Mining toxicity data to expand the domain of applicability of - PowerPoint PPT Presentation

Mining toxicity data to expand the domain of applicability of chemical activity Philipp Mayer Technical University of Denmark

LRI-ECO 30 Length: 2015-2017 La 50 €150 000 Budget: Main Participants ARC (Lead) – Jon A. Arnot, James M. Armitage, Trevor Brown UFZ – Beate I. Escher, Stefan Scholz, Annika Jahnke, Nils Klüver DTU - Philipp Mayer, Stine N. Schmidt THI – Barbara A. Wetmore* DMER/TU – Don Mackay* Malyka Galay-Burgos * Advisory role Todd Gouin Joop Hermens Mark Lampi + CEFIC LRI Monitoring Team Paul Thomas

Chemical activity µ = µ ∗ + 𝑺𝑼 × 𝒎𝒐(𝒃) Energetic state relative to pure liquid (0-1) a = 0 : no activity a = 1 : saturation for liquids a < 1 : solids form crystals below 1 Proportional to C free (a=C free /S L ) and fugacity (a=f/f L ) Diffusion & partitioning from high to low activity Equal at equilibrium a sediment = a interstitial water = a worm (Di Toro et al, 1991)

Chemical activity - well established for water! Water activity (a w , 0-1) = relative humidity (RH, 0-100%) “microbial fouling requires a certain a w ” http://wateractivity.com/education/basics-of-water-activity/ http://waterinfood.com

Baseline toxicity exerted at wide concentration range, but narrow chemical activity range Effective activity Effective concentration (Ea 50 , unitless) (EC 50 , in M) In correspondence with: 1 “Ferguson Principle” (1939) 0.1 DiToro’s Target Lipid Model 0.01 Van Wezel’s critical membrane 0.001 concent. (40-160 mM) 0.0001 0.00001 0.000001 Tadpole Mouse Algea Reichenberg & Mayer, 2006, ET&C 25: 1239-1245.

LRI-ECO30 General Objective • Further test & examine the chemical activity hypothesis for toxicity and risk assessment Methods/Approach • Compile toxicity data & apply the chemical activity approach to a series of relevant case studies

LRI-ECO30 ECO30 Research Activities Database Compilation Toxicity Data Physical-chemical (ECs, MoA) Properties QA/QC Uncertainty Chemical activity ( a ) calculations Categorization/Clustering Analyses

LRI-ECO30 ECO30 Research Activities Toxicity Data (ECs, MoA) 1. In vivo, juvenile + adult, acute + chronic • Fish data (78,206 records, 3,032 chemicals) from 4,011 studies • Mollusc and Crustacean data (39,955 records, 2,469 chemicals) • Amphibian and Reptilian data (7,172 records, 554 chemicals) • Invertebrates and other miscellaneous species data (21,117 records, 1,576 chemicals 2. Acute Fish Embryo Tests (FET) data 3. Chronic fish toxicity (Fish, Early Life Stage, FELST) data 4. Algal growth inhibition data MoA 5. C. elegans (nematode) Expert knowledge A. fischeri (bacteria) Toxtree 6. In vitro, bioassay (ToxCAST TM ) From the bioassay itself (in vitro)

1. In vivo, juvenile + adult, acute + chronic • Fish data (78,206 records, 3,032 chemicals) from 4,011 studies • Mollusc and Crustacean data (39,955 records, 2,469 chemicals) • Amphibian and Reptilian data (7,172 records, 554 chemicals) • Invertebrates and other miscellaneous species data (21,117 records, 1,576 chemicals Partner 1 - ARC 9

The ToxTest v1.0: Toxicokinetic Mass Balance Model • Toxicokinetic (bioaccumulation) model for aquatic organisms (fish) • Relates external water concentrations (e.g., LC50s) to internal concentrations (CBR50s) and internal chemical activities (La50s) • External chemical activity (i.e., CA in water phase) also provided as model output to readily allow comparisons to internal CA Ea50 External Activity > Internal Activity due to biotransformation? (i.e. disequilibrium?) Ea50 Partner 1 - ARC 10

KEY RESULTS DF = Ea50 Water / Ea50 Biota Disequilibrium factors (DF) for suspected baseline toxicants (Narc/Inert), chemicals with specific modes of action (React/Spec) and chemicals which could not be confidently assigned to either category (Uncertain). Whiskers = 1.5 IQR. NOTE: Biotransformation half-lives are predicted values based on available QSARs Partner 1 - ARC 11

SUMMARY • Database consists of ~150,000 entries for >4,500 chemicals from >1,000 species - So far, most data points categorized as “Not Assignable” are due to unconfirmed exposure concentrations • Tentative MoA classifications for 2,510 fish acute lethal data entries: 1 - 982 Narcosis/Relatively inert; 2 – 1,082 Reactive/Specific MoA; 3 - 446 Uncertain (Unknown/Unsure) • Uncertainty in physical-chemical properties is an important consideration when applying the chemical activity approach • Biotransformation can lead to large differences between the chemical activity in water (external) and in the organism – not always relevant though, as shown for Case Study Partner 1 - ARC 12

Exploring the chemical activity concept for in vitro data Task in WP 3 Approach Measures/models • Translate the existing data on Define baseline Define baseline measured/modeled cellular chemical activity 3.1 chemical activity concentrations into chemical activity E a B for in vitro for in vitro assays • Predict baseline chemical activity for assays HTS reporter gene assays • Select ToxCast and other in vitro EC W EC w , CBR assays that describe clearly defined Data mine HTS in 3.2 modes of action vitro assays S F S L • Convert reported nominal Ea concentrations into chemical activity • Define TR a threshold for baseline Define chemical Chemical activity- toxicants activity-based based Toxic Ratio • Calculate TR a for specifically acting 3.3 Toxic Ratio (TR a ) TR a = E a B compounds for in vitro assays • Explore clustering and ranges of in relation to MoA E a S excess activity in relation to MoA Partner 2 13

Exploring the chemical activity concept for in vitro data • Adapting the mass balance model (Armitage 2014) to 384 and 1536 well plate format and parameterize with experimental data 1 f cell = V w + K FBSw m FBS + K PSw V 1 1 + PS K cellw m cell K cellw m cell K cellw m cell Partner 2 Fischer, F., Henneberger, L., König, M., Bittermann, K., Linden, L., Goss, K.-U. and Escher, B. (2017) Modeling 14 exposure in the Tox21 in vitro bioassays. Chemical Research in Toxicology 30, 1197−1208.

Exploring the chemical activity concept for in vitro data Fractions in cells f cell Modelled internal effect concentrations with mass-balance model for partition in cells IEC cell coefficients are in similar range as IEC for algae, fwater fmedium fcells daphnia and fish 100 chemical fraction in compartment (%) 10000 for aquatic species 10 and EC cell for cells IEC (mmol/kg lip) 1000 100 1 10 0,1 1 f cell = 1 + K mediumw m medium V w 1 1 + e a h l l a i e s n g c i K cellw m cell K cellw m cell h f l a p a 0,01 d 0 2 4 6 8 log K ow � Escher and Fischer, 2017 15 Schwarzenbach, 2002 Partner 2 Fischer, F., Henneberger, L., König, M., Bittermann, K., Linden, L., Goss, K.-U. and Escher, B. (2017) Modeling exposure in the Tox21 in vitro bioassays. Chemical Research in Toxicology 30, 1197−1208.

Define activity-based toxic ratios for in vitro assays in relation to mode of action • First step: rescale the mass balance model to 1536 well plate and modeling the published data from Huang, R.L et al. (2011) and additional new data from ToxCAST • Second step: define baseline from unrelated cytotoxicity data (constant cellular membrane concentrations) • Third step: (related to cell concentrations) 1000 = IEC cytotoxicity activity baseline 100 TR activity = Toxic ratio TR 10 activity specific MOA IEC specific MOA 1 0.1 0.01 0.001 PR PPAR p53 ARE Partner 2 16

Partner 3 17 Schmidt and Mayer (2015) Chemosphere 120: 305-308

(1) Extending to polar and solid MOA 1 & 2 compounds - confirming the chemical activity range for baseline toxicity 3 0 2 -1 Log EC 50 (mmol L -1 ) 1 -2 Log EC 50 /S L 0 -3 -1 -4 a=1 (S L ) MOA 1 liquid (n=46) MOA 1 liquid (n=46) -2 -5 MOA 1 solid (n=4) MOA 1 solid (n=4) MOA 2 liquid (n=20) MOA 2 liquid (n=20) a=0.1 MOA 2 solid (n=38) MOA 2 solid (n=38) -1 0 1 2 3 4 5 -1 0 1 2 3 4 5 Log K ow Log K ow Aruoja et al. (2011) Chemosphere 84: 1310-1320 Aruoja et al. (2014) Chemosphere 96: 23-32

(2) Extending to more compounds, MOAs and species - identifying and quantifying excess toxicity All data from Fu et al. (2015): • awaiting publication Figure removed, Data selection: • awaiting publication awaiting publication Selected for analysis: • awaiting publication Fu et al. (2015) Chemosphere 120: 16-22

Conclusions • Transferring toxicity data to chemical activity: 1. Visually relative to regression for liquid solubility (very simple) 2. Conversions of e.g. LC 50 to La 50 • Both approaches are straight forward for a large group of neutral chemicals, but more challenging for e.g. ionics • Uncertainty/error of input data and model assumptions can be important • Baseline toxicity at chemical activity 0.01-1, generally confirmed • Toxicity at chemical activity << 0.01 shows excess toxicity • More commonalities than differences between La 50 and ILC 50