Jeremy K. Nicholson, PhDProfessor and Head of Biological

ChemistryImperial College

University of London

STATISTICAL SPECTROSCOPY AND GLOBAL SYSTEMS BIOLOGY APPROACHES IN

DISEASE MODELING

NEDMDG SYMPOSIUMWorcester, Mass. June 8th,

2006

Summary• The quest for new drugs - ‘Top-down’ Systems Biology, gene-environment interactions and the Personalized Healthcare Paradigm.

• Generating and modeling system level metabolic data in experimental disease states.– Pharmaco-metabonomics and predictive models

– Biomarker recovery via statistical spectroscopy

•NMR, UPLC-MS, UPLC-MS/NMR, Proteo-metabonomics

• Characterizing genetic,dietary and microbial contributions to Human Metabolic Phenotypes: Molecular epidemiology and “The Health of Nations”.

Can Systems Biology save the Pharmaceutical

Industry?

High technology has generally not helped the rate of practical drug

discovery!

PHARMACEUTICAL PRODUCTIVITY DECLINE: REAL COST PER DRUG HAS INCREASED 10 FOLD IN 30 YEARS!THIS IS AN UNSUSTAINABLE BUSINESS MODEL!

ONLY 11% OF DRUGS IN CLINICALTRIALS MAKE IT TO MARKET!

Lipinski Rules: Compound Bioavailability is poor if…

> 5 H-bond donors

CLog P > 5

Sum {N + O} > 10

Mass > 500 DALTONS

23,710

3,051

HUMAN GENOME23,710

DRUGGABLE GENOME3,000

DISEASEMODIFYING

GENES3,000

DRUGTARGETS 1000

LIBRARY (NME)

PHARMACOLOGY/TOXICOLOGY

ADME/ANIMALS

Ph1 Ph2 Ph3

DISCOVERY DEVELOPMENT

MARKET

CLINICAL TRIALS

INVESTIGATIONAL NEW DRUG

FATALWithdrawal

from market

OBLIGATELead Selectionand Optimization

CRITICAL$$$ failure!

Product Rescue?

EARLYChemistry and in vitro screens

© Imperial College, 2006

PHARMACEUTICAL PIPELINE ATTRITION

Quasi-DarwinianSelectionPreclinical Efficacy

Models & Biomarkers

Preclinical Safety Biomarkers and Mechanisms

Screening Studies: Transcriptomic, Proteomic and

Metabolomic/Metabonomic

Clinical Efficacy Biomarkers

Clinical Safety Biomarkers

Pre

-Le

ad

Pri

orit

iza

tion Response Analysis: Genotyping,

Proteomics, Pharmaco-genomics.Pharmaco-metabonomics

Sys

tem

s B

iolo

gy A

pplic

atio

n

CLINICALDIAGNOSTICMARKERS

NEW DRUGTARGETS

Genomics ProteomicsMetabonomics

Microbiome Xeno-metabolome

Global Systems Biology

Personalized Healthcare

© Imperial College,2006

COMPREHENSIVE PHENOTYPE

Theranostics& patient

stratification

Optimized drugefficacy &

minimized toxicity

OptimizedNutrition

Multivariate Descriptions of Metabolism

•MetaboLome Definition: The quantitative analyisis or description of all low molecular weight metabolites in specified cellular, tissue or biofluid compartments. (Metabolomics: Numbers, chemical classes, structures, concentrations: < 1KDa)

•MetaboNome Definition: The sums, products & interactions of all the individual compartments/metabolomes (including extra-genomic sources) dispersed in a complex organism…The ‘Global’ System.

METABONOMICS“Quantitative measurement of multivariate

metabolic responses of multicellular systems to pathophysiological stimuli or

genetic modification”

(AIMS TO MODEL GLOBAL METABOLICREGULATION OF COMPLEX SYSTEMS INCLUDING DYNAMIC

INTERACTIONS & COMPARTMENTALIZATION OF COMPONENTS)METABOLOMICS (various definitions)

e.g. “measurement of metabolite concentrations& fluxes in cell systems”.

OR “measurement & modelling of all metabolites & pathways in a system”

Extra-cellular metabolite pool (biofluids)

External secretions /excretion

Cellular transcriptome

Cellular proteome

Intracellular metabolome

METABONOMICS OFCOMPLEX SYSTEMS

Multiple cell linesC1,C2…C8 etc.

Intervention

with

specific

target

C1 C2

C3

C4

C5C6

C7

C8

multiple

targets?

Extra-cellular metabolite poolTissue profilesExcretion signaturesMolecular compartmentsReaction profilemeasurement & modeling

BIOLOGICAL FLUID TYPES(primary secretory and connective roles)

Key Diagnostic Fluids: Plasma, Urine.

Specialized Functions: Cerebrospinal, thyroid. Saliva (sub-lingual, parotid, sub-maxillary), Gastric, Bile, Pancreatic.Amniotic, Follicular, Milk, Seminal Vesicle, Prostatic, Epidydimal, Seminal.

Pathological Fluids: Ascites, Cystic, Blister.

Artificial Fluids: Bronchiolar lavage fluid, peritoneal dialysates, hemodialysates, fecal water, rectal dialysates, cell extracts and cell supernatants.

PHYSICAL-BIOCHEMICAL FEATURESOF URINE AND PLASMA

URINE: Variable pH, ionic strength, osmolarity.

High dielectric constant. Extreme dynamic concentration range

(>1011). Thousands of molecules < 1KDa, (polarity?).

Metal complexes and supramolecular aggregates.

Many small proteins, high enzyme activities in pathological states-dynamically reactive matrix.

PLASMA: Relatively constant pH, ionic strength, osmolarity.

Lower bulk dielectric constant. High dynamic concentration range

(>105). Hundreds of of molecules < 1KDa and

>1KDa. Metal complexes and supramolecular

complexes. Multi-compartment -multi diffusional-

matrix Many large proteins and protein

complexes.

timeaveraged

snapshot

Mass Spectrometry

Linked chromatography/MS

TLC/MS

CE-MS

GC-MS

HPLC-MS

UPLC-MSn

LC-ICPMS-MS

NMR Spectroscopy

Single pulse 1H, 13C, 31P

2+ D methods COSY TOCSY HMQC HMBC etc

LC-NMR, CE-NMR, CEC-NMR

PFG Diffusion analysis DOSY etc…

HR-MAS (cells + tissues)PFG-MAS(cryoprobes/robotic FI etc)

Standard Analytical Information: Identity, Structure, Quantity (BOTH)

Physical Biochemical Information: Interactions, Compartments (NMR)

Analytical Approaches in Metabonomics and Metabolomics

Biofluids and extractsBiofluids, extracts, cells/tissues

Single quadTriple quad

TOF-MSQTOF-MS

Ion trapLinear ion trap

FTMS

LC- NMR -MSn

Many Ionization MethodsCHEMOMETRIC MODELLING(pattern recognition for

classification, diagnostics & biomarker analysis)

STATISTICAL SPECTROSCOPY(Linking multiple spectra & spectral types for structure elucidation/pathway analysis)

CHEMOMETRIC TOOLS FOR INFORMATION RECOVERY FROM

MULTIVARIATE DATA

UNSUPERVISED

• Principal Components Analysis (PCA)

• Hierarchichal Cluster Analysis (HCA)

• Logical blocking-PCA• Non-linear Mapping (NLM)

• Supergravity Association Mapping (SAM)….etc.

SUPERVISED

• Partial Least Squares (PLS) & PLS-DA

• O-PLS & O2-PLS• Soft Independent Modeling of Class Analogy (SIMCA)

• Rule Induction• Bayes Nets/Machine Learning

• Genetic Algorithms• Neural Networks• CLOUDS…etc…© Imperial College, 2006

900 MHz 1H NMR Spectrum of Untreated Human Urine

Contains LatentBiomarker information on:

GenotypePhysiological stateNutritional state

Gut microbes‘Biological’ Age

Presence of Disease

Translatable BiomarkersDiagnosticPrognosticToxicityEfficacy

A B C D E F

Secretory Metabolomes

HOST GENOME

Cellular transcriptomes

Cellular proteomes

1o Intracellular metabolomes

Extracellular metabolite pool

Primary andco-metabolome interactions

in mammalian systems(Nicholson et al Nature, Microbiology, 2005, 3, 2-8)

Humans: > 500 functionally distinct NORMAL cell types/ca.10 trillion

parenchymal cells

GUT ‘MICROBIOME’

Species transcriptomes

Species proteomes

Species metabolomes

Enteron

Co-metabolome enters via hepatic portal + mesenteric veins

1 2 3 4 5 6

microbial and dietary2o metabolites

Biliary secretions enterduodenum fromcommon bile duct

ENTEROHEPATICCIRCULATION

Humans: > 1000 Species.> 100 trillion cells

Microbial-mammalian

metabolic axis:

COMBINATORIAL METABOLISM!

COOH

OH

HO OH

COOH

H

HO

C

OH

HO OH

ON

COOHH

C

H

HO

ON

COOHH

C

OH

HO OH

ON

H

SO3H

C

H

HO

ON

H

SO3H

1. Biosynthesis: cholic acid

deoxycholic acid

glycocholic acid glycodeoxyocholic acid taurodeoxyocholic acidtaurocholic acid

COOH

OH

HO OH

4. Regeneration ofcholic acid

2. Phase II glycine ConjugationPhase II taurine Conjugation 7. Phase II glycine ConjugationPhase II taurine Conjugation

MAMMALIAN LIVER

MAMMALIAN GUT

3. Secretion into bile

amino acid deconjugationby gut microbiota

5. 7- 7 -dehydroxylation by gut microbiota

6. reabsorption into bloodvia hepatic portal system

enterohepaticcirculation

8. Secretion into bile

9. Deconjugation further reactions

MICROBIAL-MAMMALIAN CO-METABOLISM OF CHOLIC ACID

De-conjugated bile acids areless efficient at emulsifying fats

PHARMACO-METABONOMICS

A new paradigm for personalized predictive

drug metabolism and toxicology.

Definition: The prediction of the quantitative outcome of an intervention based on a pre-treatment metabolic model: Applications in drug metabolism, xenobiotic toxicity, drug efficacy…etc.

Age Hormonal status

Genetic Factors P450 Polymorphisms

SNP variations

Understanding drug interaction responses in relation to individual metabolic variation: Gene-environment interactions determine the pre-dose

starting phenotype.

Gut Microbiome

Nutrition

Individual (dot) location is theresultant of the influencevectors in m-space.

Are there locations that are more risk averseFor particular interventions?Prognostic biomarker clusters?

Clayton et al 440 (20)1073-1077, 2006)

Conditional Metabolic Phenotype

Host Genetic ConstitutionInterspecies variations

& individual SNP variations

Specific drug metabolizing enzyme complement

(CYP450) polymorphisms

Tissue-specific CYP450induction state

(e.g., in liver & gut)

Individual Gut Microbiomemicrobial species variation

& ACTIVITY

Nutritional status& dietary composition

Metabolic Fateand Toxicity

of Drug

Global System Interactions Affecting Drug Metabolism & Toxicity

Nicholson, JK et alNature, Biotechnology

22 (10) 1268-1274. (2004)

STATISTICAL SPECTROSCOPY

“The application of multivariate statistical methods to extract

latent structural or connectivity information in multiple spectral

data sets from samples or experiments collected serially or

in parallel.”

1. STATISTICAL SEARCH SPACE REDUCTION FOR BIOMARKER

IDENTIFICATION IN SERIAL UPLC-MS DATA SETS

Crockford et al Analytical Chemistry (2006), in press

PARTIAL LEAST SQUARES DISCRIMINANT ANALYSIS (PLS-DA) SORTS FEATURES ACCORDING TO IMPORTANCE

FOR CLASS SEPARATION.

SPECTRAL LOADINGS BACK-PROJECTED DIRECTLY TO LC-MS CHROMATOGRAM TO IDENTIFY RETENTION TIMES &

MASSES OF CANDIDATE BIOMARKERS.

356 features

O-PLS-DA STATISTICAL SEARCH SPACE REDUCTION

© IMPERIAL COLLEGE 2006

Hydrazine dosed (10) vs control (10):Statistically significant peaks (r > 0.6)Back-projected into UPLC-MS time domain

51 features

r > 0.8

O-PLS-DA STATISTICAL SEARCH SPACE REDUCTION

STRONG CANDIDATEBIOMARKERS

© Imperial College, 2006

•

STOCSY

RECONSTRUCTION OF LATENT BIOMARKER INFORMATION FROM LARGE SPECTROSCOPIC SETS BY STATISTICAL

TOTAL CORRELATION SPECTROSCOPY (STOCSY) Cloarec et al Analytical Chemistry, 77 (5) 1282-

1289, 2005.

2D STOCSY: Plot / correlation matrix for all samples, color code by r2. Gives both self-molecular correlations (assignment) and also pathway and compartment correlations.

Calculate correlation matrix (C) between all computer points(/) for all 1D spectra in all datasets to be compared:

X1 and X2 are the auto-scaled experimental matrices of n x v1 and n x v2

n = number of spectra in each classv1 and v2 = number of variables in each matrix (32K)

R-Selected 2D STOCSY (30 x 1D mouse urine spectra)Only self molecular correlations r2 > 0.9 plotted

SHYSTATISTICAL HETEROSPECTRSCOPY

Analytical Chemistry (2006) 78 363-371.

NMR Control Rat urine

Hydrazine treated Rat

Hydrazine treated Rat

UPLC-MS Control Rat urine

SequentialNMR & UPLC-MSspectra can beobtained on eachsample forstatistical integration

SHYParallel NMR& MS dataCollection

© Imperial College, 2006

Direct Pathway Connection Co-variance (Am/z- B)

Direct Structure Assignment Co-varianceX-Ym/z (parent)-Zm/z (fragment)

MSdata

NMRdata

Ym/z X

BAm/z

Zm/z

SHY CONNECTIONS IN PARALLELNMR-MS SPECTROSCOPIC SETS

•

MSdomain

m/z

NMR domain

Statistical HeterospectroscopY (SHY): Expansion- shows NMR to parent ion, fragment pattern & pathway correlates.

N-acetyl-lysine

Co

rrel

atio

n/a

nti

corr

elat

ion

co

effi

cien

ts

MOLECULAR EPIDEMIOLOGY

DATA DRIVEN TOP-DOWN SYSTEM METABOLIC MODELING

Can genetic, dietary, microbial, and environmental influences in large

scale population studies be deconvolved?

J. Stamler (PI)

P. Elliot (PI)

M. Daviglus

H. Kesteloot

H. Ueshima

B. Zhou

Q. Chan

M. D Iorio

E. Maibaum

S. Bruce

C. Teague

R.L. Loo

L. Smith

The INTERMAP study has been supported by Grant 5-RO1-HL50490-09, 5-RO1 HL65461-04 and 5 RO1 HL71950-02 from the US National Heart, Lung, and

Blood Institutes, National Institutes of Health, Bethesda, MD, USA; by the Chicago Health Research Foundation; and by national agencies in Japan, People’s Republic of

China and the United Kingdom.

INTERSALT Study was supported by the Council on Epidemiology and Prevention of the International

Society and Federation of Cardiology; World Health Organisation; International Society of Hypertension;

Wellcome Trust; National Heart, Lung, and Blood Institute, US; Heart Foundations of Canada, Great

Britain, Japan and the Netherlands; Chicago Health Research Foundation; Parastatal Insurance, Company,

Belgium; and by many national agencies supporting local studies.

Acknowledgements:

Examples from the INTERMAP and INTERSALT studies.

•

CHINESE

COMBINATION OF GENETIC ENVIRONMENTAL& NUTRITIONAL FACTORS© Imperial College, 2006

JAPANESE

FUNDAMENTAL METABOTYPE DIFFERENCESPCA-DA of population data (disease outliers removed)

AMERICAN

Concluding RemarksMetabonomics is a powerful top-down systems biology tool for investigating drug toxicity, disease processes, phenotypic variation & differential gene function in vivo.

NOVEL OUTPUTS:Metabolic biomarker information on system regulation

& failure.Deeper understanding of DISEASE MECHANISMS.Models that incorporate genetic & environmental

interactions.

Omics data must be considered in an extensive biological framework with robust statistical interrogation and integration to visualize system activity

Analyzing modulations of the MICROBE-MAMMAL-METABOLIC AXIS will be crucial for understanding genotype-phenotype interactions and variation in toxicity and efficacy of drugs in man.

Top down metabolic modeling is likely to prove to be a powerful tool in the pursuit of Personalized Healthcare Solutions and understanding the Health of Nations.

The Metabonomics Engine @ IC & Collaborators

Academic: Dr Elaine Holmes, Prof John C. Lindon, Dr H. Keun Dr T.Ebbels, Dr J. Bundy, Prof James Scott, Prof Tim Aitman, Prof Paul Elliot, Dr H. Tang, Dr G. Tranter, Dr S. Mitchell. (Imperial)Post Doctoral Group: Drs, O. Cloarec, M. Dumas, A. Craig, A. Maher, B. Beckwith-Hall, E. A. Clayton, R. Barton, J., Y. Wang, E. Meibaum, I. Douarte, S. Bruce. T. Tseng, C.Stella, M. Coen. J. Sidhu, E.Skiordi, M. Bollard, ………..etcGraduate Students: T. Athersuch, I. Yap, R. C. Bailey, C. Teague, D. Parker, A. Tregay. J. Pearce, J. Bowen, S. Lowdell, L.Smith, A. Cooray, N.Jones, G. McLaughlin, D. O’Connor, R.Liu, M.Ratalainen, K. Veselkov, F.P. Martin. …etcCollaborators: Dr Rob Plum, John Shockcor (WATERS), Prof Ian D. Wilson and Dr T.Orton (AZ ), Prof J. Everett, Drs M. Reily and D. Robertson, (Pfizer), Prof Jose Ordovas (Tufts University), Prof Burt Singer (Princeton University), Drs M. Spraul (Bruker), Dr Sunil Kochhar (Nestle), Frans van D’Ouderra,J. Powell, M. Faughan et al (Unilever).Dr D. Gaugier (Oxford University), Prof D.Withers (UCL).FUNDING: NIH, The Wellcome Trust, BBSRC, MRC, EPSRC, NERC, The Royal Society, Roche Foundation, Servier, Lilly, P&G, Pfizer, AstraZeneca, Nestle, Unilever, Novo Nordisk, Roche Foundation, BMS, Hi-Q, Metabometrix, METAGRAD, WATERS CORPORATION.