High Throughput Screening as a Research Tool · High Throughput Screening as a Research Tool...

High Throughput Screening as a Research Tool

Robert Damoiseaux. Ph.D., Scientific DirectorMolecular Shared Screening Resources, UCLA

Structure of this seminar

• Applications of High Throughput Screening

•The Drug Discovery Workflow – old and new

• Adaptive Diseases

• Case Study: Biofilms

• Case Study: Prostate Cancer

• Case Study: Cancer Stem Cells

High Throughput Screening as a Research Tool

High-Throughput Screening

Drug Discovery• Targeted Libraries• Drug-like Libraries• Diverse LibrariesFunctional Genomics

• siRNA Screens• Lentiviral Screens• cDNA screens

Chemical Genomics• FDA Approved Drugs• Bioactives• Natural Products

Materials Discovery• High-throughput Chemistry• High-throughput QC

Materials Characterization• Automated Toxicity Profiling• Profiling on Live Cells Using Functional/Chemical Genomics

Disease relevant process

Disease modifying target

Disease

High Throughput Screening Campaign

Target Centric Drug Discovery- pregenomic

Disease relevant process, pathway or phenotype

Disease

High Throughput Screening Campaign

Drug Discovery- postgenomic

Target Discovery

What changed?

• More targets are known than can be reasonably screened

• Not all players in a disease relevant pathway are known-or accessible for screening

• Novel technology such as High Content Screening (HCS)allows for black box screening approaches

•Target discovery for hits from screens has become morefeasible using Functional Genomics using HighThroughput Screening

Screening StrategyAssay Technology

Library Choice

Screen Validation and Execution

Data Mining,rescreeningand secondary screening

Novel Target

Further specificityprofiling

Lead discovery

Unknown Target

Novel Target(s)

Target Discovery

Small MoleculeProbe for Chemical

Genomics

Novel agonist or antagonist

Known Target

Compound rejected

General Screening Workflow

Taken from: “Molecular Screening” by R. Damoiseaux, Ph.D.in “Development of Therapeutic Agents Handbook”

Forthcoming from Wiley and Sons

Integrated

Using HTS as a Research Tool

Examples:

• Cancer: e.g. occurrence of resistance to chemotherapy

• Infectious diseases: occurrence of resistance to e.g. anti--virals, antibiotics etc.

Adaptive Diseases

- diseases which are able to adjust to the selective pressure exerted by the treatment with drugs.

Overcoming Adaptive Diseases

Examples:

• HIV: Development of HAART

• Cancer Therapy: Multi-drug therapy

Acute Disease ⇒ Chronic Disease

HTS as Research tool : Case Studies

• Modulation of Bacterial Biofilms by compounds and drugs

• Prostate Cancer

•Cancer Stem cells

• Biofilm: A macroscopic structure on a biological (e.g. mucous membrane) or inert surface consisting of bacteria and extracellularmatrix consisting of polyglycans.

• Biofilms are not easily permeated by antibiotics

• Biofilms are not easily accessible to the immune system

⇒ Biofilms are a bacterial resistance mechanism causing many problems in the medical area.

Biofilm assay examples

In a 384 well plate biofilm forming Haemophilus Influencae bacteria or media control were incubated over night, the resulting biofilm detected in a fully

automated fashion.

media control Biofilm0

1000000

2000000

3000000

4000000

5000000

6000000B

FU

Biofilm assay examples

0

150

300

450

Frequency

% B

iofil

m M

odul

atio

n

A set of about 2 k compounds was incubated with biofilm forming bacteria and the amount of biofilm production measured.

0

70

Frequency

% B

iofil

m M

odul

atio

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Biofilm assay examples

Bacteriocidal and biofilm stimulating

Bacteriocidal and not biofilm stimulating

Exploring the HER kinase‐androgen receptor interaction in prostate cancer

Using Chemical Genomics to explore the HER‐kinase axis in Prostate Cancer

In 2008, an estimated  186,320 new cases will occur in the US.Prostate cancer is the most frequently diagnosed cancer and the leading cause of cancer death in men with an estimated 28,660 deaths in 2008 alone.

American Cancer  Society

AD-PCAndrogen-dependent

prostate cancer

PSA

PSA

AI-PCAndrogen-independent

prostate cancer

PSA

Activation of growth promoting signaling pathways:

BCL2 antiapoptotic pathwayProtein kinase A Phosphatidylinositol 3‐kinaseRas/Raf/MAPKReceptor tyrosine kinases

Activation of growth promoting signaling pathways:

BCL2 antiapoptotic pathwayProtein kinase A Phosphatidylinositol 3‐kinaseRas/Raf/MAPK

Receptor tyrosine kinases• Activating HER2/HER3 dimerization supports prostate cancer progression. 

HRG

HER2

P

HER3

P

Cell deathCell death

HRG

HER2 HER3

No Cell deathNo Cell death

2C4

Cell membrane

HER1 HER2 HER3 HER4

A: Bar Graph

Sample Replicates

Rel

ativ

e lu

min

esce

nce

light

-out

put

0

50K

100K

150K200K

250K

300K

350K

400K

450K

500K

No treatment 2C4 HRG HRG+2C4

25nM HRG50nM HRG

0 5 10 15 20 25 30 35 40 45 50

HRGHRG+2C4

B: Scatter Plot Z-factor = 0.65

AI

AI

AD

Mechanism of HRG‐induced cell kill

What is the mechanism of HRG‐induced cell kill in LNCap cells?Why does it occur in the LNCap AD prostate cancer cell model and not in the isogenic AI models?

Starved HRG 2C4

Control 2C4

HRG2.5

2C4+HRG2.5

EGF4R1881

R1881+HRG2.5

Figure 3C: Representative cellular pictures with various treatments (Hoechst 33342-PI-stained cells).

Develop a highthroughput assay to effectively screen for compounds that rescue LNCap cells from HRG‐induced cell kill.

Prestwick Chemical Library1120 molecules  dissolved in DMSO90% are marketed drugs 

Biomol bioactive lipid library201 bioactive lipids

Biomol enzyme libraryKinase phosphatase inhibitors (80 known kinaseinhibitors  and 33 known phosphatase inhibitors)

384‐well highthroughput format1200 cells/well are plated in RPMI/10%serum.10μM compound is added the next day followed by 10nM HRG. 24hrs later viable cells are quantified using the ATPlite 1step, single addition luminescence ATP detection Assay system. Percent viability of cells is calculated with respect to DMSO treated cells.Normalized data is uploaded in the CDD database to screen for hits.

DMSOcontrolHRG

High viability Low viability

HRG: average viability: 25.5 %

Add EGFR inhibitor

Prestwick compounds Rescuing  Heregulin‐induced Prostate Cell Kill

Glucocorticoid steroid

C‐21 steroid hormone

Hormone precursor to aldosterone

Cardenolide

Corticosteroid

Topical corticosteroids

0

10

20

30

40

50

60

70

80

Control

2C4

HRG 2.5n

MHRG 25

nMHRG 2.

5+ 2C

4HRG 25

+ 2C4

EGF4EGF4+

2C4

R1881

R1881

+2C4

R1881

+HRG2.5

R1881

+ HRG25

Perc

ent P

I pos

itive

LN

CaP

cells

(P

erce

nt c

ellu

lar k

illin

g no

rmal

ized

to to

tal c

ell n

umbe

r)

AR amplifications and mutations

have been involved in AI prostate cancer.

HSPAR HSP AR

androgens

AR AR

AR ARnucleus

0

200

400

600

800

1000

1200

--------HRG(25)

+-+-+-+-2C4

++--++--HRG(2.5)++++----R1881(1)

Nor

mal

ized

Rel

ativ

e L

UC

Act

ivity

Z-factor = 0.2

PSA promoter TATA Luciferase

Develop a highthroughput luminescence assay to detect cell viability in prostate cancer cells and identify several compounds and categories of compounds namely steroids that prevent HRG‐induced cell kill.

Use compound screening to connect two pathways together, to better understand the biology of HER2/HER3 mediated cell kill in AD prostate cancer cells. 

HSPAR HSP AR

androgens

AR AR

AR ARnucleus

HRG

HER2

P

HER3

P

Cell deathCell death

HSPAR HSP AR

androgens

AR AR

AR ARnucleus

HRG

HER2

P

HER3

P

Cell deathCell death

Identify molecules involved in connecting the HER2/HER3 and AR pathway by using siRNAand overexpression screens.Create several options to successfully inhibit AR in advanced prostate cancer patients.

Jack Altura, Talar Kechichian CSMC

Robert  D. Damoiseaux MSSR UCLA

Barry A Bunin CDDKellan Gregory CDD

NIHProstate Cancer Foundation