Big Data Analyses in PharmaAn Overview

Josef Scheiber, PhDManaging Director

July 2015

Geographie

Startup Center in Waldsassen

Main siteData Analyses and Software Development

Westpark CenterGarmischer Str. in MunichScientific ActivitiesSince Jan 1, 2015

Basel/SwitzerlandData Curation and customer-related activities

Prag150 km

München200 km

Berlin300 km

Frankfurt250 km

BioVariance at a Glance –Get most out of your complex data

Curate.Integrate

Analyze.Model

Visualize.ExploreDECIDE

Overview

• Background• Strategy• Examples

Background

Courtesy: M. Zeinab, slideshare

What do we need out of Big Data?1. What are the inhibitors of kinase X and the five most similar

kinases with IC50 < 1 μM and with MW < 500 from all internal and external data sources?

2. What assay technologies have been used against my kinase? Which cell lines?

3. What other proteins are in the same kinase branch as target X, where there were validated chemical hits from external or internal sources?

4. If I hit a particular kinase, what would the potential side-effect profile look like? Which known inhibitor of this kinase has the best safety profile and the fewest known IC50s?

5. Have I identified other compounds with a bioactivity profile similar to compound X and with the same core substructure?

6. Can we create a phylochemical tree of kinases and for a new kinase target place it into the tree on the basis of activity against a reference panel of compounds?

7. Have I identified all kinases with an x-ray structure (in-house or external) that are in pathway X?

Bridging Chemical and Biological Data: Implications for Pharmaceutical Drug DiscoveryJL Jenkins, J Scheiber, D Mikhailov, A Bender, A Schuffenhauer, B Cornett, V Chan, J Kondracki, B Rohde, JW Davies (2012) In: Computational Approaches in Cheminformatics andBioinformatics Edited by:A Bender, R Guha. 25-56 John Wiley & Sons, Inc.

ANSW

ERS

Context matters!

metabolitesdrugs

targets pathways

diseases (phenotypes)

Context matters

RNADNA

It´s not that simple …

Descriptive:What happened?

Diagnostic:Why did it happen?

Predictive:What will happen?

Prescriptive:How can we make it happen?

Better data for better analytics

Hindsight Insight Foresight

Need for interpretation

33,3

1020

30

70

33,3 8070

60

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33,3

10 10 1020

0%

10%

20%

30%

40%

50%

60%

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Before molecularbiology

Molecular biologygolden age

Genomics age Deep sequencingage

Very soon

Data Analysis Experiment Experimental Design

Big Data?

Volume

Genome Sequencing

Slide adapted from George Church

Genome Sequencing

Slide adapted from George Church

Cost Reduction - Example

458 Ferrari Spider - $398,000 in 2006 –

40 cents now!

Much more data for way lessmoney

Challenges for Informatics? –1 genome is roughly 500 GB/data

2011 – several 100 exomes

Drug Discovery Pipeline

Target finding

Lead FindingLead

Optimization… Phase 1 … Market

Drug candidates Patients

Velocity

Velocity

• Mutations in tumor• Resistance mechanisms in patients• long term/short term AE • compliance• Nutrition and microbiome• Data from wearables relevant for drugs

For each patient

Variety

Variety

Variety

• Bioinformatics• Clinical

• Social network• E-health

• Also text/patents

A simplified overview –Molecules in Man

Adapted from Gohlke JM, Portier CJ.Environ. Health Perspect. 115:1261-1263 (2007)

A question of complexity –They all interact …

Biology

Chemistry

Physics

Dealing with a very complex environment –i.e. many opportunities

DNA RNA Protein Interactions Clinical parameters Treatment History Tissue anatomy Surgical History Epigenetic Profiles from many

patients at different timeponits

Target Off-targets Metabolites Additional indications Unspecific effects Similar drugs

Adapted from: J. Scheiber; How can we enable drug discovery informatics for personalized healthcare?Expert Opinion on Drug Discovery, 1-6; 2/2011

… individual polypharmacology

Sequences Expression Proteomics Biological networks(but also: Cells, Tissues, Organs)

POPULATION

Veracity

Veracity

• Chemogenomics data• Gene expression data Imputation?

Veracity - Chemogenomics

Adapted from Tanrikulu et al. Missing Value Estimation for Compound-Target Activity Data, J. Mol. Inf

Veracity - Interactomics

A Proteome-Scale Map of the Human Interactome Network

Rolland, Thomas et al.Cell , Volume 159 , Issue 5 , 1212 - 1226

Veracity – Social Media

Strategy

Biological/PharmacologicalUnderstanding

drugs

targets pathways

diseases (phenotypes)

Data integration strategy

a) A central vocabulary/pointer server (informationstored are preferred names and synonyms plus pointers to data servers, where to find what)

b) semantic integration layer with domain-specificterminology and referential data

c) A database for each datatype collected, storing onlypreferred names along with raw measurements

d) Clearly defined APIs for further integration withpublic data sources and to enable large-scaleanalyses

Vocabularies needed

• Genes, Drugs, Proteins• Diseases• Organisms• Microbiome species & genes• Localization & source• Phenotype• Metabolite common names

Answering workflow

Vocabulary

Vocabulary server acts astranslator, aggregator andlocator, i.e. knows wherethe respective facts can befound

Firmicutes produce alpha-Linolein and thereby cause gut irritation

species

metabolite

Further

Data of each type isstored in a specificdatabase toenhanceperformance oflarge-scale analysesExpert tools talk todata directly or via webservices

API

API

API

API

End

use

rin

terf

ace

and

visu

aliz

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Examples

Genome data at scale

Workflow

Identify drug targets(primary and off-targets,

from DrugBank)

Call variations on a per-individuum basis

Workflow

Analyse mutation rates in the targets and in

particular drug bindingpockets

Example: Donepezil / Acetylcholinesterase

• PDB 4EY7

Image extracted from Cheung et al.,

2012 [2]

Example: Donepezil / Acetylcholinesterase

Example: Acetylcholinesterase

Integrative Genomics Viewer

Not very successful

Alignment of the 3D structures of mutant number 52 (yellow) and PDB 4EY7 AChE protein (green). The only changed residue is the Y150 (magenta) to H150 (red). The white surface represents the molecular surface of donepezil.

Why is this a bad example?

AChE a key enzyme in human biology these arethe most highly conserved, even interspecies

Learning: Look at that stuff before investingtime

Generating Vocabularies

Vocabulary generation

Extensive mapping of terms from various sources

Vocabulary generation

397211 preferred

names

598532 synonyms

102086 identifiers

The chevron diagram shows the number of samples annotated

with names. Already by looking at the numbers you can see that

mapping everything is non-trivial.

A Big Data exercise in itself …

Tweet mining

Mining Twitter for side effects

Needed Drug Name and synonyms:

AdalimumabHumiraExemptia331731-18-1L04AB04

MedDRA vocabulary

Many birds tweet lots of noise … BUT …

• [1] "Lipitor headache 0"[1] "Lipitor rash 1"[1] "Lipitor pain 27"[1] "Lipitor bleeding 0"[1] "Lipitor cough 0"[1] "Lisinopril headache 0"[1] "Lisinopril rash 0"[1] "Lisinopril pain 8"[1] "Lisinopril bleeding 0"[1] "Lisinopril cough 7"[1] "Simvastatin headache 0"[1] "Simvastatin rash 0"[1] "Simvastatin pain 0"[1] "Simvastatin bleeding 0"[1] "Simvastatin cough 0"[1] "Plavix headache 0"[1] "Plavix rash 0"[1] "Plavix pain 0"[1] "Plavix bleeding 1"[1] "Plavix cough 0"[1] "Crestor headache 0"[1] "Crestor rash 0"[1] "Crestor pain 0"[1] "Crestor bleeding 0"[1] "Crestor cough 0"

Top 200 drugs

- Cutoff is at 1500 tweets that a few drugs easily surpass (althoughit's mostly only pharmaciesadvertizing) - Others are not mentioned once(probably a synonym issue as I restricted to English as language). -- top drugs are tweeted moreoften, but e.g. Tarceva (in 2006) at the very bottom also reaches thetop number of tweets (109 on list).

089 – 189 6582 – 80Garmischer Str. 4/V80339 München

josef.scheiber@biovariance.com: 09632 – 9248 325Konnersreuther Str. 6g95652 Waldsassen

Questions?