This document discusses how informatics tools can be used across various stages of the drug discovery process in the 21st century. It outlines how genomics, bioinformatics, structural biology, high-throughput screening, combinatorial chemistry, and QSAR/read-across methods can help with target identification, hit discovery, candidate selection, and ADMET profiling. The document emphasizes using integrated informatics approaches rather than single tools to better link biological and chemical data for risk assessment and developing safer drug candidates. It also highlights the need for validation of models and moving beyond black box methods.

1. Use of Informatics for Risk Assessments in 21st
Century
Sandeep Modi
November 2010

2. SEAC
Discovery Process : The challenges
Only 1-2 chemicals reach market out of hundreds of leads, which might come from
thousands of chemicals synthesized. Currently the whole process may take about
14-15 years and ~ $800. million.
http://csdd.tufts.edu/

3. SEAC
in-vivo Decides
in-vitro Guides
in-silico Designs
Discovery / Role of Informatics in 20th
Century

4. SEAC
Why use informatics tools?
HTS (“Fail fast, fail cheap”–new mantra for R&D)
• need of decisions, more quickly
e.g. Library Design (can be done on virtual compds)
Need to do more than just screen molecules
• need of understanding SAR relationships
e.g. how to “alter” undesirable properties

5. Commit to
product
type
Commit to
target
Tractable
hit
Candidate
selection
FTIM PoC
Target to
lead
Gene-
function-
target
association
FTIM to PoC
Pre-clinical
/ Safety
Lead to
candidate
Target
family
selection
Disease
selection
Decision
points
Where in the discovery process informatics
methods could be used?

6. SEAC
Identify
Problem/disease
Isolate protein
Find comp
Which binds target
Animal testing/
Clinical Trials
How informatics can help in different steps for
candidate selections
Genomics / Proteomics / Bioinformatics
Assay development, HTS screening,
Analysis, Combinatorial chemistry /
Libraries, Virtual screening
Structural Biology
Xray structures,
molecular modelling
In-vitro and in-silico
ADMET models
PBPK modelling
(Exposure / Population
differences)

7. SEAC
Genomics / Bioinformatics
● Genomics involves determining the entire DNA
sequence of organisms and fine-scale genetic mapping
efforts.
● Bioinformatics entails the creation and advancement of
databases, algorithms, computational and statistical
techniques.
– Sequence analysis
– Genome annotation
– Computational biology
– Analysis of gene expression / regulation
– Comparative genomics
– Prediction of protein structures

8. SEAC
Known Structures (similar sequece to
target) : MTIKEMPQPKTFGELKNLPL……
Unknown Structure (target) :
MGLEALVPLAVIVAIFLLLV……..
Copy Conserved Region :
Add loops and calculate structure of
non- conserved parts
Structural Biology (e.g. Homology Modelling)

9. SEAC
Structural Biology (in 21st
Century)
21st Century:
● We now have
access to more
structures.
● And also
computational
methods are
becoming better
and more
intelligent

10. SEAC
High throughput screening
● Assumptions
– If we screen large no. of compounds, we will find right
chemicals
– In-vitro data is good measure of reality
• We understand biology enough that hitting a given
target will have desired effect on the disease.
● 20th
Century
– Far too many hits
– False +ve rate due to expt errors / purity of sample
– Bad ADMET profile (safety needs to be considered)
● 21st
Century
– Include safety in selection / screening.
– Understanding of ADR.
– Need to have smart screening instead of blind screening
• Use of informatics and QSAR models
– Use of diverse library of compounds (diverse set)

11. SEAC
Combinatorial Chemistry : Chemical reactions
in plate (Use of informatics approaches)
R3
R1
R2
R3
O
R1
R2
● Better design using ADMET / Safety
considerations (coming later)

12. SEAC
Beside activity, it needs to be able to reach target, maintains its conc.,
doesn’t reaches toxicity levels & have no side effects
Balance of activity with safety (ADMET)
Good Potency towards desired TARGET
ABSORPTION (Gut-Blood)
DISTRIBUTION (Blood-Tissues)
METABOLISM (Enzymes)
EXCRETION (Urine, Bile, Faeces)
TOXICITY (Complex)
These issues
are important
for all
industries

13. SEAC
Plasma Drug Concentrations Following Oral Dosing
1
10
100
1000
10000
0 4 8 12 16 20 24
Time (hours)
Toxicity
Activity
Need to be Safe, and also effective concentrations
needs to be maintained in circulations
Depending on target/needs (e.g. in
food or personal care Industries
we may not like to have any
plasma levels.

14. SEAC
Therefore lots of efforts are going into in-silico
modelling in ADMET area
Reasons for termination of development of New
Chemical Entities by 7 UK based companies
0
5
10
15
20
25
30
35
40
C
linicalSafetyToxicology
PK/Bioavailability
EfficacyForm
ulationC
om
m
ercial
C
ostofG
oods
O
ther
1991
2001
The continuing
High Safety Failure, about 30%
(Clinical Safety & Toxicology)
40% PK/Efficacy failure?

15. SEAC
QSAR
Experimental Data (E)
Description of
Molecules (P1,P2...)
Statistical method Model e.g E=f(P1,P2...)
Validated
Released for use
Refined based
on new data

16. SEAC
O
OH
N
H
N2H
O
O
OH
N
H
N
H
OH
OH
OH
propranololsalbutamol atenolol
LogP 0.11 -0.11 2.75
PSA 80 93 43
logD@pH7.4 -1.79 -2.21 0.59
Common Molecular Features
Property Salbutamol Atenolol Propranolol
Clearance route renal/hepatic renal hepatic
Vd (lkg-1) 3.4 0.7 3
Protein binding ~10% ~5% ~90%
CNS penetration low low high
Different Properties
Descriptors: Relate Structure to Properties which can reflect expt data
QSAR

17. SEAC
Different Methods for Predictive Chemistry
SAR / alerts
●
Simplest approach
●
Only works on +ves
QSAR
●
Work equally on +ve & -ves
●
Can be a black box
Read across / kNN
Prediction based on analogues from same chemical class with experimental data
●
Can work on +ve & -ves
●
How to define “SIMILARITY”

18. SEAC
What is available currently ?
Enzyme
Inhibition
1A2, 2D6, 2C9,
2C19, 3A4
Metabolic
(P450
Mediated)
Biliary
Systemic Exposure Bioavailability
First Pass Met AbsorptionDistribution Clearance
PPB Vol Tissue
(e.g CNS)
Renal Hepatic
Gut Stability
Solubility
Permeation
Drug-drug
interactions
Enzyme
Induction
Pgp (Transporters)
PXR (induction)
hERG (Tox)
Genetic Tox
hepatoTox

19. SEAC
It has now been possible to suggest changes for
desired ADMET property
•Predicted as:
• Pgp non-substrate
• high brain penetration
N
O
F
N
H
F
compA
New Suggestion
•Pgp non-substrate
•Low brain penetration
BB ratio of < 0.05:1 BB ratio of 1.8:1

20. SEAC
QSAR / Read-across in 21st
Century
● Data availability and integration
● Role of integrated approaches
● Validation sets / models applicability domain
● Move away from black box methods
● Building on gaps in Models

21. SEAC
QSARs
Data and
Text Mining
Structure
Alerts
Bioinformatics
Tools
Safety Risk
Assessments
Metabolites
In-vitro
Assays
ADMET
Profile
Physchem
Properties
Hazard
Identification
Hazard
Characterisation
QSAR / Read-across in 21st
Century
Tox
Pathways
Exposure
PKPD
Modelling

22. SEAC
0
10
20
30
40
50
60
70
80
90
100
3Q01 4Q01 1Q02 2Q02 3Q02
% cpds with poor AUC median AUC/20
0
10
20
30
40
50
60
70
80
90
2Q01 3Q01 4Q01 1Q02 2Q02 3Q02 4Q02 1Q03 2Q03
Time
%tested
low IC50
medium IC50
high IC50
Project1 (oral PK)
Time
Time
Project2 (CYP2C9)
Project3 (AUC)
0
5
10
15
20
25
30
35
40
45
Mar01–Jul01 Aug01–Nov01 Dec01-Jan02 Feb02–Mar02
Date
Average AUC (rat po)
% of Cmpds. with AUC=0
0
0.2
0.4
0.6
0.8
1
1-2Q03 2-3Q03 3-4Q03 4Q03-1Q04 1Q04-2Q04
L
M
H
Project4 (CNS)
Time
Time
H
L
M
Application of informatics in 20th
Century:
1D approach

23. SEAC
Could also highlight the potential
problems at very early stage
Multi-optimisation (21st
Century)
Solubility
Absorption
Metabolic
stability
Potency
SafetyX
Lead
XDrug
Property 1
Property2 Skin
penetration
Reactivity
Peptide
Depletion
SafetyDesired
Effect
A possible scenario
in case of consumer
products

24. SEAC
Absorption
Solubility
Metabolic
stability
Potency
Safety
X
X
Lead
Drug
Property 1
Property2
Assessing the path of Lead Optimisation

25. SEAC
AUC
T1/2
CYP
Plasma Binding
Potency
Profile plot shows that the
compounds with the highest scores
have good properties for multiple
endpoints
Ranking using Multi-optimisation

26. SEAC
Ability to visualize multiple databases

27. SEAC
Need for all steps to come together (21st
Century)
Identify
Problem/disease
Isolate protein
Find comp
Which binds target
Safety Risk
Assessments
Genomics / Proteomics / Bioinformatics
Assay development, HTS screening,
Analysis, Combinatorial chemistry /
Libraries, Virtual screening
Structural Biology
Xray structures,
molecular modelling
In-vitro and in-silico
ADMET models
PBPK modelling
(Exposure / Population
differences)

28. SEAC
Linking biology with chemistry

29. SEAC
Exposure
Internal Real Dose
Biologically Effective Dose
Early Biological Effects
Metabolites
(Altered Structures)
Clinical Disease
Route / Bioavailability
PPB / Transporters
Exposure-Dose Response Paradigm

30. SEAC
Use of PBPK models
78k
77k 83c79k
76k
83c
82m
82m
81m
81m
80r
75k
75k
83c
RESPONSEAPPLIED DOSE
BBDR MODELPBPK MODEL
Chemical Disposition
(bodies effect on the chemical)
Information to Develop the PBPK Model
• Target site (s) (organ, tissue, cell).
• Chemical specific ADME rates.
• Species specific parameter values (tissue
volumes, blood flow rates.
• Which internal dose metric to use (based on
mode of action).
0.1
1
Biological Response
(chemical’s effect on the body)
Information to Develop BBDR Model
• Target site.
• Adverse effect (what constitutes a significant
deviation from normal).
• Mode of Action (i.e., key events leading to an
effect).
• Best measure of effect (s).
INTERNAL DOSE AT TARGET
(e.g., TISSUE, ORGAN)
0.1
1
Slide adopted from Kenyon et al, EPA

31. SEAC
Me
S
O
S
N
O
Important structural features
Chemistry
Structural Biology
Linking biology/chemistry with other data
•Auruus
•WOMBAT
•GVKBio
•DrugEBIlity (soon to be public)
Tox end Pt
QSAR
Target specific
QSAR
X-ray/NMR Homology
Information
Biology Assays
Activity, e.g, pos/neg
Text/Data Mining
Exposure

32. SEAC
• Good/bad Chemical Features
• Mechanism / mode of action
• QSAR predictions
How Chemical is bound to Tox target Pathway Analysis
Chemical / Biological similarity
Linking biology/chemistry with other data

33. SEAC
GENET
GENOM
PROTEOM
BIOINFORMAT
MEDINFORMAT
CHEMOGENOM
CHEMOINFORMAT
PROTEOCHEMOMETR-
“-ics” – an old Latin suffix that means “way too much / organised knowledge”
-ICS
One of the challenges in 21st
Century is how we convert this information rich –ICS technologies,
to knowledge
Question Answer
Process
Information
Methods
In-slico
In-vitro
Expert Opinon
Knowledge
Information Rich “-ICS” approaches

34. SEAC
Need for Intelligent Information Harvesting
Integrated
Information

35. SEAC
Safety
Integrated approach

36. SEAC
in-vivo Decides
in-vitro Guides
in-silico Designs
ADMET in 21st Century (where would like to be)
20th
Century
Decides
21st
Century

37. SEAC
● “Welcome in-silicoids to
the ‘real world, real time
zone’; get this right and
do it now, and we’ll make
you the President.”
And Finally - our challenge
(Dennis Smith (Pfizer), DDT, 7, 2002, 1080-1081)
● “Hello…. I am from Insilico, take me to your
President”

38. SEAC
Acknowledgements
“Part of Unilever’s ongoing effort to develop novel ways of
delivering consumer safety”
● Andy White
● Andrew Garrow
● Michael Hughes
● Yeyejide Adeleye
● Matt Dent
● Paul Carmichael
● Jin Li
● Carl Westmoreland
● And other members of Unilever, SEAC