Moving Beyond Animal Data as the Gold Standard...Moving Beyond Animal Data as the Gold Standard Nicole Kleinstreuer Deputy Director, NICEATM SACATM 5-6 September, 2018 Agency for Toxic

Advancing Alternatives to Animal Testing

lnteragency Coordinating Committee on the Validation of Alternative Methods

Moving Beyond Animal Data as the Gold Standard

Nicole Kleinstreuer Deputy Director, NICEATM

SACATM 5-6 September, 2018

Agency for Toxic Substances and Disease Registry • Consumer Product Safety Commission • Department of Agriculture Department of Defense • Department of Energy • Department of the Interior • Department of Transportation

Environmental Protection Agency • Food and Drug Administration • National Institute for Occupational Safety and Health National Institutes of Health • National Cancer Institute • National Institute of Environmental Health Sciences Institute • National

Institute of Standards and Technology • Occupational Safety and Health Administration

- lnteragency Coordinating Committee on the Validation of Alternative Methods

When Mice Mislead Tackl81oa long-U111adingdisccinr..ctbatwt•allinlancl hum•stlldies..•••ctiargalhll

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922 22NOVEMSER2Dll VOl.3'2 SCIENa ~oe~ --

HOVI SLOPPY SCIENCE

CREATES WORTHLESS

CURES, CRUSHES HOPE,

AND WASTES BILLIONS

RICHARD HARRIS

Sloppy reporting on animal, studies proves hard to change Scientists appear LO ignore guidelines adopted 7 years ago

califd ARR.IVE guidclint's-$b0rt i>r AnimaJ It~~: Reporting ¢I In Vi,,:, l-~mtnl$. ~ has no(Offlda11)' endor&ed Ulffl\ but cnroungcs ilulhors to ro1JIJ)ly.) But 7 )~ars i._r, $l,1


QAssay 0 Key Event Node [jdAR Agonist Pathway td AR Antagonist Pathway Q Interference (assay- or node-specific examples) ~: i A4 7

Cofactor Recruitment AS

~ ~:.::::.:~~::~~:::;~~;on ~=~::~~;,;;,;~; ~ ~ Protein Production

R2 (N1, N2, NS) ~ AR Antagonist Pathway R1 ( N1-N4) t:ii AR Agonist Pathway

Studies

Systematic Review Process

V Reference Data

Validation Workflow Importance of Curated Reference Data

Kleinstreuer et al. 2018 RTX in press


Addressing Data QualityEx: Rat oral acute toxicity LD50 Database

Data source Number of LD50 values Number of

unique chemicals

ECHA ChemProp 5,533 2,136

NLM HSDB 3,981 2,205

JRC AcutoxBase 637 138

NLM ChemIDplus 13,072 12,977

NICEATM PAI 364 293

OECD eChemPortal 10,119 2,290

• Identify transcription errors (e.g. 20005000 mg/kg, >10 mg/kg, confidence intervals as values)

• Manual curation of highly variable chemicals; identify source data

In omm1ttee o Y oordinating C . ion of Alternaf n the Validaf 0 S

Defining a Confidence Range Bootstrapping of the standard deviations for repeattest chemicals identified a 95% confidence interval for LD50 values of ±0.31 log10(mg/kg)

LD50 (log10(mg/kg))


Variation in Classification Ex: ECHA Ocular Data

CASRN ECHA Data 100-41-4 Not Irritating 100-41-4 Category 2A 100-74-3 Category 1 100-74-3 Category 2A 102-06-7 Category 1 102-06-7 Category 2

10213-78-2 Category 1 10213-78-2 Category 2A

103-50-4 Not Irritating 103-50-4 Category 2B

10361-93-0 Category 1 10361-93-0 Category 2A


rt7:'\,A I i(,:f _ . - ~ ; . ' -~ , 1 • , .,,,..,,,;,,;., i- .. "{q,-_l . ,.,,.H/Y

- . 'I ,


Prior Type 1 2A 2B Non Total

1 73% 16 .1 % 0.4% : 10.4%) 46 .-

/ - ' 2A 4 .2% 32.9% 3.5% s9Ao/v 138 2B 0 .2% 4% 15.5%

, - - ' '-80.2%,.; 86

Non 1.1 % 3.5% 1.5% 93.9% 400

Reproducibility of Animal Data Ocular Potency Categorization

Conditional probability of Draize evaluations given a previous test result

491 substances with at least two Draize studies and extractable eye irritation category in REACH registrations 2008-2014

Leuchtefeld et al. 2017


How to Benchmark Alternative Models


Interim Science Policy: Use of Alternative Approaches for Skin Sensitization as a Replacement for Laboratory

Animal Testing

DRAFr FOR PUBLIC COMMENT April 4, 2018

EPA's Office of Chemical Safety and Pollution Prevention:

Office of Pesticide Programs Office of Pollution Prevention and Toxics

Test Chemical

Concordant?

concordance Classify

based on2/3 concordance

Test Chemical

Positive,-----

P06itiw

Nonsensitizer

How to Benchmark Alternative Models Animal data reproducibility as threshold for

performance

Defined Approaches (AOP WoE and KE 1/3 STS) accepted by EPA based on comparison to LLNA

(mouse) data


Development of Predictive Models for Acute Oral Toxicity

• Use large database of rat oral LD50 values to train (and test) QSAR models to predict acute oral systemic toxicity

• 32 groups from the US, Europe, and Asia responded with 135 models for LD50, EPA and GHS categories, and binary nontoxic vs all others and very toxic vs all others.

• Models were qualitatively and quantitatively assessedand combined into consensus models.

• Consensus model performance compared with animal test reproducibility for binary, categorical, and quantitative models

https://ntp.niehs.nih.gov/go/tox-models

https://ntp.niehs.nih.gov/go/tox-models

Oral LDSO: Reproducibility

Sensitivity Specificity

63% 99% 81%

96% 82% 89%

74% 91% 82%

66% 92% 79%

LDSO 0.8 0.42 I

. . \ .. ~ ' . . I I . --

~ -~ A): . --... ; ~ . ~ w~ .. ",_ ~ -' -~--~ . . ,,,. . -- .

Consensus Model Performance (Tr/Ts Avg)

Specificity

77% 95% 86%

82% 92% 87%

62% 94% 78%

54% 92% 73%

RMSE

0.74 0.42

Predictive Models for Acute Toxicity: Performance

vs Animal Data


Communicating Variability to Stakeholders

Review ,._ • ..,,, _...., ir .. ,.,.,u n _ __...,..,u,,.,_ ~ - W i • ,o loll ~""' " " " • .JIU'l.i/"'• •1'1. ' 11

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hl ~ .. ,1,, 20-11 , U',: ln tiirt i g~l'K\'CUU:dirl• l iri11Cu,nm illw ( 0 1 u·1~ 1/c li~ • lou11 UI All..,, .-1. liw M~Uluh (IW IAM] published t he •str.alciiC A.oa:Sn ap fo r Est.1blish r,c- New Approoc,r~ to h a l .. t11c t hc s ~~cty o~ Chem icals and Mtd ic,11 P'T,oduc::; 1n the Unit ed ~utc:s~ [ICC'IA!.4 20 UI). Cro-n•ai cncv f : dcfa w or1o:e,oups have been establ s:i"ed to mplernent : r s roadmap fot 1rar1ous oo:


Recent Workshop: Modelers + Regulators

Predictive Models for Acute Oral

Systemic Toxicity

William H. Natcher Conference Center National Institutes of Health, Bethesda, Maryland

April 11 – 12, 2018

Attendees in-person: 89; webcast: 215

Model Accessibility and Transparency

Chcriisby Das~ board

Bispheno1 £.~ SC-QC'r.71 DT.X31D70201e2

~ R':'!UIG

,,._,..1..,.r,11,n"m\'......,~"· liacL......,•w-Lr •""'- ~'J•'- 0M!O

au-,v.o,cJ1,c,,c,;t1•lill-N\LVl\1"•

l,',H.twtll': !.25

(PERA Models LogP: Odanol-Water

.l.lgoph ................ n


How to Benchmark Alternative Models

Human data and human biology as the gold standard

C. Chandrasekera/CCAAM


~II -----~

How to Benchmark Alternative Models Example: Skin Sensitization

Chemical Structure & Properties

Molecular Initiating Event

Cellular Response

Organ Response Organism Response

Metabolism Penetration

Electrophilic substance

Covalent interaction with skin proteins

• Induction of inflammatory cytokines and surface molecules

• Mobilisation of DCs

• Activation of inflammatory cytokines

• Induction of cytoprotective genes

• Histocompatibility complexes presentation by DCs

• Activation of T cells • Proliferation of activated T-cells

• Inflammation upon challenge with allergen

Dendritic Cells (DCs)

Keratinocytes responses

Key Event 1

Key Event 2

Key Event 3 Key Event 4 Adverse

Outcome T-cell proliferation

TG442C

TG442E

TG442D

QSAR

QSAR

Defined Approaches (DAs) combine in vitro and in silico data using simple decision trees or machine learning algorithms to predict skin sensitization.


\.ht:mical ~ truclure & Properties

tAcle culor lnd,at ,ng l:.vent

How to Benchmark Alternative Models Example: Skin Sensitization

All non-animal defined approaches evaluated perform as well orbetter than the mouse at predicting human skin sensitization:

Hazard: 74% (mouse) vs. 75-85% (DAs)

3-class Potency: 59% (mouse) vs. 55-69% (DAs)

Drug Oisi::C/r/12f'y Today•Vdtffl€' 00.Ntrnbef00•Jtrie 2018

Te,1.ier lt11pro,-etl tr,111.dt1tio11 ofrese,1rcl1 is 11ee1.led to i11for,11 st1f"e ,utd effective tln,g ,le,-rlopme11l. fl,ls u:111 rt.•,111lre ,, brot1,I coll,1bor,1tfre t•lfort, opt·11 ,l,llo 5,lli,rlug, ,u,,I

1,rloritV~•,I f1111di11g for l1111m111-relet.•,111t n •., e,1rd1.

Summary of major recommendations

A true shift in paradigm will require greater emphasis to be placedRecommen


on human relevance, from top-down funding decisions to datapathway-b generation, to building of databases and/or knowledge

research management tools. Lindsay J. M~rsha111,_c~ International and interagency collaboration is critical: formal Suzanne C. F1tzpatr1ck· II b . b • • · I d f d. b d. ,..,man.Sod,tyO,tema_.__..,... co a oration etween maJor organizat1ona an un mg o 1es ~1:::.."!'".,..,.,,_..,_~c..... 1o should be established. etthtsdl,. MOlOl17,USA

'Nadonal T°"""'logy"'°ll"""l-..gonc) F d' h Id b • •t• d & h• h b d........,.,........,.,,...........,H.... un mg s ou e pnon 1ze ,or researc mg uman- ase ~-:;::-:=:::':!•''""'0 ""' biology (versus 'improved' animal models) and promoting open Fai lures In the CUm!nt para, access data. soarin g research and develot

app10vais. 0ver9(111>ofnew, Human data should be collected in collaborative, open-access t he level of Phase 2/3 c linic,

unexplalnedtoxldty.Arece high-quality databases. researd, inst!tutlons, reguJa • • nongovernmental o rganir.al Common reporting formats and common ontologies should be be better applied to underst • • • • d isco~-ery. Recommendatio, established for collecting and collating human biology :~:.~~=::::.:: information, from different 'omics technologies to human clinical

data. ::,;~.!~...ttnen, o1blu1oru, There is a need to establish formal processes for cross-sector dlinse lnt~ntkmsrt':maln ~lushoc • •

,esu,ch•nd-lopmentfo,asu< commun1cat1on. numba or new drugs apprm·cd pt:r

>""""inc, 1950 1z~ Mo., lhan 9( There is an immediate need for the creation of case studies to regulatO?y ~roval, mainly ,lJ .i. re

bnd ZOOl, wing dau I

::;,:;;:i~::::·::i:.=:~!.:'. based approaches in the context of translation and human disease lowsuc=rn,.. (S(). lt l>d


OECD/OCDE 492 AdqJ!ed:

25 Juru,2018

OECD GUIDELINE FOR TIIE TESTING OF CIIEMICALS

R•rnmlrncl,d huwau Corn,a-lik, Epitl.i,limn (Rl,CE) 1,,1 w,lhuJ fu,. identifying: cla.em.ic.ah not requ.i1;ng dusification aad labe-Iling for eye

i.ni.tation or se-riow eye damage

Il\TRODt:CTI0'.'1

1. $qriGw 'Y" Jam.ig" refers to the pr oductiOJI cf tim:e duu3e i:i the- ~Y-!, o~ serious i:•hysical dec1y of ;.1:iiion., foilcv."iDg application of a test chemical m ch~ amerior surface of thP P..~'P, ,.,,}ii r.h ii:; nnt fnl ly ..-~~nil:1,- u·ithin )1 ,fayi:; nf :q,rl r.lltinn, si:; M f.iw.d hy rh"' {J. itP.,n Nil.liuus C-k ,ln.lly Hi1u.11uu..it.cd S~ tUl.l uf Cla..-.:s.ificaliuu nu.l I.a~ uf Chcw.il.::d~ (INCTH") (1) Al~, ar.c-.nnti,e 1n liN ITHS, RJ,'11!' ;mtnfit)JJ. iP."P..tt. to th~ p1ndir.tinn nf chmgcs in the CJC folbwiag the application of • ic:!>t c.hcm.ical to the m krior sufac.e o f the eye. wtuc-.h are fitly revemtle witb.m 11 dJys of a.pp.11.cat!oil. 1 ~ t clem:cili mdu:.mg xrious eye dama~c arc du3ificd u UN GHS Category 1, while tho3C i».duci.J:g eye irrrtation ue cbs~ifie:I as UN GHS Cate,:«y 2. Tes: chemicals aot cLts;ified for eye ilritation or terioru eye d.:am:at e :are de.f~ :a;: thol~ th.at do not ~et i:he requireme:::i.t~ for classukaion as lN GHS Cu~~ry 1 or 1 (2A or 2B) i.e., they are refmed to as UN OHS Nn CaiP.tnry

J . Th! uee~s:mut o)f ElP:r.oue: eye dam35e/ey! initatio:a bse: ::ypic-ally involve.cl the \!£Es cf labontory animals (OECD :'est C-u:deline (fG) 4C5; ad•>ped iii '.981 and revised U: 1Q:t7. :-O'.)'), ?Cll') jlnd ) 017) (?) The chnit:P.n:t'hP most :-1:p:rropri~P.tf!sf mflth!ld :wul lhf! 1:sf! l.'f LLis T =~t Oa.id,£.i.u.c sluu.111.ie :il:t:U Ul. the lunl~'d gf Llx OECD O ii:Lw.a: 0 1XU1.UCLI u.t. an Integrated Appmaches on -:es ting an:i Assessment OAT A) for Serious Eye Damal e and E)-c inittition (3}.

3. Tb:is T~st Guidcl.inc dcscn"b~ an i,j , ·itro rroced:u-c a llow~ the i


Eye Irritation Classification:

OECD TG 492 Proficiency Chemicals vs. Sigma SDS

Chemical Name CASRN OECD TG 492 SDS (in vivo data) (in vivo data)

Methythioglycolate 2365-48-2 Category 1 Category 2A 2,5-Dimethyl-2,5-hexanediol

110-03-2 Category 1 Not classified

1-Ethyl-3-methylimidazolium ethylsulphate

342573-75-5 Not Classified Category 1

Diethyl toluamide 134-62-3 Category 2B Category 2A Camphene 79-92-5 Category 2B Category 2A


Secretome ~ ~

-w ~ ~ ,

»f ~ ~ .~ ~

Mechanistic Mapping of HTS Assays Example: Developmental Toxicity

Human Teratogenic Mechanisms

• Endocrine disruption

• Oxidative stress

• Vascular disruption

II

• 'a • - -. -· ---- - - - -- -• t .1 I I; ; :, 1 IT 1 It t;,

--..,..-10-t ,__, • ._'II __ ,t.,, ~--••,_t -tO ... I ""5..,.,~1••1

~ '" ~ --~ - 1- 'r- 1-~---•-~1 -10111111 ,.._.. __ 10-'IIJ •-10t -•O


Sustaining pmliteratlve

signaling

Evading grow1h

svppressors

Inducing Activating angiogenesrs invasion &

metastasis

Enab'1ng replicative 1mmortal1ty

./ /

5. Oxidative stress

4. Epigenetic alt erations

1. M etabolic activation

I 0

6. Induces inflammation

10. Cell death/ proliferation

8. Nuclear receptors

0.2 0 .4

ToxPi Score

0.6 0.8

Mechanistic Mapping of HTS Assays Example: Carcinogenicity

Hallmarks of Cancer & Characteristics of Carcinogens • Inflammation

• Oxidative stress

• Genotoxicity/instablitiy

• Angiogenesis

• Immortalization/proliferation

• Immunosuppression

• Invasion/metastasis

• Specific receptor- or enzyme-mediated

Hanahan & Weingberg 2011; Smith et al. 2016; Guyton et al. 2018; Chiu et al. 2018


Prior

Posterior

Risk + Human-Relevant Mechanistic Information (from HTS assays, 3D organotypic systems, QSAR models, targeted animal studies, etc.)

+ Exposure Data and Population Genetics (from biomonitoring studies, high-throughput

transcriptomics, GWAS studies, etc.)

0.1 1 10 % Population

Addressing Risk Probabilistically


Challenges • Scientific – Considering population/genetic variability

– Incorporating metabolic competence

– Developing complex systems models

– Reporting and collection of reference data

• Non-scientific – Increasing awareness, education, and training

– Cross-sector communication

– Funding for human-centric research and education

Moving Beyond Animal Data as the Gold StandardThe Reproducibility CrisisValidation Workflow�Importance of Curated Reference DataAddressing Data Quality�Ex: Rat oral acute toxicity LD50 DatabaseDefining a Confidence RangeVariation in Classification�Ex: ECHA Ocular DataReproducibility of Animal DataReproducibility of Animal Data�Ocular Potency CategorizationHow to Benchmark Alternative ModelsHow to Benchmark Alternative ModelsDevelopment of Predictive Models for �Acute Oral ToxicitySlide Number 12Communicating Variability to StakeholdersPredictive Models for Acute Oral Systemic ToxicityModel Accessibility and TransparencyHow to Benchmark Alternative ModelsHow to Benchmark Alternative ModelsHow to Benchmark Alternative ModelsSummary of Major RecommendationsExample: Eye IrritationEye Irritation Classification: �OECD TG 492 Proficiency Chemicals vs. Sigma SDSMechanistic Mapping of HTS AssaysMechanistic Mapping of HTS AssaysAddressing Risk ProbabilisticallyChallengesTableforNK.pdfPredictive Modes for Acute Toxicity: Performance

Documents

Moving Beyond Animal Data as the Gold Standard...Moving Beyond Animal Data as the Gold Standard Nicole Kleinstreuer Deputy Director, NICEATM SACATM 5-6 September, 2018 Agency for Toxic