1. Human risk assessment perspectives for high risk conditions
Jean Lou Dorne
Institute of Human Nutrition
University of Southampton, UK

2. Resveratrol
Allyl sulphides (Allicin…)
Isothiocyanates,Sulphorafane
Vitamine C, limonene
Lycopene
Isoflavones

5. “All things are toxic and there is nothing without poisonous qualities: it is only the dose which makes something a poison”
PARACELSUS ( )
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/ toxicity
Target receptor/cell/organ

6. RISK ASSESSMENT METHODS INTAKE WITH NO APPRECIABLE EFFECTS eg ADI
LOW - DOSE
EXTRAPOLATION
RISK ASSOCIATED
WITH THE KNOWN INTAKE
QUANTITATIVE
RISK ASSESSMENT
NO THRESHOLD
THRESHOLD
NOAEL AND
SAFETY FACTORS
INTAKE WITH NO APPRECIABLE EFFECTS eg ADI
NON - QUANTITATIVE
RISK ASSESSMENT

7. Derivation of the Acceptable Daily Intake (ADI)
ADI (mg/kg/day) = NOAEL(mg/kg) / 100

8. The use of uncertainty or safety factors (UFs)
SPECIES DIFFERENCES
HUMAN VARIABILITY 10 10
KINETICS
DYNAMICS
Extrapolation from group of test animals to average human and from average humans to potentially sensitive sub-populations

9. 100 - FOLD UNCERTAINTY FACTOR
INTER-SPECIES
DIFFERENCES
10 - FOLD
INTER-INDIVIDUAL
TOXICO-
DYNAMIC
10 0.4
2.5
KINETIC
10 0.6
4.0
TOXICO-
DYNAMIC
10 0.5
3.2
KINETIC
Chemical specific adjustment factors can replace the default uncertainty factors (WHO, 2001; IPCS, 2006)

10. Towards a more flexible framework
Toxicokinetics
Toxicodynamics
Data-derived or
Pathway-related
Uncertainty factors
general default
Data-derived or
process related
Uncertainty factors
general default
Interspecies differences
Human variability
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

11. Major Routes of chemical metabolism and excretion
Phase I enzymes
Cytochrome P-450, ADH, Esterases
Phase II enzymes
Conjugation reactions
Glucuronidation
Sulphation
N-acetylation (Polymorphic)
Amino acid conjugation
% of Pharmaceuticals Metabolized by Individual Cytochrome P450’s in man
P4502D6
P4501A2
P4502A6
P4503A
P4502C9
P4502C19
P4502E1
Renal excretion
CYP2C9, CYP2C19, CYP2D6* Polymorphic (Extensive and Poor metabolisers, EMs and PMs)
*Caucasian 8% PMs 92% EMs

12. CYP2D6 Substrates Antiarrhythmics Encainide S-mexiletine Analgesics
Dextromethorphan
Codeine
Tramadol
Antipsychotics
Risperidone
Haloperidol
Antidepressants
Fluoxetine
Paroxetine
Amitriptylline
Desipramine
Imipramine
Venlafaxine
Pesticides
Chlorpyrifos
Diazinon
Methoxychlor
Beta Blockers
Bufuralol
Propafenone
Metoprolol
propranolol
Carvedilol
Adapted from Dorne et al., 2002 FCT 41,

13. Introducing metabolic and toxicokinetic data into risk assessment

14. Aims 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

15. Methods 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

16. Methods II
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

17. Results 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)

18. Healthy adults Monomorphic pathways
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)
CYP1A
CYP3A
Glucuronidation
Renal excretion
Pathway-related UFs below the kinetic default factor (3.2)

19. 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)
Pathway n compounds n CV Pathway-related UFs (99th)
CYP2C19 (EM)
CYP2C19 (PM)
CYP2D6 (EM)
CYP2D6 (PM)

20. Quantitative involvement of dose handling
on kinetic differences: CYP2D6
Ratio
EM/PM 20 40 60 80
100
% CYP2D6 metabolism in EMs
Exponential relationships between ratio EM/PM and % CYP2D6 metabolism
PMs covered by pathway-related UFs for substrates with up to 25% (dose) of CYP2D6 metabolism in EMs

21. Quantitative involvement of dose handling
on kinetic differences: CYP2C19
PMs covered by UFs for substrates with up to 20-25% (dose) of CYP2C19 metabolism in EMs.

22. Results: Subgroups of the population Interethnic differences
Historically smaller database for non-Caucasian subjects: Modern man : mixture of ethnic groups and more so in the future !
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
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 !)

23. 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

24. Neonates
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 UFs GM 95th 99th
CYP1A
CYP3A
Glucuronidation
Glycine Conjugation
Renal excretion
All data from the IV route

25. Children
Limited data-Susceptible subgroup for both polymorphic CYP2C19 and CYP2D6
Pathway Nc n CV Ratio Pathway-related UFs GM 95th 99th
CYP1A2*
CYP2C
CYP2D
CYP3A
Glucuronidation
Renal Excretion*
* IV data (all other data PO route)

26. 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 fold variation in clearance in healthy adults PMs (including 2 PM children)
Holden, C. Prozac Treatment of Newborn Mice Raises Anxiety. Science 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)

27. Predicting human variability in toxicokinetics using Monte Carlo modelling

28. 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.

29. 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)

30. Non-phenotyped healthy adults: Uncertainty factors (99th centile)
Published
Simulated

31. Phenotyped healthy adults: Uncertainty factors (99th centile)
CYP2D6 EMs
CYP2D6 PMs
Combined EMs and PMs

32. Pharmacokinetic interaction between probe substrates of polymorphic CYPs
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
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.

33. CYP2D6 CYP2D6 NON-COMPETETIVE CYP2D6 INHIBITION BY CIMETIDINE DRUG A
ACTIVE SITE
CYP2D6
CYP2D6
Cimetidine binds away from active site, changing structure so that Drug A can no longer fits
Cimetidine
Cimetidine

34. Paroxetine binds reversibly with drug A to the active site
COMPETITIVE INHIBITION OF CYP2D6 BY PAROXETINE
DRUG A
Paroxetine
ACTIVE SITE
Paroxetine binds reversibly with drug A to the active site
CYP2D6
CYP2D6

35. CYP Enzyme Induction ↑↑CYP expression Pregnane X receptor
↑↑ mRNA transcription
Hyperforin
CYP3 A4 inductions by SJW occurs when Hyperforin, the active ingredient in SJW binds to the pregnane x receptor. Pregnane x receptor binds with retinoid x receptor which that resulting in mrna transcription and finally cyp3a4 transcription
Rifampin
Pregnane X receptor
Retinoid X Receptor

36. Polymorphic 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

37. 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.

38. RELEVANCE TO HUMAN RISK ASSESSMENT
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).

39. CONCLUSIONS Human data are essential Most susceptible subgroups
To replace default uncertainty factors with chemical-specific data
To identify high risk subgroups regarding susceptibility to chemical toxicity
Most susceptible subgroups
Poor metabolisers (Healthy adults), neonates, children for polymorphic enzymes but very little data
Most suceptible subgroups (mixtures)
Extensive metabolisers for polymorphic enzymes with inhibitors if metabolite toxic
Need for well characterised metabolism before compound on the market Use of in vitro techniques
Many pesticides metabolised via polymorphic CYPs

40. CONCLUSIONS II Advanced statistical techniques
Uncertainty analysis, Probabilistic and Bayesian approaches
Analysis of toxicodynamics (mechanisms of toxicity)
Very little data, use of pharmacodynamic data
In vitro, in silico data and OMICS
Regulatory bodies, Risk managers ?
Integrate data (including susceptible subgroups…) in the risk assessment process
Industry
Integrate relevant data (compound specific metabolism PK, PD, TK, TD…) and relevant modelling techniques for risk assessment of compounds before market

41. 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