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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.

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Philipp Mayer Technical University of Denmark

Mining toxicity data to expand the domain of applicability of chemical activity

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LRI-ECO 30

Length: 2015-2017 Budget: €150 000

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* * Advisory role + CEFIC LRI Monitoring Team

Malyka Galay-Burgos Todd Gouin Joop Hermens Mark Lampi Paul Thomas

La50

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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 Cfree (a=Cfree/SL) and fugacity (a=f/fL) Diffusion & partitioning from high to low activity Equal at equilibrium asediment = ainterstitial water = aworm (Di Toro et al, 1991)

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Chemical activity - well established for water!

Water activity (aw, 0-1) = relative humidity (RH, 0-100%) “microbial fouling requires a certain aw”

http://wateractivity.com/education/basics-of-water-activity/ http://waterinfood.com

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Baseline toxicity exerted at wide concentration range, but narrow chemical activity range

Reichenberg & Mayer, 2006, ET&C 25: 1239-1245. In correspondence with: “Ferguson Principle” (1939) DiToro’s Target Lipid Model Van Wezel’s critical membrane

  • concent. (40-160 mM)

Effective activity

(Ea50, unitless) 0.000001 Tadpole Mouse Algea 0.00001 0.0001 0.001 0.01 0.1 1

Effective concentration

(EC50, in M)

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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

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LRI-ECO30

ECO30 Research Activities

Database Compilation Physical-chemical Properties

QA/QC

Toxicity Data (ECs, MoA) Chemical activity (a) calculations Categorization/Clustering Analyses Uncertainty

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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
  • 5. C. elegans (nematode)
  • A. fischeri (bacteria)
  • 6. In vitro, bioassay (ToxCASTTM)

MoA

Expert knowledge Toxtree From the bioassay itself (in vitro)

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9

  • 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

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10

Partner 1 - ARC

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
  • utput to readily allow comparisons to internal CA

Ea50 Ea50

External Activity > Internal Activity due to biotransformation? (i.e. disequilibrium?)

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11

Partner 1 - ARC

KEY RESULTS

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

DF = Ea50Water / Ea50Biota

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12

Partner 1 - ARC

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

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Exploring the chemical activity concept for in vitro data

13

Partner 2

Define baseline chemical activity for in vitro assays

  • Translate the existing data on

measured/modeled cellular concentrations into chemical activity

  • Predict baseline chemical activity for

HTS reporter gene assays Task in WP 3 Approach Data mine HTS in vitro assays

  • Select ToxCast and other in vitro

assays that describe clearly defined modes of action

  • Convert reported nominal

concentrations into chemical activity Define chemical activity-based Toxic Ratio (TRa) for in vitro assays in relation to MoA

  • Define TRa threshold for baseline

toxicants

  • Calculate TRa for specifically acting

compounds

  • Explore clustering and ranges of

excess activity in relation to MoA 3.1 3.2 3.3 Define baseline chemical activity EaB for in vitro assays Measures/models

TRa = Ea

B

Ea

S

SL Ea SF ECW

ECw, CBR

Chemical activity- based Toxic Ratio

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14

Partner 2

  • Adapting the mass balance model (Armitage 2014) to 384 and 1536

well plate format and parameterize with experimental data

fcell = 1 1+ 1 Kcellw Vw mcell + KFBSw Kcellw mFBS mcell + KPSw Kcellw V

PS

mcell

Exploring the chemical activity concept for in vitro data

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.

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15

Partner 2

Exploring the chemical activity concept for in vitro data

Fractions in cells fcell with mass-balance model for partition coefficients

log Kow

2 4 6 8

chemical fraction in compartment (%)

0,01 0,1 1 10 100

fwater fmedium fcells

  • fcell =

1 1+ Kmediumw Kcellw mmedium mcell + 1 Kcellw Vw mcell

Modelled internal effect concentrations in cells IECcell are in similar range as IEC for algae, daphnia and fish

a l g a e d a p h n i a f i s h c e l l 1 10 100 1000 10000

IEC (mmol/kg lip) for aquatic species and ECcell for cells

Escher and Schwarzenbach, 2002

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.

Fischer, 2017

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Define activity-based toxic ratios for in vitro assays in relation to mode of action

16

Partner 2

  • 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:

TRactivity = activitybaseline activityspecific MOA = IECcytotoxicity IECspecific MOA

PR PPAR p53 ARE 0.001 0.01 0.1 1 10 100 1000

Toxic ratio TR (related to cell concentrations)

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

Partner 3

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(1) Extending to polar and solid MOA 1 & 2 compounds

  • confirming the chemical activity range for baseline toxicity

Aruoja et al. (2011) Chemosphere 84: 1310-1320 Aruoja et al. (2014) Chemosphere 96: 23-32

  • 1

1 2 3 4 5

  • 5
  • 4
  • 3
  • 2
  • 1

MOA 1 liquid (n=46) MOA 1 solid (n=4) MOA 2 liquid (n=20) MOA 2 solid (n=38)

Log Kow Log EC50/SL

  • 1

1 2 3 4 5

  • 2
  • 1

1 2 3

MOA 1 liquid (n=46) MOA 1 solid (n=4) MOA 2 liquid (n=20) MOA 2 solid (n=38)

a=1 (SL) a=0.1

Log Kow Log EC50 (mmol L-1)

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(2) Extending to more compounds, MOAs and species

  • identifying and quantifying excess toxicity

All data from Fu et al. (2015):

  • awaiting publication

Data selection:

  • awaiting publication

Selected for analysis:

  • awaiting publication

Fu et al. (2015) Chemosphere 120: 16-22

Figure removed, awaiting publication

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Conclusions

  • Transferring toxicity data to chemical activity:

1. Visually relative to regression for liquid solubility (very simple) 2. Conversions of e.g. LC50 to La50

  • 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 La50 and ILC50
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Articles, published

1. Fischer, F.C.; Henneberger, L.; König, M.; Bittermann, K.; Linden, L.; Goss, K.U.; Escher, B.I. 2017. Modeling exposure in the Tox21 in vitro bioassays. Chemical Research in Toxicology 30, 1197–1208. 2. Mayer P & SN Schmidt. 2017. Comment on “Assessing Aromatic-Hydrocarbon Toxicity to Fish Early Life Stages Using Passive-Dosing Methods and Target-Lipid and Chemical-Activity Models” Environmental Science & Technology 51, 3584−85. 3. Stibany F, Schmidt SN, Schäffer A & P Mayer. 2017. Aquatic toxicity testing of liquid hydrophobic chemicals - Passive dosing exactly at the saturation limit. Chemosphere 167: 551-557. 4. Klüver, N.; Vogs, C.; Altenburger, R.; Escher, B. I.; Scholz, S., 2016. Development of a general baseline toxicity QSAR model for the fish embryo acute toxicity test. Chemosphere, 164, 164-173. 5. Thomas P, Mackay D, Mayer P, Arnot J and MG Burgos. 2016. Response to Comment on “Application of the Activity Framework for Assessing Aquatic Ecotoxicology Data for Organic Chemicals”. Environ. Sci.

  • Technol. 50, 4141-4142.
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Manuscripts

1. Scholz, S.; Duis, K.; Schreiber, R.; Lidzba, A.; Armitage, J.M.; Mayer, P.; Léonard, M.; Altenburger, R. Retrospective analysis of fish early life stage tests – association of toxic ratios and acute chronic ratios with modes of action. Submitted. 2. Gobas FAPC, Mayer P, Parkerton TF, Burgess RM, van de Meent D & T Gouin. A Chemical Activity Approach to Exposure and Risk Assessment of Chemicals. Minor revisions. 3. Hermens JLM, Cronin MTD, Escher BI, Mayer P, Roex EWM & P Thomas. Linking aquatic toxicity data to chemical activity and target site concentrations - beyond non-polar narcosis. In revision. 4. Schmidt SN, Armitage JM, Arnot JA, Mackay D & P Mayer. Expanding the chemical activity domain for algal growth inhibition tests for non-polar organic compounds. To be submitted. 5. Winding A, Modrzyński JM, Christensen JH, Brandt KK and P Mayer. Soil bacteria and protists show different sensitivity to polycyclic aromatic hydrocarbons at controlled chemical activity. To be submitted. 6. Arnot, J.A.; Armitage, J.M.; Orazietti, A.; Gouin, T.; McCarty, L.S.; Mackay, D. Toxicokinetic evaluation

  • f critical body residue and chemical activity data for fish. In preparation.

7. Various ECO.30 Project Authors. Exploring the merits and limitations of the chemical activity approach for chemical hazard and risk assessment. Planned.

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Presentations (posters/platforms)

1. Armitage JM, Arnot JA, Orazietti A, Brown TN, Celsie A, McCarty LS, Mackay D. 2017. Expanding the evaluation of the chemical activity hypothesis for toxicity assessment. SETAC Conference, Brussels, Belgium. 2. Schmidt SN, Armitage JM, Arnot JA, Kusk KO, Mayer

  • P. 2016. Linking algal growth inhibition to chemical

activity: A tool for identifying excess toxicity. SETAC Conference, Nantes, France. 3. Schmidt SN, Armitage JM, Arnot JA, Kusk KO, Mayer

  • P. 2015. Linking algal growth inhibition to chemical
  • activity. SETAC Conference, Salt Lake City, UT.

4. Armitage JM, Arnot JA. Mackay D. 2015. Why is chemical activity successful as a metric of aquatic toxicity? A gedanken experiment explains why. SETAC Conference, Salt Lake City, UT. 5. Brown TN, Armitage JM, Arnot JA. 2015. Addressing uncertainty in sub-cooled liquid property estimation: Applications for chemical activity calculations. SETAC Conference, Salt Lake City, UT.

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THANK YOU!

24/11/2017 LRI – PRESENTATION TITLE