High Throughput Screening as a Research Tool. Robert Damoiseaux. Ph.D., Scientific Director Molecular Shared Screening Resources, UCLA

Transcription

1 High Throughput Screening as a Research Tool Robert Damoiseaux. Ph.D., Scientific Director Molecular Shared Screening Resources, UCLA

2 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

3 High Throughput Screening as a Research Tool Functional Genomics sirna Screens Lentiviral Screens cdna screens Drug Discovery Targeted Libraries Druglike Libraries Diverse Libraries HighThroughput Screening Materials Characterization Automated Toxicity Profiling Profiling on Live Cells Using Functional/Chemical Genomics Chemical Genomics FDA Approved Drugs Bioactives Natural Products Materials Discovery Highthroughput Chemistry Highthroughput QC

4 Target Centric Drug Discovery pregenomic Disease Disease relevant process Disease modifying target High Throughput Screening Campaign

5 Drug Discovery postgenomic Disease Disease relevant process, pathway or phenotype High Throughput Screening Campaign Target Discovery

6 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 more feasible using Functional Genomics using High Throughput Screening

7 General Screening Workflow Integrated Novel Target Screening Strategy Assay Technology Library Choice Taken from: Molecular Screening by R. Damoiseaux, Ph.D. in Development of Therapeutic Agents Handbook Forthcoming from Wiley and Sons Novel Target(s) Screen Validation and Execution Small Molecule Probe for Chemical Genomics Target Discovery Data Mining,rescreening and secondary screening Novel agonist or antagonist Unknown Target Further specificity profiling Known Target Compound rejected Lead discovery

8 Using HTS as a Research Tool Adaptive Diseases diseases which are able to adjust to the selective pressure exerted by the treatment with drugs. Examples: Cancer: e.g. occurrence of resistance to chemotherapy Infectious diseases: occurrence of resistance to e.g. anti virals, antibiotics etc.

9 Overcoming Adaptive Diseases Examples: HIV: Development of HAART Cancer Therapy: Multidrug therapy Acute Disease Chronic Disease

10 HTS as Research tool : Case Studies Biofilm: Modulation A macroscopic of Bacterial structure Biofilms by on compounds a biological and (e.g. drugs mucous membrane) or inert surface consisting of bacteria and extracellular matrix Prostate consisting Cancerof polyglycans. Cancer Biofilms Stem are not cellseasily 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.

11 Biofilm assay examples BFU media control Biofilm 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.

12 Biofilm assay examples % Biofilm Modulation % Biofilm Modulation 70 0 Frequency 0 Frequency A set of about 2 k compounds was incubated with biofilm forming bacteria and the amount of biofilm production measured.

13 Biofilm assay examples Bacteriocidal and biofilm stimulating Bacteriocidal and not biofilm stimulating

14 Exploring the HER kinase androgen receptor interaction in prostate cancer Using Chemical Genomics to explore the HER kinase axis in Prostate Cancer

15 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

16 ADPC Androgendependent prostate cancer PSA PSA AIPC Androgenindependent prostate cancer PSA

17 Activation of growth promoting signaling pathways: BCL2 antiapoptotic pathway Protein kinase A Phosphatidylinositol 3 kinase Ras/Raf/MAPK Receptor tyrosine kinases

18 Activation of growth promoting signaling pathways: BCL2 antiapoptotic pathway Protein kinase A Phosphatidylinositol 3 kinase Ras/Raf/MAPK Receptor tyrosine kinases Activating HER2/HER3 dimerization supports prostate cancer progression.

19 Cell membrane HER1 HER2 HER3 HER4 HRG 2C4 HRG HER2 HER3 HER2 HER3 P P Cell death No Cell death

20 Relative luminescence lightoutput 500K 450K 400K 350K 300K 250K 200K 150K 100K 50K A: Bar Graph 25nM HRG 50nM HRG 0 No treatment 2C4 HRG HRG+2C4 B: Scatter Plot Zfactor = 0.65 HRG HRG+2C Sample Replicates

21 AD AI AI

22 Mechanism of HRG induced cell kill Starved HRG 2C4 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?

23 Figure 3C: Representative cellular pictures with various treatments (Hoechst 33342PIstained cells). Control 2C4 R1881 EGF4 HRG2.5 2C4+HRG2.5 R1881+HRG2.5

24 Develop a highthroughput assay to effectively screen for compounds that rescue LNCap cells from HRG induced cell kill. Prestwick Chemical Library 1120 molecules dissolved in DMSO 90% are marketed drugs Biomol bioactive lipid library 201 bioactive lipids Biomol enzyme library Kinase phosphatase inhibitors (80 known kinase inhibitors and 33 known phosphatase inhibitors)

25 384 well highthroughput format 1200 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.

26 HRG High viability Low viability DMSO control

27 HRG: average viability: 25.5 %

28

29 Add EGFR inhibitor

30 Prestwick compounds Rescuing Heregulin induced Prostate Cell Kill Glucocorticoid steroid C 21 steroid hormone Hormone precursor to aldosterone Cardenolide Corticosteroid Topical corticosteroids

31 C4 R1881+ HRG25 R1881+HRG2.5 R1881 R1881+2C4 EGF4+2C4 EGF4 HRG 25+ 2C4 HRG C4 HRG 25nM HRG 2.5nM Control Percent PI positive LNCaP cells (Percent cellular killing normalized to total cell number)

32 AR amplifications and mutations have been involved in AI prostate cancer. androgens AR HSP HSP AR AR AR nucleus AR AR

33 Normalized Relative LUC Activity PSA promoter TATA Luciferase Zfactor = R1881(1) HRG(2.5) HRG(25) 2C

34 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.

35 HRG HER2 HER3 androgens P P AR HSP HSP AR AR AR Cell death nucleus AR AR

36 HRG HER2 HER3 androgens P P AR HSP HSP AR AR AR Cell death nucleus AR AR

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

38 Jack Altura, Talar Kechichian CSMC Robert D. Damoiseaux MSSR UCLA Barry A Bunin CDD Kellan Gregory CDD NIH Prostate Cancer Foundation