1

Evaluation of Alternative Methods for Assessing Acute Toxicity of Mixtures

Raja S. Settivari, BVSc&AH, PhD, DABT

Dow Chemical Company

2

Purpose

Evaluation of alternative methods for:

Testing formulations/mixtures for acute contact toxicity endpoints

Application of alternative methods for sustainable formulations development

3

Development of Alternative Testing Methods

An active area of research to develop and validate alternative methods

Acute toxicity and Receptor binding endpoints

Increased focus to: Explore applicability domain and identify limitations/gaps

Identify complementary assays and develop integrated testing strategies (ITS)

Performance of an ITS depends on the extent to which the different assays are able to compensate for each other’s technical limitations

Promote utilization and acceptance globally

Application of alternative methods are growing

Endocrine Screening program

Dermal sensitization, Dermal irritation, Eye irritation

Lautenberg Chemical Safety Act: Non-animal models

4

Testing strategies

Tools: QSAR, Analog ID, Read Across, Data mining

(Internal and publically available data)

Tier 1 -Cheminformatics

In vitro predictive assays

(selected based on specific question/need)

Tier 2 –In vitro biological profiling

Knowledge

integration

In vivo (animal) assays

(selection based on regulatory need)

Tier 3 – Standard Regulatory Toxicology

Imp

lem

en

tati

on

Approach that supports implementation of IATAs

5

ICCVAM. 1999. NIH Publication No. 99-4494 ICCVAM. 2010. NIH Publication No. 11-7709

Urbisch et al. 2015. Reg Tox Pharm 71:337-351. Dumont et al. 2016. Tox In Vitro 34: 220-228

ENV/JM/MONO(2013)12/PART2 Hoffmann et al. 2017 submitted

Hazard 72%-82%

Potency 54% - 60%

Hazard ~72%

Potency ~60%

GPMT / Buehler LLNA

Reproducibility of Multiple Tests (~100 chemicals)

Hazard ~78%

Potency ~62%

Accuracy Against Human Clinical Data (~150 chems)

Draize Eye Irritation Test Limited within- and between-laboratory reproducibility High variability for moderate to mild irritating compounds

6

A way to deliver an active ingredient (AI) in a usable form

Consists of multiple components Active Ingredient(s) (AI)

Inert ingredients Surfactants Dry fillers and carriers Solvents, Thickeners, Biocides, Odorants, Anti-freeze, Stabilizers, Dyes, Pigments

Effective implementation of alternative methods may lead to: Development of sustainable products

Greater reduction in animal usage

Formulation/Mixtures For Everyday Use

• Components with different toxicity potential • Compatibility issues at higher conc. • Can’t be defined by Molarity

Challenges

7

Dermal Sensitization The key mechanistic events underpinning the skin sensitization process that leads to

Allergic Contact Dermatitis (ACD) in humans have been identified

1

4

5 2

3

DPRA KeratinoSens

M&K

Buehler

LLNA

Human Cell Line

Activation Test

Myeloid U937 Skin Sensitization Test;

IL-8 luc Test

8

8 agrochemical AIs and 10 corresponding multi-component formulations (Commercial quality)

All samples had prior in vivo Buehler, M&K or LLNA results

Three different types of formulations were tested in the KeratinoSens and DPRA:

Soluble Concentrate (SL) AI is presented as a water soluble salt

Emulsion Concentrate (EC) AI is solubilized in an organic solvent to be applied as an emulsion after

dilution in water

Suspension Concentrate (SC) Low water soluble and high melting point AI is suspended in water

Dermal Sensitization

9

KeratinoSens Assay

Cells: HaCaT cells with stably integrated ARE-luciferase reporter gene

Dose Levels: Limit concentration of 2000 µM or 4 mg/mL or 0.4%

Provides concentration-response information (12 concentrations)

Study measurements/readouts (separate plates):

Luciferase induction

Cytotoxicity

Keap1

S S

Nrf2

Nrf2

ARETarget Genes

Luciferase

Activated state

Figure adapted from Natsch, 2010

Keap1

SH SH

Nrf2

Nrf2Ub

Non-activated state

HaCaT cell line

Stably integrated ARE reporter gene

Keap1

S S

Nrf2

Nrf2

ARETarget Genes

Luciferase

Activated state

Figure adapted from Natsch, 2010

Keap1

SH SH

Nrf2

Nrf2Ub

Non-activated state

HaCaT cell line

Stably integrated ARE reporter gene

Nucleus

Cytoplasm

Ub-dependent proteasomal

degradation

10

Approaches for Dose Selection

Approach 1: • Based on the MW and concentration of AI in the formulation

(ranged from 4.9% to 81.8%)

• Potential challenges: • Results in testing co-formulants at higher concentrations • Test material compatibility issues and false positive results

Approach 2: • Test at a constant maximum “mg/ml” concentration • Based on the total formulation (considered formulation as single entity) • Avoid testing the co-formulants at higher concentrations

• Potential challenges:

• False negative results

Results were compared to existing in vivo data and human data (when available)

11

Test Material Type AI conc, %

In vivo data

KeratinoSens assay data

(Corrected to MW and purity)

(Approach 1)

KeratinoSens assay data

(Constant max conc)

(Approach 2)

GF-700 (AI: Acetochlor) EC 81.8 Sensitizer Sensitizer Sensitizer

GF-1478 (AI: Meptyldinocap) EC 34.4 Sensitizer Non-sensitizer Sensitizer

GF-2870 (AI: Triclopyr choline) SL 54.6 Sensitizer Non-sensitizer Sensitizer

XRM-4714 (AI: Triclopyr butotyl) EC 61.8 Sensitizer Sensitizer Sensitizer

GF-871 (AI: Aminopyralid

triisopropanolammonium SL 40.1 Non-sensitizer Non-sensitizer Non-sensitizer

GF-2000 (AI: Clopyralid

monoethanolammonium) SL 43.2 Non-sensitizer Non-sensitizer Non-sensitizer

EF-1343* (AI: Florasulam) SC 4.91 Non-sensitizer Sensitizer Non-sensitizer

GF-837 (AI: Methoxyfenozide) SC 22.8 Non-sensitizer Non-sensitizer Non-sensitizer

GF-1243 (AI: Oxyfluorfen) EC 22.3 Equivocal Sensitizer Non-sensitizer

GF-1191 (AI: Oxyfluorfen) EC 22.6 Equivocal Sensitizer Non-sensitizer

Testing co-formulants at higher concentrations resulted in false predictions in Approach-1

Approach-2 provided better predictions compared to in vivo results

Settivari et al., RTP, 2015

KeratinoSens Test Results for Formulations

12

Formulations

with single AI KeratinoSens

analysis

n % Correct

predictions

Correct 27 96.4%

False Positives 0 NA

False Negatives 1 3.6%

Formulations

with multiple AIs KeratinoSens

analysis

n % Correct

predictions Correct 7 78% False Positives 1 11% False Negatives 1 11%

KeratinoSens Test Results for Formulations

In vivo classification

for respective AIs In vitro KeratinoSens results

In vivo

results

Formulations with single AI LLNA/Guinea pig studies EC1.5

(µM)

Cell

viability

(%)1 Interpretation In vivo studies

Acetochlor-EC Sensitizer 1.43 >70% Sensitizer Sensitizer

Meptyldinocap-EC Sensitizer 1.79 >70% Sensitizer Sensitizer

Triclopyr Butoxyethyl Ester-EC Sensitizer 71.25 >70% Sensitizer Sensitizer

Triclopyr choline-SL Sensitizer 623.09 >70% Sensitizer Sensitizer

Cyhalofop butyl-EC-1 Non-sensitizer 26.4 >70% Sensitizer Sensitizer

Cyhalofop butyl-EC-2 Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Aminopyralid

triisopropanolammonium-SL Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Clopyralid monoethanolammonium-SL Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Florasulam-SC Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Methoxyfenozide-SC Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Oxyfluorfen-EC-1 Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Oxyfluorfen-EC-2 Non-sensitizer 545.21 >70% Non-sensitizer Non-sensitizer

Propiconazole-SC Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Spinetoram-SC Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Spinosad-SC Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Sulfoxaflor-SC Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Formulations with multiple AIs

Triclopyr salt + Aminopyralid-SL Sensitizer + Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Triclopyr ester + Aminopyralid-EC Sensitizer + Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Triclopyr ester + Florasulam-EC Sensitizer + Non-sensitizer NA2 >70% Non-sensitizer Sensitizer

Aminopyralid + Halauxifen methyl-SC Non-sensitizer + Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Aminopyralid + Clopyralid-SL Non-sensitizer + Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Aminopyralid + Fluroxypyr-EW Non-sensitizer + Non-sensitizer 114.1 >70% Sensitizer Sensitizer

Picloram potassium + 2,4-D-SL Non-sensitizer + Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Aminopyralid + Picloram potassium +

Clopyralid-SL Non-sensitizer + Non-sensitizer

+ Non-sensitizer NA2 >70% Non-sensitizer Non-sensitizer

Aminopyralid + Picloram potassium +

Fluroxypyr-EW Non-sensitizer + Non-sensitizer

+ Non-sensitizer 140.3 >70% Sensitizer Non-sensitizer

13

Direct Peptide Reactivity Assay (DPRA)

In chemico procedure

Measures depletion of a target peptide following incubation with a test material.

Depletion quantified using HPLC-UV.

O

O

F

F

F

O

O

F

F

F

Hapten

E

Protein

O

O

F

F

F

Protein

Hapten O

O

F

F

F

:Nu

The hepta-peptides are incubated with test material at for 24 h.

Lysine Peptide (K): Ac-RFAAKAA-COOH (Peptide:TM – 1:50 ratio)

Cysteine Peptide (C): Ac-RFAACAA-COOH (Peptide:TM – 1:10 ratio)

Interpretation criteria:

Avg of Cysteine & Lysine model: Peptide depletion threshold – 6.38%

Cysteine model: Peptide depletion threshold – 13.89%

14

DPRA Test Results for Formulations

Formulations

with single AI DPRA analysis

n % Correct

predictions

Correct 14 87.5%

False Positives 0 NA

False Negatives 2 12.5%

Formulations

with multiple AIs DPRA analysis

n % Correct

predictions Correct 8 89% False Positives 0 NA False Negatives 1 11%

Liquid Chromatography with Tandem Mass Spectrometry (LC/MS-MS) to improve sensitivity

and specificity

Selected in vitro methods provided good predictions for testing formulations

15

Active

Ingredients (AI)

Formulations w/ single

AI

Formulations 2 or

more AIs

N 20 16 9

KeratinoSens 85% 100% 78%

DPRA 91% 87.5% 89%

GHS Threshold - 87.5% 78%

WoE (2 of 3) 100% 100% 89%

Accuracy relative to

in vivo data

Integrated Testing Results for Formulations

Threshold-based approach (Formulations)

GHS approach: Read-across from sensitizing ingredients present ≥ 1% to the mixture

A substance testing positive or negative in any two methods are classified as

sensitizers or non-sensitizers, respectively

Key learnings

In silico:

Closer attention to applicability domain

KeratinoSens:

Dose response, Cytotoxicity, LogKoW, Physical properties of the test substance

DPRA:

Solvent, Non-covalent interaction b/n TM and peptide

Conclusion

Selected alternative methods provided promising performance for evaluating skin sensitization potential of multi-component mixtures

17

Ocular Irritation Assays 1. Organotypic models

Hen’s egg test – Chorioallantoic membrane test

Isolated rabbit eye test

Isolated chicken eye test (OECD TG 438)

Bovine corneal opacity and permeability test (OECD TG 437)

2. Cell-based models

Short Time Exposure In Vitro Test Method (OECD TG 491)

Red blood cell hemolysis test

Cytosensor Microphysiometer

Fluorescence leakage test (OECD TG 460)

Neutral red release assay

3. Reconstructed human tissue models Reconstructed Human Cornea-like Epithelium Test (OECD TG 492)

18

Ocular Irritation study:

Selected 64 formulations for evaluating the performance of the NRR and EpiOcular assays

Selection based on availability of high quality in vivo data

11 types of formulations

Represented both solids and liquid type formulations

Settivari et al., RTP, 2016

19

Neutral Red Release (NRR) Assay Performance

Accuracy (%) Sensitivity (%) Specificity (%) Sample size

78 85 76 64

20

G H S

C a t . 1 + 2

G H S

N o t c la s s if ie d

0

2 0

4 0

6 0

G H S c a te g o r ie s ( in v iv o d a ta )

ET

40

va

lue

s

G H S C a t N C

G H S C a t 1 + 2

EpiOcular Assay Performance

Accuracy

(%)

Sensitivity

(%)

Specificity

(%)

Sample

size

65 58 75 51

21

Tiered Testing Approach

Accuracy

(%)

Sensitivity

(%)

Specificity

(%)

Sample

size

75 73 77 64

Tiered Testing Approach for Ocular Irritation

Key learnings

In silico: Closer attention to applicability domain

NRR assay:

Dose response, Physical properties, Coloring agents

EpiOcular:

Damaged tissue during shipping, Physical properties, Coloring agents

Conclusion

Selected alternative methods provided promising performance for evaluating ocular irritation potential of multi-component mixtures

Inclusion of methods to assess post-exposure recovery may improve predictive performance of alternative methods

23

OECD 435: In vitro membrane barrier test method OECD 431: Reconstructed human epidermis test method OECD 430: Transcutaneous electrical resistance test method OECD 439: Reconstructed human epidermis test method

Dermal Irritation Testing Strategy

Corrosives

OECD 435/431/430

Corrosive STOP

+ - Irritants

OECD 439

+

Irritant (Cat-2)

-

NC

Non irritant STOP

No testing for chemicals with extreme phys-chem properties

8/10 Formulations were correctly predicted compared to existing in vivo data Good predictions for corrosives, however lower predictions for moderate and

milder irritants

24

Applications of In Vitro Assays Evaluation of multiple molecules as part of lead-candidate prioritization

during early stage development

In silico data is used to determine metabolism requirement and selection of in vitro tests. *GHS Threshold approach as part of WoE assessment for mixtures

25

Applications of In Vitro Assays

Formulations development Co-formulants testing to identify least toxic alternatives

Determine potential threshold (non-toxic) levels for co-formulants

Hypothesis driven research to develop sustainable formulations

Selected alternative methods identified formulations with reduced sensitization potential

26

Conclusions

Selected testing methods demonstrated promising performance for evaluating acute toxicity of multi-component mixtures.

Establishing a sound scientific understanding on the applicability domain of the alternative test methods improves confidence and public trust.

Resource efficient and Reduce the requirement for animal testing. Applicable at earlier stages of sustainable formulation development.

27

Acknowledgements

Dow Chemical

Darrell Boverhof

Sue Marty

Sean Gehen

Marco Corvaro

Dan Wilson

Ricardo Acosta Amado

Nicolo Visconti

Fagen Zhang

Jeremy McFadden

Matt Koehler

Julie Wheatley

Givaudan

Andreas Natsch

28

Thank you