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NUOVI MODELLI IN SILICO UTILI PER LA VALUTAZIONE DELLA

TOSSICITÀ SISTEMICA

Nuovi approcci in silico e strategie integrate nella valutazione del rischio

Milano, 27 maggio 2015

Alessandra Roncaglioni [email protected]

IRCCS - Istituto di Ricerche Farmacologiche “Mario Negri”

mailto:[email protected]

• Mutagenicity

• Carcinogenicity

• Developmental toxicity

• Repeated dose toxicity

Addressed endpoint

MUTAGENICITY

Mutagenicity (Ames test) Detection of mutations that restore the functional capability of the bacteria to synthesize an essential amino acid. Positive results indicate that a substance induces point mutations by base substitutions or frameshifts in Salmonella typhimurium and/or Escherichia coli.

In vitro bacterial mutagenesis

Ames mutagenicity is also one of the most modeled endpoints by predictive computational methods.

A critical component for the assessment of carcinogenesis

Approaches to model mutagenicity

QSAR models of noncongeneric compounds to predict

mutagenicity can use TWO APPROACHES:

1 : STRUCTURAL ALERTS

2 : STATISTICS

Classification models

Expert knowledge codified in SA • All available scientific

information for a specific toxicological endpoint collected, evaluated, and weighted

• A generalized relationship between structural moieties (alerts) and biological activity identified

• Structural information translated into a knowledge base by human experts

• Prediction of new chemical entitiesis based on the existence of alerts

Ashby, 1985

Overlapping: lists of SA for Ames

Benigni /Bossa 28 (46)

genotoxic SAs

Ashby 19 SAs

Bailey 33 SAs

LMC 17 SAs

Kazius 29 SAs

ToxTree – genotoxicity

Genotoxic SA (predictivity for mutagenesis)

ToxTree – cancerogenesis

CAESAR model for mutagenicity

Dataset

• Extracted by Kazius-Bursi Mutagenicity Dataset (Kazius et al. J Med Chem, 2005), containing 4225 compounds, 2358 classified as mutagens and 1867 classified as non-mutagens by Ames test

• For validation, the dataset has been divided into training (80%) and test (20%) sets

Descriptors

• 2D descriptors: MDL software

Models

• Classification: SVM (Support Vector Machines)

• 10 fold cross-validation

CAESAR integrated model

Support Vector Machines (basic model)

ToxTree 1st checkpoint

ToxTree 2nd checkpoint

if negative…

if negative…

if negative…

positives

positives

positives

MUTAGENIC

MUTAGENIC

NON-MUTAGENIC

3 STEPS IN CASCADE:

statistical model (based on chemical descriptors)

knowledge-based

filter (based on structural alerts)

The tested models Software Method Free Applicability

domain

ADMET predictor (Simulation Plus v

6.06.0007) Artificial Neural Network Ensembles No Outsider of the

sub-models

T.E.S.T v 4.0.1 Consensus method: average of 3 models: NN, FDA and Hierarchical clustering Yes

The model processes only the compounds inside

AD

TOPKAT (Accelerys discovery studio v

3.1)

QSAR statistical method on 2D descriptors (e-state, topological) No Optimum

Predictive Space

Tox Suite (ACD Labs v 2.95)

Statistical model employing binominal PLS, predefined set of fragmental descriptors, local

correction to baseline, using experimental data for similar compounds

No Reliability index

VEGA Caesar v 2.1.10 QSAR statistical model based on Artificial Neural Network + rule based model Yes Quantitative AD

measurement

VEGA SARpy v 1.0.5-Beta Structural Alerts data mining Yes Quantitative AD

measurement

Derek Nexus v 2.0 Collection of knowledge - based rules No Not applicable

Toxtree v 2.5.0 Collection of knowledge - based (SA) Yes Not applicable

Benchmark Data set for in silico prediction of Ames mutagenicity, Hansen et al., 6512 compounds

Evaluation of models

Statistical Models

Knowledge Based

Models

Statistical Rule Based

Models

FRAGMENTATION

EVALUATION

RULESET EXTRACTION +

VALIDATION

PREDICTOR

Data mining: SARpy steps

SMILES ACTIVITY

O=CC 1

O/N=C/C 0

O=C(N)C 1

… …

NEW MOLECULE

PREDICTION

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

ACD CAESAR Toxtree T.E.S.T. SARpy Topkat Derek ADMET

Sensitivity

Specificity

Accuracy

Performance on the whole Dataset

6065 chemicals

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

CAESAR SARpy ACD T.E.S.T. ADMET

Accuracy

out AD

in AD

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Sensitivity

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Specificity

Performance out of training in/out AD

Evaluation on REACH compounds

27,144 studies corresponding to 2,975 unique CAS RN

START

Keep only data respecting OECD 471 and eliminate UVCB/inorganics, false mono constituents and ambiguous (neither positive or negative)

results

765 CAS RN 170 P and 595 N

Keep Klimisch codes = 1 & 2

Results for chemicals in AD

MCC

0.65 0.65 0.71 0.34 0.78

75%

94%

97%

75%

57%

83%

Without AD!

free models

Reminder : “Consensus VEGA + MN rules” (765 chemicals) = 0.82, 0.74, 0.84, MCC = 0.53

commercial models

Results for chemicals in AD

• Among systemic toxicity endpoints mutagenicity is one of the endpoints with greater success in the modelling

• Qualitative assessment, primarily focused on in vitro bacterial reverse test

• Used for the assessment of potential genotoxic impurities in pharmaceuticals

Mutagenicity conclusions

CARCINOGENICITY

Data to assess POTENTIAL CARCINOGEN:

Animal carcinogenicity studies (rat, mouse)

Epidemiological studies

Studies of genotoxicity in short-term in-vitro and in-

vivo

QSAR, read-across, general toxicity (subchronic) and specific (organ toxicity), toxicokinetics, pathology

Carcinogenicity

Berkeley Carcinogenic Potency Database (CPDB)

> 1300 chemical structures

Data fromTechnical Reports of NCI/NTP (rodent assays and data from peer reviewed sources)

Selection of 805 compounds (only male and female rats, no other species have been taken into account)

TD50

CAESAR model

Counter Propagation ANN

Step1: mapping of molecule Xs

(vector representing structure)

into the Kohonen layer

Step2: correction of weights in both, the Kohonen and the Output layer

Step3: prediction of the four-dementional target (toxicity) Ts=carcinogenicity

Model Training set Test set

Total compounds (number) 644 161

Accuracy,% 91 73

Cross-validation (leave 20% out), % 66

False Positive (FP) (number) 44 22

False Positive Rate, % 14 31

False Negative (FN) (number) 13 22

False Negative Rate, % 4 25

Positive Predictive Value (PPV) (precision), %

88 75

Negative Predictive Value (NPV), % 95 69

Sensitivity (Carcinogen), % 96 75

Specificity (Non-Carcinogen), % 86 69

CAESAR statistics

Caesar v 1.0.0.6

Free

QSAR statistical model based on CP-ANN

Rat (both sexes) yes

Lazar Free

K-nearest neighbours, escluding

identical compound Rat (both sexes)

yes

MultiCASE AF1 Module

Commercial Statistical method, fragment based +

modulating factors Male rat yes

Topkat Commercial Qsar statistical method on 2D

descriptors

Rat (both sexes)

yes

Toxtree V 1.60

Free

Structural Alerts interpretation

Studies in different

species

Not based on training set

Derek for Windows DfW_11.0.0

Commercial Collection of knowledge - based rules Studies in different

species

Not based on training

set

Hazard Expert Module in Pallas 3.0

Commercial Collection of Structural Alerts +

modulating factors for bioavailability

Studies in different

species

Not based on training set

software availability method Input basis Info training set

Software/tested models

1544 (805+739) chemicals with carcinogenicity values towards rat (males or females)

Berkeley Carcinogenic Potency Database (CPDB)

FDA 2009 SAR Carcinogenicity – SAR Structures

(from the Leadscope database)

Public; More than 1300 chemical structures

Commercial database containing 2090 compounds

Data extracted from the Technical Reports of the NCI / NTP concerning rodent essays and data extracted from multiple sources subject to review.

Information about chemical structures and carcinogen activity based on different species of mammalians

Assays data from other species as well as rat and mouse and includes a wide variety of experimental protocols; selected 805 compounds related to rats (male and female)

Compounds in common with CPDB (655) evaluated to determine the suitability of the experimental class of carcinogenicity assigned from the two sources (concordance 95%)

Quantitative measure of the carcinogenic potential (TD50)

Compounds already present in the CPDB excluded; selected 739 compounds with experimental data on rats reliable

Dataset

0.81 0.87

0.57

0.89

0.72

0.61

0.52

0.43

Topkat CAESAR LAZAR MultiCase

Sensitivity

In DB

Out DB

0.68

0.82

0.73

0.89

0.41

0.58

0.8 0.78


Specificity

In DB

Out DB

0.74

0.85

0.64

0.89

0.57 0.6

0.67 0.6


Accuracy

In DB

Out DB

Performance of the models for the training (In DB) and test sets (Out DB)

Results

Percentage of matched predictions stratified by mechanism of carcinogenicity

Mechanisms of carcinogenicity

TOXTREE HAZARDEXPERT DEREK LAZAR CAESAR TOPKAT

Acylating direct acting

77,8 55,6 66,7 44,4 77,8 44,4

Alkylating direct acting

58,6 53,7 58,6 61,5 70,9 57,8

Alkylating indirect acting

79,2 70,7 78,8 75,7 83,0 67,6

Intercalating and DNA adduct

forming Indirect acting

68,0 68,9 76,7 45,6 70,9 54,4

Aminoaryl DNA adducts forming Indirect acting

64,8 60,6 65,4 63,5 74,3 58,7

Non genotoxic 41,6 56,2 65,2 64,0 71,9 59,6

No Alerts 65,2 63,4 63,1 64,9 70,5 49,5

MoA information

• Complex endpoint, data uncertainty

• Advantage of the use of mutagenicity information

• Combination with read-across in a weight of evidence approach would increase reliability

Carcinogenicity conclusions

DEVELOPMENTAL TOXICITY

Targeted endpoints

Sexual maturation

Release of gametes

Fertilisation

Transport of the zygote

Implantation

Embryogenesis

Foetogenesis

Birth

Postnatal development

Growth and development

ReproTox (fertility)

DevTox

- Different effects: • death of the developing organism(fetal death, pre/post-

implantation loss) • structural abnormality • altered growth (growth retardation, weight variation) • functional deficiency

- Confounding factors (maternal toxicity)

Developmental toxicity is a complex endpoint because:

DevTox – issues (1)

- Different tests • Prenatal developmental toxicity test [OECD TG 414] • Reproductive/Developmental toxicity screening assay

[OECD TG 421] • Two-generation reproduction toxicity study [OECD TG 416] • Extended one-generation reproductive toxicity study

[OECD TG 443]

- Existing QSAR models are mainly classifiers (POS/NEG)

• Translation of NOAEL/LOAEL values in classification scheme (POS/NEG)

• Classification based on different lines of evidence (risk-based, epidemiological findings)

DevTox – issues (2)

Model Type of model

CAESAR Descriptor-based

SARpy Fragment-based, extracted by the

software

DART (from P&G) Categories and

substituents identified by experts

DevTox – freely available models

Model Type of model

LeadScope Model Applier Fragment & descriptor based

statistical software

MultiCASE Rules extracted by the

software

Derek Nexus Rules identified by experts

Kindly provided by S. Kulkarni and T. Barton-Maclaren Health Canada

DevTox – commercial models

FDA pregnancy categories

A No risk in controlled human studies

B No risk in other studies: Negative animal studies, no human studies OR Positive animal studies but negative human studies

C Risk not ruled out: Positive animal studies, no human studies, but risk-benefit analysis may warrant use of the drug in pregnant women

D Positive evidence of risk: Positive evidence of effects in human studies, but risk-benefit analysis may warrant use of the drug in pregnant women

X Contraindicated in Pregnancy: Animal OR human studies show abnormalities AND/OR evidence of fetal risk based on human experience, risks for pregnant women clearly outweigh potential benefits

Negative n = 91

Positive n = 201

Random forest (RF) model based on 13 2D-descriptors

CAESAR model

P&G dataset

total compounds 672

positive compounds 615

negative compounds 57

Library of about 185000 positive

chemicals

P&G model

main symptoms sub symptom

fetal growth

fetal growth

retardation

fetal weight decrease

fetal survival

fetal death

post-implantation lost

pre-implantation lost

structural

dysmorphogenesis

visceral organ toxicity

LeadScope dataset

total compounds 1320

positive compounds 692

negative compounds 628

LeadScope dataset classification

Two steps model

First step: rules with high accuracy

Positive fragment 53

Negative fragment 51

Second step: rules with low accuracy

Positive fragment 77

Negative fragment 77

Compounds not predicted

SARpy model

Rules comparison - DART

ReproTox DevTox

• Devtox is a difficult endpoint to be estimated for the high number of sub-endpoints, relatively low number of chemicals with experimental data, classification of data and heterogeneity of source information

• Few models exists but so far without satisfactory performance

DevTox conclusions

REPEATED DOSE TOXICITY

1. Repeated dose toxicity approaches towards LO(A)EL

and NO(A)EL

2. k-nearest neighbors (k-NN) approach

3. Chemical categories for hepatotoxicity and

nephrotoxicity

4. Structural alerts for hepatotoxicity and

nephrotoxicity

Quantitative

Qualitative

General overview

This study provides information on:

• adverse effects on organs and tissues

• lowest observed (adverse) effect level (LO(A)EL) and no observed (adverse)

effect level (NO(A)EL)

Repeated Dose Toxicity

Lowest Observed (Adverse) Effect Level (LO(A)EL)

the lowest dosage where (adverse) effects can be observed

No Observed (Adverse) Effect Level (NO(A)EL)

It is the highest dosage for which no (adverse) effects can be observed

20 mg/kg/die 40 mg/kg/die 60 mg/kg/die 80 mg/kg/die

NO(A)EL LO(A)EL

Control

The NO(A)EL and LO(A)EL should be expressed as mg/kg body weight/day.

Many different effects (steatosis, seizure, hematuria etc…) on organs and

tissues (liver, kidney, blood, CNS etc…)

Experimental design: different species used, strain used, spacing between

doses, exposure time, exposure route, variability of the responses between

animals within the dose groups, the definition of “adversity” of an effect,

specific life stages differences between animals…

NO(A)EL and LO(A)EL values are not derived from a dose-response curve

Uncertainty and variability of Repeated Dose Toxicity data

*In the calculation of the MoS, the lowest obtained LO(A)EL value is used when a NO(A)EL is

not available (SCCS, 2012).

Munro database

613 compounds

HESS database

502 compounds

IRIS’EPA database

557 compounds

Final dataset

255 compounds

(138 HESS, 99

Munro, 18 IRIS)

84-98 days

Oral exposure (gavage, feed,

drinking water)

Rats studies (interspecies

difference)

Organic compounds

When more data were

available for the same

compound: used the lowest

K-NN:Training set

LO(A)EL data

Final dataset 179 compounds

84-98 days

Oral exposure (gavage, feed,

drinking water)

Rats studies

Organic compounds

Rejected data already present

in the training set

Fraunhofer RepDose

database

Over 650 compounds

K-NN:Validation set

LO(A)EL data

Refined algorithm K-NN: quantitative approach

Target compd

k most similar

compds

SI ≥ 0.85

Δ exp values < 1

SI ≥ 0.90

Error

in prediction of

similar compds

< 1

Similar

compds have another

similar compd

Cross-validation

of most similar

compds

Not

predicted

Prediction as

average of exp

values

Prediction as

average of exp

values

Not

predicted

Not

predicted

Not

predicted

Not

predicted

n = 1 n = 0

n > 2

Non predicted Predicted

Yes

Yes

Yes

Yes

No

No

No

No

k 2 3 4 5 6

q2 0.699 0.778 0.797 0.793 0.792

RMSE 0.451 0.401 0.388 0.392 0.398

n 68 / 255 52 / 255 51 / 255 51 / 255 49 / 255

% 27% 20% 20% 20% 19%

Training set (255 compounds)

k = 2 k = 3 k = 4

k = 5 k = 6

K-NN: quantitative approach

k 2 3 4 5 6

r2 0.534 0.549 0.551 0.672 0.679

RMSE 0.623 0.663 0.658 0.632 0.647

n 27 / 179 24 / 179 23 /179 21 / 179 20 /179

% 15% 13% 13% 12% 11%

Validation set (179 compounds)

k = 2 k = 3 k = 4

k = 5 k = 6


The algorithm was developed by using only freely available tool

(IstSimilarity) and can be easily implemented in VEGA

platform

The models could be easily improved by simply updating the

model dataset, without any need of new training procedures

The real model’s predictivity power was evaluated using an

external dataset

The models provide reliable predictions only for few chemicals.


LO(A)EL

Chemical categories and structural alerts: qualitative approach

Organ toxicity

liver

kidney

Chemical categories and structural alerts: qualitative approach

Hepatotoxicity

Nephrotoxicity

Hepatotoxicity dataset (218 compounds)

Nephrotoxicity dataset (200 compounds)

a) effects on LIVER at the LOAEL ACTIVE 121 compds

INACTIVE 97 compds b) effects on other organs than

liver at the LOAEL

a) effects on KIDNEY at the

LOAEL ACTIVE 87 compds

INACTIVE 113 compds b) effects on other organs than

kidney at the LOAEL

If a compound reported effects both on kidney and liver: included in both dataset

HESS database (OECD Toolbox)

JAVA application based on the VEGA core libraries

(http://vega-qsar.eu) that performs the analysis of a given dataset of

molecules on the basis of a set of relevant chemical features (functional

groups and atom centered fragments).

INPUT: dataset where each molecule is assigned to a class of toxicity

(ACTIVE-INACTIVE)

OUTPUT: statistics for each relevant feature, with the n. of matching and

the % for each user defined class

IstChemFeat-Chemical categories

The application is released under the GPLv3 open source license. The application has

been developed under the EC funded project CALEIDOS (http://www.caleidos-

life.eu/) by Kode s.r.l. (http://kode-solutions.net).


Chemical category Occurences

n. TP n. % TP

Aromatic Nitriles 4 4 100

Sulfonamides 8 7 87

Aromatic 2° amines 7 6 86

Aromatic ketones 6 5 83

Esters (aromatic and aliphatic) 9 6 67

Chemical category Occurences

n. TP n. % TP

Aromatic ethers 14 11 79

Aromatic hydroxyl 22 17 77

3° amines (aromatic and aliphatic) 8 6 75

Aliphatic esters 7 5 71

Aromatic ketones 10 7 70

Aromatic Carboxylic acids 3 2 67

Furanes 6 4 67

Aromatic nitro group 18 12 67

Halobenzenes 16 10 62

Hepatotoxicity

Nephrotoxicity


Halobenzenes Cyp 450 Cyp 450 Oxidation

Convalently

bound to

macromolecules

Liver damage

Chan et al., 2007, Sakuratani et al., 2013

Sulfonamides Metabolites, N-acetyl

derivatives

Low solubility

excreted in large amounts

in the urine

Intratubular crystal

deposition

Renal damage

Parazzella, 1999

A: any atoms except H

AH: any atoms including H

X: halogens

02

All compounds

(active-inactive)

SARpy rules

extraction

Human expert rules

analysis

STEP 1

65% TP

LR ≥ 2

Ruleset procedure

STEP 2

Human expert rules

extraction

Literature

information and

chemical categories

(IstChemFeat)

F

i

n

a

l

R

u

l

e

s

e

t

SARpy-Hepatotoxicity alerts

8 7 86

19 16 84

8 6 75

8 6 75

Structures Tot TP %TP

16 11 69

53 36 68

11 7 64



SARpy fragments Human-based fragments

Halobenzenes,

at least 2 X

SARpy-Nephrotoxicity alerts

4 4 100

14 12 86

7 5 71

17 12 70

9 6 67


4 4 100


SARpy fragments

Human-based fragments

10 7 70


SARpy alerts

IstChemFeat

sulphanilamide

4-hydroxylaminobenzenesulfonamide

(4-HABSA)

Severe renal damage

(Interstitial nephritis,

polyarteritis, intratubular

crystal deposition)

Eyanagi et al., 1985; Parazzella, 1999, Mustafa et al., 2014

Ahmed et al., 2007

naphtalene

decrease in GSH levels, hepatic

lipid peroxidation,

increase of ALT, AST and AP

Liver injury

oxidative damage,

cholestasis,

DNA fragmentation


Sulfonamides

This approach focuses not on whole body toxicity but on single

organ and tissue toxicity (liver and kidney)

Structural alerts and chemical categories

The alerts and the chemical categories are not already tested with

external data

For some alerts and chemical categories it is possible to find a

mechanistic explanation in the literature

These SA and chemical categories will be implemented in VEGA for

predicting renal and liver toxicity and they will be implemented

also in the new VEGA read across-tool

• Models highly affected by the study design

• Quantitative models capable to deal with a low number of compounds

• Category approach/SA can support read-across and weight of evidence

Repeated dose conclusions

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