ChemBioNet: Chemical Biology supported by Networks of Chemists and Biologists. Affinity Proteomics Meeting Alpbach. Michael Lisurek 15.3.
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1 ChemBioet: Chemical Biology supported by etworks of Chemists and Biologists Affinity Proteomics Meeting Alpbach Michael Lisurek
2 - Introduction - Equipment Screening Unit - Screening Compound Collection - Screening Project
3 Status of Usage of HTS Pharmaceutical Companies: - Standard Tool for Drug Development since Decades! Academic Institutions: - Lack of Available Compound Libraries - Lack of Automation and Know-How - Lack of Supporting Chemical Synthesis Resources - Lack of Support by Computational Drug Design
4 Medicinal Chemistry/ Molecular Modeling Automated Screening Unique Technology nline Database Screening Unit Compound Library Mobility Shift on Chips HT Cellular Imaging
5 ChemBioet: Academic Screening etwork Chemistry Compounds meets Biology Assays & Targets Bioprofiles Screening Units Compound Libraries & Technological Know-How for HTS Molecular Probes Combined Database in silico Methods
6 Equipment Screening Unit Supporting Academic Projects
7 Liquid Handling Robot for Library Transfer 384 Well Dispenser 384 Well Washer MTP Speed-Vac
8 Automated Mobility Shift Screening (LabChip-250) Caliper Life Sciences Leibniz-Institut für Molekulare Pharmakologie Boston - Berlin
9 Automated Mobility Shift Screening (LabChip-250) Caliper Life Sciences, Screening Technology
10 Reader-Equipment Safire 2 Genios Pro - Absorption Scans (Continuous Wavelength Range) - Fluorescence Intensity - Luminescence - Fluorescence Polarization - Luminescence and Chemiluminescence - Luciferase Assays - Realtime Kinetics
11 Automated Microscope : Automated Cell Culture and Sample Preparation Automated Image Analysis Software: - Drugs affecting Proliferation of Tumor Cells (Microtubuli) - euroregeneration (Differentiation of euronal Cells) - Translocations (Inflammation, Leukemia)
12 Automated Image Recognition: Inflammation Inactive Leibniz-Institut für Molekulare Pharmakologie Active Picture- Documents Multiparameter- Spreadsheets Cell nucleus: Cytoplasm:
13 Screening Compound Collection Strategy for the Assembly of a Screening Compound Collection
14 Assembly of a Screening Compound Collection Enriched with Bioactive Scaffolds > Lowest Estimate of Stable Drug Candidates: Million Tests / Day Years ~ (< 1 %) Rational Pre-selection of Bioactive & Chemically Diverse Compounds for ChemBioet
15 Assembly of a Screening Compound Collection Enriched with Bioactive Scaffolds > Lowest Estimate of Stable Drug Candidates: Million Tests / Day Years ~ (< 1 %) Rational Pre-selection of Bioactive & Chemically Diverse Compounds for ChemBioet
16 Strategy for the Assembly of a Screening Compound Collection 1 st Step: Removing reactive, unstable and irrelevant Compounds S Hal 2 nd Step: Search for Compounds containing bioactive Substructures, derived from the WDI Hal S S Hal H C C 3 rd Step: Selection of a calculated Diverse Subset C CH 2 I Se 4 th Step: Lipinskis Rule of 5 Self-made Filter / Sybyl (Tripos)
17 1 st Step: Removing reactive, unstable and irrelevant Compounds S 2 nd Step: Search for Compounds containing bioactive Substructures, derived from the WDI S 3 rd Step: Selection of a calculated Diverse Subset 4 th Step: Lipinskis Rule of 5 Self-made Filter / Sybyl (Tripos)
18 1 st Step: Removing reactive, unstable and irrelevant Compounds Br Br Br 2 nd Step: Search for Compounds containing bioactive Substructures, derived from the WDI S H F Br S H 3 rd Step: Selection of a calculated Diverse Subset H H H 4 th Step: Lipinskis Rule of 5
19 1 st Step: Removing reactive, unstable and irrelevant Compounds Br Br Br 2 nd Step: Search for Compounds containing bioactive Substructures, derived from the WDI S H F Br S H 3 rd Step: Selection of a calculated Diverse Subset H H H 4 th Step: Lipinskis Rule of 5
20 1 st Step: Removing reactive, unstable and irrelevant Compounds Br Br Br 2 nd Step: Search for Compounds containing bioactive Substructures, derived from the WDI S H F Br S H 3 rd Step: Selection of a calculated Diverse Subset H H H 4 th Step: Lipinskis Rule of 5 Diverse Subset Tool / ME (CCG) MW, H ACC, H D,,, RT, Substructures, logp, logs, logd
21 1 st Step: Removing reactive, unstable and irrelevant Compounds 2 nd Step: Search for Compounds containing bioactive Substructures, derived from the WDI 3 rd Step: Selection of a calculated Diverse Subset 4 th Step: Lipinskis Rule of 5 C. Lipinski et al. (1997) Adv. Drug Del. Rev. 23, 3-25 H-Bond Donors < 5 H-Bond Acceptors <10 LogP < 5 MW < 500 (< 800) Compute Descriptors / ME (CCG)
22 Where are the Bioactive Substructures derived from? World Drug Index (WDI): Database of Biologically Active Small Molecules Search for Maximum Common Substructures: ClassPharmer (BioReason)
23 Where are the Bioactive Substructures derived from? World Drug Index (WDI): Database of Biologically Active Small Molecules Search for Maximum Common Substructures: ClassPharmer (BioReason) CH CH 2 S CH
24 Where are the Bioactive Substructures derived from? World Drug Index (WDI): Database of Biologically Active Small Molecules Search for Maximum Common Substructures: ClassPharmer (BioReason) CH CH 2 S CH 573 Maximum Common Substructures
25 Screening Projects Recent Successful Example
26 M. tuberculosis Myo-Inositol-Phosphatase Antagonists Primary Screen: 100 µm Compound Substrate: Sorbitol-6-Phosphate Detection: P i -Malachitegreen-Mo Complex o Inhibition Blank ovel Antagonists with K i < 1 µm Phosphate Reference 500 µm 400 µm 300 µm 200 µm 100 µm 50 µm 0 µm
27 ovel Small Molecule Antagonist of M. tuberculosis Growth Inhibition in Medium A C* µm B Medium only Tb only Growth Inhibition in Macrophages CFU/ml C C Day
28 FMP: - Med. Chem.: J. Rademann R. Raz M. Schmidt S. Al-Gharabli - Drug Design: R. Kühne - MR: H. schkinat S. Hübel - Screening Unit: J.P. von Kries C. Erdmann S. Behnken V. Pütter M. Schade MDC: K. Hellmuth W. Birchmeier. Rötzschke K. Falk C. Scheidereit E. Wanker M. Lipp MPI, Dortmund: H. Prinz, A. ören H. Waldmann (*HWA) Structural Genomics Consortium EMBL-Hamburg: M. Wilmanns M. Weiss P. Tucker MPG-Hamburg: H. Bartunik MPI-IB, Berlin S. Kaufmann SGC M. Sundström S. Knapp University of xford
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