药学学报

Drug	Biomark	Clinical implications and guideline suggestion
Atazanavir	UGT1A1	Markedly decreased UGT1A1 activity would be observed in poor metabolizers of UGT1A128/28, 28/37 or 37/37, resulting in high likelihood of bilirubin-related discontinuation of atazanavir. Consider an alternative agent particularly where jaundice would be of concern to the patient.
Azathioprine, mercaptopurine, thioguanine	TPMT	Design the starting dose based on genotyping of TPMT in order to avoid severe myelosuppression. Consider alternative nonthiopurine immunosuppressant therapy for nonmalignant patients carrying 3A/3A, 2/3A, 3C/3A, 3C/4, 3C/2, or 3A/4.
Capecitabine, fluorouracil, tegafur	DPYD	Design the starting dose based on genotyping of DPYD in order to avoid severe and possibly fatal toxicities. Consider alternatives for patients carrying 2A/2A, 13/13 or rs67376798A/ rs67376798A.
Citalopram, escitalopram, sertraline	CYP2C19	Genotyping of CYP2C19 is recommended prior to prescribing. Consider an alternative drug not predominantly metabolized by CYP2C19 in order to avoid treatment failure in ultrarapid metabolizers carrying 17/17 or 1/17. Consider a 50% reduction of recommended starting dose and titrate to response or select alternative drug not predominantly metabolized by CYP2C19 in poor metabolizers (2/2, 2/3, 3/3) in order to avoid the increased probability of side effects.
Desipramine, fluvoxamine, paroxetine, nortriptyline	CYP2D6	Genotyping of CYP2D6 is recommended prior to prescribing. Consider an alternative drug not predominantly metabolized by CYP2D6 in order to avoid treatment failure in ultrarapid metabolizers carrying CYP2D61/1×N, 1/2×N, or 2/2×N. Consider a 25%-50% reduction of recommended starting dose or select alternative drug not predominantly metabolized by CYP2D6 in poor metabolizers (3/4, 4/4, 5/5, 5/6) in order to avoid the increased probability of side effects.
Amitriptyline, imipramine, doxepin, clomipramine	CYP2C19, CYP2D6	Avoid the use of tricyclic antidepressants in ultrarapid, intermediate and poor metabolizers of CYP2D6 and carriers of extensive metabolizers of CYP2D6 also having ultrarapid metabolizers of CYP2C19. Consider 50% reduction of recommended starting dose in subjects with extensive metabolizers of CYP2D6 also having poor metabolizers of CYP2C19. Utilize therapeutic drug monitoring to guide dose adjustments.
Clopidogrel	CYP2C19	Increased risk for adverse cardiovascular events would be observed in intermediate metabolizers (CYP2C191/2, 1/3, 2/17) and poor metabolizers (CYP2C192/2, 2/3, 3/3) . Clinicians may consider alternative antiplatelet agents such as prasugrel and ticagrelor (if no contraindication).
Warfarin	CYP2C9, VKORC1	Genotyping of VKORC1-1639G>A, CYP2C92 and CYP2C9**3 could help to design the starting warfarin dose.
Codeine	CYP2D6	Genotyping of CYP2D6 is recommended prior to prescribing in order to avoid increased opioid toxicities in ultrarapid metabolizers (CYP2D61/1×N, CYP2D61/2×N), and insufficient pain relief in poor metabolizers (CYP2D64/4, CYP2D64/5, CYP2D65/5, CYP2D64/6) . If necessary, consider alternatives that are not affected by this CYP2D6 phenotype include morphine and non-opioid analgesics.
Phenytoin sodium	CYP2C9	Consider 50% reduction of recommended starting maintenance dose in order to avoid probability of toxicities due to reduced phenytoin metabolism and higher plasma concentrations.
Simvastatin	SLCO1B1	Prescribe a lower dose or consider an alternative statin (e.g., pravastatin or rosuvastatin) for subjects with intermediate myopathy risk (SLCO1B11a/5, 1a/15, 1a/17, 1b/5, 1b/15, 1b/17) , and subjects with high myopathy risk (SLCO1B15/5, 5/15, 5/17, 15/15, 15/17, 17/17) . Routine creatine kinase surveillance should be considered.
Tacrolimus	CYP3A5	Initiate therapy with standard recommended dose for subjects with CYP3A53/3, CYP3A56/6, CYP3A57/7, CYP3A53/6, CYP3A53/7, or CYP3A56/7; however, a dose 1.5-2 times higher than standard dosing, but not to exceed 0.3 mg·kg^-1·day^-1, can be initiated among subjects with CYP3A51/3, CYP3A51/6, CYP3A51/7 or CYP3A51/1. Dosage should be adjusted according to therapeutic drug monitoring of tacrolimus plasma concentrations.

Drug

Biomark

Clinical implications and guideline suggestion

Atazanavir

UGT1A1

Markedly decreased UGT1A1 activity would be observed in poor metabolizers of UGT1A1*28/*28, *28/*37 or *37/*37, resulting in high likelihood of bilirubin-related discontinuation of atazanavir. Consider an alternative agent particularly where jaundice would be of concern to the patient.

Azathioprine, mercaptopurine, thioguanine

TPMT

Design the starting dose based on genotyping of TPMT in order to avoid severe myelosuppression. Consider alternative nonthiopurine immunosuppressant therapy for nonmalignant patients carrying *3A/*3A, *2/*3A, *3C/*3A, *3C/*4, *3C/*2, or *3A/*4.

Capecitabine, fluorouracil, tegafur

DPYD

Design the starting dose based on genotyping of DPYD in order to avoid severe and possibly fatal toxicities. Consider alternatives for patients carrying *2A/*2A, *13/*13 or rs67376798A/ rs67376798A.

Citalopram, escitalopram, sertraline

CYP2C19

Genotyping of CYP2C19 is recommended prior to prescribing. Consider an alternative drug not predominantly metabolized by CYP2C19 in order to avoid treatment failure in ultrarapid metabolizers carrying *17/*17 or *1/*17. Consider a 50% reduction of recommended starting dose and titrate to response or select alternative drug not predominantly metabolized by CYP2C19 in poor metabolizers (*2/*2, *2/*3, *3/*3) in order to avoid the increased probability of side effects.

Desipramine, fluvoxamine, paroxetine, nortriptyline

CYP2D6

Genotyping of CYP2D6 is recommended prior to prescribing. Consider an alternative drug not predominantly metabolized by CYP2D6 in order to avoid treatment failure in ultrarapid metabolizers carrying CYP2D6*1/*1×N, *1/*2×N, or *2/*2×N. Consider a 25%-50% reduction of recommended starting dose or select alternative drug not predominantly metabolized by CYP2D6 in poor metabolizers (*3/*4, *4/*4, *5/*5, *5/*6) in order to avoid the increased probability of side effects.

Amitriptyline, imipramine, doxepin, clomipramine

CYP2C19, CYP2D6

Avoid the use of tricyclic antidepressants in ultrarapid, intermediate and poor metabolizers of CYP2D6 and carriers of extensive metabolizers of CYP2D6 also having ultrarapid metabolizers of CYP2C19. Consider 50% reduction of recommended starting dose in subjects with extensive metabolizers of CYP2D6 also having poor metabolizers of CYP2C19. Utilize therapeutic drug monitoring to guide dose adjustments.

Clopidogrel

CYP2C19

Increased risk for adverse cardiovascular events would be observed in intermediate metabolizers (CYP2C19*1/*2, *1/*3, *2/*17) and poor metabolizers (CYP2C19*2/*2, *2/*3, *3/*3) . Clinicians may consider alternative antiplatelet agents such as prasugrel and ticagrelor (if no contraindication).

Warfarin

CYP2C9, VKORC1

Genotyping of VKORC1-1639G>A, CYP2C9*2 and CYP2C9*3 could help to design the starting warfarin dose.

Codeine

CYP2D6

Genotyping of CYP2D6 is recommended prior to prescribing in order to avoid increased opioid toxicities in ultrarapid metabolizers (CYP2D6*1/*1×N, CYP2D6*1/*2×N), and insufficient pain relief in poor metabolizers (CYP2D6*4/*4, CYP2D6*4/*5, CYP2D6*5/*5, CYP2D6*4/*6) . If necessary, consider alternatives that are not affected by this CYP2D6 phenotype include morphine and non-opioid analgesics.

Phenytoin sodium

CYP2C9

Consider 50% reduction of recommended starting maintenance dose in order to avoid probability of toxicities due to reduced phenytoin metabolism and higher plasma concentrations.

Simvastatin

SLCO1B1

Prescribe a lower dose or consider an alternative statin (e.g., pravastatin or rosuvastatin) for subjects with intermediate myopathy risk (SLCO1B1*1a/*5, *1a/*15, *1a/*17, *1b/*5, *1b/*15, *1b/*17) , and subjects with high myopathy risk (SLCO1B1*5/*5, *5/*15, *5/*17, *15/*15, *15/*17, *17/*17) . Routine creatine kinase surveillance should be considered.

Tacrolimus

CYP3A5

Initiate therapy with standard recommended dose for subjects with CYP3A5*3/*3, CYP3A5*6/*6, CYP3A5*7/*7, CYP3A5*3/*6, CYP3A5*3/*7, or CYP3A5*6/*7; however, a dose 1.5-2 times higher than standard dosing, but not to exceed 0.3 mg·kg^-1·day^-1, can be initiated among subjects with CYP3A5*1/*3, CYP3A5*1/*6, CYP3A5*1/*7 or CYP3A5*1/*1. Dosage should be adjusted according to therapeutic drug monitoring of tacrolimus plasma concentrations.

Drug	Biomark	Clinical implications and guideline suggestion
Atazanavir	UGT1A1	Markedly decreased UGT1A1 activity would be observed in poor metabolizers of UGT1A128/28, 28/37 or 37/37, resulting in high likelihood of bilirubin-related discontinuation of atazanavir. Consider an alternative agent particularly where jaundice would be of concern to the patient.
Azathioprine, mercaptopurine, thioguanine	TPMT	Design the starting dose based on genotyping of TPMT in order to avoid severe myelosuppression. Consider alternative nonthiopurine immunosuppressant therapy for nonmalignant patients carrying 3A/3A, 2/3A, 3C/3A, 3C/4, 3C/2, or 3A/4.
Capecitabine, fluorouracil, tegafur	DPYD	Design the starting dose based on genotyping of DPYD in order to avoid severe and possibly fatal toxicities. Consider alternatives for patients carrying 2A/2A, 13/13 or rs67376798A/ rs67376798A.
Citalopram, escitalopram, sertraline	CYP2C19	Genotyping of CYP2C19 is recommended prior to prescribing. Consider an alternative drug not predominantly metabolized by CYP2C19 in order to avoid treatment failure in ultrarapid metabolizers carrying 17/17 or 1/17. Consider a 50% reduction of recommended starting dose and titrate to response or select alternative drug not predominantly metabolized by CYP2C19 in poor metabolizers (2/2, 2/3, 3/3) in order to avoid the increased probability of side effects.
Desipramine, fluvoxamine, paroxetine, nortriptyline	CYP2D6	Genotyping of CYP2D6 is recommended prior to prescribing. Consider an alternative drug not predominantly metabolized by CYP2D6 in order to avoid treatment failure in ultrarapid metabolizers carrying CYP2D61/1×N, 1/2×N, or 2/2×N. Consider a 25%-50% reduction of recommended starting dose or select alternative drug not predominantly metabolized by CYP2D6 in poor metabolizers (3/4, 4/4, 5/5, 5/6) in order to avoid the increased probability of side effects.
Amitriptyline, imipramine, doxepin, clomipramine	CYP2C19, CYP2D6	Avoid the use of tricyclic antidepressants in ultrarapid, intermediate and poor metabolizers of CYP2D6 and carriers of extensive metabolizers of CYP2D6 also having ultrarapid metabolizers of CYP2C19. Consider 50% reduction of recommended starting dose in subjects with extensive metabolizers of CYP2D6 also having poor metabolizers of CYP2C19. Utilize therapeutic drug monitoring to guide dose adjustments.
Clopidogrel	CYP2C19	Increased risk for adverse cardiovascular events would be observed in intermediate metabolizers (CYP2C191/2, 1/3, 2/17) and poor metabolizers (CYP2C192/2, 2/3, 3/3) . Clinicians may consider alternative antiplatelet agents such as prasugrel and ticagrelor (if no contraindication).
Warfarin	CYP2C9, VKORC1	Genotyping of VKORC1-1639G>A, CYP2C92 and CYP2C9**3 could help to design the starting warfarin dose.
Codeine	CYP2D6	Genotyping of CYP2D6 is recommended prior to prescribing in order to avoid increased opioid toxicities in ultrarapid metabolizers (CYP2D61/1×N, CYP2D61/2×N), and insufficient pain relief in poor metabolizers (CYP2D64/4, CYP2D64/5, CYP2D65/5, CYP2D64/6) . If necessary, consider alternatives that are not affected by this CYP2D6 phenotype include morphine and non-opioid analgesics.
Phenytoin sodium	CYP2C9	Consider 50% reduction of recommended starting maintenance dose in order to avoid probability of toxicities due to reduced phenytoin metabolism and higher plasma concentrations.
Simvastatin	SLCO1B1	Prescribe a lower dose or consider an alternative statin (e.g., pravastatin or rosuvastatin) for subjects with intermediate myopathy risk (SLCO1B11a/5, 1a/15, 1a/17, 1b/5, 1b/15, 1b/17) , and subjects with high myopathy risk (SLCO1B15/5, 5/15, 5/17, 15/15, 15/17, 17/17) . Routine creatine kinase surveillance should be considered.
Tacrolimus	CYP3A5	Initiate therapy with standard recommended dose for subjects with CYP3A53/3, CYP3A56/6, CYP3A57/7, CYP3A53/6, CYP3A53/7, or CYP3A56/7; however, a dose 1.5-2 times higher than standard dosing, but not to exceed 0.3 mg·kg^-1·day^-1, can be initiated among subjects with CYP3A51/3, CYP3A51/6, CYP3A51/7 or CYP3A51/1. Dosage should be adjusted according to therapeutic drug monitoring of tacrolimus plasma concentrations.

Drug

Biomark

Clinical implications and guideline suggestion

Atazanavir

UGT1A1

Azathioprine, mercaptopurine, thioguanine

TPMT

Capecitabine, fluorouracil, tegafur

DPYD

Citalopram, escitalopram, sertraline

CYP2C19

Desipramine, fluvoxamine, paroxetine, nortriptyline

CYP2D6

Amitriptyline, imipramine, doxepin, clomipramine

CYP2C19, CYP2D6

Clopidogrel

CYP2C19

Warfarin

CYP2C9, VKORC1

Genotyping of VKORC1-1639G>A, CYP2C9*2 and CYP2C9*3 could help to design the starting warfarin dose.

Codeine

CYP2D6

Phenytoin sodium

CYP2C9

Consider 50% reduction of recommended starting maintenance dose in order to avoid probability of toxicities due to reduced phenytoin metabolism and higher plasma concentrations.

Simvastatin

SLCO1B1

Tacrolimus

CYP3A5

Biomark	Clinical application	Genotype
CYP3A5	Predict the starting dose and chronic renal toxicity of tacrolimus, and dose of cyclosporin in renal transplant patients^{[6, 7]}	CYP3A5*3
CYP2D6	Predict the efficacy of adjuvant tamoxifen for breast cancer^[8], and safety of tramadol, amitriptyline, aripiprazole, perphenazine, tomoxetine, citalopram, clomipramine, doxepin, fluoxetine, vortioxetine, tomoxetine, venlafaxine, metoprolol, propafenone, quinidine or tolterodine in poor metabolizers and ultrarapid metabolizers receiving codeine^[3]	Multi-allelic loci
CYP2C9	Predict the dose of celecoxib in poor metabolizers, warfarin in intermediate metabolizers and poor metabolizers^[3]	2/2, 3/3, 2/3(celecoxib); 1/3, 1/2, 2/2, 3/3, 2/3 (warfarin)
VKORC1	Predict the starting dose and drug toxicity of warfarin^[3]	VKORC1-1639G>A
CYP2C19	Predict clopidogrel resistance, voriconazole dose and safety of citalopram and diazepam in poor metabolizers^[3]	CYP2C192, CYP2C19**3
CYP1B1	Predict the anti breast cancer efficacy of paclitaxel^[9]	CYP1B1*3
GSTP1	Predict the efficacy of cisplatin and oxaliplatin^[10]	GSTP1 A313G
UGT1A1	Predict the toxicity of irinotecan (e.g., diarrhea and bone marrow suppression)^[3], and nilotinib-induced hyperbilirubinemia^[3]	UGT1A128, UGT1A1*6 (irinotecan); UGT1A1**28 (nilotinib)
TPMT	Predict the efficacy and toxicity of mercaptopurine and azathioprine^[3]	TPMT2 (G238C), TPMT*3A (G460A/A719G), TPMT*3B (G460A), TPMT**3C (A719G)
MTHFR	Predict the relapse risk in acute lymphoblastic leukemiapatients receiving methotrexate^[11]	MTHFR C677T
DPYD	Predict toxicity of 5-fluorouracil (gastrointestinal cancer) and capecitabine (recurrent breast cancer)^[12]	DPYDc.1679T>G, c.1236G>A/HapB3, DPYD*2A, c.2846A>T
NAT2	Predict hepatic toxicity of anti tuberculosis drugs^[3]	NAT2*4
SLCO1B1	Predict the probability of enalapril-induced cough^[13], and down-regulation of simvastatin dose or therapeutic switch to other statins^[14]	c.521T>C
ABCB1	Predict imatinib response in chronic myeloid leukemia patients^[15] and drug response of platinum-based chemotherapy in non small cell lung cancer patients^[10]	c.1236C>T, c.2677G>T/A, c.3435C>T
ABCG2	Predict the uric acid-lowering effect of allopurinol^[16]	Q141K (rs2231142)

Biomark

Clinical application

Genotype

CYP3A5

Predict the starting dose and chronic renal toxicity of tacrolimus, and dose of cyclosporin in renal transplant patients^{[6, 7]}

CYP3A5*3

CYP2D6

Predict the efficacy of adjuvant tamoxifen for breast cancer^[8], and safety of tramadol, amitriptyline, aripiprazole, perphenazine, tomoxetine, citalopram, clomipramine, doxepin, fluoxetine, vortioxetine, tomoxetine, venlafaxine, metoprolol, propafenone, quinidine or tolterodine in poor metabolizers and ultrarapid metabolizers receiving codeine^[3]

Multi-allelic loci

CYP2C9

Predict the dose of celecoxib in poor metabolizers, warfarin in intermediate metabolizers and poor metabolizers^[3]

*2/*2, *3/*3, *2/*3(celecoxib); *1/*3, *1/*2, *2/*2, *3/*3, *2/*3 (warfarin)

VKORC1

Predict the starting dose and drug toxicity of warfarin^[3]

VKORC1-1639G>A

CYP2C19

Predict clopidogrel resistance, voriconazole dose and safety of citalopram and diazepam in poor metabolizers^[3]

CYP2C19*2, CYP2C19*3

CYP1B1

Predict the anti breast cancer efficacy of paclitaxel^[9]

CYP1B1*3

GSTP1

Predict the efficacy of cisplatin and oxaliplatin^[10]

GSTP1 A313G

UGT1A1

Predict the toxicity of irinotecan (e.g., diarrhea and bone marrow suppression)^[3], and nilotinib-induced hyperbilirubinemia^[3]

UGT1A1*28, UGT1A1*6 (irinotecan); UGT1A1*28 (nilotinib)

TPMT

Predict the efficacy and toxicity of mercaptopurine and azathioprine^[3]

TPMT*2 (G238C), TPMT*3A (G460A/A719G), TPMT*3B (G460A), TPMT*3C (A719G)

MTHFR

Predict the relapse risk in acute lymphoblastic leukemiapatients receiving methotrexate^[11]

MTHFR C677T

DPYD

Predict toxicity of 5-fluorouracil (gastrointestinal cancer) and capecitabine (recurrent breast cancer)^[12]

DPYDc.1679T>G, c.1236G>A/HapB3, DPYD*2A, c.2846A>T

NAT2

Predict hepatic toxicity of anti tuberculosis drugs^[3]

NAT2*4

SLCO1B1

Predict the probability of enalapril-induced cough^[13], and down-regulation of simvastatin dose or therapeutic switch to other statins^[14]

c.521T>C

ABCB1

Predict imatinib response in chronic myeloid leukemia patients^[15] and drug response of platinum-based chemotherapy in non small cell lung cancer patients^[10]

c.1236C>T, c.2677G>T/A, c.3435C>T

ABCG2

Predict the uric acid-lowering effect of allopurinol^[16]

Q141K (rs2231142)

Biomark	Clinical application	Genotype
CYP3A5	Predict the starting dose and chronic renal toxicity of tacrolimus, and dose of cyclosporin in renal transplant patients^{[6, 7]}	CYP3A5*3
CYP2D6	Predict the efficacy of adjuvant tamoxifen for breast cancer^[8], and safety of tramadol, amitriptyline, aripiprazole, perphenazine, tomoxetine, citalopram, clomipramine, doxepin, fluoxetine, vortioxetine, tomoxetine, venlafaxine, metoprolol, propafenone, quinidine or tolterodine in poor metabolizers and ultrarapid metabolizers receiving codeine^[3]	Multi-allelic loci
CYP2C9	Predict the dose of celecoxib in poor metabolizers, warfarin in intermediate metabolizers and poor metabolizers^[3]	2/2, 3/3, 2/3(celecoxib); 1/3, 1/2, 2/2, 3/3, 2/3 (warfarin)
VKORC1	Predict the starting dose and drug toxicity of warfarin^[3]	VKORC1-1639G>A
CYP2C19	Predict clopidogrel resistance, voriconazole dose and safety of citalopram and diazepam in poor metabolizers^[3]	CYP2C192, CYP2C19**3
CYP1B1	Predict the anti breast cancer efficacy of paclitaxel^[9]	CYP1B1*3
GSTP1	Predict the efficacy of cisplatin and oxaliplatin^[10]	GSTP1 A313G
UGT1A1	Predict the toxicity of irinotecan (e.g., diarrhea and bone marrow suppression)^[3], and nilotinib-induced hyperbilirubinemia^[3]	UGT1A128, UGT1A1*6 (irinotecan); UGT1A1**28 (nilotinib)
TPMT	Predict the efficacy and toxicity of mercaptopurine and azathioprine^[3]	TPMT2 (G238C), TPMT*3A (G460A/A719G), TPMT*3B (G460A), TPMT**3C (A719G)
MTHFR	Predict the relapse risk in acute lymphoblastic leukemiapatients receiving methotrexate^[11]	MTHFR C677T
DPYD	Predict toxicity of 5-fluorouracil (gastrointestinal cancer) and capecitabine (recurrent breast cancer)^[12]	DPYDc.1679T>G, c.1236G>A/HapB3, DPYD*2A, c.2846A>T
NAT2	Predict hepatic toxicity of anti tuberculosis drugs^[3]	NAT2*4
SLCO1B1	Predict the probability of enalapril-induced cough^[13], and down-regulation of simvastatin dose or therapeutic switch to other statins^[14]	c.521T>C
ABCB1	Predict imatinib response in chronic myeloid leukemia patients^[15] and drug response of platinum-based chemotherapy in non small cell lung cancer patients^[10]	c.1236C>T, c.2677G>T/A, c.3435C>T
ABCG2	Predict the uric acid-lowering effect of allopurinol^[16]	Q141K (rs2231142)

Biomark

Clinical application

Genotype

CYP3A5

Predict the starting dose and chronic renal toxicity of tacrolimus, and dose of cyclosporin in renal transplant patients^{[6, 7]}

CYP3A5*3

CYP2D6

Multi-allelic loci

CYP2C9

Predict the dose of celecoxib in poor metabolizers, warfarin in intermediate metabolizers and poor metabolizers^[3]

*2/*2, *3/*3, *2/*3(celecoxib); *1/*3, *1/*2, *2/*2, *3/*3, *2/*3 (warfarin)

VKORC1

Predict the starting dose and drug toxicity of warfarin^[3]

VKORC1-1639G>A

CYP2C19

Predict clopidogrel resistance, voriconazole dose and safety of citalopram and diazepam in poor metabolizers^[3]

CYP2C19*2, CYP2C19*3

CYP1B1

Predict the anti breast cancer efficacy of paclitaxel^[9]

CYP1B1*3

GSTP1

Predict the efficacy of cisplatin and oxaliplatin^[10]

GSTP1 A313G

UGT1A1

Predict the toxicity of irinotecan (e.g., diarrhea and bone marrow suppression)^[3], and nilotinib-induced hyperbilirubinemia^[3]

UGT1A1*28, UGT1A1*6 (irinotecan); UGT1A1*28 (nilotinib)

TPMT

Predict the efficacy and toxicity of mercaptopurine and azathioprine^[3]

TPMT*2 (G238C), TPMT*3A (G460A/A719G), TPMT*3B (G460A), TPMT*3C (A719G)

MTHFR

Predict the relapse risk in acute lymphoblastic leukemiapatients receiving methotrexate^[11]

MTHFR C677T

DPYD

Predict toxicity of 5-fluorouracil (gastrointestinal cancer) and capecitabine (recurrent breast cancer)^[12]

DPYDc.1679T>G, c.1236G>A/HapB3, DPYD*2A, c.2846A>T

NAT2

Predict hepatic toxicity of anti tuberculosis drugs^[3]

NAT2*4

SLCO1B1

Predict the probability of enalapril-induced cough^[13], and down-regulation of simvastatin dose or therapeutic switch to other statins^[14]

c.521T>C

ABCB1

Predict imatinib response in chronic myeloid leukemia patients^[15] and drug response of platinum-based chemotherapy in non small cell lung cancer patients^[10]

c.1236C>T, c.2677G>T/A, c.3435C>T

ABCG2

Predict the uric acid-lowering effect of allopurinol^[16]

Q141K (rs2231142)

基于药物代谢酶和转运体基因组学的药物精准治疗

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周权 ¹^,^* , 余露山 ² , 曾苏 ²

药学学报 | 专题报道 2017,52(1): 1-7

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药学学报 | 专题报道 2017, 52(1): 1-7

基于药物代谢酶和转运体基因组学的药物精准治疗

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周权¹^,^*, 余露山², 曾苏²

作者信息

1.浙江大学医学院附属第二医院, 浙江杭州 310009

2.浙江大学药学院药物代谢和药物分析研究所, 浙江省抗肿瘤药物研究重点实验室, 浙江杭州 310058

通讯作者:

* Tel:86-571-88208407,Fax:86-571-88208405,E-mail:zhouquan142602@zju.edu.cn

Personalized dosing from perspective of pharmacogenomics of drug metabolizing enzymes and transporters

Quan ZHOU¹^,^*, Lu-shan YU², Su ZENG²

Affiliations

1. The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China

2. Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China

出版时间: 2017-01-12 doi: 10.16438/j.0513-4870.2016-0562

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摘要

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药物基因组学旨在研究药物效应（药物体内过程、安全性和有效性）的个体差异与基因变异（药物代谢酶、转运体和药物靶点）之间的关系。药物精准治疗是以药物基因组学为基础，结合患者的其他个体情况实施量体裁衣式的药物治疗。本文总结了基于药物代谢酶和转运体基因组学的药物精准治疗的临床应用进展，提出应重点关注的科学问题（包括多基因和非遗传因素对药物效应的影响、治疗药物监测与药物基因组学检测的整合），同时提出临床应用中面临的瓶颈问题及其相关对策。

关键词

药物代谢酶 / 转运体 / 精准治疗 / 药物基因组学 / 组合药物基因组学 / 表型转换

Abstract

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Pharmacogenomics is defined as research into the relationship between inherited genetic variations in drug metabolizing enzymes, transporters and targets and individual variations in person's response to drugs (fate of drug in human body, safety and efficacy). Personalized dosing is pharmacogenomics-based therapeutic regimen tailored to other individual characteristics. This article summarizes the progress in clinical application of personalized dosing from the perspective of pharmacogenomics of drug metabolizing enzymes and transporters, and proposes to draw attention to key scientific issues (e.g., the effect of multi-genes and non-genetic factors on drug effects, the integration of therapeutic drug monitoring and pharmacogenomics); meanwhile, bottle necks in the clinical application and corresponding strategies are proposed.

Key words

drug metabolism / transporter / personalized dosing / pharmacogenomics / combinatorial pharmacogenomics / phenoconversio

引用本文

周权, 余露山, 曾苏. 基于药物代谢酶和转运体基因组学的药物精准治疗. 药学学报, 2017 , 52 (1) : 1 -7 . DOI: 10.16438/j.0513-4870.2016-0562

Quan ZHOU, Lu-shan YU, Su ZENG. Personalized dosing from perspective of pharmacogenomics of drug metabolizing enzymes and transporters[J]. Acta Pharmaceutica Sinica, 2017 , 52 (1) : 1 -7 . DOI: 10.16438/j.0513-4870.2016-0562

正文

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药物基因组学 (pharmacogenomics) 旨在研究药物效应 (药物体内过程、安全性和有效性) 的个体差异与药物代谢酶、转运体和药物靶点基因多态性之间的关系。药物精准治疗 (personalized dosing) 是以药物基因组学为基础, 根据患者的个体情况实施量体裁衣式的药物治疗 (即特定患者适合用什么药、精准预估药物效应和给药剂量、预测和预防药物毒性)。积极利用药物代谢与转运基因组学的知识, 可以增进疗效、减少药品不良反应以及降低药物治疗费用^[1]。近五年来基于药物代谢酶和转运体基因组学的药物精准治疗成为精准医学的热点之一, 本文总结了该领域的临床应用进展。

1 具有临床指导意义的药物基因组学标志物

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药物基因组学从基础到临床、从理论到实践、从群体到个体, 已经取得了可喜的成果。已有许多文献揭示药物代谢酶和转运体的基因多态性与药动学参数、疗效或药品不良反应发生率之间的关系。2016年3月美国临床药物基因组学应用委员会 (Clinical Pharmacogenetics Implementation Consortium, CPIC) 更新了33个药物的药物基因组学应用指南, 其中涉及药物代谢转运的有25个 (表 1) ^[2]。

美国食品药物管理局 (Food and Drug Administration, FDA) 于2013年1月发布了《临床药物基因组学指导原则: 早期临床研究的上市前评价和对药品说明书的建议》^[3], 对药品说明书中药物基因组学资料的描述有相关要求。“药物基因组学”会出现在“临床药理学”项下。当这种资料对安全有效用药有重要影响, 或遗传变异会导致限制药物使用、调整剂量、禁忌或警告时, 应该在药品说明书中加框警告, 适应证、用法用量、禁忌、警告、注意事项或药物相互作用中进行相关概述。例如, 在波立维^® (硫酸氯吡格雷片) 药品说明书的加框警告部分可见描述“慢代谢者中的疗效降低。本品的疗效依赖于细胞色素P450 (主要是CYP2C19) 介导的药物代谢激活 (见警告和注意事项)。采用氯吡格雷推荐剂量进行治疗的急性冠脉综合征或经皮冠脉介入的慢代谢者, 显示较CYP2C19功能正常患者的心血管事件发生率更高。目前已有确定CYP2C19基因型患者的检测方法, 这些方法可用于帮助确定治疗方案 (见临床药理学)。在确定为CYP2C19慢代谢的患者中, 应考虑替代的治疗或治疗方案 (见用法用量)”。2015年FDA列出140种在药品说明书中提及药物基因组学生物标志物的药物, 其中涉及药物代谢酶的有66种药物, 但还没有收录转运体生物标志物^[4]。有人评价了FDA药品说明书中药物基因组学检测的临床证据强度, 发现15.1% 的说明书是有明确的临床实用性证据, 51.3% 的说明书相关描述仅仅是推荐用于临床决策参考^[5]。

表 2收录了近5年PubMed数据库荟萃分析证据支持或FDA药品说明书建议的药物代谢转运相关的药物基因组学标志物^{[3, 6-16]}。

2 重点关注的科学问题

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2.1 多基因因素

药物总的药理学作用并不是单基因性状, 而是由编码参与多种药物代谢途径、药物处置和药物效应的多种蛋白的若干基因决定的。单因素的考察可能不能很好地解释药动学的个体间差异。例如, 传统认为CYP2C9基因型是氟伐他汀血浆水平个体差异的主要决定因素, 作者发现CYP2C9*3 (1075A>C) 对氟伐他汀药时曲线下面积 (AUC) 的影响在统计学上具有极显著性 (P< 0.01) , CA型携带者的AUC比AA型携带者平均高出46.2%; 而且发现ABCB1 C3435T显著影响了氟伐他汀的血浆峰浓度 (C_max)。T突变基因 (TT+CT) 携带者中的C_max比野生型纯合子 (CC) 平均高出39.9% (P<0.05) ^[17]。也需要考虑多种转运体的共同影响, 例如研究发现ABCB1和ABCG2基因多态性对瑞舒伐他汀药动学有显著影响。ABCB1 1236TT纯合子突变型个体中C_max和AUC显著高于1236CT杂合子突变型个体。ABCG2 421AA纯合子突变体比野生型421CC个体有更高的C_max和AUC^[18]。

另外, 需要综合考虑药物代谢转运与药效学靶点多态性对药物效应的联合影响。例如, 美托洛尔主要经CYP2D6代谢, 美托洛尔在CYP2D6慢代谢者体内的血药浓度较高, 假如患者的β1-受体敏感性强或受体数量较多, 则伴随高的疗效。假如受体敏感性差或受体数量少, 则疗效高但同时不良反应大。在应用同一个剂量后理论可产生9种不同的药物反应。代谢酶和受体均为野生型纯合子的个体将产生较高的疗效和最小的毒性; 相反, 两者均为纯合子突变基因型的个体则效应最低、毒性最大。组合药物基因组学 (combinatorial pharmacogenomics) 已开始用于指导药物精准治疗。例如, 与单基因测试相比, 集合CYP2D6、CYP2C19、CYP2C9、CYP1A2、5-羟色胺转移蛋白基因 (SLC6A4) 和5-羟色胺2A受体 (HTR2A) 的组合药物基因组学已被证实可以为抑郁症治疗提供更佳的临床有效性和成本效益^[19]。周宏灏院士团队以高血压病的药物治疗为切入点, 创新性研发了国内第一张基因芯片。利用该基因芯片检测6个基因多态性 (CYP2C9*3、CYP2C19*2、CYP2C19*3、CYP2D6*10、β1-受体Gly389Arg和血管紧张素Ⅱ1型受体A1166C) , 来进行高血压病的药物精准治疗。

2.2 非遗传因素

患者具有复杂性, 体现在生理、病理、医学因素 (多种疾病、疾病类型、多重用药、遗传因素、药品不良反应史、用药史) 、用药依从性、药品可及性、经济条件、健康素养和文化。一般认为, 遗传因素也只能解释60%左右的个体间变异, 因此, 广义的药物精准治疗必须考虑到非遗传因素的影响, 不能只局限于药物基因组学检测, 而是应该以患者为中心、团队协作的综合干预, 才能制定出适合于个体的精准方案。

Kim等^[20]评价了2 188名经皮冠状动脉介入治疗 (PCI) 术后接受氯吡格雷治疗患者的CYP2C19基因型对临床结果的影响, 结果发现CYP2C19慢代谢基因型与急性心肌梗死患者亚组中的心脑血管不良事件密切相关 (风险比为2.88, P= 0.011) , 但在稳定型心绞痛患者亚组中却没有观察到这种相关性。作者研究了匹伐他汀酸和酯的药动学个体间差异的分子机制, 发现除CYP2C9*3, ABCB1 G2677T/A, SLCO1B1 c.521T>C, SLCO1B1 g.11187G>A和SLCO1B1*17外, 性别对个体间差异也有重要贡献。与521TT男性受试者相比, SLCO1B1 521TT女性受试者中匹伐他汀酯的C_max和AUC_(0-∞)更高^[21]。

以药物基因组学为基础, 开展群体药动学 (population pharmacokinetics) 研究和模型构建, 可能是解决复杂患者药物精准治疗的重要方向。例如, van Schie等^[22]给出了相应的公式, 揭示了苯香豆素的平均维持剂量的平方根与患者的基因型 (CYP2C9和VKORC1)、年龄、身高、体重、是否女性、是否合并使用胺碘酮之间的定量关系。Tan等^[23]建立了中国心脏瓣膜置换患者中华法林的剂量估算方法, 华法林维持剂量与VKORC1-1639G>A、CYP2C9*3、体表面积、年龄、能引起INR增高的合并用药个数、吸烟习惯、术前中风史和高血压密切相关。应用该模型可预测华法林维持剂量个体间差异的56.4%。

在临床实践中发现表型转换 (phenoconversion) 现象, 多发生于治疗药物对代谢酶或转运体有明显抑制作用的情况。因此, 单一依赖基因型检测, 有时会误导临床决策^{[24, 25]}。研究显示, 在接受文拉法辛缓释制剂治疗长达8周的900名抑郁症患者中, 在不是CYP2D6慢代谢基因型的865名患者中有24% 的患者变成了慢代谢表型。CYP2D6慢代谢表型的发生率是慢代谢基因型发生率的7倍, 机制是文拉法辛是CYP2D6强抑制剂^[26]。CYP2C19也存在表型转换, 连续服用奥美拉唑或埃索美拉唑4周的受试者中CYP2C19慢代谢表型的发生率是慢代谢基因型发生率的10倍^[27]。也就是说, 药物代谢酶和转运体介导的药物相互作用对药物精准治疗的实施有重要影响。恰当的合并用药可以为药物精准治疗所用。例如, 奥美拉唑竞争性抑制肝移植患者中他克莫司经CYP3A4的代谢, 在CYP2C19慢代谢者中这种影响尤为突出。因此, 合用奥美拉唑时他克莫司的药物精准治疗有必要同时检测CYP2C19*2、CYP2C19*3和CYP3A5*3。然而, 选择雷贝拉唑、泮托拉唑就可以降低与他克莫司相互作用的风险, 而且不受CYP2C19基因型影响^[28]。

2.3 药物基因组学与治疗药物监测的整合

治疗药物监测 (therapeutic drug monitoring, TDM) 是指在临床药物治疗过程中, 观察药物疗效的同时, 定时采集患者的血液 (有时采集尿液、唾液等样本), 测定其中的药物浓度, 以药动学和药效学基础理论为指导, 根据患者的具体情况, 拟定给药方案。根据药物基因组学标志物测试结果, 设定一个初始治疗剂量, 然后通过TDM, 进一步优化个体化用药方案, 使疗效最大化和不良反应最小化。欲使临床效益最大化, 药物基因组学的检测宜在治疗前或疗程的早期阶段进行。

这种药物基因组学与TDM的整合已经在伏立康唑、硫嘌呤类药物、镇痛药物和肾移植抗排异药物的精准治疗中发挥重要作用^[29-32]。例如, CYP2C19基因型与伏立康唑血药浓度密切相关, 而后者又与伏立康唑的抗真菌临床疗效和不良反应显著相关。伏立康唑的目标治疗谷浓度为2～6 mg·L^-1, 在携带超快代谢者基因CYP2C19*17的个体中通常达不到治疗谷浓度。TDM结果显示, 治疗谷浓度低于0.2 mg·L^-1的个体中超快代谢者基因发生频率显著高于对照组。一名62岁的急性髓性白血病患者因疑似播散性真菌感染而接受伏立康唑治疗。首日剂量为一天两次, 每次400 mg。次日起一天两次, 每次200 mg。5天后TDM显示谷浓度低于0.3 mg·L^-1; 改用静脉给予伏立康唑 (首次负荷剂量为6 mg·kg^-1, 然后一天两次, 每次5 mg·kg^-1), 4天后谷浓度为0.5 mg·L^-1。药物基因组学检测显示患者属于超快代谢者基因纯合子CYP2C19*17/*17携带者, 这是导致应用高剂量伏立康唑后谷浓度仍低的主要原因。治疗方案改为静脉用卡泊芬净, 病情逐渐改善, 出院后以口服泊沙康唑序贯治疗。这个案例提示, 单纯实施TDM, 可能不能及时达到伏立康唑的目标治疗浓度, 存在延误病情的风险; 而用药前进行药物基因组学测试, 一旦显示超快代谢者基因, 医生可第一时间改用替代药物^[29]。对于硫嘌呤类药物而言, 药物基因组学检测与TDM的整合有三方面益处, 即检出巯基嘌呤甲基转移酶 (TPMT) 极低活性或酶缺失的个体并启用替代药物治疗方案、根据硫唑嘌呤代谢物浓度来监测患者的用药依从性、以及识别具有耐药性高风险的患者 (人红细胞内硫嘌呤药物活性毒性代谢物6-甲巯基嘌呤核苷酸与6-硫鸟嘌呤核苷酸浓度比值≥20) ^[30]。Manvizhi等^[32]报道的一个案例强调了用药前药物基因组学检测结合疗程中持续进行TDM的重要性。肾移植后患者接受他克莫司、霉酚酸钠、激素三联的标准免疫抑制抗排异治疗。他克莫司初始剂量为5.5 mg·d^-1, TDM显示谷浓度30 ng·mL^-1, 经过连续4次剂量下调, 谷浓度仍持续高于推荐治疗浓度范围。在移植后一年, 剂量已下调到2 mg·d^-1, 采用有限采样法估算AUC为140.9～165.6 μg·h·L^-1。2年后由于同时接受了环丙沙星治疗, AUC增加到281.7 μg·h·L^-1; 停用环丙沙星一周后AUC降至217.3 μg·h·L^-1。4年后经患者知情同意后进行药物基因组学检测, 发现患者携带CYP3A5*3 (GG) 和ABCB1-2677 (TT) 纯合子突变基因, 这是移植后一年内他克莫司谷浓度持续高于治疗浓度范围的主要原因。

3 生物标志物临床应用中面临的瓶颈问题和对策

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在个体化用药的大背景下, 随着高特异性、高通量及高灵敏度的基因检测新技术的快速发展, 具体的分析技术问题已经不再是难题。然而, 证据级别高的或公认的药物基因组学检测项目在临床应用还很少, 其瓶颈在于检测项目尚未纳入医保范围, 使得患者不愿意支付额外的医疗费用而接受测试。其次, 药物基因检测的一些试剂还没有获得国家食品药品监督管理局注册证, 这两大原因导致不少医院的药物精准治疗仅仅停留在科研水平, 没有真正投入临床应用。建议有关部门出台相应的法规, 制定标准的实验室检测体系, 规范个体化治疗检测市场, 理顺价格支付机制, 切实为药物精准治疗的推广而保驾护航。

相对来说, 为避免严重不良反应的药物基因组学测试项目更受临床医生欢迎, 从表 1和表 2也可见, 基于安全性的药物基因组学检测项目比基于疗效预测的项目要多, 因此, 围绕治疗安全性的生物标志物挖掘与应用, 是药物精准治疗在临床推广的突破口。

合理用药强调安全、有效、经济、适当。因此, 药物经济学也是开展药物精准治疗所必须考虑的实际因素。系统评价显示, 氯吡格雷用药前的CYP2C19基因检测、阿巴卡韦用药前的人类白细胞抗原HLA-B*5701检测、卡马西平用药前的HLA-B*1502和HLA-A*3101检测、以及别嘌醇用药前HLA-B*5801检测被证实具有成本效益, 而对于硫嘌呤药物用药前TPMT检测、香豆素类药物用药前CYP2C9和VKORC1检测、甲氨蝶呤用药前MTHFR检测是否具有经济学意义, 结论尚未统一^[33]。Donnan等^[34]也证实, 与基于体重的给药方案相比, 急性淋巴细胞性白血病儿童患者中基于TPMT基因型检测或基于TPMT酶活性检测的给药方案并不具有成本效益。更有研究表明, 香豆素类药物用药前药物基因组学检测的临床执行率依赖于检测的成本以及新型口服抗凝药的可及性。与华法林相比, 一些新颖的口服抗凝药无需在用药前进行药物基因组学检测^[35]。也有人提出对需要使用口服抗凝药的房颤患者进行用药前药物基因组学检测, 根据基因检测结果对患者进行分诊。假如患者具有对华法林敏感性正常的基因型 (CYP2C9 *1/*1和VKORC1 G/A), 则选择使用华法林; 假如患者具有对华法林敏感性高 (CYP2C9*2、CYP2C9*3或VKORC1 A/A) 或敏感性低 (VKORC1 G/G且CYP2C9*1/*1) 的基因型, 则选择新颖口服抗凝药 (如达比加群、利伐沙班、阿哌沙班)。与常规接受华法林抗凝的治疗模式相比, 基于药物基因组学的分诊模式具有更好的成本-效果比^[36]。随着药物精准治疗理念的推行, 未来将会有更多药物经济学研究来为政府和临床决策提供证据。目前而言, 担心成本效益问题仍是影响开展基于药物代谢酶和转运体基因组学的药物精准治疗的一个制约因素。美国一家基因检测技术公司评价了药物基因组学检测用于接受5种或5种以上药物的长期照护患者精准治疗的经济获益, 发现平均每个患者每年节约成本621美元^[37]。该研究提供了一个重要启示, 即基于药物基因组学的精准治疗可在特殊群体中优先应用。

另外, 一些生物标志物在临床证据总体级别不高, 还需要通过开展进一步的基础研究和大样本量的数据分析来论证。荟萃分析通过合并资料而增大样本量, 可增加结论的可信度, 解决研究结果的不一致性。因此, 可尝试开展药物基因组学检测临床价值的荟萃分析研究。

4 结语

收起

精准医学开启了个体化治疗新时代。基于药物代谢酶和转运体的药物精准治疗, 具有重要的临床意义和应用前景^[38]。应重点关注多基因和非遗传因素对药物效应的影响, 重视TDM与药物基因组学检测的整合, 同时需解决临床应用中面临的瓶颈问题。临床上的药物精准治疗涉及药物表观基因组学、药物基因组学、药物代谢学、生物统计学、临床药理学、药物治疗学和药物经济学等, 因此, 需要多学科交叉, 基础与临床协同研究, 加强研发和挖掘证据级别高的新的生物标志物, 积极推动临床转化应用, 为临床药物精准治疗提供依据。

基金

收起

国家自然科学基金资助项目(81373488)

参考文献

收起

文献

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2017年第52卷第1期

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文章信息

doi: 10.16438/j.0513-4870.2016-0562

接收时间：2016-06-08
首发时间：2026-01-14
出版时间：2017-01-12

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收稿日期：2016-06-08
修回日期：2016-08-21

基金

国家自然科学基金资助项目(81373488)

作者信息

1.浙江大学医学院附属第二医院, 浙江杭州 310009

2.浙江大学药学院药物代谢和药物分析研究所, 浙江省抗肿瘤药物研究重点实验室, 浙江杭州 310058

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* Tel:86-571-88208407,Fax:86-571-88208405,E-mail:zhouquan142602@zju.edu.cn

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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Drug	Biomark	Clinical implications and guideline suggestion
Atazanavir	UGT1A1	Markedly decreased UGT1A1 activity would be observed in poor metabolizers of UGT1A128/28, 28/37 or 37/37, resulting in high likelihood of bilirubin-related discontinuation of atazanavir. Consider an alternative agent particularly where jaundice would be of concern to the patient.
Azathioprine, mercaptopurine, thioguanine	TPMT	Design the starting dose based on genotyping of TPMT in order to avoid severe myelosuppression. Consider alternative nonthiopurine immunosuppressant therapy for nonmalignant patients carrying 3A/3A, 2/3A, 3C/3A, 3C/4, 3C/2, or 3A/4.
Capecitabine, fluorouracil, tegafur	DPYD	Design the starting dose based on genotyping of DPYD in order to avoid severe and possibly fatal toxicities. Consider alternatives for patients carrying 2A/2A, 13/13 or rs67376798A/ rs67376798A.
Citalopram, escitalopram, sertraline	CYP2C19	Genotyping of CYP2C19 is recommended prior to prescribing. Consider an alternative drug not predominantly metabolized by CYP2C19 in order to avoid treatment failure in ultrarapid metabolizers carrying 17/17 or 1/17. Consider a 50% reduction of recommended starting dose and titrate to response or select alternative drug not predominantly metabolized by CYP2C19 in poor metabolizers (2/2, 2/3, 3/3) in order to avoid the increased probability of side effects.
Desipramine, fluvoxamine, paroxetine, nortriptyline	CYP2D6	Genotyping of CYP2D6 is recommended prior to prescribing. Consider an alternative drug not predominantly metabolized by CYP2D6 in order to avoid treatment failure in ultrarapid metabolizers carrying CYP2D61/1×N, 1/2×N, or 2/2×N. Consider a 25%-50% reduction of recommended starting dose or select alternative drug not predominantly metabolized by CYP2D6 in poor metabolizers (3/4, 4/4, 5/5, 5/6) in order to avoid the increased probability of side effects.
Amitriptyline, imipramine, doxepin, clomipramine	CYP2C19, CYP2D6	Avoid the use of tricyclic antidepressants in ultrarapid, intermediate and poor metabolizers of CYP2D6 and carriers of extensive metabolizers of CYP2D6 also having ultrarapid metabolizers of CYP2C19. Consider 50% reduction of recommended starting dose in subjects with extensive metabolizers of CYP2D6 also having poor metabolizers of CYP2C19. Utilize therapeutic drug monitoring to guide dose adjustments.
Clopidogrel	CYP2C19	Increased risk for adverse cardiovascular events would be observed in intermediate metabolizers (CYP2C191/2, 1/3, 2/17) and poor metabolizers (CYP2C192/2, 2/3, 3/3) . Clinicians may consider alternative antiplatelet agents such as prasugrel and ticagrelor (if no contraindication).
Warfarin	CYP2C9, VKORC1	Genotyping of VKORC1-1639G>A, CYP2C92 and CYP2C9**3 could help to design the starting warfarin dose.
Codeine	CYP2D6	Genotyping of CYP2D6 is recommended prior to prescribing in order to avoid increased opioid toxicities in ultrarapid metabolizers (CYP2D61/1×N, CYP2D61/2×N), and insufficient pain relief in poor metabolizers (CYP2D64/4, CYP2D64/5, CYP2D65/5, CYP2D64/6) . If necessary, consider alternatives that are not affected by this CYP2D6 phenotype include morphine and non-opioid analgesics.
Phenytoin sodium	CYP2C9	Consider 50% reduction of recommended starting maintenance dose in order to avoid probability of toxicities due to reduced phenytoin metabolism and higher plasma concentrations.
Simvastatin	SLCO1B1	Prescribe a lower dose or consider an alternative statin (e.g., pravastatin or rosuvastatin) for subjects with intermediate myopathy risk (SLCO1B11a/5, 1a/15, 1a/17, 1b/5, 1b/15, 1b/17) , and subjects with high myopathy risk (SLCO1B15/5, 5/15, 5/17, 15/15, 15/17, 17/17) . Routine creatine kinase surveillance should be considered.
Tacrolimus	CYP3A5	Initiate therapy with standard recommended dose for subjects with CYP3A53/3, CYP3A56/6, CYP3A57/7, CYP3A53/6, CYP3A53/7, or CYP3A56/7; however, a dose 1.5-2 times higher than standard dosing, but not to exceed 0.3 mg·kg^-1·day^-1, can be initiated among subjects with CYP3A51/3, CYP3A51/6, CYP3A51/7 or CYP3A51/1. Dosage should be adjusted according to therapeutic drug monitoring of tacrolimus plasma concentrations.

Drug

Biomark

Clinical implications and guideline suggestion

Atazanavir

UGT1A1

Azathioprine, mercaptopurine, thioguanine

TPMT

Capecitabine, fluorouracil, tegafur

DPYD

Citalopram, escitalopram, sertraline

CYP2C19

Desipramine, fluvoxamine, paroxetine, nortriptyline

CYP2D6

Amitriptyline, imipramine, doxepin, clomipramine

CYP2C19, CYP2D6

Clopidogrel

CYP2C19

Warfarin

CYP2C9, VKORC1

Genotyping of VKORC1-1639G>A, CYP2C9*2 and CYP2C9*3 could help to design the starting warfarin dose.

Codeine

CYP2D6

Phenytoin sodium

CYP2C9

Consider 50% reduction of recommended starting maintenance dose in order to avoid probability of toxicities due to reduced phenytoin metabolism and higher plasma concentrations.

Simvastatin

SLCO1B1

Tacrolimus

CYP3A5

Biomark	Clinical application	Genotype
CYP3A5	Predict the starting dose and chronic renal toxicity of tacrolimus, and dose of cyclosporin in renal transplant patients^{[6, 7]}	CYP3A5*3
CYP2D6	Predict the efficacy of adjuvant tamoxifen for breast cancer^[8], and safety of tramadol, amitriptyline, aripiprazole, perphenazine, tomoxetine, citalopram, clomipramine, doxepin, fluoxetine, vortioxetine, tomoxetine, venlafaxine, metoprolol, propafenone, quinidine or tolterodine in poor metabolizers and ultrarapid metabolizers receiving codeine^[3]	Multi-allelic loci
CYP2C9	Predict the dose of celecoxib in poor metabolizers, warfarin in intermediate metabolizers and poor metabolizers^[3]	2/2, 3/3, 2/3(celecoxib); 1/3, 1/2, 2/2, 3/3, 2/3 (warfarin)
VKORC1	Predict the starting dose and drug toxicity of warfarin^[3]	VKORC1-1639G>A
CYP2C19	Predict clopidogrel resistance, voriconazole dose and safety of citalopram and diazepam in poor metabolizers^[3]	CYP2C192, CYP2C19**3
CYP1B1	Predict the anti breast cancer efficacy of paclitaxel^[9]	CYP1B1*3
GSTP1	Predict the efficacy of cisplatin and oxaliplatin^[10]	GSTP1 A313G
UGT1A1	Predict the toxicity of irinotecan (e.g., diarrhea and bone marrow suppression)^[3], and nilotinib-induced hyperbilirubinemia^[3]	UGT1A128, UGT1A1*6 (irinotecan); UGT1A1**28 (nilotinib)
TPMT	Predict the efficacy and toxicity of mercaptopurine and azathioprine^[3]	TPMT2 (G238C), TPMT*3A (G460A/A719G), TPMT*3B (G460A), TPMT**3C (A719G)
MTHFR	Predict the relapse risk in acute lymphoblastic leukemiapatients receiving methotrexate^[11]	MTHFR C677T
DPYD	Predict toxicity of 5-fluorouracil (gastrointestinal cancer) and capecitabine (recurrent breast cancer)^[12]	DPYDc.1679T>G, c.1236G>A/HapB3, DPYD*2A, c.2846A>T
NAT2	Predict hepatic toxicity of anti tuberculosis drugs^[3]	NAT2*4
SLCO1B1	Predict the probability of enalapril-induced cough^[13], and down-regulation of simvastatin dose or therapeutic switch to other statins^[14]	c.521T>C
ABCB1	Predict imatinib response in chronic myeloid leukemia patients^[15] and drug response of platinum-based chemotherapy in non small cell lung cancer patients^[10]	c.1236C>T, c.2677G>T/A, c.3435C>T
ABCG2	Predict the uric acid-lowering effect of allopurinol^[16]	Q141K (rs2231142)

Biomark

Clinical application

Genotype

CYP3A5

Predict the starting dose and chronic renal toxicity of tacrolimus, and dose of cyclosporin in renal transplant patients^{[6, 7]}

CYP3A5*3

CYP2D6

Multi-allelic loci

CYP2C9

Predict the dose of celecoxib in poor metabolizers, warfarin in intermediate metabolizers and poor metabolizers^[3]

*2/*2, *3/*3, *2/*3(celecoxib); *1/*3, *1/*2, *2/*2, *3/*3, *2/*3 (warfarin)

VKORC1

Predict the starting dose and drug toxicity of warfarin^[3]

VKORC1-1639G>A

CYP2C19

Predict clopidogrel resistance, voriconazole dose and safety of citalopram and diazepam in poor metabolizers^[3]

CYP2C19*2, CYP2C19*3

CYP1B1

Predict the anti breast cancer efficacy of paclitaxel^[9]

CYP1B1*3

GSTP1

Predict the efficacy of cisplatin and oxaliplatin^[10]

GSTP1 A313G

UGT1A1

Predict the toxicity of irinotecan (e.g., diarrhea and bone marrow suppression)^[3], and nilotinib-induced hyperbilirubinemia^[3]

UGT1A1*28, UGT1A1*6 (irinotecan); UGT1A1*28 (nilotinib)

TPMT

Predict the efficacy and toxicity of mercaptopurine and azathioprine^[3]

TPMT*2 (G238C), TPMT*3A (G460A/A719G), TPMT*3B (G460A), TPMT*3C (A719G)

MTHFR

Predict the relapse risk in acute lymphoblastic leukemiapatients receiving methotrexate^[11]

MTHFR C677T

DPYD

Predict toxicity of 5-fluorouracil (gastrointestinal cancer) and capecitabine (recurrent breast cancer)^[12]

DPYDc.1679T>G, c.1236G>A/HapB3, DPYD*2A, c.2846A>T

NAT2

Predict hepatic toxicity of anti tuberculosis drugs^[3]

NAT2*4

SLCO1B1

Predict the probability of enalapril-induced cough^[13], and down-regulation of simvastatin dose or therapeutic switch to other statins^[14]

c.521T>C

ABCB1

Predict imatinib response in chronic myeloid leukemia patients^[15] and drug response of platinum-based chemotherapy in non small cell lung cancer patients^[10]

c.1236C>T, c.2677G>T/A, c.3435C>T

ABCG2

Predict the uric acid-lowering effect of allopurinol^[16]

Q141K (rs2231142)