Predictive Models for Mechanism of Action Classification from Phenotypic Assay Data – Application to Phenotypic Drug Discovery
Presentation at SLAS 2014 conference in San Diego, 21 January 2014
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Predictive Models for Mechanism of Action Classification from Phenotypic Assay Data – Application to Phenotypic Drug Discovery
1. Predictive Models for Mechanism of Action Classification
from Phenotypic Assay Data – Application to Phenotypic
Drug Discovery
Ellen L. Berg, PhD
21 January 2014
SLAS 2014, San Diego, CA
2. • Problem:
- Drug discovery productivity is at an all time low
- We are swimming in oceans of data
• High throughput technologies
• New assay models and platforms
• Needed:
- New tools or new approaches
- Framework for integrating information
Extracting Meaning from Complex Data
3. The Challenge of Drug Discovery
Scale (meters)
molecules pathways cells tissues humans
10-9 M 10-8 M 10-7 M 10-6 M 10-5 M 10-4 M 10-3 M 10-2 M 10-1 M 1 M
Human exposureMolecular targets
3
• Human biology is complex
• Multiple modular, highly interconnected networks
4. Context is Key
• Target validation
- Biology has a modular architecture
- Function depends on “context”
• Target selectivity (poly-pharmacy)
- Most drugs interact with more than one target
4
5. BioMAP® Technology Platform
BioMAP®
Assay Systems
Reference
Profile Database
Predictive
Informatics Tools
Standardized human
primary cell disease models
Database of reference profiles Analysis and data mining tools
A Primary Human Cell and Co-Culture-Based Assay Platform for PDD
5
6. Human primary cell-based assays
Tissue & disease models
BioMAP® Systems – Key Features
6
• Primary human cell types
• Physiologically relevant “context”
- Complex activation settings
- Co-cultures
• Translational biomarker endpoints
7. Feature Mice Man
Lifespan 2 Years 70 Years
Size 60 g 60 kg
Environment
Animal facility, cage-
mates
Outside world, people,
animals, etc.
Why Human?
• Key differences between mouse and man:
- DNA repair mechanisms
- Control of blood flow, hemostasis
- Immune system status
7
8. • Two approaches:
- (1) Measure everything
• Whole genome mRNA, proteomics, metabolomics, etc.
- (2) Measure what is “decision-making”
• Translational biomarkers, known disease biomarkers, are
downstream of multiple pathways and integrate information
Why Translational Biomarkers?
mRNA,
epigenome
Phospho-sites,
intracellular proteins,
metabolome
Cell surface,
secreted molecules
8
9. BioMAP Profiling: Example Profile
Reference p38 MAPK Inhibitor
Logexpressionratio
(Drug/DMSOcontrol)
Control (no drug)
99%
significance
envelope
BioMAP Systems
Readout Parameters (Biomarkers)
Dose
Response
Cytotoxicity Readouts
9
BioMAP profiles retain shape over multiple concentrations
10. BioMAP Profiling: Example Profile
Reference p38 MAPK Inhibitor
Logexpressionratio
(Drug/DMSOcontrol)
10
Activities relevant to the role of p38 in monocyte / Th1-type inflammation
p38 kinase is important for Th1-dependent inflammatory responses
Takanami-Ohnishi Y, et al., Essential role of p38 mitogen-activated protein kinase
in contact hypersensitivity. J Biol Chem. 2002, 277:37896-903.
IL-8
HLA-DR
Monocyte
activation
IL-6IL-1aCD38
HLA-DR
TNF-a
11. BioMAP Profiling: Example Profile
Reference p38 MAPK Inhibitor
Logexpressionratio
(Drug/DMSOcontrol)
11
Activities relevant to anti-thrombotic effects of p38 inhibitors
Tissue factor is the primary cellular initiator of coagulation
p38α deficiency impairs thrombus formation
Sakurai K, et al. Role of p38 mitogen-activated protein kinase in thrombus
formation. J Recept Signal Transduct Res. 2004;24(4):283-96.
Tissue
Factor
12. BioMAP Profiling: Example Profile
Reference p38 MAPK Inhibitor
Logexpressionratio
(Drug/DMSOcontrol)
12
Activities relevant to side effects – clinical finding: skin rash
Upregulation of VCAM and ITAC are characteristic of skin hyperreactivity
Melikoglu M, et al., Characterization of the divergent wound-healing responses
occurring in the pathergy reaction and normal healthy volunteers. J Immunol.
2006, 177:6415-21.
ITAC
VCAM
MMP1
VCAM
13. Similarity Analysis of Profiles
• Highly correlated Similar
- Pearson’s correlation of r > 0.7
• Low correlation Not similar
- Pearson’s correlation of r < 0.7
13
18. • Can we use these signatures to build classifiers?
- Predictive models for specific mechanism classes would
enable automated mechanism assignments
• Certain mechanisms are known to be associated with
certain human outcomes (safety and/or toxicity)
- Automated mechanism assignment could be used as a tool
to help in compound assessment and prioritization
- Triage of phenotypic drug discovery hits
Consensus Profiles – Phenotypic Signatures
18
19. • Generate reference dataset
- Compounds from 28 mechanism classes
• Well characterized, target selective
- Test in 8 BioMAP systems
• Multiple concentrations
• Build a series of Two-class models using
support vector machines
- Use profiles in the “known” class versus a
“null” set
- Model output is “decision value”
Automated Mechanism Class Assignment -
Predictive Models
19
20. • Test reference data set in each model
- PPV – positive predictive value, for a given decision value
cut off, what fraction (percentage) of profiles are correctly
classified? (=TP/(TP+FP))
- Sensitivity – for a given decision value, what percentage of
profiles were assigned to the class? (=TP/(TP+FN))
Assessing Model Performance
Mitochondrial
Inhibitor
p38 MAPK
Inhibitor
Berg, Yang and Polokoff, 201320
21. Prediction Results – p38 MAPK
• Among Phase II chemicals (800) tested as part of the
EPA’s ToxCast program:
- The 2 named p38 MAPK inhibitors were both classified as
p38 MAPK inhibitors (highest decision values were 1.01
and 0.91)
- Manuscript submitted (Nicole Kleinstreuer, Keith Houck et
al)
• Final results will depend on disclosure of target
mechanisms for compounds donated by ToxCast
Pharma partners
22. • Screening and library characterization
• Triage of hits/actives from discovery programs
- Phenotypic drug discovery programs
• Elucidation of Adverse (Efficacy) Outcome Pathways
- Connecting initiating events (targets) with clinical outcomes
Applications
22
23. 2323
Source Library Type Conc. % Active % Cytotoxic
A Secreted proteins 10 mg/ml 18% 0%
B Peptides 1 mM 7% 0.2%
C Kinase 3 mM 25% 6%
D Diversity 3.3 mM 31% 6%
E Natural Product 5 mM 50% 11%
F Kinase 1.6 mM 66% 1%
Screening and Library Characterization
• Phenotypic assays higher hit rates
• Small molecule libraries and collections can be prioritized
24. Mechanism Classification for Triage of
Phenotypic Actives
Environmental BioactivesKinase Focused Collection
Mitochondria
Microtubule
cAMP Elevator
mTOR
Proteasome
AhR
EGFR
Other
Unclassified
Mit
Mic
cAM
mT
Pro
AhR
EGF
Oth
Unc
• ~50% of phenotypic actives from two collections could be
classified
• Mitochondrial and microtubule inhibitors are common
mechanisms in both sets of compounds24
25. Adverse Outcome Pathway Framework
MIE
Key
Event
Adverse
Outcome
Key
Event
Key
Event
Molecular
Initiating Event Clinical Effect
• Framework for integrating mode of action hypotheses to
outcomes for chemical risk assessment (OECD)
• Focused on the clinical outcome
27. An Adverse Outcome Pathway for Skin Rash
MIE
Key
Event
Adverse
Outcome
Inhibition of
p38 MAPK
Upregulation
of VCAM
Skin Rash
MIE
Inhibition of
MEK
Inflammatory
Cell
Recruitment?
Key
Event
Key
Event
JNK Pathway
Activation?
Molecular
Initiating Event Clinical Effect
HDF3CGF
In vitro
disease model
28. • Phenotypic data sets from primary human cell and co-
culture models can be used to classify mechanisms of
action
- Assays are sufficiently reproducible
- Mechanisms are distinguishable
• Applications
- Screening & Library characterization
- Triage of discovery program hits
- Outcome pathway knowledge
- Phenotypic drug discovery
Summary
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29. • Using biological systems to discover new drugs
- Target agnostic approach
• Neoclassic Drug Discovery
- The combination of using biologically complex model
systems & high throughput approaches (JAL & EB, 2013)
- Screening assays that are extraordinarily well characterized
• Tool compounds
• Omics and genetic technologies
- Integration of target-based and phenotypic drug discovery
• Please attend:
- Phenotypic Drug Discovery SIG, Wednesday 8:00 AM
Phenotypic Drug Discovery
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31. • BioSeek
- Mark A. Polokoff
- Alison O’Mahony
- Jian Yang
- Antal Berenyi
Acknowledgements
• EPA
- Keith Houck
- Nicole Kleinstreuer
31
32. BioSeek, A Division of DiscoveRx
310 Utah, Suite 100
South San Francisco, CA 94030
650-416-7600
Ellen L. Berg, PhD
eberg@bioseekinc.com
www.biomapsystems.com
CONTACTS
Editor's Notes
Depth of coverage in all target classes, with the largest menus of any provider in both GPCRs and Kinases unparalleled in the industry.Focus on expanding our pathway signaling portfolio and several additional technology platforms under development in R&D.