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Training Event E Wastewater treatment by advanced technologies and risk assessment
framework
1st ANSWER Workshop Risk prognosis of environmental and public health aspects of
antibiotics and antibiotic-resistant bacteria and antibiotic resistance genes (A&ARB&ARGs)
September 4-6, 2017, Fisciano (SA) Italy
Where are we going with chemical risk assessment? The challenges for the future
Emanuela Testai
Istituto Superiore di Sanità Environment and Health Department
Rome, Italy [email protected]
The present RA paradigm generally focuses on hazard identification and characterisation as first steps. There is a demand for changing the basis of RA, giving more focus on 1) modes of action
(mechanistic approach) 2) a progressive reduction of
tests using laboratory animals
3) exposure driven process Towards the Tox21 and the EU SC document on New challenges for RA (2013)
Current safety testing methods
Toxicological profile
Exposure
vs
Risk assessment
Phys-chem
properties
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Exposure: Advances in exposure assessment
1. Determination of the internal dose plus a shift from single to individual/groups of chemicals
2. Exposure data increasingly based on human surveillance data (biomonitoring) using biomarkers of exposure
3. Exposome approach: Embracement of the lifetime exposure of a human to chemicals from conception to death: how these exposures relate to the development of disease?
Hazard: Reduction in animal use
1. Application of modelling and Non testing methods (TTC or Read across) and the TK-first approach No or limited testing for ‘hazard identification/ characterization’ in the absence of absorption
2. Development of in vitro preparations maintaining in vivo characteristics over long periods, identification of MoA and key events (AOP) and assessment of in vitro biokinetics
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External
Exposure
ADME X X*
absorption
distribution
metabolism
escretion
Interaction
between
X o X*
and the
target
Pharmaco/
toxico-kinetics
Pharmaco/
toxico-dynamics
The processes integrating ADME, determining the internal dose following ‘external’ exposure is usually referred to as toxicokinetics (TK)
Time (hrs)
Blo
od
Co
ncen
trati
on
i.v.
oral
Metabolite
EFFECT
Internal
Exposure
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The time dependent concentration of a certain pollutant in human tissues can be predicted using physiologically-based pharmaco-kinetic (PBPK) models. These models consider the human body as a set of well stirred compartments linked by the blood flow. Physiological processes are represented by a set of ordinary differential equations describing the ADME processes of a specific chemical. The final result is a model that simulates the time course distribution of a substance in the human body, which helps to quantify the relationship between measures of external exposure and internal dose
Ibuprofen
CSA
PBPK models have the potential for extrapolation from observed kinetic data.
The expansion of application of PBPK-models also to in vitro conditions will result in more precise quantification of tolerable exposures and extrapolation from in vitro to in vivo.
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http://publications.jrc.ec.europa.eu/repository/bitstream/JRC96418/eurl%20
ecvam%20toxicokinetics%20strategy.pdf
From the ABSTRACT: Information on human toxicokinetics plays an important role in the safety assessment of chemicals, even though there are few data requirements in the EU regulatory framework……
Published July 2015
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Female rat t1/2 PFOA = 3 h
Male rat t1/2 = 5 d
Human t1/2 = 3.8 ys (average); 1.5–13.5 ys (range).
Mouse t1/2 = 17-19 d
PFOA
PFOS
Male Rat t1/2 PFOS = 43 d
Cynomolgus t1/2 = 2.3 ys
Human t1/2 =5.4 ys
Comparison among studies in different species should be carried out by using the on the basis of the internal dose rather than on the external one.
Reprotox study on female rats are poorly representative for human extrapolation
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The time dependent concentration of PFOS and PFOA in human tissues can be predicted using PBPK models. The PBPK model was developed by considering the main target tissues of toxicological relevance for PFOA and PFOS Trends in the simulation results indicate that the urinary PFAS resorption-based PBPK model seems to be a reliable approach to explain the relatively longer half-life of PFOA and PFOS in human plasma. The model had been successfully validated by using experimental data in human blood, but good validation results were not achieved for other human tissues (knowledge is very limited; uncertainty and variability) Fabrega et al., Toxicology Letters 230 (2014) 244–251
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How blood levels are representative for tissue levels?
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An association between PFOA and PFOS serum levels and delayed age at menarche was reported in a cross-sectional study of adolescents. Growth dilution and the new route of excretion (menstruation) could account for some of the reported association.
A Monte Carlo (MC) physiologically-based pharmacokinetic (PBPK) model of PFAS was developed to simulate plasma PFAS levels in a hypothetical female population aged 2 to 20 years old, incorporating realistic distributions of physiological parameters as well as timing of growth spurts and menarche to assess how much of the apparent epidemiological association during puberty can be explained by pharmacokinetic variability. .
Wu et al, Environment International 82 (2015) 61–68
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Parameters in the simulated subjects were comparable to those reported in the epidemiologic study. Individual variations in PFAS kinetics associated with rapid growth around the onset of menstruation may contribute to the reported relationship between serum PFAS levels and age at menarche. The reported relationship between PFAS and age at menarche appears to be at least partly explained by pharmacokinetics rather than a toxic effect of these substances.
Wu et al, Environment International 82 (2015) 61–68
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(Angerer et al., Int J Hyg Envir Heal 2007)
EXPOSURE DOSE Response
Chemicals Toxins,
Metabolites
Protein adducts,
DNA adducts
Cytogenetic,
Immunological
parameters
Ambient Monitoring
In air, soil,
water, diet
Biological monitoring
In blood, urine
Health Surveillance
significance for risk assessment
External
Exposure
Internal
Exposure
Biochemical
Effect
Biological
Effect
Health Impairment,
Illness
Genetic Susceptibility
Impact of individual’s genotypic state on Exposure-Response
Continuum
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Genetic and Phenotipic differences
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Gene-environment interaction
Multifactorial diseases result from the interaction of individual
susceptibility genetic or acquired factors and exposure to modifiable
environmental factors.
No Exposure
n-Fold Increased ER
Increased background
Risk associated to
exposure (ER)
Background
Risk Level
(low)
Susceptible
Genotype
Susceptible
Genotype
Resistant
Genotype
Resistant
Genotype
Exposure
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• Studies at the molecular level in
humans suggest that there is an
individual variability in genetic
parameters, consistent with
different susceptibility to disease.
• An individual may be at high risk
due to genetic predisposition or
susceptibility.
For genetically
predisposed individuals
risk is strongly
dependent on exposure.
Gene-environment interaction ANSWER Workshop 2017
In Silico non testing methods
1. Read Across principle
The toxicological profile of chemical A is known and data are available
No or scant data are available for chemical B
When it can be demonstrated that A and B are structurally and toxicologically related, it is possible to use data on hazard identification available on A to evaluate hazard caused by B. Obviously for RA the exposure scenarios of B should be considered (which could be different from the A one)
The structural analogy should be supported by
1) an in silico analysis (SAR Structure activity relationship)
or
2) ‘bridging studies’ showing a similar toxic behaviour
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ECHA: Practical Guidance 6 http://echa.europa.eu/documents/10162/13655/pg_report_readacross_it.pdf
http://echa.europa.eu/support/grouping-of-substances-and-read-across
Read-across – predicting unknown properties of one chemical from known properties of similar chemicals – is a scientific method for filling data gaps on the effects of chemicals.
The aim of the Read-Across Assessment Framework (RAAF) is to provide a structured approach to the scientific evaluation of read-across justifications made by registrants in their dossiers.
OECD
GUIDANCE ON GROUPING OF CHEMICALS, SECOND EDITION Series
on Testing & Assessment No. 194 (2014)
http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/
jm/mono(2014)4&doclanguage=en
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Read across from data of the structurally related QUAT Didecyldimethyl-ammonium chloride (DDAC) is requested for metabolism, teratogenicity/ reproduction, chronic toxicity carcinogenicity, bioaccumulation and chronic ecotoxicity for the active substance Bardap 26.
Quaternary ammonium compound Bardap 26
The read across is supported by a set of bridging studies for DDAC demonstrating the similarity in physico-chemical and toxicological properties of these quaternary substances.
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Twinning ProjectN. EE05-IB-TWP-ESC-
09 21 Tallin April 2008
Physico-chemical properties
Physico-chemical prop. DDAC Bardap 26
Physical state (at ntp) Light-coloured solid Yellow liquid
Melting temperature Melted at 188 – 205°C followed by decomposition at ca 280°C.
<-50°C. No a melting point or a freezing point down to –50°C.
Boiling temperature Decomposition at ca 280°C 180 – 195°C
Relative density 0.902 at 20°C 0.942 at 20°C
Vapour pressure 5.9 x 10-6 Pa, 20 °C 1.8 x 10-6 Pa, 20°C
Henry’s Law constant 4.27E-09 Pa•m3/mol H monomer= 3.03E-11 Pa.m3/mol
Partition coefficient Not determined as substance is ionic and surface active (~ 1)
Not determined as the substance is ionic and surface active (~ 1)
Water solubility 500 g/l (20°C pH ca 2.2-9.2) Completely miscible with water (> 500 g/l)
Dissociation constant Not applicable, the substance is irreversibly ionised.
Not applicable, the substance is irreversibly ionised.
Surface tension 27.0 mN/m at 20°C (1g/l) 30.5 mN/m at 20°C (1g/l)
Solubility in ethanol > 250 g/l at 20°C > 250 g/l at 20°C
Solubility in octanol > 250 g/l at 20°C >250 g/l at 20°C
Flammability Not highly flammable Not highly flammable
Self ignition temperature ca. 195°C > 400°C
Explosive properties Non explosive Non explosive
Oxidising properties Non oxidising Non oxidising
Reactivity towards container materials
Non-reactive to metals and plastics Non-reactive to metals and plastics
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The acute hazardous properties of Bardap and DDAC mainly relate to the local effects of the reactive QUATs and are characterized by severe irritation and primary tissue damage by corrosion at the site of application. Other effects are considered to be secondary to local ones.
The subchronic toxicity endpoints and health based values (HBV) are in a similar range for Bardap and DDAC. Both compounds were negative in the mutagenicity test battery DDAC has comparable TK behaviour with another structurally related compound with the same MoA, namely ADBAC, as well as HBV for developmental and chronic toxicity and showed no effects in 2-generation and carcinogenicity studies.
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Endpoint DDAC Bardap 26
Acute toxicity LD50 oral rat LD50 dermal rabbit
238 mg/kg >2000 mg/kg
788 mg/kg read across
Skin irritation rabbit Eye irritation rabbit
corrosive corrosive
corrosive read across
Sensitization (Buehler) (M+K)
not sensitizing not sensitizing
not sensitizing
Subchronic tox NOAEL 90 day oral rats NOAEL 90 day oral mice NOAEL 8 weeks oral dogs NOAEL 90 day dermal rats
61 mg/kg/d 107 mg/kg/d 30 mg/kg/d 12 mg/kg/d
90 mg/kg/d
Mutagenicity Ames Mouse lymphoma cells Chromosome aberration
negative negative negative
negative negative negative
Bridging studies
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Twinning ProjectN. EE05-IB-TWP-ESC-
09 24 Tallin April 2008
Endpoint DDAC ADBAC Bardap 26
Developmental toxicity Rats, oral: NOAEL maternal toxicity NOAEL teratogenicity Rabbits, oral: NOAEL maternal toxicity NOAEL teratogenicity
10 mg/kg/d >20 mg/kg/d 10 mg/kg/d >20 mg/kg/d
10 mg/kg/d >100 mg/kg/d 3 mg/kg/d >9 mg/kg/d
read across
2-Generations, rats NOAEL parental NOAEL F1 NOAEL F2
no effects 750 ppm 750 ppm 750 ppm
no effects 1000 ppm 1000 ppm 1000 ppm
read across
Chronic toxicity 104 weeks, rats NOAEL 52 weeks, dog NOAEL
37 mg/kg/d 10 mg/kg/d
44 mg/kg/d 13 mg/kg/d
read across
Carcinogenicity 104 weeks combined, rats 78 weeks, mice
no effects no effects
no effects no effects
read across
ADME, rats <2.5% urine 89-99% faeces <1% in tissues
5-8% urine 87-99% faeces <1%in tissues
read across
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Based on The bridging studies’ results The structural similarity The similar MoA and the results showing similarity between
other QUATs (e.g. the similar metabolism pattern of DDAC and ADBAC)
it can reasonably be assumed that also for Bardap 26 similar results would be found as they have similar physico-chemical properties and similar chemical structure
The read across for the above mentioned toxicological end-points
from DDAC data to Bardap 26 is acceptable.
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2. In silico methods In silico is an expression used to mean "performed on computer or via computer simulation." The phrase was coined in 1989 as an allusion to the Latin phrases in vivo, in vitro. They include expert system as QSAR (quantitative structure-activity relationships) models, that is mathematical models that correlates a quantitative measure of chemical structure to either a physical property or a biological effect (e.g., toxic outcome). The term quantitative in QSAR refers to the nature of the parameters (also called descriptors) used to make the prediction. A molecular descriptor provides a means of representing molecular structures in a numerical form.
An assessment of QSAR model validity should be performed by reference to the internationally agreed principles for the validation of QSARs. An important issue in model validation is the definition of its applicability domain. The applicability domain of a QSAR is the physico-chemical, structural or biological space, knowledge or information on which the training set of the model has been developed, and for which it is applicable to make predictions for new compounds. Therefore QSAR models are associated with limitations as to what they can reliably be used
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3. Threshold of Toxicological Concern (TTC) The TTC approach is a science-based pragmatic tool for
screening and prioritizing chemicals for their safety assessment when hazard data are incomplete and human exposure can be estimated.
It can be used to evaluate chemicals for which toxicological data are not available (or to prioritise among a number of chemicals those for which there is a need to produce data)
It represent a ‘generic’ toxicological alert threshold giving possible concern, applicable to all chemicals (excluding some specific categories), below which the probability to have significan health risk is very low.
The approach was initially developed to evaluate the risk of substances present in very low amount in food items (e.f. flavourings); nowadays it is used in many other sectors.
SCCS, SCHER, SCENIHR, Joint Opinion on the Use of the Threshold of Toxicological Concern (TTC)
Approach for Human Safety Assessment of Chemical Substances with focus on Cosmetics and Consumer
Products, 8 June 2012 http://ec.europa.eu/health/scientific_committees/environmental_risks/index_en.htm
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TTC values have been derived by a statistical analysis of large toxicological data bases containing data on systemic toxicity reference values (NOEL, LOAEL, etc).
Threshold of Toxicological Concern (TTC)
Starting point: NOAELs distribution for different chemicals . The value corresponding to the 5th percentile divided per 100 (uncertainty factor) gives rise to the TTC value
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30 µg/kg BW
1.5 µg/kg BW
It is absolutely crucial to have available reliable data on the level of exposure. When the human exposure is below the TTC value, it is considered that the probability of experiencing any adverse effect is very low.
Cramer class I
Cramer class II e III
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TTC Applicability domain
Pesticides relevant metabolites in groundwater
Introduced for the first time in the previous Dir. 91/414/CEE.
Metabolites are defined ‘relevant’ when accounting for > 10% of the parent in environmental matrixes or are (eco)toxicologically relevant.
The EU directive 98/83/CE (Quality of water for human consumption) indicates that the maximun tolerated levels for any pesticide or its metabolite should be 0.1- 0.5 µg/l (single and total).
A Guidance Document on relevant metabolites in groundwater is available (EU Comm.,2003) establishing a procedure to be followed
The evaluation is described as a step-wise procedure in 5 phases, following the general principle pf RA, but introducing the application of the TTC.
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Starting from the analysis of all metabolites, some are excuded during phase 1:
Metabolites with no or scant toxicological relevance (es.CO2),
Inorganic compounds,
Aliphatic organic compounds with <4 C chain lacking any eteroatoms and structural alert (es. epoxides, nitrosamine).
These are defined as irrelevant metabolites (independently on their concetrations) .
The remaining ones proceed to phase II
Phase 1
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Groundwater concentrations are considered (predictive models ⇒ PECgw or monitoring data)
Metabolites with estimated or measured concentrations <0.1 g/l are excluded from further analysis as not relevant
Phase 2
Phase 3
For those metabolites with estimated or measured concentrations >0.1 g/l toxicological data should be evaluated along three steps : not meeting only one out of the three steps requirement is sufficient to define the metabolite as relevant.
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Step 1: comparison with the parent acute toxicity
Toxicity parent ⇒ relevant metabolite (<0.1 g/l) Toxicity <50% parentale ⇒ step 2
Step 2: genotoxicity assessment. Genotoxic ⇒ relevant metabolite (<0.1 g/l)
Non genotoxic ⇒ step 3
Step 3: toxicity assessment.
• T+, T, reprotox ⇒ relevant metabolite (<0.1 g/l)
• carcinogen 1a,1b⇒ relevant metabolite (<0.1 g/l)
• carcinogen 2⇒ relevant metabolite (<0.1 g/l),
unless data can demonstrate the lack of relevance
For the last case and remaining ones ⇒ phase 4
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The exposure level is compared with the TTC = 1.5 g/person/day (0.02 g/kg bw/d)
Considering a 2L of drinking water daily intake, the maximum level acceptable in water is = 0.75 µg/L.
When conc. < 0.75 µg/l ⇒ no further evaluation
When conc. > 0.75 µg/l ⇒ phase 5
Phase 4
Phase 5
When conc. is in the range 0.75-10 µg/L a more detailed RA is required It is assumed as a general quality criteria for GW that the 10 µg/L should not be exceeded (this limit as no toxicological basis) and the expert should evaluated the situation on a case-by-case basis
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Limited use for risk assessment purposes difficulties in carrying out quantitative in vitro to in vivo extrapolation
(QIVIVE) translate in vitro effect concentration into human toxicologically equivalent dose
Need of translating information from the cell level, to organs and subsequently to organisms and to distinguish between adaption vs. adversity, likely identifying actual in vitro markers of adversity (Blaauboer et al, 2012) or Key Events of AOPs
Integrated approach: in silico and in vitro IATA Lack of information on actual cell exposure in vitro biokinetics Battery KE (TD) + kinetics PBTK models
In vitro studies in RA
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Kinetics is finally considered the crucial body of information for the design and performance of ‘traditional’ in vivo toxicological tests,
toxicity data interpretation, identification of internal dose…….
Why not to include kinetics in alternative/non
animal testing strategy ?
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Biokinetics processes have been evoked to explain the in vitro/in vivo differences, but… …in vitro the nominal applied concentration rather than the actual level of cell exposure is usually associated to the observed effects.
Figure from Heringa et al., ES&T, 2004
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Cells
Test Item
Plastic adsorption
Evaporation
Protein binding
Uptake
Free Concentration in the medium
Target
Metabolism Free intracellular
Concentration
Characterization of the cell model
Passive/Active
(Transporters)
In vitro biokinetics
Chemical instability
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Adsorption to plastics and attachment to matrices
Dependent on:
Lipophilicity : LogD7.4>2.5 (e.g. amiodarone, CsA, Chlorpro-mazine) up to 70% plastic bound. Negligible binding for Ibuprofen, cisplatin, adefovir
Time : increase with time of treatment
Dose: increase with dose up to a plateau
Serum competes with plastics
Possibility of sequestration by Collagen; lower by Gelltrex
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Binding to protein in the medium
Low albumin
concentration
High albumin
concentration
Gülden M. et al. Toxicol. Letters 2003, 137, 159-168.
Cytotoxicity depends on protein binding in the medium Cell uptake is reduced by protein binding in the medium
2
10
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Bellwon et al, TIV, 2015a Bellwon et al, TIV, 2015b Wilmes A., et al. Journal of Proteomics , 2013
It depends on: the cell type Transporter activity and Metabolic competence Dose time CsA
Accumulation in cells
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AOP : Pathways/sequence of biological perturbations/events leading to adverse effects
MOA : a specific AOP is relevant for the chemical under evaluation? Verify the presence of key events
Key Events in AOP: endpoints (readouts) to be measured to verify if the chemical acts according to a specific AOP .
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OECD TG 442C Adopted: 4 February 2015 In Chemico Skin Sensitisation: Direct Peptide Reactivity Assay (DPRA) OECDTG 442D Adopted: February 2015 In Vitro Skin Sensitisation: ARE-Nrf2 Luciferase Test Method OECDTG 442E: Adopted: July 2016
In Vitro Skin Sensitisation: Human Cell Line Activation Test (h-CLAT)
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OECD TG 442C : DPRA + sensitiser OECDTG 442D : KeratinoSense + Sens. - OECD TG 442E : h-CLAT
DPRA or protein reactivity: represents the initiating molecular event in the skin sensitization AOP.
KeratinoSenseTM or luciferases test provide data on the second key event in the AOP with induction of gene which are regulated by ARE (Antioxidant Response Element).
h-CLAT readout represents the 3rd key event in the AOP and quantify the expression changes of cellular surface markers associated to the monocytes and dendritic cells activation.
-
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The issue of exposure to multiple chemicals
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Modified from A. Boobis, 2016
The RA process up to now has been essentially carried out referring to single chemicals. In many cases it addressed a single route of exposure or refers to a single use (the same chemicals can be used in different sectors and the routes and exposure scenarios can be very complex . Exposomes approach and biomonitoring consider this opportunity (the internal dose sums up all the different contributions) What about exposure to multiple chemicals ? What about multiple stressors exposure?
Testing of mixtures is practically unfeasible (high number of components and combinations; variation in the environment due to bacterial metabolism; bio/photo and chemical degradation different for different chemicals, altering the relative content; variation over time)
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When data on MoA are available:
RPF (Relative Potency Factor) used for OPT and carbamates ; AChE inhibition as critical effect toward a reference compound (RPF=1) within the family: summing up the relative potency of single components the inhibitory activity of the mixture is obtained
TEF (Toxic Equivalent Factor), similar to RPF, used for complex mixtures of dioxin like compounds (2,3,7,8,-TCDD is the reference compound for which TEF=1 and the critical effect is the binding to AhR receptor).
The toxicity of the mixture is then obtained by summing up the product of specific RFP or TEF with the concentration of the single components.
Dose Additivity : the component-based approach
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Hazard Index
The RfD or HBV of single components is needed. The relative contribution of each single is derived as the ratio between its concentration and the crresponding RfD. HI of the mixture is obtained by summing up single contributions :
HI= Conc1/RfD1 + Conc2/RfD2+.....+ Concn/RfDn
When HI>1 it is necessary to refine the RA on the basis of the ‘expert judgement’, since interactions (other than additivity) cannot be excluded
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When mechanistic data are not available : HI (Hazard Index) : assumption is that there is addivity as a worst case.
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HazDaT
Data base on line (ATSDR, 1997) containing data on the environmental contamination of >2000 sites on which ATSDR carried out an assessment aimed to protect public health
Identification of more frequent combinations of environmental contaminants in water, soil, air, or in highly risky areas and description of interaction profiles (IP)
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• IP for POPs in fish and in breast milk (dioxine-like chemicals, DDE,
HCB, PCBs, Methyl-Hg);
•IP for 1,1,1-TCEthane; 1,1-DCEthane; TCE e PCE;
•IP for benzene, toluene, ettylbenzene e xilene (BTEX);
•IP for Cu, Pb, Mn e Zn and for Cd, As, Cr, and Pb
•IP for atrazine, simazine, desetilatrazine, diazinon and nitrates.
Toxin i.p. LD50 (µg/kg)
M.W. Structure
MC-LR 50 994 cyclo -(D-Ala-L-Leu-D-MeAsp-L-Arg-Adda-D-Glu-Mdha-)
[D-Asp3]MC-LR 50 970 cyclo -(D-Ala-L-Leu-D-Asp-L-Arg-Adda-D-Glu-Mdha-)
MC-LA 50 909 cyclo -(D-Ala-L-Leu-D-MeAsp-L-Ala-Adda-D-Glu-Mdha-)
MC-YA 60-70 959 cyclo- (D-Ala-L-Tyr-D-MeAsp-L-Ala-Adda-D-Glu-Mdha-)
MC-YR 150-200 1044 cyclo -(D-Ala-L-Tyr-D-MeAsp-L-Arg-Adda-D-Glu-Mdha-)
[Dha7]MC-RR 180 980 cyclo -(D-Ala-L-Arg-D-MeAsp-L-Arg-Adda-D-Glu-Dha-)
MC-RR 500 1037 cyclo -(D-Ala-L-Arg-D-MeAsp-L-Arg-Adda-D-Glu-Mdha-)
MCs acute hepatotoxic potential is congener-dependent
2
(L-Arginine) 4
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In analogy with the method used for polychlorinated dibenzo[p]dioxins (PCDD), it has been proposed that one derive a “toxicity equivalent factor” (TEF) from the available acute toxicological data for MCs and NODs (Wolf and Frank, 2002; Funari and Testai, 2008). The approach should at present be limited to acute toxicity, since repeated toxicity data on different congeners are not available. The reference cyanotoxin is MC-LR, with TEF = 1; the TEF of a specific toxin (X) is derived as the ratio between the LD50 values, according to the equation: TEFX = LD50 MC-LR/LD50X
The total acute toxicity of the mixture is estimated by the sum of all the individual toxicity equivalents obtained as the product between the specific TEF and the toxin concentration.
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By using this approach a more realistic assessment is obtained for a hypothetic mixture when compared with the “worst case” approach, considering all the component as toxic as MC-LR, which is generally higher.
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Council Conclusion on Chemical Mixtures (2009) Kortenkamp et al. (2009) State of the Art on Mixture Toxicity.
Report to the EU ECETOC (2011) Report “Development of guidance for assessing the impact of mixtures of chemicals in the aquatic environment” Meek et al. (2011) Risk assessment of combined exposure to multiple chemicals: A WHO/IPCS framework, Reg Tox Pharm SCHER, SCENIHR, SCCS (2012): Toxicity and Assessment of Chemical Mixtures Euromix Project funded by EU Workshop EFSA RIVM on mixture toxicty (Utrecht, 2016) CURRENT EFSA WG on MIXTURES to adopt a harmonised opinion on the issue
International activity on mixtures: some examples
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Panel on Plant Protection Products and their Residues EFSA (2008) suitability of existing methodologies assessing cumulative and synergistic risks from pesticides to human health to set MRLs (Regulation (EC) 396/2005). EFSA (2009) Risk assessment Cumulative Effects- Triazole fungicides EFSA (2012) Science behind the development of a risk assessment of Plant Protection Products on bees EFSA (2013) 1.Identification of pesticides to be included in cumulative assessment groups on the basis of their toxicological profile. 2.Relevance of dissimilar mode of action and its appropriate application for cumulative risk assessment of pesticides residues in food. Panel on Contaminants in the Food Chain EFSA (2008) Polycyclic Aromatic Hydrocarbons in Food EFSA (2009) TEF approach-Non-ortho polybrominated biphenyls Marine biotoxins –Saxitoxin Group Pectenotoxin Group EFSA (2011) Whole mixture approach applied to Mineral Oil Saturated Hydrocarbons EFSA (2012) dose addition approach-Pyrrolizidine and Ergot alkaloids
The EFSA Activity
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The WHO framework
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Start with exposure Use pattern Is exposure even possible? Physicochemical properties Is systemic exposure possible? Threshold of toxicological concern Is exposure so low that it can be ignored? If additivity is a potential
concern, consider comparing exposure with a fraction of the TTC
Health based guidance values If exposure to a single
chemical exceeds its HBGV (e.g. TDI), address this issue before Cumulative RA
Modified from A. Boobis, 2016
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Modified from J-L Dorne, 2016
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In silico Kinetics ‘omics’
Imaging ‘organ on a chip’
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