Lang Li Department of Medical and Molecular Genetics Indiana Institute of Personalized Medicine Center for Computational Biology and Bioinformatics Indiana University School of Medicine

A Tamoxifen Story: background

The selective estrogen receptor modulator tamoxifen (TAM) was first approved in 1977 by the FDA for the treatment of women with metastatic breast cancer and in ensuing years for adjuvant treatment of breast cancer.

TAM is an established hormonal treatment for all stages of estrogen receptor (ER)-positive breast cancer and is widely used as a chemo-preventive agent in women at risk for developing the disease.

However, there is wide inter-individual variability in the clinical efficacy and side effects of TAM: some patients may be refractory to TAM, and a significant proportion of patients experience side effects that include hot flashes.

(Osborne, 1998)

A Tamoxifen Story: a pharmacokinetics idea

4-hydroxy-Tam is 30-100 times more potent than TAM in suppressing Estrogen-dependent cell proliferation. (Jordan et al. 1977, 1982) CYP3A, CYP2B6, CYP2D6 are responsible for TAM primary metabolism. (Lonning et al. 1992) There were evidences of secondary TAM metabolites, but their functions and metabolism pathways were not clear. (Stearns et al., 2003)

A Tamoxifen Story: a pharmacokinetics idea

CYP3A and CYP2D6 are responsible for TAM secondary metabolism. (Desta et al . 2004) Endoxifen is 10 times more potent than 4-hydroxy-TAM (Johnson et al. 2004)

A Tamoxifen Story: pharmacogenetics and drug interaction hypotheses

1. Can CYP2D6 genetic polymorphisms predict endoxifen variation and breast cancer patient outcome? 2. Some of the breast cancer patients also take antidepressants, and many antidepressants are strong CYP2D6 inhibitors. Will these co-medications predict endoxifen variation and breast cancer patient outcome?

Genetic and Drug Interaction Effect on Tamoxifen Metabolism

-1 0 1 2 3 4

0.02

0.05

0.10

0.20

0.50

Proposed CYP2D6 Gene Score

En

do

xife

n/N

DM

pla

sm

a r

atio

r²= 0.24

-1 0 1 2 3 4

0.02

0.05

0.10

0.20

0.50

System 1 CYP2D6 Gene Score

r²= 0.22

-1 0 1 2 3 4

0.02

0.05

0.10

0.20

0.50

System 2 CYP2D6 Gene Score

r²= 0.18

-1 0 1 2 3 4

0.01

0.02

0.05

0.10

0.20

Proposed CYP2D6 Gene Score

En

do

xife

n/N

DM

pla

sm

a r

atio

r²= 0.3

-1 0 1 2 3 4

0.01

0.02

0.05

0.10

0.20

System 1 CYP2D6 Gene Score

r²= 0.24

-1 0 1 2 3 4

0.01

0.02

0.05

0.10

0.20

System 2 CYP2D6 Gene Score

r²= 0.15

-1 0 1 2 3 4

0.02

0.05

0.10

0.20

0.50

Proposed CYP2D6 Gene Score

En

do

xife

n/N

DM

pla

sm

a r

atio

r²= 0.43

-1 0 1 2 3 4

0.02

0.05

0.10

0.20

0.50

System 1 CYP2D6 Gene Score

r²= 0.42

-1 0 1 2 3 4

0.02

0.05

0.10

0.20

0.50

System 2 CYP2D6 Gene Score

r²= 0.38

-1 0 1 2 3 4

0.01

0.02

0.05

0.10

0.20

Proposed CYP2D6 Gene Score

En

do

xife

n/N

DM

pla

sm

a r

atio

r²= 0.52

-1 0 1 2 3 4

0.01

0.02

0.05

0.10

0.20

System 1 CYP2D6 Gene Score

r²= 0.48

-1 0 1 2 3 4

0.01

0.02

0.05

0.10

0.20

System 2 CYP2D6 Gene Score

r²= 0.38

CYP2D6 Genetic Activity Score

Borges et al. Journal of Clinical Pharmacology 2009

CYP2D6 Genetic /Drug- Inhibition Score

CYP2D6 Functional Genotype Predicts Patient Survival After Tamoxifen Treatment

Goetz et al. 2005, JCO.

CYP2D6 Functional Genotypes and Co-medications (CYP2D6 inhibitor) Predicts Patient Survival After Tamoxifen Treatment

EM: CYP2D6 *1/*1, no CYP2D6 inhibitor IM: CYP2D6 *1/*4 no CYP2D6 inhibitor PM: CYP2D6 *4/*4 or CYP2D6 inhibitor

Goetz et al. 2007, BCRT.

Time to Breast Cancer Relapse Relapse Free Survival

Disease Free Survival Overall Survival

Traditional Pharmacology Approaches in Pharmacogenomics and Drug Interaction Studies

in vitro studies (Human liver microsome, hepatocyte, recombinant system, and targeted tissue cells) that determine the drug metabolism pathways and drug inhibition effects, and sometime drug effects.

Clinical studies that investigate genetic effect or drug interaction effect on drug exposure change or clinical endpoints.

Read the literature!!! Read the literature!!!

A Computational Biologist Approaches

Read the literature Literature based discovery

Clinical Study Large scale clinical database data mining

in vitro studies System pharmacology based discovery

Drug A

Enzyme E

Drug B

Literature Based Discovery

Existing Literatures

Percha B., Garten Y., and Altman R. B., Discovery and explanation of drug-drug interaction via text mining. PSB, 2011.

Segura-Bedmar I., Martinez P., and Pablo-Sanchez C. de. Using a shallow linguistic kernel for drug-drug interaction extraction. Journal of Biomedical Informatics, 2011, 44, 789-804.

Segura-Bedmar, I., Martínez, P., de Pablo-Sánchez, C. (2011). "A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents." BMC Bioinformatics 12(suppl 2): S1

Boyce, R., Collins, C., Horn, J., and Kale, I. (2009). "Computing with

evidence Part II: An evidential approach to predicting metabolic drug-drug interactions." J Biomed Inform 42(6): 990-1003.

in silico: DDI Prediction from PubMed Based Text Mining

Pharmacokinetics and Drug Interaction Ontology

Pharmacokinetics and Drug Interaction Corpus

CYP substrates and CYP inhibitors Text Mining

CYP enzyme based DDI prediction

PK and DDI Ontology

PK Corpus

XML format is also available.

• Single drug in vivo PK studies: 60

• Single drug in vivo PG studies: 60

• in vivo drug interaction studies: 218

• in vitro drug interaction studies: 208

Term Annotation: Sentences with Drug Name, Dose information, Enzyme Name, PK

parameter, Units, Sample size, P-value, Mechanism, Adj word, Verb, Action work.

Clear DDI Sentence (CDDIS)

Vague DDI Sentence (VDDIS)

DDI ADDI Non-DDI

Term

Sentence

IN-VIVO DDI

C3 or C4

DDI DEI ADD

I ADEI

Non-DDI

Non-DEI

IN-VITRO DDI

PMID DDI sentence Relationship and commend

20012601 The pharmacokinetic parameters of verapamil were

significantly altered by the co-administration of

lovastatin compared to the control.

Because of the words,

“significantly”, (Verapamil,

lovastatin) is a DDI.

20209646 The clearance of mitoxantrone and etoposide was

decreased by 64% and 60%, respectively, when

combined with valspodar.

Because of the fold changes were

less than 0.67, (mitoxantrone,

valspodar.) and (etoposide,

valspodar) are DDIs.

20012601 The (AUC (0-infinity)) of norverapamil and the

terminal half-life of verapamil did not significantly

changed with lovastatin coadministration.

Because of the words, “ not

significantly changed”,

(verapamil , ovastatin) is a

NDDI.

13129991 The mean (SD) urinary ratio of dextromethorphan

to its metabolite was 0.006 (0.010) at baseline and

0.014 (0.025) after St John’s wort administration

(P=.26)

The change in PK parameter is

more than 1.5 fold but P-value

is >0.05. Thus,

(dextromethorphan , St John’s

wort) is an ADDI.

19904008 The obtained results show that perazine at its

therapeutic concentrations is a potent inhibitor of

human CYP1A2.

Because of words, “potent

inhibitor”, (perazine , CYP1A2)

is a DEI.

19230594 After human hepatocytes were exposed to 10

microM YM758, microsomal activity and mRNA

level for CYP1A2 were not induced while those for

CYP3A4 were slightly induced.

Because of words, “not

induced” and “slightly

induced”, (YM758, CYP1A2)

and (YM758, CYP1A2) are

NDEIs.

Key Terms

Annotation

Categories

Frequencies Krippendorff's

alpha

Drug 8633

0.953

CYP 3801

PK Parameter 1508

Number 3042

Mechanism 2732

Change 1828

Total words 97291

DDI

sentences

CDDI

sentences

1191

0.921 VDDI

sentences

120

Total sentences 4724

DDI Pairs

DDI 1239

0.905

ADDI 300

NDDI 294

DEI 565

ADEI 95

NDEI 181

Total Drug

Pairs

12399

Drug Interaction Information Extraction

in vitro DDI Abstracts

Clinical DDI Abstracts

DDI Relevant

Sentences

DDIs, ADDIs, NDDIs DEIs, ADEIs, NDEIs

Abstract Identification

Sentence Identification

DDI Extraction

DDI Extraction

Datasets Precision Recall F-measure

in vivo DDI Training 0.67 0.78 0.72

in vivo DDI Testing 0.67 0.79 0.73

in vitro DDI Training 0.51 0.59 0.55

in vitro DDI Testing 0.47 0.58 0.52

DDI Extraction Performance

Predict Potentially Interacting Drug Pairs Using Text Mining of PubMed Abstracts

Text mining of PubMed abstracts for in vitro drug metabolism studies involving major CYP450 isoforms using FDA recommended probes

and HLMs or recombinant CYP450s

Substrates Inhibitors

1A2 2A6 2B6 2C8 2C9 2C19 2D6 2E1 3A

+ + … …

13,197 Predicted Potentially Interacting Drug Pairs

FDA recommended CYP2C9 inhibitors

FDA recommended CYP2C9 substrates

Predicted potentially interacting drug pairs

Large Scale Database DDI Data Mining

Tatonetti, NP, Denny, J.C., Murphy, S.N., Fernald, G.H., Krishnan, G., Castro, V., Yue, P., Tsau, P.S., Kohane, I., Roden, D.M., Altman, R.B. (2011). "Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels." Journal of Clinical Pharmacology and Therapeutics 90(1): 133-142.

Tatonetti, N. P., Fernald, G.H., Altman, R.B. (2012). "A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports." J Am Med Inform Assoc 19(1): 79-85.

Tatonetti NP, Ye PP, Daneshjou R, Altman RB. 1. Data-driven prediction of drug effects and interactions. Science Translational Medicine, 2012 Mar 14, 4 (125).

Observational Medical Outcome Partnership - Common Data Model

Data

Medication Data

Diagnosis

Lab Tests

2.2 Million De-identified Patient Data in the Indiana Patient Care Network (2004 – 2009)

Drug Safety Outcome – Myopathy CDM Code (54 items) Myositis

Muscle weakness

Polymyositis

Myoglobinuria

Myositis unspecified

[D]Myoglobinuria

March myoglobinuria

Idiopathic myoglobinuria

Exertional rhabdomyolysis

Rhabdomyolysis

Traumatic rhabdomyolysis Non-traumatic rhabdomyolysis

Rhabdomyolysis

Myopathy, unspecified

Myopathy, unspecified

Myalgia and myositis, unspecified

Muscle weakness (generalized)

Polymyositis

Myoglobinuria

Rhabdomyolysis

Other myopathies

Toxic myopathy

Antilipemic and antiarteriosclerotic drugs causing adverse effects in therapeutic use

Myoglobinuria

Rhabdomyolysis

Polymyositis

Muscle Weakness

Myositis

Muscle Weakness

Myoglobinuria

Myoglobinuria

Polymyositis Polymyositis Myopathy toxic

Myopathy toxic

Muscle weakness conditions Myositis Myositis-like syndrome

Myopathy

Rhabdomyolysis Myositis Myositis-like syndrome

Muscle weakness Generalised muscle weakness Generalized muscle weakness Myopathy

Myopathy, unspecified

Rhabdomyolysis Rhabdomyolysis-induced renal failure

Myalgia and myositis, unspecified

Antilipemic and antiarteriosclerotic drugs causing adverse effects in therapeutic use

Myopathy unspecified Mylagia and myositis unspecified Muscle weakness

Myopathy

Drug Exposure Window

Baseline co-Meds (confounder)

Intermediate co-Meds (Modifier)

D1 + D2

D1 only

D2 only

No D1/D2

Baseline Exposure Window

Pharmaco-epidemiologic Study Design

Causal Inference Propensity Score (Donald B. Rubin, 1981)

Propensity score construction: multinomial logistic regression.

Case control selection based on matched propensity score.

Control group selection sensitivity analysis.

Inverse Weighted Method (James M. Robins, 1999)

Propensity score construction: multinomial logistic regression.

Inverse weighted based regression

Loratadine Only

Simvastatin Only

Loratadine +

Simvastatin

Risk 1 0.03

Risk 2 0.05

+ Risk 12 0.13

< ?

Myopathy Risk

Synergistic Effect Model

Identify DDIs Associated with Increased Risk of Myopathy Using Electronic Medical Records

• Logistic regression

• Adjust for age, sex, and medication frequency

• Drugs that treated pain were removed.

• Bonferroni corrected

Predicted CYP450 Pathways Based DDIs and Their Associations with Myopathy Risk

(p-value < 0.01)

drug 1 drug 2 myopathy risk 1

myopathy risk 2

combined risk

Relative Risk P-value

loratadine alprazolam 0.07 0.03 0.16 1.56 1.06E-09

loratadine duloxetine 0.14 0.03 0.28 1.56 7.43E-09

loratadine omeprazole 0.03 0.06 0.13 1.33 4.45E-07

loratadine simvastatin 0.03 0.05 0.13 1.60 4.75E-07

promethazine tegaserod 0.03 0.07 0.21 2.20 1.28E-05

loratadine ropinirole 0.03 0.12 0.31 2.05 1.27E-05

Six Significant DDI Pairs

Removed drug pairs involving drugs used to treat pain, including: chloroquine, hydroxychloroquine, acetaminophen, oxycodone, hydrocodone, fentanyl, tizanidine

Duke J., Han X., Wang Z., et al, 2012 PLoS Computational Biology

Do the identified drugs inhibit CYP enzymes?

Inhibitor concentration high to low control Backgro

und

1 2 3 4 5 6 7 8 9 10 11 12

Positive inhibitor

A

B

Test inhibitor 1

C

D

Test inhibitor 2

E

F

Test inhibitor 3

G

H

CYP Enzyme Substrate Fluorescent metabolite

Inhibitor

+ + +

Cofactors

37C for 15~45 min

Estimate IC50 by fitting to

Summary of Metabolic and Inhibitory Profiles

Metabolic pathway IC50

1A2 2B6 2C9 2C19 2D6 3A 1A2 2B6 2C9 2C19 2D6 3A4

Duloxetine

Loratadine

Ropinirole

Promethazine

Simvastatin

Tegaserod

Not/Unknown Minor Major

IC50 > 100 uM or ND 20uM < IC50 < 100 uM IC50 < 20 uM

Drug 1 Drug 2 pathways metabolism inhibition DDI Prediction

loratadine alprazolam CYP3A4 major moderate Moderate

loratadine duloxetine CYP2D6 minor strong Moderate

loratadine simvastatin CYP3A4 major strong Strong

promethazine tegaserod CYP2D6 major strong Strong

loratadine ropinirole CYP3A minor strong moderate

Metabolism Based Inhibition Interpretation of Six DDI Pairs

System Pharmacology: A trans-eQTL Analysis in identify a-SNPs for CYP2D6

Genetic Variation

CYP2D6 Enzyme Activity

Gene Expression

SNP (Illumina) 207

Gene Expression

466

Enzyme Activity

488

167

Samples Publically Available

SNP (Affymetrix) 204

Gene Expression

466

Enzyme Activity

488

180

Samples Publically Available

Mediation Analysis

A mediation analysis method was developed to assess the indirect SNP effects to CYP2D6 activity mediated by gene expressions. The mediated effect is estimated by product of coefficients

Type #genes

(Affy)

#genes

(illum)

cytokine 5 7

growth factor 5 11

ligand-dependent nuclear receptor 6 8

translation regulator 6 10

transmembrane receptor 11 14

ion channel 13 18

phosphatase 15 22

G-protein coupled receptor 17 20

peptidase 31 39

kinase 52 63

transporter 76 127

transcription regulator 80 113

enzyme 245 365

other 382 609

Functional categories of Mediators

What have we learned?

The new translational biomedical information research paradigm works!

Literature Based DDI Discovery

EMR data based validation

in vitro validation

System pharmacology based discovery

What is a drug interaction?

DDI changes Drug ADME

in vitro!

DDI changes Drug ADME

in vivo!

DDI changes Efficacy and

ADE!

Drug Interaction Evidences

It is a drug metabolism based DDI!

It is a drug transporter based DDI!

It is a drug target based DDI!

Drug Interaction Mechanisms

Why do we care about all the information?

Only knowing the clinical effect of a DDI won’t help prevent the DDI. For example, polypharmacy.

Only knowing the mechanisms of a DDI won’t be enough to understand its clinical impact.

Even we understand both the mechanism and clinical effect of a DDI, we will have to worry about implementation.

Myocyte Metabolism

CYP

CYP CYP

Feces Urine

Liver

Portal vein

CYP

CYP

CYP

We all need each other!

David A. Flockhart Clinical Pharm, IUSM

Sara K. Quinney OBGYN, IUSM

Richard B. Kim Pharmacology

UNIVERSITY of WESTERN ONTARIO

Jeffrey S. Elmendorf Physiology, IUSM

Wanqing Liu Medicinal Chemistry Purdue

We all need each other!

PK Ontology Lang Li

Drug Interaction Text Mining Luis M. Rocha

Myopathy Definition In CDM Jon Duke

Drug Interaction Pharmacoepidemiology Study Design Xiaochun Li

The true heroes! PK Knowledge Database DDI corpus Abhinita Subhadarshini M.S. Bioinformatics

DDI Corpus DDI text mining Shreyas Karnik M.S. Bioinformatics

Pharmacogenetics Corpus Santosh Philips Ph.D. Bioinformatics

Transport Ontology Chienwei Chiang Ph.D. Bioinformatics

Ontology Construction Hengyi Wu Ph.D. Bioinformatics

DDI Graphical Presentation Hrishikesh Lokhande M.S. Bioinformatics

EMR data processing PK data text mining Zhiping Wang Ph.D. Computer Science

in vitro validation Xu Han Ph.D. Pharm and Tox

Funding support are from NIGMS, AHRQ, and IUCRG.

Thank you!