Sarigiannis biological connectivity in cra

1Environmental Health 2013 Boston, USA 3-6 March 2013

The connectivity paradigm to cumulative risk assessment

Denis A. SarigiannisSpyros KarakitsiosAlberto GottiEnvironmental Engineering Laboratory (EnvE-Lab)Department of Chemical Engineering, Aristotle University of Thessaloniki - 54124, Greece

Graziella Cimino-RealeNational Cancer Institute, Italy

GI tract – portal vein

Liver

Heart

Brain

Muscles

Skin

Kidneys

Adipose

Bones

Breast

Uterus - gonads

Lungs


Liver

Heart

Brain

Muscles

Skin

Kidneys

Adipose

Bones

Breast

Uterus - gonads

Lungs

metaboliteformation

Arterial blood

Venous blood

Arterial blood

Venous blood


What’s the exposome?

• The record of all exposures, both internal and external, an individual receives over his or her lifetime, from conception onward. These exposures range from chemicals in the environment to the body’s response to infection or psychological stress

(C. Wild, IARC, 2005)

• It is important to keep an unbiased (agnostic) stance to coupling chemical exposure to health status

S M Rappaport, M T Smith Science 2010;330:460-461

Is this enough?


Advancing exposome – The integrative approach

Aim:To draw the maximum benefit from the exposure information related to the currently evermore enhanced biomonitoring data

How?• advanced –omics technologies (A)• systems biology (B)• physiology-based biokinetic modeling (C)

cell organ organism

“Systems Biology” Approach

“Physiome” Approach


Liver

Heart

Brain

Muscles

Skin

Kidneys

Adipose

Bones

Breast

Uterus - gonads

Lungs


Liver

Heart

Brain

Muscles

Skin

Kidneys

Adipose

Bones

Breast

Uterus - gonads

Lungs

metaboliteformation

Arterial blood Venous blood Arterial blood Venous blood


Coupled EWAS-GWAS Methodology The HEALS paradigm


(A) Integrated Multi-layer computational Approach

Characterization of exposure factors

Aggregate and cumulative exposure models

Probabilistic exposure

Biomarkers of exposure effects

Dose-effect modelsBiologically effective dose-

early biological effects

Omics (expression profiles)

-Life styles-Polymorphisms

Individual responseBiomarkers of individual

susceptibility

Assessment of Risk FactorsMolecular dosimetryPopulation studies

Toxicological analysis

Individual profiles


Environmental exposure: in-/outdoor/personal


Biomarkers andSystems Toxicology

Models

Gene Identification

Validation by Quantitative PCR Statistical Evaluation

Tissues RNAMice, Rats, Humans

Whole Genome Discovery Systems (32.000 genes)

Experimental DesignEnvironment and Health

Signature of chemicals in productsImplementation of Risk Assessment

BIOINFORMATICS

Integrated approachwith

Proteomics/Metabonomics

Genes ModulationGenes Classification

Genes Pathway

(A) Expressomics for the Exposome


Cell surface receptomediated signaling

Electron transport

mRNA transcription

Protein biosynthesis

Protein metabolism

Signal transduction

0 50 100 150 200 250 300

10ug/l

100ng/l

10ng/l Mix A

0 50 100 150 200 250 300

Mix B

(A) Transcriptomics responses to chemical mixtures


0 20 40 60 80 100

Mix B

Cell proliferation and differentiation

Cytokine and chemokine mediated

signaling

Hematopoiesis

mRNA transcription

mRNA transcription regulation

Protein biosynthesis

Protein metabolism

Protein modification

0 20 40 60 80 10010ug/l100ng/l10ng/l Mix A

(A) Transcriptomics responses to chemical mixtures


(B) Metabolic profiling and systems biology integration

Exposome

GeneticsEpigenetics

Diet, behavior

Health outcomes



Liver

Heart

Brain

Muscles

Skin

Kidneys

Adipose

Bones

Breast

Uterus - gonads

Lungs


Liver

Heart

Brain

Muscles

Skin

Kidneys

Adipose

Bones

Breast

Uterus - gonads

Lungs

metaboliteformation

Arterial blood

Venous blood

Arterial blood

Venous blood

( ) lim Priji i j ij ij ij ij ij

dCV Q CA CV Metab E Absorp Bindingdt

PBPK models serve three main purposes:

- internal dose – Biologically Effective Dose (BED) assessment - for refined exposure characterization (I)

- the capability to derive an exposure conversion factor (ECF)/advanced exposure reconstruction for biomonitoring data assimilation (II)

- the capability to derive Biomonitoring Equivalents (BEs) - link to BED for direct comparison to legislative/toxicological thresholds (III)

Physiology Based PharmacoKinetic (PBPK) models are modeling tools that describe the mechanisms of absorption, distribution, metabolism and elimination (ADME) of chemicals in the body resulting from acute and/or chronic exposure regimes

(C) Internal dosimetry models


Coupling biokinetics and metabolism regulation


(C) (I) External and internal exposure intra-day variability

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14Benzene exposure (μg/m3)NNK exposure (ng/m3)Formaldehyde exposure (μg/m3)Benzene toxic metabolites (μg/L)NNK metabolites (ng/L)DPX (μM∙105)

Time (h)

Exte

rnal

exp

osur

e

Inte

rnal

exp

osur

e


(C) (II) Exposure reconstruction from HBM data

B*nPBTK model run with E*n input

B*3

B*2

Distribution of exposures consistent with HBM data

Optimization algorithm

Com

paris

on w

ith b

iom

arke

r dat

a

Small proportion of rejected model simulations

Biomarker data

Companion data(Exposure

related)

Exposure model INTERA TAGS ProTEGE

B*1PBTK model run with E*1 input

Potential exposure estimation

Poten

tial E

xpos

ures

(Sam

ples)

E1

E2

E3

En

Improved sampling

? ?


Bisphenol A - risk characterization based on ToxCast assay derived BPAD

Fetu

s

Prem

atur

e in

fant

s

New

born

(for

mul

a+bo

tt...

New

born

(bre

ast f

ed)

3 m

onth

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6 m

onth

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9 m

onth

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12 m

onth

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18 m

onth

s

Teen

-Adu

lts

0.0000

0.0004

0.0040

0.0400

0.4000

4.0000

40.0000

400.0000

4,000.0000Smokers

RC

R


Conclusions

• Advancing risk assessment from a “hazard based” to an “exposure based” process is made easier by exposomics

• Key for the development of the exposome is the exploitation of biomonitoring data, in combination to advanced PBPK models for relating exposure biomarkers to actual exposure scenarios

• Omics technologies hold a key role for understanding the intermediate pathways between exposure and disease, especially in the case of exposure to mixtures or latency

• Advanced computational tools are needed for understanding the interaction between environmental, exposure and biological responses.

• PBPK and systems biology modeling is the connecting layer for incorporating the above dynamics within a continuous frame

Sarigiannis biological connectivity in cra

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