Post on 26-Jun-2020
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!