Computational Drug Design

Transcription

1 Computational Drug Design Univ.-Prof. Dr. Gerhard F. Ecker Pharmacoinformatics Research Group Department of Medicinal Chemistry, University of Vienna Althanstrasse 14, A-1090 Wien, Austria

2 Pharmacoinformatics Target identification Target validation Identification & obtention of compounds Preclinical assessment Clinical trials Postmarketing monitoring Computational genomics Virtual screening Structure-based drug design ADMET prediction Medical Informatics Computational systems biology Biosimulation Based on the molecular structure

3 Drug Discovery Process / Genes Compounds Million 12 Years Decreasing number of NCEs Safety problems (Withdrawals)

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6 Drug Development - Failures Kubinyi, Nature Rev Drug Discov 2, 665 (2003)

7 Target Identification Target: protein associated with a given disease drugable Target: can be used for drug therapy (opinion can change with knowledge kinases) in 2001 approx. 440 genes were targeted by around compounds; HUGO: genes

8 Genome Information overflow Function of single genes? Activation/deactivation of genes - chronopharmacology Proteomics - differences in expression profiles healthy/sick?

9 Chemogenomics H. Kubinyi, Vienna 2006

10 H. Kubinyi, Vienna 2006

11 Drug Discovery Serendipity Drugs from Nature Metabolism Side effects in clinical studies Mimicry Hit from HTS-screening in silico Screen

12 Serendipity Penicillin Antibiotics were one of the biggest steps for increasing life span Inhalation narkotics benzodiazepines

13 Drugs from Nature

14 Mimicry golden age in drug discovery Introduce chemical groups which mimic the active transition-state of the drug/enzyme complex classical examples are enzyme inhibitors O OH O OH * N H * * * * P X * X = -CH 2 -, -NH-, -O- Substrat O OH HO OH * N H * * H O als * OH HO OH Übergangszustand * X als * X X = -CF 3, -CF 2 -, -Aryl

15 Mimicry Pentostatin: enzymatic reaction of adenosin to inosin is blocked by pentostatin in picomolar concentrations N H 2 OH NH 2 HN N O H OH N N N N sugar Adenosin N N sugar Ü HN N Inosin N N sugar N HN N N sugar Pentostatin

16 High throughput screening HTS: high throughput screen compounds maximum diversity Automatic single point detection of biological activity Gives only hits, no leads Subsequently often synthesis and testing of focused Libraries

17 High throughput screen Up to compounds per day

18 Combinatorial Chemistry Automatic chemical synthesis allows generation of s of compounds within several months theoretisch up to possibilities Which compounds to synthesize? Pharmacoinformatics

19 In silico Screening Ligand-based screening Classification Pharmacophore modeling Target-based screening docking De novo design

20 Ligand-based Screening

21 Target-based Screening

22 Paradigm Shifts One drug one target One drug multiple targets (kinases)

23 Paradigm Shifts Many drugs one target (herg channel)

24 Paradigm Shifts Many drugs many targets (ABC-transporter)

25 ABC-Transporter and Cholestasis Pauli-Magnus, Hepatology 44, 778 (2006)

26 Virtual Screening

27 In silico Profiling

28 Pharmakophormodelle

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30 Classifier Performance

31 Target-based Design Structure-based drug design Rely on homology models Side chain rotamers Domain flexibility Ambiguous binding regions O Mara, FEBS Lett. 2007, 4217?

32 Experimental Data guided Docking and Scoring Homology Models Data from Literature Labeling Data 12 Medizinische Universität Wien. Werner Sieghart, Margot Ernst Docking Universität Wien Gerhard Ecker, Lars Richter Universität Bern. Erwin Sigel, Kelly Tan

33 Target-based Design meets QSAR N OH log(1/ec 50 ) calcd logp

34 Common scaffold based Docking Q725 Q725 Q990 F303 Q990 F303

35 The Systems Biology View ABC-transporter and Cytochromes are regulated by nuclear receptors ABC-transporter and CYPs show overlapping substrate specificity Selectivity has to be addressed on a systems level rather than on individual targets

36 A Network of Targets Ekins S, unpublished