nick tatonetti's presentation on systems pharmacology at amia 2015

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

Technological Augmentation

• Tech companies are becoming really good at observing (and recording) the moments of life

• Facebook

• Google

• 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


Myocardial infarction


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


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%


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 [email protected]

@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

nick tatonetti's presentation on systems pharmacology at amia 2015

Science