Post on 15-Apr-2017
Systems pharmacology for drug safety
November 15th, 2015
Nicholas P. Tatonetti, PhD Herbert Irving Assistant Professor of Biomedical Informatics
Columbia University
Observation is the starting point of biological discovery
• Charles Darwin observed relationship between geography and phenotype
• William McBride & Widukind Lenz observed association between thalidamide use and birth defects
The tools of observation are advancing
• Human senses
• sight, touch, hearing, smell, taste
• Mechanical augmentation
• binoculars, telescopes, microscopes, microphones
• Chemical and Biological augmentations
• chemical screening, microarrays, high throughput sequencing technology
• What’s next?
Bytes to KB
Megabytes to Terabytes
The tools of observation are advancing
• Human senses
• sight, touch, hearing, smell, taste
• Mechanical augmentation
• binoculars, telescopes, microscopes, microphones
• Chemical and Biological augmentations
• chemical screening, microarrays, high throughput sequencing technology
• What’s next?
Bytes to KB
Megabytes to Terabytes
Technological Augmentation
• Tech companies are becoming really good at observing (and recording) the moments of life
• Apple (iCloud)
• 2015, the year of the zetabyte
• 1 zetabyte = 1,000 exabytes = 1 billion terabytes
Your doctor is observing you like never before
>99% of Hospitals have Electronic Health Records
Your doctor is observing you like never before
>60% of ALL Physicians
Every drug order is an experiment.
Observation analysis in a petabyte world
• Darwin, McBride, and Lenz were working with kilobytes of data
• Today’s scientists are observing terabytes and petabytes of data
• The human mind simply cannot make sense of that much information
• Data mining is about making the tools of data analysis (“hypothesis generation”) catch up to the tools of observation
But, there’s a problem…
Bias confounds observations
Databases of drug effects are confounded
• Most drug side effects are only discovered after drugs hit the market using observational data
• This leads to high false positive and false negative rates when using EHR and adverse event data to find side effects
A
B
A
MWAS
Ryan et al. CPT: Pharmacometrics & Systems Pharmacology (2013)
False positivesFalse negatives
FN: Estradiol, DesipramineSensitivity: 67% 70%Specificity: 60% 60%
Myocardial InfarctionMedication-wide association study
AE Protein
Protein
Protein
AETarget
Drug
We can use prior biological knowledge to improve pharmacovigilance programs.
Systems pharmacology
• Integration of physiological, biochemical, genomic data to analyze drug actions and side effects in the context of the interactome
• Key method: network analysis
• Nodes = proteins and small molecules
• Edges = interactions
(aka systems pharmacology)
MADSS
• Use network analysis to build AE neighborhoods: a subset of the interactome surrounding AE “seed” proteins
• Score each protein on connectivity to seeds
• Overarching hypothesis: drugs targeting proteins within an AE neighborhood more likely to be involved in mediating that AE
Modular Assembly of Drug Safety Subnetworks
Lorberbaum, et al. Clin. Pharmacol. Ther. (2015)
Protein
InteractionSeed protein
Adverse eventDrug known to cause AEDrug predicted to cause AE
• For each AE, use four adapted pairwise connectivity metrics to score every protein in interactome on its connectivity to the seed set
• Mean first passage time (MFPT)
• Betweenness centrality (BC)
• Shared neighbors (SN)
• Inverse shortest path (ISP)
Building AE neighborhoods
Lorberbaum, et al. Clin. Pharmacol. Ther. (2015)
Myocardial infarction
Lorberbaum, et al. Clin. Pharmacol. Ther. (2015)
Serotonin agonists (triptans): serotonin receptor activation can lead to vaso-constriction and increased synthesis of IL-6 (MI seed) in vascular smooth muscle
Can we use these molecular data to
predict results of clinical trials or
post-market surveillance?
Evaluating MADSS: Drug safety gold standard
• Gold standard for 4 AEs created using systematic literature review and natural language processing of structured product labeling
GI Bleeding (73) 24 positives49 negatives
Myocardial Infarct (73) 33 positives40 negatives
Liver Failure (89) 63 positives26 negatives
Kidney Failure (49) 19 positives30 negatives
MWAS
Ryan et al. CPT: Pharmacometrics & Systems Pharmacology (2013)
False positivesFalse negatives
FN: Estradiol, DesipramineSensitivity: 67% 70%Specificity: 60% 60%
Myocardial InfarctionMedication-wide association study
Evaluating MADSS: Subnetwork (SubNet) models
• Trained SubNet model for each AE individually using connectivity scores as features
• Evaluated MWAS alone, SubNet alone, MWAS+SubNet
GI Bleeding Liver Failure
Kidney FailureMyocardial Infarct
MWAS + SubNetSystems PharmacologyAlone (SubNet)
SubNet (0.81)MWAS+SubNet (0.85)
MWAS (0.69)
Statistics Alone (MWAS)
Comparing network biology to post-market analysis
Lorberbaum, et al. Clin. Pharmacol. Ther. (2015)
MWAS+SubNet outperforms either model alone
Drug MWAS SubNet Both
Desipramine 70% 85% 100%
Darbepoetin Alfa 49% 73% 100%
Estradiol 67% 52% 75%
Frovatriptan 42% 64% 75%
Imipramine 64% 58% 67%
Myocardial InfarctSensitivity Specificity
MWAS SubNet Both
60% 74% 100%
80% 86% 100%
60% 89% 100%
89% 86% 100%
71% 89% 100%
Lorberbaum, et al. Clin. Pharmacol. Ther. (2015)
Observational analysis is the fuel of scientific discovery
• Data-mining has the potential to generate billions of hypotheses we could not have conceived of
• However, like all good hypotheses, these must be rigorously tested
• Systems pharmacology reveals the molecular hypothesis of drug side effects enabling experimental validation
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tatonettilab.org nick.tatonetti@columbia.edu
@nicktatonetti
Current Lab MembersRobert Moskovitch, PhD Rami Vanguri, PhD Alexandra Jacunski Tal Lorberbaum**Mary Boland Joseph Romano Yun Hao Phyllis Thangaraj Alexandre Yahi
CollaboratorsBrent Stockwell, PhD George Hripcsak, MD, MS Ziad Ali, MD, DPhil Santiago Vilar, PhD Konrad Karczewski, PhD (Broad/MGH) Joel Dudley, PhD (Mount Sinai) Patrick Ryan, PhD (OHDSI) Eric Horvitz (Microsoft Research) Ryen White (Microsoft Research) Russ Altman (Stanford)
FundingNIGMS R01GM107145 Herbert Irving Fellowship NCI P30CA013696 NIMH R03MH103957 PhRMA Foundation AstraZeneca
Thank you