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Chemoinformatics in Drug Design
Biological Sequence Analysis, June 8, 2010
Irene Kouskoumvekaki, Associate Professor, Computational Chemical Biology, CBS, DTU-Systems Biology
2 CBS, Department of Systems Biology
Computational Chemical Biology group
Irene Kouskoumvekaki
Associate Professor
Olivier Taboureau
Associate Professor
Sonny Kim Nielsen
PhD student Jens Eric Pontoppidan Larsen
PhD student
Honey Polur
MSc student
4 CBS, Department of Systems Biology
A small molecule drug ... is a (ligand) compound that binds to a biological
target (protein, enzyme, receptor, ...) and in this way either initiates a process (agonist) or inhibits the natural signal transmitters in binding (antagonist)
The structure/conformation of the ligand is complementary to the space defined by the protein’s active site
The binding is caused by favorable interactions between the ligand and the side chains of the amino acids in the active site. (electrostatic interactions, hydrogen bonds, hydrophobic contacts...)
5 CBS, Department of Systems Biology
Drug discovery process
Screening collection
HTS
Actives
103 actives 106 cmp.
6 CBS, Department of Systems Biology
Drug discovery process
Screening collection
HTS
Actives
103 actives 106 cmp.
High rate of false positives !!!
High throughput is not enough … to get high output…..
7 CBS, Department of Systems Biology
Drug discovery process
Screening collection
HTS
Actives
103 actives 106 cmp.
Follow-up Chemical structure Purity Mechanism Activity value
8 CBS, Department of Systems Biology
Drug discovery process
Screening collection
HTS
Actives
103 actives 106 cmp.
Follow-up
Hits
1-10 hits
9 CBS, Department of Systems Biology
Drug discovery process
Screening collection
HTS
Actives
103 actives 106 cmp.
Follow-up
Hits
1-10 hits
SAR
Analogues synthesis and tesiting
10 CBS, Department of Systems Biology
Drug discovery process
Screening collection
HTS
Actives
103 actives 106 cmp.
Follow-up
Hits
1-10 hits
Lead series
0-3 lead series
Hit-to-lead
11 CBS, Department of Systems Biology
Drug discovery process
Screening collection
HTS
Actives
103 actives 106 cmp.
Follow-up
Hits
1-10 hits
Lead series
0-3 lead series
Hit-to-lead
Drug candidate
0-1
Lead-to-drug
12 CBS, Department of Systems Biology
Drug discovery process
Screening collection
HTS
Actives
103 actives 106 cmp.
Follow-up
Hits
1-10 hits
Lead series
0-3 lead series
Hit-to-lead
Shift on time requirements
Drug candidate
0-1
Lead-to-drug
In vivo experiments
ADMET properties
Selectivity profile Safety
13 CBS, Department of Systems Biology
Failures
Clinical efficacy (53%)
Side effects and toxicity (35%)
Pharmacokinetics (4%)
Portfolio (4%) Other (4%)
Phase III failures 1992 – 2002
Schuster et al, Curr. Pharm. Des. 2005, 11, 3545-3559
15 CBS, Department of Systems Biology
Drug discovery
• Diverse set of molecules
HTS Virtual Screening • Computational methods to select
subsets based on prediction of drug-likeness, solubility, binding, pharmacokinetics, toxicity, side effects, ...
16 CBS, Department of Systems Biology
The Lipinski ‘rule of five’ for drug-likeness prediction Octanol-water partition coefficient (logP) ≤ 5 Molecular weight ≤ 500 # hydrogen bond acceptors (HBA) ≤ 10 # hydrogen bond donors (HBD) ≤ 5 If two or more of these rules are violated, the compound might
have problems with oral bioavailability. (Lipinski et al., Adv. Drug Delivery Rev., 23, 1997, 3.)
Rules have always exceptions.
(antibiotics, antibacterial and antimicrobials,…)
19 CBS, Department of Systems Biology
In silico Databases
Canonical SMILES:
C1=CC=C2C(=C1)C(=O)NS2(=O)=O -OEChem-06071012303D
17 18 0 0 0 0 0 0 0999 V2000
-1.6163 -0.8146 0.0000 S 0 0 0 0
-2.0135 -1.3702 1.2749 O 0 0 0 0
-2.0120 -1.3733 -1.2741 O 0 0 0 0
-0.5478 2.8682 0.0022 O 0 0 0 0
20 CBS, Department of Systems Biology
BioAssay: Bioactivity screens of chemical substances
Compound: Unique chemical structures
Substances: sample descriptions
Yesterdays count: 433,863 BioAssays 27,111,073 Compounds (R05: 18,917,923)
22 CBS, Department of Systems Biology
fingerprints
1D descriptors:
MW, number of features,…
2D descriptors:
Topological, physichochemical,
BCUT,…
2D/3D pharmacophores
Descriptors
23 CBS, Department of Systems Biology
Choosing the right descriptors can be tricky…
Wolfgang Sauer, SMI 2004
24 CBS, Department of Systems Biology
Virtual Screening
• Computational techniques for a rapid assessment of large libraries of chemical structures in order to guide the selection of likely drug candidates.
• Exploit knowledge of target(s) and/or active ligand(s) and/or target family.
Similarity-based /
Pharmacophore-based LIGAND INFORMATION
Docking PROTEIN STRUCTURE
26 CBS, Department of Systems Biology
Identification by VS of new Generation of Bacterial Biofilm Inhibitors > 60% of all infections are related to biofilm formation
Scanning Electron Micrograph of a Pseudomonas aeruginosa Biofilm found on
a Daily Wear Soft Contact Lens Dürig A., et al. Appl Microbiol Biotechnol, (2010) EPub
27 CBS, Department of Systems Biology
Green tea, Camellia sinensis
Has been used for over 5000 years
Green tea is known to have health enhancing qualities
28 CBS, Department of Systems Biology
Virtual Screening of Natural Compound Database for new anti-biofilm compounds
Fisetin inhibits biofilm formation of both S. aureus and Strep. dysgalactiae at ~ 10 fold lower concentrations than the 1st and 2nd generation queries
30 CBS, Department of Systems Biology
Discovery of selective PPAR-γ ligands
Kouskoumvekaki I. et al. , Petersen RK. et al. In preparation
31 CBS, Department of Systems Biology
Docking
• Explore the binding sites of new crystal structures
• Explore the effect of mutations on the binding affinity
• Search of new ligands
32 CBS, Department of Systems Biology
Docking analysis of identified PPAR-γ ligands
Kouskoumvekaki I. et al. , Petersen RK. et al. In preparation
34 CBS, Department of Systems Biology
Systems Chemical Biology
S. Berger and R. Iyengar, Bioinformatics, 2009, 25(19), 2466-72
35 CBS, Department of Systems Biology
Systems Chemical Biology
S. Berger and R. Iyengar, Bioinformatics, 2009, 25(19), 2466-72
• drug repurposing
• side effect, toxicity
• new druggable targets
• effective drug combinations
36 CBS, Department of Systems Biology
Systems Chemical Biology
S. Berger and R. Iyengar, Bioinformatics, 2009, 25(19), 2466-72
38 CBS, Department of Systems Biology
From Network Visualization to Biological Activity Prediction
Comp 1 Comp 2
Target
39 CBS, Department of Systems Biology
Chemical-Protein Network www.cbs.dtu.dk/services/Chem-Prot-1.0
Chemical libraries
600,000 chemicals annotated to 15,800 proteins
Inweb
(941629 ppi)
Small compound – protein – disease associations
Taboureau O. et al. , In preparation
40 CBS, Department of Systems Biology
Recent publications using Systems Chemical Biology Approaches
• Yildirim MA et al., Nature Biotechnology (2007), 25, 1119-1126 (Drug-Target-Disease Networks)
• Campillos M et al, Science (2008) 321, 263-266 (Side Effect similarity – Target Identification)
• Keiser MJ et al. Nature, (2009), 12(462), 175-81 (Drug-Target Networks)
• Hansen NT et al. Clinical Pharm & Therap. (2009), 86(2), 183-9 (PGx Gene Prediction)
• Adams JC et al. PLOS Comp Biol (2009), 5(8) (Drug-Metabolite Networks)
• Chen B et al. J Chem Inf Mod (2009), 49(9), 2044-55 (BioAssay Networks - Target Prediction)
• Iorio F et al., J Comput Biol (2009), 16(2), 241-251 (Gene Expression Profiling of Drug Action)
• Qu XA et al., BMC Bioinformatics (2009), 10(Suppl 5):S4 (Drug-Disease Networks)
• Schadt EE, et al, Nat Rev Drug Discov (2009), 8, 286-295 (Perspective on Drug-Disease Networks)
• Xie L et al., PLOS Comp Biol (2009), 5(5) (Drug-Target Networks – Side Effects)
• Yamanishi Y et al., Bioinformatics, (2010), 26, i246-254 (Drug-Target Predictions)
• Audouze K et al., PLOS Comp Biol (2010), 6(5) (Toxicogenomics Networks)
• Suthram S et al., PLOS Comp Biol (2010), 6(2) (Human Disease Network)
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