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University of Pittsburgh Drug Discovery Institute

Fifth Annual Ri.MED Scientific Symposium

October 24, 2011

11/4/2011 1 Novel Chemistries and Systems Biology Power Discovery

D. Lansing Taylor, Ph.D. Director Professor of Computational and Systems Biology

The Role of Systems Biology in Drug Discovery

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Agenda

1. Introduction to the Challenge/Opportunity in Academic Drug Discovery

2. Overview of the UPDDI

3. Cellular Systems Biology Program (CSBP)

4. Platform for Protein-Protein Interactions

11/4/2011 Novel Chemistries and Systems Biology Power Discovery

2

Target identification and validation

Lead identification

and optimization

Lab and animal testing

Clinical trials

Traditional Steps in Drug Discovery and Development at Pharma: Why is it Not Working Well?

Novel Chemistries and Systems Biology Power Discovery 3

Pre-discovery

Discovery

Preclinical studies Humans

Up to 15 years and $ 1 billion High attrition rates

Very low success rates

Chemical libraries/Biologics


What is the State of the Drug Discovery Industry?

11/4/2011 Novel Chemistries and Systems Biology Power Discovery 4

In the Last 15 Years • Cost of discovering a drug up more than 270% • Number of NME’s approved down more than 50% • Major revenue generators (block busters) going off patent

How has the Pharmaceutical Industry Responded?

• Merging with other pharma’s to gain short-term pipeline • Decreasing costs by laying off tens of thousands of researchers • Stopping research and development (R&D) in some therapeutic areas • Shifting more R&D to China and India • Exploring Personalized Medicine-drug candidate with diagnostic • Developing more collaborations with academia

Some Thoughts On How To Be Successful in Academic Drug Discovery

1. Understand what other academic discovery programs are doing

2. Identify and then Integrate strengths across the Institution and key partners

3. Create a “marketing” program to educate industry on capabilities

4. Establish a pharmaceutical industry collaboration program (e.g.Italian Institute of Technology’s D3)

5. Engage industry involved in personalized medicine

6. Select some initial therapeutic focus areas for internal discovery


Some Existing Pharmaceutical Company Collaborations at the University of Pittsburgh


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Pharmaceutical Company Therapeutic Area

Johnson & Johnson Cancer

Janssen Biotech (Centocor) Asthma

Janssen Biotech (Centocor) Scleroderma

Janssen Biotech (Centocor) COPD

Novartis Alpha-1 antitrypsin deficiency

Hawthorn Pharmaceuticals Cancer

GE Healthcare Alzheimer's Disease

Abbott Necrotizing enterocolitis

Arno Therapeutics Cancer

Examples of Successful “Academic” Drugs: This Can Be Done!

• Remicade (Infleximab)-tumor necrosis factor α Jan Vilcek and Junming Le New York University-Centocor, Inc.

• NYU has received $650M in royalties

• Paclitaxel (Taxol)-MT stabilizer (mitotic inhibitor) Robert Holton, Chemist-total synthesis Florida State-Bristol Myers Squibb

• Florida State has received $350M in royalties


Example: Drug Discovered in Italy

Biphosphates-Osteoporosis

Giorio Staibano and Sergio Rosini

Instituto Gentili Research Laboratories in Pisa


Agenda






9

Drug Discovery in BST-3 at the University of Pittsburgh

University of Pittsburgh Biomedical Science Tower 3

4 Novel Chemistries and Systems Biology Power Discovery

1

1

Leadership of the UPDDI

Director Lans Taylor

Associate Director Chemistry Peter Wipf

Associate Director Med-Chemistry

Barry Gold

Associate Director Comp & Sys Biol

Ivet Bahar

Associate Director Cancer Institute Dept. Medicine

Edward, Chu, MD

Novel Chemistries and Systems Biology Power Discovery

Distributed Structure Of UPDDI


Technology Developments &

Corporate Collaborations &

Licensing

Drug Discovery & Development

Core

Screening Support for

Collaborative Development

Pre-Clinical Studies

Formulations & Delivery

Animal Tox & Pharmacokinetics

Animal Efficacy

Educational Programs

Focused Discovery and Development

Portfolio

In Vitro Safety &

Metabolism

Biologics

Chemistry &

Medicinal Chemistry

Focused Discovery &

Development Projects


We have the Capabilities:

Prominent Centers, Institutes & Departments

Initial Focus Areas

• Selected two therapeutic areas for initial focus where we have great strengths, while supporting any therapeutic area brought to us by faculty or industry

• Cancer

• Neurological Diseases

• Selected three technical areas for initial focus where we have unique strengths, while harnessing all technologies

• Novel Chemistries including Biologics

• Computational Chemistry (Includes Structural Biology)

• Computational Biology and Systems Biology


Novel Chemistries from Pitt: Two Examples from Multiple Faculty in A&S, SOM & SOPharm


Combinatorial Chemistry Center

Chemical Diversity Center

Peter Wipf, Ph. D. Distinguished University Professor of Chemistry

Phase 2 Clinical trials (Glioblasoma multiforme and Prostate) -PI3 Kinase

Edward Chu, M.D. Professor of Medicine Chief, Division of Hematology/Oncology Deputy Director of UPCI

Chinese Herbal Medicines

PHY906 -Combination with cytotoxic chemotherapy in metastatic colorectal cancer -Phase 1-2

http://ccc.chem.pitt.edu/UPCDC/index.htmlhttp://ccc.chem.pitt.edu/

Computational Chemistry/ Biology & Systems Biology


Ivet Bahar, Ph. D. Chair, Department of Computational and Systems Biology

Mapping of complete set of FDA approved drugs and their targets

Extracted from DrugBank (Sept 2010).

Predicting protein interaction dynamics

using elastic network models1

(1) Bahar et al (2010) Annual Rev Biophys 39, 23-42.; (2) Liu, Eyal & Bahar (2008) Bioinformatics 24, 1243-50.

http://www.ccbb.pitt.edu/Faculty/Faeder/index.html


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To build an Institute that applies novel and traditional technologies to optimize the discovery and development of new molecular entities (NME’s) through integrated activities across departments, institutes and commercial partners, while advancing the science and technology of drug discovery

Vision

Agenda






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-------- 25 Å ------

Bridge between Molecular and Cellular Dynamics and Physiology: Computational and Systems Biology

Lei Yang, Ph.D. Cecilia Lo, Ph.D

Neil Hukreide, Ph.D. Andreas Vogt, Ph.D. Gary Silverman, M.D., Ph.D.

David Perlmutter, M.D.

Bert Gough, Ph.D., Tim Lezon, Ph.D. Stephen Thorne, Ph.D.


Addressing the Biological Complexities

Center for Biologic Imaging

Heterogeneity of Response to Therapeutics in Cell Populations: Treatment of Tumors


Bert Gough, Ph.D.

Tim Lezon, Ph.D. Lans Taylor, Ph.D. & Jennifer Grandis, M.D., FACS Tobacco Grant Funding

Cancer Cell Lines TME Patient Samples

Heterogeneity of Drug Responses in Tumors and Pathway Modulations


Heterogeneity in a cell population

Extrinsic

Non-genetic clonal population

Genetic

Population noise Temporal noise

Intrinsic

Macroheterogeneity Microheterogeneity

Adapted from Huang (2009) Development 136: 3853

cell parameter 1

Cel

l co

un

t

Microheterogeneity

Macroheterogeneity

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Platform for Cellular Systems Biology


Cell Data Database

Store Database

Sample Database

Protocol Development

Sample Prep

Data Process

Data Mining/Visualization

Computational & Systems Biology

InCell 6000 Data Acquisition

6 -384 Well Plates

Server Room

Wireless

Classroom

Laptops (23)

Miscellaneous

Laptops (20+)

Workstation

VLAN

Internet

802.11g Wireless Access Points (4)

PITTnet

Network Printers, Copiers, Scanners

Desktops /

Workstations (90+)

Gig-E

Gig-EVMware ESXi hosts (3)

Promise Vtrak (11TB)

4Gbps FC optical

Mis

c S

erv

ers

Each Cluster has

login node

and dedicated Gig-E

private networkCluster 1

864 CPU cores

1388 GB ram

75 nodes on Infiniband

Cluster 2

46 CPU cores

124 GB ram

Cluster 3

124 CPU cores

132 GB ram

Cluster 4

40 CPU cores

40 GB ram

DMZ

VLAN


384 Well Plates

Single Well 4x 10,000 cells Higher Magnification

Multiple Biomarkers

High Content Analysis

High Throughput, Hyperplexed Imaging to Identify Heterogeneous Subpopulations


Cell 1

Cell 2

Cell N

Dose

Assay Plates Hyperplexed Images

TOR1 cAMP

PKA

RPP1A

UTH1

BMH1

HFD1

CMD1

cytoskeleton

ARP2/3

ARC15

FPR1

rapamycin

Calcineurin

Mitochondria

Nutrients

Nucleus

Profiles of Subpopulations

Pathway Identification

IL-6 STAT3 Pathway

MLKs

TYK2 JAK2

MKK1/2

Rac

GTP

P P P

P

BCL2

Anti-Apoptosis

Cell Growth, Survival,

Differentiation and

Oncogenesis

P

P

WAF1

Growth Arrest

and Progression

G1 to S

Cell Cycle

Progression

Gene

Expression

P

P

P

STAT3

STAT3 P

ISRE

P

MKKs

Raf1

ERK1/2

P Src

Pim1

p38 JNK

SOCS

STAT3

STAT3

c-Myc

CDC25A

STAT3

STAT3

P

P

P

P

P

STAT1

P

P

STAT1

STAT1

P P

Anti-tumor

Inflammatory

Response

IFNgR1

I

F

N

g

R

2

JAK2

STAT1

P

NF-κB

STAT1 Pathway

IKK

IkBs NF-κB

IkBs P

G

P

1

3

0

25

11 Cell Feature STAT 3 Heterogeneity & Pathway Analysis in Cal-33 Cancer Line (HNSCC)

Cell Features

Cell Number

Nuclear Size

DNA Content/ Cell Cycle

DNA Texture

Mitochondrial Membrane Potential

STAT3 Activation

STAT1 Activation

NFkB Activation

ERK

MT Stability

Apoptosis

Live

Rea

do

ut

1st

pan

el

2n

d p

anel



Heterogeneity in STAT3 and STAT1 Activation by IL-6 and IFNg

0.00E+00

5.00E+05

1.00E+06

1.50E+06

2.00E+06

2.50E+06

3.00E+06

0 50000 100000 150000 200000 250000 300000

tota

l Nu

cle

ar I

nte

nsi

ty

pSTAT3 (TotalCircAvgInten)

Unstimulated Cells - 30 min

0.00E+00

5.00E+05

1.00E+06

1.50E+06

2.00E+06

2.50E+06

3.00E+06

0 50000 100000 150000 200000 250000 300000

tota

l Nu

cle

ar I

nte

nsi

ty


Max Stim IL-6 - 30 min

0.00E+00

5.00E+05

1.00E+06

1.50E+06

2.00E+06

2.50E+06

3.00E+06

0 50000 100000 150000 200000 250000 300000

tota

l Nu

cle

ar I

nte

nsi

ty


Unstimulated Cells - 60 min

0.00E+00

5.00E+05

1.00E+06

1.50E+06

2.00E+06

2.50E+06

3.00E+06

0 50000 100000 150000 200000 250000 300000

tota

l Nu

cle

ar I

nte

nsi

ty


Max Stim IFN-g - 60 min

STAT3 Activation with IL-6 STAT1 Activation with IFNg

DN

A C

on

ten

t D

NA

Co

nte

nt

DN

A C

on

ten

t D

NA

Co

nte

nt

pSTAT3 Activity

pSTAT3 Activity

pSTAT1 Activity

pSTAT1 Activity

pSTAT3 active In some cells pSTAT1 Active

Apoptotic?

pSTAT3 Active - Proliferation

pSTAT1 Active

No Response

Statistical Networks are Calculated from Feature Data


Den

sity

Ener

gy

Probability density is used to determine an energy function

Multivariate distribution for data defines a

probability density Effective interactions

between features define statistical network that accounts for system’s

behavior

pH3.

Microtubule Stability (MS)

Nuclear Cond (NC)

Nuclear Area

DNA Content

var(pH3)

var(NC)

var(MS)

Clinical Impact of Understanding Heterogeneity: Personalized Medicine

11/4/2011


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Patient Sample

Genomic Tests

Proteomics Tests

Hyperplexed Pathology

Optimal Treatments &

Clinical Trails

Bioinformatics/ Systems Biology

Agenda






30

Computational Chemistry and Systems Biology Approach to Finding Inhibitors of P-P Interactions

11/4/2011


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David Koes

Alexander Dömling

Carlos Camacho

Protein-Protein Interactions as Targets

•PPIs are involved in all biochemical and cell biological events in cells •PPIs comprise a rather novel target class for pharmaceutical interventions •Orders of magnitude more PPIs are known than “druggable proteins” •The complete interaction map of proteins during the lifetime of an organism is called the “interactome” •Compounds interacting with PPIs are currently discovered by HTS •Jacoby (NOVARTIS) recently analyzed the HTS screening success of Large Pharma libraries for PPIs (

Goal: Enable Rational Design, Synthesis and Testing of Novel P-P Interactions

11/4/2011


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• Expand chemical space • Design suitable chemical libraries that can increase hit rates • Implement a truly interactive virtual screening technology • Utilize Cell-based biosensors for functional testing

• Short time between design, synthesis and testing of leads

• Synergy of chemists, biologists and experts on specific protein

interaction pairs

Key Points

• P-P Interactions often mediated by only a few key amino acids

“Hot Spots” or “Anchor” Key is how deeply buried!

• Anchor amino acid side chains might serve as a reasonable starting point for the design of antagonists of the P-P interaction

• Particular amino acid side chain as an initial anchor for

screening virtual libraries of low-molecular weight scaffolds

• Multicomponent reactions (MCR) allow assembly of many diverse and complex scaffolds


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35

Selection of Disease

Relevant P-P Interactions

from PDB

Select Optimal P-P Pairs Based on

Ligand Chemistry

Build Biosensor and Profile Early Safety Assessment

Design and synthesis of Compounds:

MCR

Validation: Crystallographic

SPR/FP Cell based

Pre-Clinical Efficacy &

Safety

Virtual screening:

MCR Biased to Anchor

I

II

III

IV

V

Steps to Identify Inhibitors of Protein-Protein Interactions: New Thinking

p53

p53

p53 HDM2

P P

HDM2

P

p53

p53 P

Xx

P

Therapeutic Target: Maintain Elevated p53 by Inhibiting p53-Mdm2 Interactions

Nutlin-3


p53-hDM2 Biosensor Design

p53

hdm2

NLS

NES/NLS TagRFP Nuclear – Cytoplasm Shuttling Component

Nuclear Anchored Component

Co-Express (Adenovirus)

Selective Disruption


Giuliano, Premkumar and Taylor, 2006

p53-hDM2 Biosensor: Nutlin-3 Control


Nutlin 3

M

1x10 -7

1x10 -5

Bio

sen

sor

Act

ivit

y

0.2

0.5

0.8

1.1

1.4

1.7 1.1 M

Nutlin-3 control

Plate 1 - Min/Max

-50

0

50

100

150

200

250

300

350

400

0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384

Min1 Max1 Mean +3sdev

-3sdev Mean +3sdev -3sdev

Plate 2 - Min/Max

-100

0

100

200

300

400

500

600

0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384

Min2 Max2 Mean +3sdev -3sdev

Mean +3sdev -3sdev

Z’=0.72 Z’=0.68

Validation plates showing consistency and reproducibility of the response of the adenovirus-delivered p53:hdm2 biosensor to Nutlin3 challenge

Quantitative Profiling

+ Nutlin 3


Cellular Systems Biology Profile Integrates Biosensor Activity with Pathway and Off-Target Effects


Three p53-Mdm2 Inhibitor Leads in Pre-Clinical Testing

11/4/2011


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Characteristic Pitt Leads

Nutlin Derivative

Amgen J&J Mich.

Ki

Novel Chemistries/ Computational Chemistry & Computational and Systems Biology


….

Computational Chemistry/Biology and Systems Biology

Target Molecules Cells Tissues/Organs Human Animals

Novel Therapeutic Molecules



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Grazie molto!

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