Issues for metabolomics and

Issues for metabolomics and systems biology Douglas Kell School of Chemistry, University of Manchester, MANCHESTER M60 1QD, U.K. dbk@manchester.ac.uk http://dbkgroup.org/ http://www.mib.ac.uk www.mcisb.org

What I can’t do now and would like to

Some facts I ‘know’ (i.e. think I can remember…) • Epidemiologically, statins enhance longevity • Cholesterol is barely a risk factor when within the normal range of 120-240 mg% • Statins supposedly act (only) via HMG- CoA reductase to lower cholesterol • Actually many have (and from the above logically must have) off-target effects

More ‘facts’ • Although originating as natural products, many/most statins can bear comparatively little structural relationships to them or to each other • Are there QSAR-type relations between the various off-target effects and the drugs that cause them? lovastatin atorvastatin

The software tool I want would integrate all of those questions by: • Finding the facts from the literature (and the Web) by reading the articles ‘intelligently’ • Displaying and setting out the facts sensibly • Allowing the QSARs directly from the papers as the structures and substructures would be ‘known’ (or knowlable via PubChem, DrugBank etc) • Classify/cluster the off-target effects and the papers that described them (via TM and ML) • Without me having to write any actual code

Westerhoff & Palsson NBT 22, 1249-52 (2004) But despite everything science is in some ways becoming LESS effective in an applied context

Declining numbers of drug launches Leeson & Springthorpe, NRDD 6, 881-890 (2007)

Drug Discovery/Development Pipeline • Multifaceted, complicated, lengthy process gyy o llo g o yy co ac g y y et ffe t rrm m a ll llo g a o o Sa ll S a haa c a co P hy niic a c lii n ma ca iic a P y all fett cl a --c arrm inn iic a a fe clli --c c iin S a N e h e n S Prr P h e l HO OH e Cl & OH HO N Prr OH OH O P P O O C & O O N O- P O O N O H Products F OH O O NH2 OH N N NHCH3 N N O HN N N H 2N O C O 2H N N N S F O O 2S N N N N CF3 F Cl Cl Discovery Exploratory Development Exploratory Development Full Development Cl Discovery Full Development O O CH 3O O N H O NH2 Phase II Phase Phase II Phase II Phase III Phase III 0 5 10 15 Idea 12 -15 Years Drug Peter S. Dragovich, Pfizer

Attrition Kola & Landis, NRDD 3, 711-5 (2004)

Issues of attrition • PK/PD less of an issue in last decade • Now mostly due to (i) lack of efficacy, (ii) toxicity • Both problems are underpinned by the fact that drugs are typically first developed on the basis of molecular assays before being tested in the intact system • These failures turn drug discovery – if it was not already – into a problem of systems biology

Nature Rev Drug Disc 7, 205-220 (March 2008)

Poor correlation between different artificial membrane (Corti & PAMPA) assays Corti et al EJ Pharm Sci 7, 354-362 (2006)

Poor correlation between Caco-2 cells and artificial membrane (PAMPA) assays Note axis scales Balimane et al., AAPS J 8, e1-e13 (2006)

Poor relationship between PAMPA permeability and log Ko/w Corti et al. EJ Pharm Sci 7, 354-362 (2006)

Poor relationship between Caco-2 permeability and log Ko/w r2 = 0.097 THESE THEORIES OF DRUG UPTAKE WERE BIOPHYSICAL, ‘LIPID-ONLY’ THEORIES Corti et al. EJ Pharm Sci 7, 354-362 (2006)

Narcotics (‘general anaesthetics’) • Potency also correlates with log P (up to a cut- off) (Meyer & Overton) • Negligible structure-activity relationships • Was assumed that they also act by a ‘biophysical’ mechanism by partitioning ‘nonspecifically’ into membrane and e.g. ‘squeezing’ nerve channels • This too was a ‘lipid-only’ theory • None of this now stands up

Anaesthetic potency does largely correlate with partitioning into membrane, suggesting (to many) a ‘lipid-only’ mechanism P. Seeman, Pharmacol Rev 24, 583-655 (1972)

But…narcotics inhibit luciferase, a soluble protein, with the same potency with which they anaesthetise animals, over 5 logs! No lipid involved! Franks & Lieb, Nature 310, 599-601 (1984)

The structural basis is known Binding of bromoform to luciferase Franks et al, Biophys J 75, 2205-11 (1998)

Halothane affects narcosis in part via a TREK-1 K+ channel Heurteaux et al. EMBO J 23, 2684-95 (2004)

How to integrate all this information with biological and physiological networks? • One strategy is Integrative Systems Biology

One view of systems biology Experiment Theory Computation/ Technology Modelling

Bringing together metabolomics and systems biology models Drug Discovery Today 11, 1085-1092 (2006)

There is a convergence between systems biology models from whole-genome reconstruction and the number of experimental metabolome peaks (ca 3000 for human serum)

The human metabolic network (1) • 8 cellular compartments • 2,712 compartment-specific metabolites • ~ 1,500 different chemical entities • 1,496 genes • 2,233 metabolic reactions (1,795 unique) • 1,078 transport reactions (32.6%) PNAS 104, 1777-1782 (2007)

The human metabolic network (2) • Not yet compartmentalised • 2,823 reactions (incl 300 ‘orphans’), of which 2,215 have disease associations, plus 1189 transport reactions and 457 exchange reactions • 2,322 genes (1069 common with Palsson model) Molecular Systems Biology 3, 135 (2007)

Systems biology and modelling are all about representation

The main representation for systems biology models is SBML www.sbml.org

VISUALISE edit Literature mining create Layouts and views SBGN Store in dB BIOCHEMICAL Overlays, dynamics MODEL (assumed to Model merging: (not) Compare with LEGO blocks be in SBML) other models Cheminformatic THERE analyses ARE MANYRun, analyse THINGS THAT ONEfit to real POSSIBLE Compare with and MIGHT DO WITH THIS REPRESENTATION, AND and (sensitivities, etc) data (parameters variables) with constraints THESE ACTIONS CAN BE SEEN AS MODULES Integrate various levels Store results of manipulations How to deal with fitting, including as f(global LINK WORKFLOWS Network Motif parameters like pH) discovery Soaplab, Taverna, Automatic characterisation Web services, etc. Optimal DoE for of parameter space and Sys Identification, constraint checking incl identifiability

BIOCHEMICAL MODEL (assumed to be in SBML) FEBS J 274, 5576-5585 Compare with and fit to real data (parameters and variables) with constraints 4, 74-97

The Data Management Infrastructure of the Manchester Centre for Integrated Systems Biology Norman Paton University of Manchester

Capabilities • We require software to support: – Data capture: Pedro. – Data access: Pierre. – Integration of data and analyses: Taverna.

Pipeline Pilot workflow etc…

LITERATURE STORE NEW MINING ANNOTATE MODEL IN DB CREATE VISUALISE MODEL COMPARE WITH ‘REAL’ DATA METABOLIC MODEL IN SBML SCAN RUN BASE MODEL PARAMETER STORE SPACE MODEL IN DB SENSITIVITY ANALYSES COMPARE DIFFERENT MODELS METABOLIC MODEL IN SBML STORE DIFFERENCES AS NEW MODEL IN SYSTEMS BIOLOGY WORKFLOWS DB

Scientists Decoupled suppliers & consumers tion ora o llab C dge t wle en n o em K ag M an Science

‘Warehouse’ vs distributed workflows • Different ‘modules’ developed in different labs can reside on different computers anywhere, and expose themselves as Web Services • Labs can then specialise in what they are best at • All that is then needed is an environment for enacting bioinformatic workflows by coupling together these service- oriented architectures • One such is Taverna • This is arguably the best way to combine metabolomic SBML models with metabolomic data, and is what are using at MCISB

Overall Architecture Workflow Repository Data Analysis1 Integration Model Using Workflows Repository Analysisn Consistent Web Service Interfaces Repository1 … Repositoryn Experiment1 … Experimentn Consistent Web Interfaces

The Taverna API consumer along with libSBML allows many of these transformations to be performed Details: http://www.mcisb.org/software/taverna/libsbml/index.html

Relating Models to Expression Read gene names of enzymes from SBML model Query maxd transcriptome database using gene names Create new Compute colour SBMLmodel for expression readings

Visualise Models Using Cell Designer JC_C-0.07-1_Measurement JC_N-0.07-1_Measurement

Potential Solutions • Semantic annotation • Chemical and bio-text mining • RDF annotations – that can also be included within the SBML • Integrated reasoning engine • Allowing literature-based discovery • But we still lack a proper and useful (bio)chemical ontology integrating roles, pathways, diseases, chemical (sub)structures, targets, etc. • This last is probably the most damaging lack and thus most important need

Issues for metabolomics and systems biology Douglas Kell School of Chemistry, University of Manchester, MANCHESTER M60 1QD, U.K. dbk@manchester.ac.uk http://dbkgroup.org/ http://www.mib.ac.uk www.mcisb.org

Issues for metabolomics and

by Duncan Hull on Mar 06, 2008

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