Human risk assessment Human risk assessment perspectives perspectives

for high risk conditionsfor high risk conditions

Jean Lou DorneJean Lou Dorne

Institute of Human NutritionInstitute of Human Nutrition

University of Southampton, UKUniversity of Southampton, UK

Resveratrol

Lycopene

Allyl sulphides (Allicin…)Isothiocyanates,Sulphorafane

Isoflavones Vitamine C, limonene

PARACELSUS (1493-1541)

“All things are toxic and there is nothing without poisonous qualities: it is only the dose which makes something a poison”

Pharmaco/Toxicokinetics

How the chemical is eliminated from the body or activated into a toxic species (ADME)

Pharmaco/Toxicodynamics

How the chemical exerts its pharmacological effect/ toxicityTarget receptor/cell/organ

RISK ASSESSMENT METHODS

LOW - DOSEEXTRAPOLATION

RISK ASSOCIATEDWITH THE KNOWN

INTAKE

QUANTITATIVERISK ASSESSMENT

NO THRESHOLD THRESHOLD

NOAEL ANDSAFETY FACTORS

INTAKE WITH NO APPRECIABLE

EFFECTS eg ADI

NON - QUANTITATIVERISK ASSESSMENT

ADI (mg/kg/day) = NOAEL(mg/kg) / 100

Derivation of the Acceptable Daily Intake Derivation of the Acceptable Daily Intake (ADI)(ADI)

KINETICS DYNAMICSKINETICS DYNAMICS

SPECIESDIFFERENCES

HUMANVARIABILITY

Extrapolation from group of test animals to average human and

from average humans to potentially sensitive sub-populations

10 10

The use of uncertainty or safety factors The use of uncertainty or safety factors (UFs)(UFs)

Chemical specific adjustment factors can replace the default uncertainty factors (WHO, 2001; IPCS, 2006)

100 - FOLD UNCERTAINTY FACTOR

INTER-SPECIES

DIFFERENCES

10 - FOLD

INTER-INDIVIDUAL

DIFFERENCES

10 - FOLD

TOXICO-DYNAMIC

10 0.4

2.5

TOXICO-KINETIC

10 0.6

4.0

TOXICO-DYNAMIC

10 0.5

3.2

TOXICO-KINETIC

10 0.5

3.2

Towards a more flexible frameworkTowards a more flexible framework

Data-derivedorPathway-relatedUncertainty factorsor

general default

Data-derivedorprocess relatedUncertainty factorsor general default

Interspecies differencesHuman variability

Toxicokinetics Toxicodynamics

UFs for main routes of metabolism in test species and humans –intermediate option between default factor and chemical specific adjustment factors

Adapted from Dorne and Renwick, 2005 Toxicol Sci 86, 20-26

Phase I enzymesCytochrome P-450, ADH, Esterases

% of Pharmaceuticals Metabolized by Individual Cytochrome P450’s in man

P4502D6 P4501A2

P4502A6

P4503A

P4502C9

P4502C19

P4502E1

Phase II enzymes Conjugation reactions

Glucuronidation

Sulphation

N-acetylation (Polymorphic)

Amino acid conjugation

Renal excretion CYP2C9, CYP2C19, CYP2D6* Polymorphic (Extensive and Poor metabolisers, EMs and PMs)

*Caucasian 8% PMs 92% EMs

Major Routes of chemical metabolism and Major Routes of chemical metabolism and excretionexcretion

Beta Blockers

BufuralolPropafenone

Metoprololpropranolol

Carvedilol

Antiarrhythmics

EncainideS-mexiletine

Analgesics

Dextromethorphan

CodeineTramadol

Antidepressants

FluoxetineParoxetine

AmitriptyllineDesipramine

ImipramineVenlafaxine

Pesticides

ChlorpyrifosDiazinon

Methoxychlor

Adapted from Dorne et al., 2002 FCT 41, 1633-1656

CYP2D6 SubstratesCYP2D6 Substrates

Antipsychotics

Risperidone Haloperidol

Introducing metabolic and toxicokinetic data into risk

assessment

AimsAims

Quantify human variability in kinetics for major metabolic routes

•Markers of chronic exposure (plasma Clearance)

•Markers of acute exposure (plasma peak concentration Cmax)

•Prefer the oral route (gut + liver): relevance to environmental

contaminants

•Comparison to the IV route (liver)

Identify susceptible subgroups of the population

Derive pathway-related uncertainty factors for each subgroup

MethodsMethods

Literature searches Medline, Toxline and EMBASE (1966-current)

•Compounds metabolised by single route (complete oral absorption, >60% of dose)

•In vitro metabolism data (cell line, liver microsomes): metabolic route

•In vivo excretion data: HPLC detects parent compound and metabolites

•In vivo pharmacokinetic studies for human subgroups

Meta-analysis of studies reporting PK parameters for each

compound/ parameter/ subgroup of the population:

•Mean, SD and CVN (normal distribution) transform to

geometric mean and GSD, CVLN (lognormal distribution)

•Derive Coefficient of variation (CV) for each compound/parameter and pool CVs to get overall value for metabolic route (pathway-related variability)

•Derive Pathway-related uncertainty factors (to cover 95, 97.5 and 99th centiles) using CV and magnitude of difference in internal dose (clearance or Cmax) between healthy adults and subgroups

Methods IIMethods II

ResultsResults

Database for >200 compounds

•HPLC method for the detection of parent compound and metabolites

•In vitro metabolism of compound inter-species and human

•In vivo metabolism data (% excretion for compound and each metabolite HPLC data)

Kinetic studies for each compound (> 2500 studies)

•Subgroups of the human population (healthy adults, genetic polymorphism, interethnic differences, neonates, children and the elderly)

Monomorphic pathways

Pathway-related UFs below the kinetic default factor (3.2)

Low variability in healthy adults (<30%), exception of CYP3A4 : role of gut

CYP3A4, P-glycoprotein, polymorphism

Pathway n compounds n CV Pathway-related UFs

(99th)

CYP1A2 4 379 30 2.0

CYP3A4 12 1381 46 2.7

Glucuronidation 15 906 29 2.0

Renal excretion 6 444 21 1.6

Healthy adultsHealthy adults

Pathway n compounds n CV Pathway-related UFs (99th)

CYP2C19 (EM) 2 56 60 3.8

CYP2C19 (PM) 2 21 20 52

CYP2D6 (EM) 9 192 66 5.8

CYP2D6 (PM) 7 74 29 26

Polymorphic pathways

Variability for polymorphic pathways larger than for monomorphic pathways

Large difference in internal dose between EMs and PMs for CYP2D6 (9-fold) and CYP2C19 (12-fold)

Pathway-related uncertainty factors above the current kinetic default factor (3.2)

Exponential relationships between ratio EM/PM and % CYP2D6 metabolism

Ratio EM/PM

0

20

40

60

80

0 20 40 60 80 100

% CYP2D6 metabolism in EMs

PMs covered by pathway-related UFs for substrates with up to 25% (dose)

of CYP2D6 metabolism in EMs

Quantitative involvement of dose handling Quantitative involvement of dose handling

on kinetic differences: CYP2D6on kinetic differences: CYP2D6

Quantitative involvement of dose handling Quantitative involvement of dose handling

on kinetic differences: CYP2C19on kinetic differences: CYP2C19

PMs covered by UFs for substrates with up to 20-25% (dose) of CYP2C19

metabolism in EMs.

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

0 20 40 60 80 100

% CYP2C19 in EM

Rati

o E

M/P

M

Results: Subgroups of the population Interethnic differences

Less variability in Asian vs Caucasian for CYP2D6 and CYP2C19 (+

different frequencies of phenotypes)

Pathway-related uncertainty factors above kinetic default

for CYP2C19 and NAT metabolism

Historically smaller database for non-Caucasian subjects:

Modern man : mixture of ethnic groups and more so in

the future !

Ex relationship for CYP2C19 and ratio EMs/PMs in Asian healthy

adults (R2=0.87) : Slope 100% metabolism via CYP2C19 gives a

ratio of 30 (80 in Caucasian !)

Children and neonates

Potential susceptible subgroups of the population:

-Immaturity of phase I, phase II and renal excretion (particularly for

neonates)

-Quantify differences in internal dose from in vivo PK database

-Provide pathway-related UFs for these subgroups

-Identify datagaps

NeonatesNeonates

The most susceptible subgroup for all pathways with data:

immaturity of phase I, II metabolism and renal excretion. No reliable

data available for polymorphic pathways.

Pathway Nc n CV Ratio Pathway-related UFsGM 95th 99th

CYP1A2 2 251 35 6.2 11 14

CYP3A4 2 35 65 3.0 8.1 12

Glucuronidation 4 94 50 3.9 8.6 12

Glycine Conjugation 1 10 16 19 25 28

Renal excretion 7 656 32 1.7 2.8 3.4

All data from the IV route

ChildrenChildren

Limited data-Susceptible subgroup for both polymorphic CYP2C19 and CYP2D6

Pathway Nc n CV Ratio Pathway-related UFsGM 95th 99th

CYP1A2* 1 195 34 0.82 1.4 1.8

CYP2C19 1 25 86 1.6 5.4 9.0

CYP2D6 1 173 140 4.0 22 45

CYP3A4 3 16 45 0.70 1.4 1.8

Glucuronidation 5 131 23 0.86 1.3 1.5

Renal Excretion* 6 126 30 0.70 1.2 1.5

* IV data (all other data PO route)

Polymorphism in metabolism and Children and neonates: Examples

Fluoxetine and paroxetine metabolised largely via CYP2D6 and

other CYP isoforms (CYP2C9, CYP3A4 and CYP2C19)

Large inter-individual differences in kinetics in healthy adults and

children: up to 10-18-fold variation in clearance in healthy adults

PMs (including 2 PM children)

Holden, C. Prozac Treatment of Newborn Mice Raises Anxiety. Science. 2004 Oct 29;306(5697):792.

Ibuprofen and indomethacin in preterm neonates : up to 10-fold

difference decrease in clearance : immature CYP2C9,

glucuronidation and renal excretion.

Lansoprazole (CYP2C19-CYP3A4): 1 neonate and 1 infant PM (3-

and 7-fold decrease in clearance)

Predicting human variability in toxicokinetics using Monte

Carlo modelling

Latin hypercube sampling: variant of Monte Carlo (random),

stratified sampling throughout the distribution.

Compounds handled by multiple pathways : predict variability

and uncertainty factors for healthy adults, children and neonates.

Combine distributions describing pathway –related variability and

quantitative metabolism data.

Compare Simulated data and published kinetic data.

Poor metabolisers, neonates and children :

-GM ratio of internal dose (mean) compared to healthy adults and pathway-specific variability (GSD) for each pathway.

-Neonates and children: ideally use metabolism data but often not available: liver microsome / in vitro and/or healthy adult data

-Polymorphic pathways : Combine distribution for EM and PM using frequency of EM and PMs ( for CYP2D6 7.4% PM in Caucasian)

PM

combinedEM PM

EMs

2.32.3

2.7

2.0

3.4

2.7

2.9

3.5

1.92.0

2.1

1.8

3.03.0antipyrine

codeine

diazepam

imipramine

paracetamol

proguanil

propranolol

Non-phenotyped healthy adults: Uncertainty factors (99th centile)

Published Simulated

Phenotyped healthy adults: Uncertainty factors (99th centile)

3.6

2.8

2.11.8

codeine propranolol

3.6

2.8

2.11.8

codeine propranolol

CYP2D6 EMs CYP2D6 PMs

1.91.8

4.3

5.2

codeine

propranolol

Combined EMs and PMs

•Literature searches for interaction studies between major probe substrates (> 70% of the dose metabolised by each CYP) of CYP2D6 and CYP2C19, inhibitors and inducers of each enzyme.

•UFs to cover percentiles for subgroup of population

Pharmacokinetic interaction between probe substrates of polymorphic CYPs

Relevance: a number of pesticides are substrates and inhibit polymorphic CYPs (chlorpyrifos, diazinon)..

Extensive metabolisers (EMs) are at risk if the metabolite produced the toxicant. Poor metabolisers (PMs) would be at risk if the parent compound is the toxicant.

DRUG ADRUG A

ACTIVE SITE

CYP2D6 CYP2D6

Cimetidine

CimetidineCimetidine binds away

from active site, changing structure so that Drug A

can no longer fits

NON-COMPETETIVE CYP2D6 INHIBITION BY CIMETIDINE

CYP2D6 CYP2D6

DRUG A

DRUG A

ACTIVE SITE

Paroxetine

Paroxetine binds reversibly with drug A to the

active site

COMPETITIVE INHIBITION OF CYP2D6 BY PAROXETINE

CYP Enzyme Induction

Hyperforin

↑↑CYP expression

↑↑ mRNA transcription

Pregnane X receptor

Retinoid X Receptor

Rifampin

Polymorphic CYP inhibitionPolymorphic CYP inhibition

•CYP2D6 Inhibition will increase internal dose in EMs and UF for toxicokinetic UF (3.16) would not cover this subgroup for binary mixtures. PMs not affected : alternative pathways of metabolism, slow extensive metabolisers (SEMs) are an intermediate

0

5

10

15

20

25

Un

cert

ain

ty F

acto

rs (

95 t

h c

enti

le)

EM non competitive

PM non competitive

EM Competitive

INHIBITION/ INDUCTION INHIBITION/ INDUCTION

•Inhibition/induction of polymorphic CYP increase/decrease exposure to therapeutic drugs in EMs (and PMs for induction). Current UF for human variability in toxicokinetics (3.16) would not cater for these interactions

•Results variable ; detailed analysis to classify interaction according to constant of inhibition (Ki)

• In vivo database on therapeutic doses much higher than pesticide levels but only in vivo data quantifying human variability in toxicokinetic interactions.

RELEVANCE TO HUMAN RISK RELEVANCE TO HUMAN RISK ASSESSMENTASSESSMENT

•Current levels of exposure of organophosphates (< 10 uM) : shown to inhibit imipramine metabolism in human recombinant enzymes and liver microsomes (Di Consiglio et al., 2005).

•Many pesticides known to either inhibit or induce cytochrome P-450 isoforms in animals and man

• More work to characterise their potential in vivo effects at the current level of exposure using recombinant technology and toxicokinetic assays (Hodgson and Rose, 2005).

CONCLUSIONS CONCLUSIONS

Most suceptible subgroups (mixtures)Extensive metabolisers for polymorphic enzymes with inhibitors if metabolite toxic

Human data are essential To replace default uncertainty factors with chemical-specific dataTo identify high risk subgroups regarding susceptibility to chemical toxicity

Most susceptible subgroupsPoor metabolisers (Healthy adults), neonates, children for polymorphic enzymes

but very little data

Need for well characterised metabolism before compound on the market Use of in vitro techniques

Many pesticides metabolised via polymorphic CYPs

Regulatory bodies, Risk managers ?Integrate data (including susceptible subgroups…) in the risk assessment

process

In vitro, in silico data and OMICS

Analysis of toxicodynamics (mechanisms of toxicity) Very little data, use of pharmacodynamic data

Advanced statistical techniquesUncertainty analysis, Probabilistic and Bayesian approaches

IndustryIntegrate relevant data (compound specific metabolism PK, PD, TK, TD…)

and relevant modelling techniques for risk assessment of compounds before market

CONCLUSIONS II CONCLUSIONS II

Many thanks to

Professor Emeritus Andrew Renwick OBE and

-The Department of Health (UK), -Health and Safety Executive (UK), -Food Standard Agency (UK),-European Commission within NO MIRACLE

for funding this work