This presentation gives an general overview of Big Data issues and approaches in Pharma and shows a few use cases from our work.

1. Big Data Analyses in Pharma
An Overview
Josef Scheiber, PhD
Managing Director
July 2015

2. Geographie
Startup Center in Waldsassen
Main site
Data Analyses and Software
Development
Westpark Center
Garmischer Str. in Munich
Scientific ActivitiesSince Jan 1, 2015
Basel/Switzerland
Data Curation and customer-
related activities
Prag
150 km
München
200 km
Berlin
300 km
Frankfurt
250 km

3. BioVariance at a Glance –
Get most out of your complex data
Curate.Integrate
Analyze.Model
Visualize.Explore
DECIDE

4. Overview
• Background
• Strategy
• Examples

5. Background

6. Courtesy: M. Zeinab, slideshare

7. 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 Discovery
JL 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 and
Bioinformatics Edited by:A Bender, R Guha. 25-56 John Wiley & Sons, Inc.
ANSWERS

8. Context matters!
metabolites
drugs
targets pathways
diseases (phenotypes)

9. Context matters
RNADNA
It´s not that simple …

10. 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

11. Need for interpretation
33,3
10
20
30
70
33,3 80
70
60
10
33,3
10 10 10
20
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Before molecular
biology
Molecular biology
golden age
Genomics age Deep sequencing
age
Very soon
Data Analysis Experiment Experimental Design

12. Big Data?

13. Volume

14. Genome Sequencing
Slide adapted from George Church

15. Genome Sequencing
Slide adapted from George Church

16. Cost Reduction - Example
458 Ferrari Spider - $398,000 in 2006 –
40 cents now!

17.  Much more data for way less
money

18. Challenges for Informatics? –
1 genome is roughly 500 GB/data
2011 – several 100 exomes

19. Drug Discovery Pipeline
Target
finding
Lead Finding
Lead
Optimization
… Phase 1 … Market
Drug candidates Patients

20. Velocity

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

22. For each patient

23. Variety

24. Variety

25. Variety
• Bioinformatics
• Clinical
• Social network
• E-health
• Also text/patents

26. A simplified overview –
Molecules in Man
Adapted from Gohlke JM, Portier CJ.
Environ. Health Perspect. 115:1261-1263 (2007)

27. A question of complexity –They all
interact …
Biology
Chemistry
Physics

28. 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

29. … individual polypharmacology

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

31. Veracity

32. Veracity
• Chemogenomics data
• Gene expression data
 Imputation?

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

34. Veracity - Interactomics
A Proteome-Scale Map of the Human
Interactome Network
Rolland, Thomas et al.
Cell , Volume 159 , Issue 5 , 1212 - 1226

35. Veracity – Social Media

37. Strategy

38. Biological/Pharmacological
Understanding
drugs
targets pathways
diseases (phenotypes)

39. Data integration strategy
a) A central vocabulary/pointer server (information
stored are preferred names and synonyms plus
pointers to data servers, where to find what)
b)  semantic integration layer with domain-specific
terminology and referential data
c) A database for each datatype collected, storing only
preferred names along with raw measurements
d) Clearly defined APIs for further integration with
public data sources and to enable large-scale
analyses

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

41. Answering workflow
Vocabulary
Vocabulary server acts as
translator, aggregator and
locator, i.e. knows where
the respective facts can be
found
Firmicutes produce alpha-Linolein and thereby cause gut irritation
species
metabolite
Further
Data of each type is
stored in a specific
database to
enhance
performance of
large-scale analyses
Expert tools talk to
data directly or via
webservices
API
API
API
API
Enduserinterfaceand
visualization

42. Examples

43. Genome data at scale

44. Workflow
Identify drug targets
(primary and off-targets,
from DrugBank)
Call variations on a per-
individuum basis

45. Workflow
Analyse mutation rates in
the targets and in
particular drug binding
pockets

46. Example: Donepezil /
Acetylcholinesterase
• PDB 4EY7
Image extracted from Cheung et al.,
2012 [2]

47. Example: Donepezil /
Acetylcholinesterase

48. Example: Acetylcholinesterase
Integrative Genomics Viewer

49. 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.

50. Why is this a bad example?
AChE a key enzyme in human biology  these are
the most highly conserved, even interspecies
 Learning: Look at that stuff before investing
time 

51. Generating
Vocabularies

52. Vocabulary generation
Extensive mapping of terms from various sources

53. 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 tha
mapping everything is non-trivial.
A Big Data exercise in itself …

54. Tweet mining

55. Mining Twitter for side effects
Needed Drug Name
and synonyms:
Adalimumab
Humira
Exemptia
331731-18-1
L04AB04
MedDRA vocabulary

56. 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"

57. Top 200 drugs
- Cutoff is at 1500 tweets that a
few drugs easily surpass (although
it's mostly only pharmacies
advertizing)
- Others are not mentioned once
(probably a synonym issue as I
restricted to English as language). -
- top drugs are tweeted more
often, but e.g. Tarceva (in 2006) at
the very bottom also reaches the
top number of tweets (109 on list).

58. 089 – 189 6582 – 80
Garmischer Str. 4/V
80339 München
josef.scheiber@biovariance.com:
09632 – 9248 325
Konnersreuther Str. 6g
95652 Waldsassen
Questions?