Biological Read-Across: Species-Species and Endpoint- Endpoint Extrapolation Mark Cronin School of Pharmacy and Chemistry Liverpool John Moores University England m.t.cronin@ljmu.ac.uk
Integrated Testing Strategies (ITS) Existing Data In Silico Assessment In Chemico Assessment In Vitro Assessment In Vivo Assessment
An Initial Stage of an ITS is the Use of In Silico and In Chemico Techniques Existing data Category formation Filling data gaps Existing Data In Silico Assessment In Chemico Assessment In Vitro Assessment In Vivo Assessment
Category Formation Developing groups of similar compounds btaining toxicological data and information Performing read-across to interpolate toxicological endpoints
Category Formation Structural Analogues H H H H Mechanistic Analogues H N Toxicologically Meaningful Analogues H H H H H
Chemical Read-Across H H H H Toxicity Toxicity SAR / Read- Across Interpolation ECD Guidance on Grouping of Chemicals
Biological Read-Across Read-Down H H H H Species 1 Species 2 Single Endpoint Species 3
Inter-Species Relationships Fish to Fish Miscellaneous Chemicals 3 Rainbow Trout Toxicity 2 1 0 Trout LD 50 = 0.97 Bluegill LD 50 0.11 n = 13 r 2 = 0.93-1 -1 0 1 2 Bluegill Toxicity 3 4 Data from: LeBlanc GA (1984) Environ. Toxicol. Chem. 3: 47-60
Biological Read-Across Trophic Level Read-Down H H H H Species 1 Species 2 Single Endpoint Species 3 Single Effect H H H H Species 1 Species 2 Single Endpoint Species 3
Fathead Minnow vs Tetrahymena pyriformis Toxicity 5 4 Fish Toxicity 3 2 1 0-1 -2-3 Fish LD 50 = 1.00 Protozoan IGC 50 + 0.56 n = 364 r 2 = 0.75-3 -2-1 0 Protozoan Toxicity 1 2 3 Kahn I et al (2007) ATLA 35: 1-10
Fathead Minnow vs Tetrahymena pyriformis Toxicity of Non-Polar Narcotics 3 2 Fish Toxicity 1 0-1 -2 Fish LD 50 = 1.15 Protozoan IGC 50 + 0.44 n = 54 r 2 = 0.96-3 -3-2 -1 0 Protozoan Toxicity 1 2 3 Cronin MTD et al (1991) Sci Tot Environ 109-110: 431-439
Between Taxa Extrapolations are Stronger Within a Mechanism Narcotic potency is often consistent across trophic levels, only differing by species sensitivity Reactive chemicals show more significant inter-species variability Species specific metabolism can be identified Esterase in fish and not in Tetrahymena Form categories on mechanisms for biological read-across
Tools to Form Categories: Acute Aquatic Toxicity Verhaar rules and updates US EPA (Aster) Protein reactivity rules Metabolic groups
Biological Read-Across Endpoint to Endpoint Read-Down H H H H Species 1 Species 2 Species 3 Single Endpoint Single Effect H H H H Species 1 Species 2 Species 3 Single Endpoint H H H H Species 1 Species 2 Species 3 Single Endpoint Single Effect
(Protein) Reactive Toxicity A number of toxic effects are a result of reactivity with biological macromolecules Skin sensitisation, excess acute aquatic toxicity, mutagenicity Reactivity is the formation of a covalent bond with e.g. a protein or DNA Reactive toxicity has been a challenge to model (quantitatively) in silico
Predicting Reactive Toxicity Reactivity can be associated with mechanistic organic chemistry Relative reactivity can be quantified by in chemico reactivity Domains of reactivity have been defined SMARTS strings for five classic mechanisms Endpoints superimposed across a domain
Cl Br H Cl H Cl Category: Michael Acceptors Cl Cl Cl - N + Cl Cl Cl - N + - N + N H Cl N + - Br
Michael Acceptors Show Excess Acute Aquatic Toxicity 2 Non-Polar Narcosis 1 Toxicity 0-1 -2 0 1 Log P 2 3 4
Skin Sensitisers? LLNA Moderate Sensitiser Read-Across LLNA Weak Sensitiser H Br
Further Lines of Evidence: In Chemico Reactivity Read-Across Reactivity with a nucleophile e.g. glutathione is associated with sensitisation In chemico reactivity can be measured (and predicted) In chemico reactivity will extend the domain of the category and assist in readacross Br H
Category Formation: Filling an Incomplete Data Matrix by Weight of Evidence H H H H Species 1 Species 2 Species 3 Single Endpoint Structure H H H H Species 1 Species 2 Single Endpoint In Chemico Species 3 Species 1 Species 2 H H H H Single Endpoint In Vitro Species 3
A special supplement of ATLA is available with details of these ITS Integrated Testing Strategies 1. Are there existing data to suggest that the substance is, or is not, sensitising to the skin? No Yes C&L and/or RA 2. Define mechanistic domain (if applicable) and collect any available data on the reactive chemistry of the test substance (or its chemical class; nonvalidated). 3. Use in silico methods (such as DEREK, TIMES, ECD QSAR Application Toolbox) to make predictions on skin sensitisation (non-validated). 4. Perform in vitro skin penetration study (ECD TG 428). 5. Perform in vitro protein binding test (in house method). 6. Perform in vitro cell based assays such as those involving dendritic/langerhans cells and/or T Lymphocytes (non-validated). Grindon C et al (2006) ATLA 34: 651-664 7. Perform weight of evidence evaluation on all data so far. Does this show whether the substance is a skin sensitiser or not? No 8. Is a full quantitative risk assessment required? Yes C&L and/or RA
Conclusions Mechanisms of action can be used to form categories Categories allow for biological readacross Species to species Endpoint to endpoint Tools are available to assist in the formation of robust categories Categories can be implemented through ITS
Acknowledgements Terry Schultz, Steve Enoch, Mark Hewitt, Yana Koleva, Judith Madden EU FP6 SIRIS Integrated Project (GCE-CT-2007-037017) EU FP6 InSilicoTox Marie Curie Project (MTKD-CT-2006-42328)
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