Transcript INTRA080606

Human risk assessment
perspectives
for high risk conditions
Jean Lou Dorne
Institute of Human Nutrition
University of Southampton, UK
Resveratrol
Lycopene
Isothiocyanates,
Sulphorafane
Isoflavones
Allyl sulphides (Allicin…)
Vitamine C, limonene
“All things are toxic and there is nothing
without poisonous qualities: it is only the
dose which makes something a poison”
PARACELSUS (1493-1541)
Pharmaco/Toxicokinetics
Pharmaco/Toxicodynamics
How the chemical is eliminated
from the body or activated into
a toxic species (ADME )
How the chemical exerts its
pharmacological effect/ toxicity
Target receptor/cell/organ
RISK ASSESSMENT METHODS
NO THRESHOLD
THRESHOLD
QUANTITATIVE
RISK ASSESSMENT
NON - QUANTITATIVE
RISK ASSESSMENT
LOW - DOSE
EXTRAPOLATION
NOAEL AND
SAFETY FACTORS
RISK ASSOCIATED
WITH THE KNOWN
INTAKE
INTAKE WITH NO
APPRECIABLE
EFFECTS eg ADI
Derivation of the Acceptable Daily Intake
(ADI)
ADI (mg/kg/day) = NOAEL(mg/kg) / 100
The use of uncertainty or safety factors
(UFs)
SPECIES
DIFFERENCES
10
KINETICS
HUMAN
VARIABILITY
10
DYNAMICS
KINETICS
DYNAMICS
Extrapolation from group of test animals to average human and
from average humans to potentially sensitive sub-populations
100 - FOLD UNCERTAINTY FACTOR
INTER-SPECIES
INTER-INDIVIDUAL
DIFFERENCES
DIFFERENCES
10 - FOLD
10 - FOLD
TOXICODYNAMIC
TOXICOKINETIC
TOXICODYNAMIC
TOXICOKINETIC
10 0.4
2.5
10 0.6
4.0
10 0.5
3.2
10 0.5
3.2
Chemical specific adjustment factors can replace the default
uncertainty factors (WHO, 2001; IPCS, 2006)
Towards a more flexible framework
Toxicokinetics
Toxicodynamics
Data-derived
or
Pathway-related
Uncertainty factors
or
Data-derived
or
process related
Uncertainty factors
or
general default
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
Major Routes of chemical metabolism and
excretion
Phase I enzymes
Phase II enzymes
Cytochrome P-450, ADH, Esterases
Conjugation reactions
% of Pharmaceuticals Metabolized by
Individual Cytochrome P450’s in man
P4502D6
P4502C9
P4501A2
P4502A6
P4502C19
Glucuronidation
Sulphation
N-acetylation (Polymorphic)
P4502E1
Amino acid conjugation
P4503A
CYP2C9, CYP2C19, CYP2D6* Polymorphic
(Extensive and Poor metabolisers, EMs and PMs)
*Caucasian 8% PMs 92% EMs
Renal excretion
CYP2D6 Substrates
Antiarrhythmics
Analgesics
Antipsychotics
Encainide
S-mexiletine
Dextromethorphan
Codeine
Tramadol
Risperidone
Haloperidol
Beta Blockers
Antidepressants
Pesticides
Bufuralol
Propafenone
Metoprolol
propranolol
Carvedilol
Fluoxetine
Paroxetine
Amitriptylline
Desipramine
Imipramine
Venlafaxine
Chlorpyrifos
Diazinon
Methoxychlor
Adapted from Dorne et al., 2002 FCT 41, 1633-1656
Introducing metabolic and
toxicokinetic data into risk
assessment
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
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
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
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)
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)
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
Pathway-related UFs below the kinetic default factor (3.2)
Polymorphic pathways
Variability for polymorphic pathways larger than for monomorphic
pathways
Large difference in internal dose between EMs and PMs for CYP2D6 (9fold) 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)
2
56
60
3.8
CYP2C19 (PM)
2
21
20
52
CYP2D6 (EM)
9
192
66
5.8
CYP2D6 (PM)
7
74
29
26
Quantitative involvement of dose handling
on kinetic differences: CYP2D6
80
60
Ratio
EM/PM
40
20
0
0
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
Quantitative involvement of dose handling
on kinetic differences: CYP2C19
90.0
80.0
70.0
Ratio EM/PM
60.0
50.0
40.0
30.0
20.0
10.0
0.0
0
20
40
60
80
100
% CYP2C19 in EM
PMs covered by UFs for substrates with up to 20-25% (dose) of CYP2C19
metabolism in EMs.
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 !)
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
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
GM
Pathway-related UFs
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
Children
Limited data-Susceptible subgroup for both polymorphic CYP2C19
and CYP2D6
Pathway
Nc
n
CV
Ratio
GM
Pathway-related UFs
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
Holden, C. Prozac Treatment of Newborn Mice Raises
children: up to 10-18-fold variation in clearance in healthy adults
Anxiety. Science. 2004 Oct 29;306(5697):792.
PMs (including 2 PM children)
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 (3and 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
combined
EM PM
EMs
Non-phenotyped healthy adults:
Uncertainty factors (99th centile)
Published
Simulated
3.5
3.4
3.0
2.9
3.0
antip yrine
2.7
2.7
co d eine
d iazep am
2.3
2.3
1.9
imip ramine
2.0
1.8
2.0
2.1
p aracetamo l
p ro g uanil
p ro p rano lo l
Phenotyped healthy adults:
Uncertainty factors (99th centile)
3.6
3.6
2.8
2.8
2.1
1.8
codeine
2.1
1.8
codeine
propranolol
CYP2D6 EMs
propranolol
CYP2D6 PMs
5.2
code ine
4.3
Combined EMs
and PMs
propranolol
1.8
1.9
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.
NON-COMPETETIVE CYP2D6 INHIBITION BY CIMETIDINE
DRUG A
DRUG A
ACTIVE SITE
CYP2D6
Cimetidine
Cimetidine binds away
from active site, changing
structure so that Drug A
can no longer fits
CYP2D6
Cimetidine
COMPETITIVE INHIBITION OF CYP2D6 BY PAROXETINE
DRUG A
Paroxetine
ACTIVE SITE
CYP2D6
Paroxetine binds
reversibly with
drug A to the
active site
CYP2D6
DRUG A
CYP Enzyme Induction
↑↑CYP expression
Hyperforin
Rifampin
Pregnane X receptor
Retinoid X Receptor
↑↑ mRNA transcription
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
25
EM non competitive
Uncertainty Factors (95 th centile)
PM non competitive
EM Competitive
20
15
10
5
0
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
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).
CONCLUSIONS
Human data are essential
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
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
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