Detailed results are shown below for one test set compound. According to Dempster-Shafer theory, each prediction consists of a reported belief and plausibility. The difference between these two is the uncertainty associated with the prediction. The global model suggests fumaric acid may be a moderate sensitizer but the prediction is uncertain. The SN2 carboxylate MoA model predicts non-sensitizer. qWoE prediction is non-sensitizer with probability 44% to 76% [belief plausibility].

A rigorous decision-theory approach based on Dempster-Shafer theory (DST) has been developed to accomplish two key tasks essential for probabilistic modeling:

1) uncertainty analysis

• a quantitative estimate of the uncertainty associated with each prediction is determined based on the reliability of the evidence source

• for QSAR models, model validation process provides reliability metrics

• applicable to binary, multinomial, and ordinal classification models

• can be applied to any source of evidence: QSAR models, structural alerts, in vivo and in vitro assay data, expert opinions, …

2) combination of multiple sources of evidence

• evidence is combined by joining DST basic probability mass structures

• much more rigorous and versatile than simplistic combination-of-evidence strategies commonly used (e.g., consensus or mean-value methods)

• enables quantitative weight-of-evidence (qWOE) strategies for combining multiple and diverse information sources

• combination reduces uncertainty when sources are in general agreement, and increases uncertainty when strong conflicts exist

J. Rathman1,2 C. Yang1,2,3 L. Terfloth3 J. Schwöbel3 A. Mostrag-Szlichtyng1 T. Magdziarz3 A. Tarkhov3

Contact: james@altamira-llc.com Acknowledgement: www.cosmostox.eu

In silico workflow for assessing skin penetration, irritation,

and sensitization potentials using chemotype-based models and alerts

INTRODUCTION SKIN SUITE COMPUTATIONAL WORKFLOW

ESTIMATING UNCERTAINTY AND

QUANTITATIVE COMBINATION OF EVIDENCE

1Altamira LLC

Columbus OH

TRAINING RESULTS AND PREDICTIONS

Evaluating chemical toxicity instigated by dermal contact requires

addressing exposure (permeation) and chemical reactivity that leads to

irritation or subsequent sensitization via well-known induction/elicitation

immune response mechanisms:

2The Ohio State University

Columbus, OH

3Molecular Networks

Erlangen, Germany

Induction Elicitation

OECD report ENV/JM/MONO(2012)10/PART1

Stratum corneum

Epidermis

Dermis

Lymph nodes

high no

global SNAr SN2 carboxylic

SN2 alkyl halides

Michael acceptors

Schiff base formers

phenols skin

sensitization models

Quantitative weight-of-evidence (qWOE) approach to combine evidence sources and estimate prediction uncertainty

alert?

non-sensitizer

Schiff base formers

Michael acceptors

Aldehyde binding to lysine

a,b-unsaturated carbonyl binding to cystein

SNAr

Aromatic nucleophilic substitution

SN2 e.g. primary-alkyl halides. etc.

X Nu -+ Nu:-

+ X

Smith Pease, C.K., Toxicology, 192, 2003, 1-22

Aptula, et. al. Chem. Res. Toxicol. 19, 2006, 1097

Database

In chemico reactivity database > 400 chemicals

‒ GSH assay

• Michael acceptors (43), SN2 (73)

‒ Methanolate assay

• SNAr (54)

‒ Reaction sites

‒ Reaction rates

Local lymph node assay (LLNA) assay database

‒ Database (> 470 structures): Master table I and II, other publications, OECD toolbox

‒ Study design (OECD TG429): 3H-Methymidine assay, vehicle (>240 used AOO,

ethanol or acetone/H2O)

‒Results: Stimulation index, EC3, LLNA potency

Exposure? • skin permeability database • models (Potts & Guy, Kasting) • QSAR (kp, Jmax, logP, ilogP, …)

query molecule (example: fumaric acid)

Irritant? • corrosion alerts • QSAR (MoA models based on

chemotypes, physchem props)

LLNA TRAINING SET

non weak moderate strong extreme

potency

freq

uen

cy

0

60

12

0 Influential descriptors

(global model):

ToxPrint chemotypes: chain:alkaneLinear_ethyl_C2

bond:CN_amine_aromatic_generic

bond:COH_alcohol_sec-alkyl

chain:alkeneBranch_mono-ene_2-butene

chain:alkaneLinear_propyl_C3

bond:N(=O)_nitro_aromatic

bond:COH_alcohol_aliphatic_generic

bond:C=O_carbonyl_ab-unsaturated_aliphatic

chain:alkaneLinear_butyl_C4

ring:aromatic_benzene

Physicochemical properties: HOMO/LUMO logP logS topological polar surface area (TPSA) dipole moment hydrogen bond acceptors and donors complexity rotatable bonds diameter principle moments of inertia https://chemotyper.org https://toxprint.org

Chemotype knowledge

https://chemotyper.org

Importance and frequency of chemical-

protein reactivity classes in training set

Each mode-of-action (MoA) model is trained independently to optimize parameters and provide reliability estimates used in subsequent decision theory analysis.

Ordinal potency classification: non-sensitizing (1), weak (2), moderate (3), strong/extreme (4)

1 2 3 4 1 111 15 11 1 2 25 51 28 0 3 9 25 70 18 4 2 4 30 56

Global model training set results

predicted

actu

al

d+1 reliabilities = [0.16 0.20 0.24]

d0 reliabilities = [0.76 0.54 0.50 0.75]

d-1 reliabilities = [0.17 0.26 0.22]

Global model applied to test set (54 compounds)

chemotype categories physicochemical properties

skin metabolic rules

yes In domain of applicability?

{1} {1,2} {2} {2,3} {3} {3,4} {4} {1,2,3,4} 1 15 4 0 1 0 0 0 0 2 5 6 0 2 0 3 0 0 3 0 0 0 3 0 2 2 1 4 0 1 0 0 0 6 3 0

predicted

actu

al

concordance = 76% ordinal agreement (g statistic) = 0.83

Uncertainty analysis results in no prediction for only one compound.

_____________Individual model results__________ Combination of evidence (qWoE)

Molecular initiating events in sensitization: hapten-protein adduct

formation reactions:

global SN2 MoA

0,0

0,2

0,4

0,6

0,8

1,0

belief plausibility

0,0

0,2

0,4

0,6

0,8

1,0

belief plausibility

0,0

0,2

0,4

0,6

0,8

1,0

belief plausibility

Altex 29, 4/12, 373-378, Novel Technologies and an Overall Strategy to Allow Hazard Assessment and Risk Prediction of Chemicals, Cosmetics, and Drugs with Animal-Free Methods