This slideset gives an overview of pharmacogenomic and pediatric dosing knowledge and various influence factors. Finally it shows an example on how to use this kind of Data within predictive approaches.

1. Pediatric Pharmacogenomics –
What knowledge do we have and how can we
use it in bioinformatics analyses for Drug
Discovery?
Josef Scheiber, PhD
www.biovariance.com
m4 Seminar
April 25, 2013

2. BioVariance - Overview
Two distinct offerings rooted in the same data:
• “Medical value content”-as-a-Service for Healthcare
• making sense of genomic & other data in context as
service offering
Bio-Variance
BaVarians ;)
• We aim for testable
hypotheses that are well-
supported by data from
scientific databases and the
literature

3. Overview
• General Introduction
– Genetics
– Impact of age (children)
– Further influence factors
• Key message
• prediction-based example

4. Significant unmet medical need
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
 diseases
Drugresponserate
NSAIDS  80 % response rate
Alzheimer  25 % response rate
Several thousand diseases without
known treatment

5. Disease understanding getting better and better
2010
1970
1960
1950
Disease of the
Blood
Leukemia
Chronic
Leukemia
Acute
Leukemia
Preleukemia
Lymphoma
Indolent
Lymphoma
Aggressive
Lymphoma
Increasing understanding of underlying biology
opens up new hypotheses
5 Year
Survival
~ 0 %
~ 70%
Example: Leukemia and Lymphoma

6. But still, mostly off-label use for
children
3.9
1.4
3.4
0
1
2
3
4
5
6
7
Inlabel / Licensed Offlabel (Unlicensed)
Adverse Drug Reactions of unlicensed / off-label
prescription in pediatrics
In hospital (UK) Outpatient clinics (FR)
(Turner et al, Acta Paed, 1999) (Horen et al, Brit J Clin Pharm, 2002)

7. Integrated Knowledge is key for good
interpretations
Genetics
Age
External
influence
factors

8. Integrated Knowledge is key for good
interpretations
Genetics
Age
External
influence
factors

9. There are ~7 billion human genomes and
each responds differently to drugs
Medically actionable annotations are key, particularly in the
area of Pharmacogenetics

10. Knowledge is key to enable
decisions – both for Drug Discovery
and Treatments
?

11. Impact of genetics on Drug Action
The human genome contains roughly 3 billion nucleotides and the
genomes of any 2 individuals vary in 3 million of them
A significant likelihood that individuals respond differently to the
same medicine
This is rooted in differences for drug
absorption, distribution, metabolism and excretion

12. Examples: Genes impacting Drugs
Biotransformation:
Phase I (Oxidation, Reduction, Hydrolysis, Hydration, Dethioacetylation, Isomerization)
Phase II (Glucuronidation, Sulfation, Methylation, Acetylation, Amino Acid
Conjugation, Gluthathione Conjugation, Fatty acid conjugation)
Gene Drug
Bcr/abl or 9:22 translocation Imatinib
HER2-neu Trastuzumab
EGFR mutations Gefitinib
Thiopurine S-methyltransferase Mercaptopurine, Azathioprine
UGT1A1 Irinotecan
CYP2D9/VKORC1 Warfarin
HLA-B*5701 Abacavir
HLA-B*1502 Carbamazepine
CYP2C19 Clopidrogel
…. Many more

13. Ultimately: An individual profile

14. Example: Carbamazepine/Steven Johnsons Syndrome
Courtesy:
Dr. Thomas Habif
dermnet.com
A single mutation can have massive impact
Difference in European and Korean populations

15. On a bigger scale – similar impact
• Example: Treatment of ALL (acute lymphoblastic
leucemia)
– Patients with ALL who have 1 wild-type allele and
intermediate TPMT activity tend to have a better response
to 6MP (Mercaptopurine) therapy than patients with 2
wild-type alleles and full activity
– Pharmacogenetic polymorphisms of several additional
genes also have the potential to influence successful
treatment of ALL
– 20% of patient with ALL who do not respond to
chemotherapy represent an additional challenge for
pharmacogenomic research

16. Understand the link between Types of
Genetic Variation and phenotype
• Single Nucleotide Aberrations
– Single Nucleotide Polymorphisms (SNPs)
– Single Nucleotide Variations (SNVs)
• Short Insertions or Deletions (indels)
• Larger Structural Variations (SVs)

17. Classes of structural variation
Alkan, C. et al. Genome structural variation discovery and genotyping. Nature Reviews Genetics
12, 363-376 (2011).

18. Raw Data
Analysis
Image Processing
and base calling
Whole
Genome
Mapping
Alignment to
reference genome
Variant
Calling
Detection of
genetic variation
(SNP, CNV etc.)
Annotation
Linking variants to
biological
information
BioVariance focus
Quick detour: Basic NGS workflow

19. Integrated Knowledge is key for good
interpretations
Genetics
Age
External
influence
factors

20. Age-related influence factors on Drug
Therapy
Physiologic Factors that influence the Oral absorption of
Medications
PARAMETER Neonate Infant Child
Gastric Acid
secretion
Reduced Normal Normal
Gastric Acid
Emptying Time
Decreased Increased Increased
Intestinal
Motility
Reduced Normal Normal
Biliary Function Reduced Normal Normal
Microbial Flora Acquiring Adult Pattern Adult pattern

21. Premature Neonate Neonate Infant Child Adolescent
Absorption
Gastric acidity Decreased Decreased Decreased Equal Equal
Gastric emptying time Decreased Decreased Equal Equal Equal
GI motility Decreased Decreased Decreased Equal Equal
Pancreatic enzyme
activity
Significantly
decreased
Decreased Decreased Equal Equal
GI surface area Increased Increased Increased Increased Equal
Skin permeability Significantly
increased
Increased Equal Equal Equal
Distribution
Body composition Equal
Blood-brain barrier Decreased Decreased Equal Equal Equal
Plasma proteins Significantly
decreased
Decreased Equal Equal Equal
Metabolism
Liver Decreased Decreased Decreased Equal/Increased Equal
Elimination
Renal blood flow Decreased Decreased Decreased Equal Equal
Glomerular filtration Decreased Decreased Decreased Equal Equal
Tubular function Decreased Decreased Decreased Equal Equal

22. Oral Drug Absorption in the Neonate vs Older
Children and Adults – no direct heigt/weight
etc. correlation
Drug Oral Absorption
Acetaminophen Decreased
Ampicillin Increased
Diazepam Normal
Digoxin Normal
Penicillin G Increased
Phenobarbital Decreased
Phenytoin Decreased
Sulfonamides Normal

23. Expression patterns different
between age groups
- There are many literature examples for many relevant
indications  way too many to detail them here
- However, nobody has yet attempted to integrate genetic
and pediatric information on a large scale, this is what we
are doing now

24. Integrated Data is Key for good
interpretations
Genetics
Age
External
influence
factors

25. Drug-Drug Interactions
• When 2 or more drugs are administered to the
same patient, the pharmacokinetic and
pharmacodynamic properties of each agent may
be modified by their interaction.
– Acetaminophen + alcohol = Increase hepatotoxicity
– Antacid + Iron = decrease absorption
– Digoxin + Cimetidine = Increase Digoxin toxicity

26. Tobacco drug interactions
Food drug interactions

27. • A full integration of genetic and additional data is
currently missing, we are addressing this in a
collaboration project
• If you want to develop a drug for a stratified
population, you want to understand the interplay
of all these factors in detail
∑

28. An example

29. Incorporating pediatric data into
predictive approaches
Starting point: Netdosis (www.netdosis.de) has collected a dataset
of pediatric on- and off-label use for many different medications
- Data is very well structured and therefore amenable for machine-
learning approaches
- Drug names linked to machine-readable description of chemical
structures

30. The idea
• Link chemical substructures to their influence on
pediatric dosage
• Use this information to predict dosage levels for
not yet tested drugs and drug candidates

31. Simplified workflow
(1) Predict dosage levels at different age ranges
(2) Investigation of dosage-related
information for hypothesis
development

32. Data input
• Your compounds
• Netdosis database
• Your internal data incorporated where applicable
• Specifically curated scientific papers around
particular usages (especially if some interesting
facts turn up in first run)

33. Computational description of
molecules
• Descriptor selection heavily impacts outcome of
analyses
• Depending on your main objectives different
technologies are the best fit, we will discuss this
in detail with you
0 1 0 0 2 0 0 0 1 0

34. Statistical modeling
• Activity is either in categories (age range)
or more granular depending on your needs
• Plenty of positive results with naïve
Bayes, therefore method of choice
• Other technologies depending on data/on
request
• Strict model validation

35. n compounds
defined activity
1
3
n
model
predict
2
3
nrepeat3xtimes
2
3
n
Internal measure for model quality
R2
CV-50%
Predictionmodel
training data set test data set
Model Validation - Example

36. 1
3
n
model
predict
2
3
nrepeat3xmal
2
3
n
Internal measure for model quality
R2
CV-50%
PredictionModel
External measure
for model quality
R2
Test,Avg
repeat
atleaste100x
Model Validation - Example

37. Prediction results
• Based on model sets for each dosage level
and age range, there are 100 prediction
results for each dosage level/age range
• These are further analyzed, usually median
predictive value taken for prediction and
ranking
• Result: A ranked list with associated
probabilities for each dosage level/age
range
T1
T2
T3
…
…

38. What does the result mean?
• Targets need to be annotated with phenotypic
outcome – i.e. what does it mean that the
compound is hitting this target?
• Do we have opportunities ( repurposing) or
liabilities ( side effects) or both?
• How do different compounds compare?
• What predictions should be confirmed by testing?

39. Possible extensions
Diving into chemical biology
• Map into
pathways
• Retrieve
marketed drugs
and clinical
candidates that
act in these
pathways

40. 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

41. Outlook
The right drug for the right patient
at the right time & right dose is only possible
if you have the right knowledge within the right context
right in place
We will further work on this!

42. Thank you for your attention!
josef.scheiber@biovariance.com
Phone: +49 – 89 – 189 6582 – 80
Garmischer Str. 4/V
80339 Munich / Germany