barb’s genotype would suggest she would be safest and
likely most successful starting at a low dose and titrating up
slowly based on response.Several years later, Barb returns for routine survivorship visit
to your office and admits, “I just feel so wiped out for the last
few days—I can barely get up to the bathroom.” You are paged
by the hematology lab urgently: her complete blood count
shows blasts and profound anemia. After hospital admission,
she is diagnosed with treatment-related acute myeloid leuke-
mia (AML). Given the poor prognosis, she starts standard chem-
otherapy and ultimately undergoes allogeneic bone marrow
transplant. In the post-transplant setting she will be maintained
on voriconazole for antifungal prophylaxis. You place the order
for the antifungal in the electronic medical record, and you get
an immediate prescriber alert that Barb has pharmacogenetic
test results that affect this order.
ANTIFUNGAL SELECTIONVoriconazole is an antifungal agent that is used for treatmentor prophylaxis of certain fungal infections. Appropriate serumconcentrations are critical for effective prevention or treatmentof invasive fungal infections (IFIs) [52, 53]. Studies have demon-strated that subtherapeutic voriconazole trough concentrationshave been strongly associated with therapeutic failure [54].Importantly, up to 50% of patients receiving the standard pro-phylactic dose of 200 mg twice daily remain subtherapeutic atsteady state [55]. There is a significant association between IFI-related mortality and subtherapeutic initial trough concentra-tions—even when therapeutic blood level monitoring is usedto direct subsequent dosing [52, 53, 56].
Importantly, CYP2C19 is responsible for the majority ofvoriconazole metabolism; thus, polymorphisms in this genecan have a significant effect on serum concentrations [57].The patients at greatest risk of inadequate drug concentra-tions and thus voriconazole failure are those with rapidCYP2C19 metabolism, which occurs in up to 30%–35% ofwhites and blacks, such that the drug is removed from thebloodstream too quickly and can never reach therapeutic lev-els [54, 58–65]. Preliminary data show that, in a population ofstem cell transplant patients, genotype-guided dosing for vor-iconazole prophylaxis (higher initial doses for CYP2C19 rapidand ultrarapid metabolizers) resulted in zero cases of subther-apeutic initial trough concentrations in this subset of patientscompared with 80% in historical controls (p< .001) [66].Another study showed reduced overall costs with genotype-directed dosing for patients with AML, even when includingthe tests of genomic analysis [67]. Currently, CPIC recom-mends that patients with rapid, ultrarapid, or poor metabo-lism at CYP2C19 should avoid voriconazole in favor of analternative antifungal [58] (See Fig. 3.).
Ketoconazole, itraconazole, and isavuconazole clearance ishighly dependent on CYP3A4 metabolism, and thus efficacy ofthese antifungal agents may be prone to variation by individualCYP3A4 genotype. As a start, studies have confirmed that theCYP3A4*22 allele results in significantly lower enzyme activity,impairing the metabolism of common CYP3A4-metabolizeddrugs [68, 69]. However, additional data are required to navi-gate the interactions between individual genotype and poten-tial CYP3A4-inducers or inhibitors that could be concomitantlyadministered.
Barb’s pharmacogenomic testing reveals she has ultrarapid
metabolism at CYP2C19—the key enzyme for voriconazole. You
consider starting her voriconazole dose higher, as suggested by
preliminary data from the genotype-directed dosing study.
However, per CPIC guidelines you ultimately decide to avoid vor-
iconazole completely and instead start isavuconazole for
Figure 3. Pharmacogenetic-driven treatment pathway for antifun-gal selection. CYP2C19 PMs, RMs, and UMs should avoid usingvoriconazole as primary prophylaxis or treatment for fungal infec-tions. CYP2C19 RMs and UMs are at risk of subtherapeutic concen-trations and increased risk of breakthrough fungal infection or lackof efficacy. CYP2C19 PMs are at risk of supratherapeutic concen-trations, which may increase the risk of related side effects.1, Further dose adjustments or selection of alternative therapy
may be necessary because of other clinical factors, such as druginteractions, hepatic function, renal function, species, site of infec-tion, therapeutic drug monitoring, and comorbidities.2, Some data suggest that higher initial doses of voriconazole in
CYP2C19 RMs and UMs may overcome subtherapeutic concentra-tions.Abbreviations: CYP2C19, cytochrome P450 2C19; IM, intermedi-
ate metabolizer; NM, normal metabolizer; PM, poor metabolizer;RM, rapid metabolizer; UM, ultrarapid metabolizer.
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prophylaxis, given that this medication does not undergo
CYP2C19-mediated metabolism.After the diagnosis of poor-risk acute leukemia and months
of prolonged hospitalization for the bone marrow transplant,
Barb admits that she has been feeling depressed, losing weight,
and feeling hopeless in the last few weeks. You consult psychia-
try at the start of a long holiday weekend and they will see her
next week. However, she says, “I just want to start feeling bet-
ter as soon as I can—I can’t wait another day.” You feel the
need to start antidepressant therapy sooner, and the electronic
alert reminds you that Barb has prior pharmacogenomic testing
that will influence your decision.
ANTIDEPRESSANT SELECTIONAt least one quarter of all patients with cancer suffer frommajordepressive disorder. Recognizing this as a major comorbidity,ASCO created guidelines for screening, assessing, and treatingdepression in patients with cancer [70]. Standard response ratesto antidepressants are 30%–50% regardless of what agent isselected [71]. There is a growing recognition that pharmacoge-nomic variationmay help explain some of the low response ratesand incidence of adverse effects. Data now clearly justify the clin-ical utility of using an individual patient’s pharmacogenomic pro-file to select the best treatment for depression. (See Fig. 4.)
CYP2C19 plays a major role in the metabolism of citalo-pram, escitalopram, and sertraline. Poor metabolizers atCYP2C19 have been shown to be at increased risk of adverseevents, including QT prolongation [72, 73]. Alternatively, UMshave lower plasma concentrations and are more likely to sufferfrom ineffectively treated depression [74]. CPIC recommends a50% dose reduction in citalopram, escitalopram, and sertralinefor CYP2C19 PMs and avoiding citalopram and escitalopram forCYP2C19 UMs [75]. For CYP2C19 UMs, sertraline can be pre-scribed at the recommended starting dose, but if a patientdoes not respond clinically, CPIC guidelines suggest considera-tion of an alternative drug not predominantly metabolized byCYP2C19.
Paroxetine and fluvoxamine are primarily metabolized byCYP2D6; thus, PMs are at increased risk of adverse effects, par-ticularly gastrointestinal [76, 77]. CYP2D6 UMs are at risk ofpoor drug response [78]. CPIC recommends avoiding paroxe-tine in CYP2D6 UMs and PMs and a 25%–50% dose reductionof fluvoxamine in CYP2D6 PMs [75]. Fluoxetine is metabolizedby CYP2D6 and CYP2C19; however, there are few data associat-ing specific genetic variants with differences in clinical responseto fluoxetine. The U.S. Food and Drug Administration (FDA)label highlights the potential for complicated drug-drug inter-actions in patients with reduced CYP2D6 function taking
Figure 4. Pharmacogenetic-driven treatment pathway for depression. Several antidepressants, including SSRIs, SNRIs, and TCAs, are avail-able to treat depressive symptoms in patients with adequate CYP2D6 and CYP2C19 activity (i.e., NM and IM patients). Patients withCYP2D6 and CYP2C19 variations (i.e., UM and PMs) are at a higher risk for altered antidepressant drug exposure. As such, treatmentoptions become limited in these populations because of potential drug-gene interactions. The newer antidepressants, levomilnacipran,vilazodone, and vortioxetine, are not included on this algorithm but can be used regardless of CYP2C19 and CYP2D6 genotype. However,the maximum recommended daily dose of vortioxetine in CYP2D6 PMs is 10 mg according to the package insert.1, Strong CYP2D6 inhibitors may result in poor metabolism.2, Other genetic variants exist that influence response to SSRIs, particularly the serotonin transporter gene, SLC6A4. Reduced response
has been noted in patients carrying the S allele.3, TCAs are not recommended for first-line therapy because of high incidence of adverse effects.Abbreviations: CYP2C19, cytochrome P450 2C19; CYP2D6, cytochrome P450 2D6; IM, intermediate metabolizer; NM, normal metabo-
lizer; PM, poor metabolizer; SNRI, serotonin and norepinephrine reuptake inhibitor; SSRI, selective serotonin reuptake inhibitor; TCA, tricy-clic antidepressant; UM, ultrarapid metabolizer.
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fluoxetine [79]. Vortioxetine, a newer antidepressant withmultimodal activity, is primarily metabolized by CYP2D6, butalso 3A4/5, 2C9, 2C19, 2A6, 2C8, and 2B6 [80], and the FDAlabel recommends a maximum dose of 10 mg per day inknown CYP2D6 PMs [81].
For serotonin and norepinephrine reuptake inhibitors, theevidence associating pharmacogenomic variation with clinicalresponse is weaker than for selective serotonin reuptakeinhibitors (SSRIs) but is growing. Venlafaxine is metabolizedto multiple active metabolites by CYP2D6 and CYP2C19,although there is not enough evidence yet for a firm guide-line on prescribing [82, 83]. Additionally, genetic variations inserotonin-related genes may influence antidepressant effi-cacy; however, these may be less actionable, as no CPICguidelines exist for these. For example, patients harboringthe S allele for the serotonin transporter gene SLC6A4 mayhave reduced response to SSRIs. Polymorphisms in the sero-tonin receptor gene HTR2A have been associated with lackof response to SSRIs [84].
Multiple studies have recently been published illustratingthe clinical value of multigene pharmacogenetic panels whentreating patients with depression. At least four rigorous studieshave shown significantly better treatment outcomes for majordepressive disorder with pharmacogenomic guidance com-pared with the standard clinical approach [85, 86].
Given that Barb is a CYP2C19 UM, you know that sertra-
line, citalopram, or escitalopram will fail to reach adequate
concentration in the bloodstream and thus are likely to be
ineffective for her depression. Per CPIC guidelines, those med-
icines should be avoided in her case. As a known CYP2D6
PM, Barb could be at risk of excessive side effects if pre-
scribed paroxetine, as it requires CYP2D6 to be broken down
and removed from the blood stream. Safer and more effec-
tive options include desvenlafaxine, low-dose vortioxetine,
mirtazapine, and bupropion. Given that she is losing weight
and her insurance will not cover desvenlafaxine or vortioxe-
tine as first-line therapy, mirtazapine is an appropriate choice
in her case, starting with the lowest dose and titrating based
on clinical response, given that mirtazapine does undergo
some metabolic inactivation via CYP2D6.
CONCLUSIONPharmacogenomic data are important to understand interpa-tient variability in drug response to many supportive oncologymedications. Barb’s case presented in this paper demonstratesthe possibilities and power from the knowledge of just a fewgenes that influence the metabolism of many drugs. As thesedata grow, seemingly exponentially, with ever-cheaper analytictechnology, it will soon be the standard of care to perform rou-tine pharmacogenomic testing on all patients with cancer priorto treatment. Ultimately the truest value of these data can onlybe fully realized when they are implemented into the routineworkflowwith care pathways of health care providers and phar-macists on the ground.
As demonstrated in the case above, even two genes canhave a major impact on medication management. BeyondCYP2D6 and CYP2C19, there are pharmacogenetic panelscommercially available to analyze many more genes with theability to minimize prescribing by trial and error. In addition
to writing drug and gene guidelines, CPIC creates supplemen-tary informatics resources to assist clinicians. These resourcesserve as clinical decision support tools to integrate pharma-cogenetic data into the electronic health record at the pointof care—when the prescription is written [7]. The value ofapplying pharmacogenomics downstream, even years afterinitial testing—as in Barb’s case—depends on clinical decisionsupport tools that are updated in real time to reflect themost recent evidence-based data. Effective integration withoncology workflow is critical and has been achieved at sev-eral prominent institutions [87]. The figures presented inthis manuscript represent pharmacogenetic-guided treat-ment algorithms to select the so-called least genetically vul-nerable drug, by avoiding known drug-gene interactionsbased on presence of pharmacogenetic test results.Although not discussed in detail in this review, itis important to consider the role of pharmacogenomics indetermining the magnitude of drug-drug interactions anddrug-drug-gene interactions—that is, polymorphisms in ametabolic pathway and inhibition or induction of the sameor minor pathway [88]. In fact, a cross-sectional studyinvolving 22,885 patients found that there were approxi-mately 6,900 drug interactions, of which drug-drug-gene,drug-gene, and drug-drug interactions accounted for 22%,25%, and 53%, respectively [89].
There will always be many demographic, biologic, psycho-logic, and pharmacologic variables that influence medicationchoice. Pharmacogenetic variation is an increasingly success-ful avenue for making objective choices about the safest and,at times, most effective treatments for patients with cancer.Ultimately, having an individual’s personalized genomic dataat the point of care has significant implications for supportiveoncology medication management throughout the care tra-jectory and can be integrated to personalize oncology caretoday.
ACKNOWLEDGMENTS
This clinical review was supported by Admera Health, SouthPlainfield, New Jersey.
AUTHOR CONTRIBUTIONSConception/design: Jai N. Patel, Lauren A. Wiebe, Henry M. Dunnenberger,Howard L. McLeod
Provision of study material or patients: Jai N. Patel, Lauren A.Wiebe, Henry M.Dunnenberger, Howard L. McLeod
Collection and/or assembly of data: Jai N. Patel, Lauren A. Wiebe, Henry M.Dunnenberger, Howard L. McLeod
Data analysis and interpretation: Jai N. Patel, Lauren A. Wiebe, Henry M.Dunnenberger, Howard L. McLeod
Manuscript writing: Jai N. Patel, Lauren A. Wiebe, Henry M. Dunnenberger,Howard L. McLeod
Final approval of manuscript: Jai N. Patel, Lauren A. Wiebe, Henry M.Dunnenberger, Howard L. McLeod
DISCLOSURES
Jai N. Patel: Janssen Pharmaceuticals (C/A); Janssen Pharaceuticals,Myriad Genetics (RF), Admera Health (H); Henry M. Dunnenberger:Admera Health (H); Howard L. McLeod: Cancer Genetics, Inc. (SAB);Saladax, Admera Health (C/A); Interpares Biomedicine (OI). The otherauthor indicated no financial relationships.(C/A) Consulting/advisory relationship; (RF) Research funding; (E) Employment; (ET) Expert
testimony; (H) Honoraria received; (OI) Ownership interests; (IP) Intellectual property rights/
inventor/patent holder; (SAB) Scientific advisory board
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