The importance of absorption, elimination and feeding pattern: using - - PowerPoint PPT Presentation

Agnieszka Bednarska(1), Peter Edwards(1), Richard Sibly(2), Pernille Thorbek(1)

The importance of absorption, elimination and feeding pattern: using toxicokinetics modelling to refine the risk assessment

• f pesticides to wildlife

(1) Syngenta, Jealott’s Hill International Research Centre, Bracknell (2) School of Biological Sciences, University of Reading, Reading, UK

University of Reading

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Content

• The current risk assessment for birds and mammals
• TK model: what, why and how?
• What do regulators say about importance of feeding patterns and avoidance?
• Case study: pros and cons of body burden model

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Background

• The current risk assessment for birds and mammals*
• Based on external exposure measurements
• Toxicity endpoints calculated from the gavage dose (acute) or the dietary

toxicity (chronic) do not represent field exposure

• No account taken of the physiological processes (e.g., absorption,

elimination), ecological factors (e.g., feeding rate, avoidance), duration of exposure

• TK model
• Absorption, Distribution, Metabolism, Excretion (ADME)

* EFSA Journal 2009, 7(12), 1438

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TK models in EFSA Guidance

“Within the registration process of PPP under Directive 91/414/ECC, often data from metabolism studies (ADME) within rat, live-stock or hen are available.” “Where risk-refinement is necessary based on results from lower tier assessment, ‘metabolism’ data should be evaluated by the risk assessor for options to reduce the uncertainty associated with the risk assessment.”

EFSA Journal 2009, 7(12), 1438

6.3. Metabolism & avoidance – application of body-burden models and dietary toxicity data

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Case study: metabolism data for an insecticide

• FIG. The concentration of an insecticide in blood of three male rats administered 0.5 mg kg-1

bw [Thiazol-2-14C] (left-hand column) or 0.5 mg kg-1 bw [Oxadiazin-4-14C] (right-hand column). Lines are the one-compartment models fit to the experimental blood data. Note different scale

• n y axis.

Data from rats metabolism study with radiolabelled insecticide Compartmental analysis of data on insecticide concentrations in blood One-compartment model fits data best Insecticide concentrations in blood highly correlated with concentrations in different tissues Up to 90% eliminated as parent compound through the urine

WinNonlin software; comparison between different models based on residual plots and AIC. Model parameters estimated using Marquardt method. ka =0.30 h-1 ke =0.30 h-1 ka =2.56 h-1 ke =0.27 h-1 ka =3.45 h-1 ke =0.13 h-1 ka =0.36 h-1 ke =0.37 h-1 ka =1.01 h-1 ke =0.36 h-1 ka =2.21 h-1 ke =0.30 h-1 Concentration [µg ml-1] Time [h]

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Case study: TK model

gut a gut

• ut

gut

C k C k I C    

int int

C k F C k C

e gut a

  

ΔC change in the gut gut or internal int concentration of pesticide in given time interval, here one min. I intake rate [mg a.i. kg-1 bw min-1] F bioavailability, here F =1 kout the rate of excretion of toxicant not absorbed into the system from the gut [min-1], here kout =0 ka the rate of toxicant absorption from the gut into the system [min-1] ke the rate of toxicant elimination from the system [min-1]

a.i. in food

gut central compartment (bloodstream)

ka ke kout I

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Case study: using metabolism data for parameterization of TK model

Parameters intravenous exposure bolus gavage exposure [Thiazol-2-14C] 0.5 mg kg-1 bw [Thiazol-2-14C] 0.5 mg kg-1 bw [Oxadiazin-4-14C] 0.5 mg kg-1 bw [Thiazol-2-14C] 100 mg kg-1 bw [Oxadiazin-4-14C] 100 mg kg-1 bw male female male female male female male female male female ka (h-1)

• 2.1±1.62

2.3±1.93 1.20±0.93 3.2±0.29 0.78±0.32 1.6±0.71 0.71±0.73 2.00±1.37 ke (h-1) 0.26±0.02 0.50±0.11 0.23±0.09 0.23±0.13 0.34±0.034 0.25±0.06 0.28±0.12 0.18±0.06 0.25±0.11 0.19±0.06 AUC (h ug-1 ml-1) 2.30±0.19 1.63±0.38 1.49±0.15 1.56±0.53 1.30±0.015 1.03±0.13 342±80 278±59 359±50 294±24 F

• 0.65±0.07 0.96±0.33 0.56±0.007

0.63±0.08 0.74±0.17 0.85±0.18 0.78±0.11 0.90±0.07 Kinetics parameters estimated from rat study based on radiolabeled test substance

2.2 1.3 0.40 0.25

Concentration [mg/kg] measured ka = 2.2 h-1 ke = 0.25 h-1 ka = 1.3 h-1 ke = 0.40 h-1

0.5 mg a.i. kg-1 bw 100 mg a.i. kg-1 bw

Concentration [mg/kg] Time [min]

• FIG. The concentration of an insecticide in blood of rats (points) after administration of 0.5 or 100 mg a.i. kg-1 bw and

model simulations (lines) for two extreme combinations of ka and ke.

Time [min]

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Feeding pattern in EFSA Guidance

EFSA Journal 2009, 7(12), 1438

6.2. Avoidance

“What rates of feeding occur in the field?“ “Do the feeding rates achieved in laboratory studies or assumed in model correspond to the maximum rates occurring in the field? “

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Case study: different exposure simulations

Time from start of feeding (min) Control food [g diet kg-1 bw] Intake rate [g kg-1 bw min-1] Intake rate of LD50 [mg a.i. kg-1 bw min-1] C7 C15 C21 C17 ..... mean mean mean

0.0 0.00 0.0 15 10.9 7.8 6.3 5.6 9.2 0.61 35.3 30 14.1 11.3 6.3 9.4 14.2 0.33 19.1 45 20.3 16.9 6.8 13.8 17.5 0.22 12.7 60 23.5 24.5 10.3 16.4 20.5 0.20 11.6 75 26.7 28.1 15.5 18.9 23.6 0.21 12.2 90 28.8 28.1 19.8 19.7 25.1 0.10 5.8 105 28.8 31.9 20.7 19.7 26.8 0.11 6.4 120 28.8 31.9 20.7 20.6 27.1 0.02 1.2 Time [min] Concentration [mg kg-1 bw]

The concentration of an insecticide in the body after eating LD50 dose according to different intake rates; scenarios for ka = 2.2 and ke = 0.25. Intake rate for rat of uncontaminated food [g diet kg-1 bw min-1] at 15-min time intervals over 2h LD50 eaten with constant intake rate over 120 min.:

13.0

bolus gavage exposure maximum feeding rate in control over 2 h constant ingestion rate over 2h

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Case study: LD50 eaten with different feeding patterns

FIG. The concentration

• f

an insecticide in the body, in the gut, and cumulative intake over time after exposure to LD50 dose according to different feeding pattern.

The slower animals eat the lower internal maximum concentrations are reached. Feeding pattern influences internal concentration of pesticide, especially if there are breaks of low/no feeding activity after short feeding bouts.

Concentration [mg kg-1 bw] Time [min]

ΔCint ΔCgut Cmax bolus gavage cumulative a.i. intake Continuous feeding 2h Continuous feeding 4h

Time [min] Time [min] Time [min]

1h feeding +2h break +1h feeding 1h feeding +4h break +1h feeding

Concentration [mg kg-1 bw]

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Avoidance in EFSA Guidance

EFSA Journal 2009, 7(12), 1438

6.2. Avoidance “A degree of avoidance of food contaminated with pesticides, commonly

seen in dietary studies with captive animals, has the potential to reduce exposure and hence risk in the field. It can be combination of several different responses including (a) a reduction in the rate of feeding due to novel or unpleasant characteristics of the contaminated food, and (b) temporary cessation of feeding due to sublethal intoxication. It is hard to determine the precise mechanism(s) of avoidance for a given pesticide. “

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Avoidance

a.i. in food

gut central compartment (bloodstream)

ka ke kout I

To be useful for TK modelling the strength of avoidance must be determined quantitatively

AVOIDANCE

‘... (b) temporary cessation of feeding due to sublethal intoxication ...’

Avoidance experiment on rats

Aim:: determine the highest dose (mg kg-1 bw) and intake rate [mg a.i. kg-1 bw min-1] of an insecticide Methods: acclimatisation to maximise the feeding 2h access to contaminated food without access to alternative food, then 10h access to untreated food video-recording of intake of food (a balance reading) during 1st day of exposure exposure for a further 2 days with a 2h deprivation period, but without video recording direct measurement of eaten diet

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Avoidance: results from study on rats

3126 6252 15630 31260 46900 96300 5 10 15 20 25 30 35 40 45 50 Treatment 3126 6252 15630 31260 46900 96300 5 10 15 20 25 30 35 40 45 50 Treatment

Food consumption at Day -2 Food consumption at Day 0

Treatment [mg a.i. kg-1 food] Food consumption [g kg-1 bw]

Results of Multifactor ANOVA: Treatment p<0.0001 Day p<0.0001 Day*Treatment p< 0.001

Food consumption [g kg-1 bw] Treatment [mg a.i. kg-1 food] Intake dose [mg a.i. kg-1 bw]

Clear evidence of avoidance in terms of eaten dose but not stable over time

• FIG. Food consumption for rats before exposure (Day-2) and on the first day of exposure (Day 0) at different treatments.

FIG. Potential intake dose (mean based

• n

food consumption at Day-2 and measured intake dose (mean) at different treatment.

Treatment [mg a.i. kg-1 food]

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Avoidance: results from study on rats

Concentration [mg kg-1 bw] Time [min] Intake

0.2 0.4 0.6 0.8 1 (X 1000) 200 400 600 800

ΔCint Δcgut cumulative a.i. intake intake rate

• FIG. The concentration of an insecticide in the body, in the gut, and cumulative dose

(upper panel) for individual rats over time after exposure according to food intake pattern (lower panel); pattern recorded at 15-min time intervals.

Intake dose [mg a.i. kg-1 bw] Concentration [mg kg-1 bw]

FIG The relationship between concentration

• f

an insecticide reached in the body at which an animal stops feeding for longer than 15 min (or concentration after 120 min exposure) and intake dose eaten over that time.

r=0.99

No evidence of fixed internal threshold

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• TK models are considered as a refinment tool for risk assessment in EU

guidance for birds and mammals.

• ADME data can be used to parameterize a body burden (or other TK) model
• Key assumptions which should be checked before using BB approaches:
• Can kinetics be described as a first order process or is it more complex?
• How many compartments should be included in the model?
• Is it necessary to represent target organ(s) as separate compartment(s) or

is the toxicant concentration in systemic circulation (blood) sufficient?

• BB model based on total radioactivity, so metabolites are not characterized

separately - PBTK models may be sometimes preferred Choice between approaches (model structure and type) depends on intended use – make model only as complex as needed

Conclusions: TK models

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Conclusions: Feeding pattern & avoidance

Thank k you for your r attention ntion

• Behavioural responses may moderate exposure - taking into account

behavioural responses, timescales of exposure and kinetics improve risk assessment

• Choose the proper metric for comparison between feeding scenarios
• There is clear evidence for avoidance, but mechanism is not yet fully understood

This research has been financially supported by the European Union under the 7th Framework Programme (project acronym CREAM, contract number PITN-GA-2009-238148)

Abstract code: WE 134