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Human risk assessment perspectives for high risk conditions

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Presentation on theme: "Human risk assessment perspectives for high risk conditions"— Presentation transcript:

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


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

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.

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

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.

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


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