profiling potential drug-induced hepatotoxicity through ... potential... · ivts 2017, 23-24...

Profiling potential drug-induced hepatotoxicity through modelling molecular initiating events (MIEs)

[email protected]

Dr. Lilia Fisk

Outline• Introduction

• Who are Lhasa Limited?• Liver toxicity

• Hepatotoxicity as an adverse effect• Types of liver damage

• Toxicity testing: past, present and future

• Liver toxicity mechanisms and AOP approaches for toxicity predictions

• Hepatotoxicity profiler

• Hepatotoxicity predictions based on AOP-based models• Data compilation• Modelling BSEP inhibition• Reactive metabolites modelling

• Hepatotoxicity profiler results

• Summary

• Future workIVTS 2017, 23-24 November 2

Who is Lhasa Limited?

IVTS 2017, 23-24 November

• Established in 1983

• Not-for-profit organisation• Educational charity• Controlled by our members

• Currently approx. 160 employees

• Main Headquarters in Leeds• Small teams of staff also based in Newcastle, Poland and USA

• Creators of scientific prediction and database systems

• Facilitate collaborative data sharing in the chemistry-related industries

• Over 350 members across 6 continents

3

Lhasa’s current products

IVTS 2017, 23-24 November

Expert rule-based toxicity prediction

Statistical mutagenicity prediction

Metabolism prediction

Toxicity database, data sharing

Degradation prediction

Impurity purge prediction 4

Data management workflow tool

Liver toxicity as an adverse outcome• The liver is a major target for drug toxicity

• Orally administered drug – first organ to be exposed

• Extensive metabolism of drugs in liver

• 462 medicinal products withdrawn from the market (1953-2013) [Onakpoya et al.]

• Highest number of adverse effects:

• Liver (29%)

• Heart (22%)

• Attrition during drug development

• Hepatotoxicity cased by environmental chemicals

Liver29%

Cardio22%

Hematologic17%

Skin14%

Immunologic12%

Carcinogenicity6%

IVTS 2017, 23-24 November 5

Types of liver damage• Intrinsic hepatotoxicity

• Dose dependent

• Reproducible in pre-clinical species

• Predictable

• Idiosyncratic hepatotoxicity

• Dose independent

• Occurs without “warning”, susceptible individuals

• Not reproducible in animals, unpredictable

• 2,000 cases of acute liver failure occur annually in the US

[Medscape]

• 52% are due to medication

• 39% - acetaminophen overdose (intrinsic liver toxicant)• 13% - idiosyncratic reaction to other drugs


Toxicity testing


• In vivo models

• Complex systemic interactions (absorption, metabolism etc.)

• Costly, time-consuming, low throughput

• Inter-species differences

• Intra-species differences

• Relevance of results to humans?

• In vitro testing

• Fast, cost-effective, high-throughput (large scale screening)

• Use of human derived cells

• Reduction of animal use

• In vitro-in vivo extrapolation =

Toxicity Profiling in Drug Discovery


ScreenDevelopment

& High Through-put Screening

Hit to LeadLead

OptimizationCandidateSeeking

TargetPoC

Primary Hits screen

Parallel Med Chem

Optimal Potency/

Selectivity

Efficacy in in vivo models

In silico/ in vitro assessment In vivo toxicity studies

Toxicity profiling


• Frequently used (traditional)

• Liver tissue slices• Perfused liver• Isolated microsomes• Immortalised hepatic cell lines • Primary hepatocytes (suspension,

cultures, sandwich)

In vitro systems for hepatotoxicity testing• Novel methodologies

• 3D culture systems (e.g. spheroids)• Primary cell co-cultures• Embryonic stem cells• Induced pluripotent stem cells• Hurel Biochip• Hollow-fiber Reactor• Multi-well perfused bioreactor• Bio-artificial liver

Soldatow et al.

Liver toxicity mechanisms• Multiple mechanisms of liver damage

• Mitochondrial dysfunction• Reactive metabolites• Inhibition of hepatic transporters• …


DrugDrug

Clearance

Adaptive response

Metabolism

Clearance

Mitochondrial impairmentnecrosis, steatosis

Reactive metabolitesnecrosis

Inhibition of billiary effluxcholestasis

Immune-mediated

Lysosomal impairment

AOP – Adverse Outcome Pathway


Molecular Initiating event

• Receptor/ligand interaction

• DNA binding• Protein oxidation

Key events

• Gene activation• Protein

production• Altered signalling• Cell-cell

interaction• Altered tissue

development• Adverse tissue

function

Adverse Outcome

• Disease• Impaired

development• Impaired

reproduction

AOP starts with MIE


KE3KE2KE1

Molecular Initiating event

• Receptor/ligand interaction

• DNA binding• Protein oxidation

Key events

• Gene activation• Protein

production• Altered signalling• Cell-cell

interaction• Altered tissue

development• Adverse tissue

function

Adverse Outcome

• Disease• Impaired

development• Impaired

reproduction

AOP starts with MIE


KE3KE2KE1

Can modelling these events help in predicting the in vivo outcome?

Plan for modellingMIEs- Identification of liver toxicity MIEs (public literature)- Compilation of hepatotoxicity MIEs list

Data- Identification of relevant sources- Collation of MIEs datasets

Modelling- Evaluation of suitability for modelling (size, bias)- Applying relevant methodology for modelling (machine learning, patterns)

Profiler - Creating a network of the MIE models, assembling into a profiler

Evaluation- Assessment of predictivity with suitable in vivohepatotoxicity data

IVTS 2017, 23-24 November14

AOP for liver fibrosis• Adopted from Landesmann et al.


AOP for cholestasis• Adopted from Vinken et al.


Identified liver toxicity MIEs (subset)

• SEURAT-1 liver gold reference compounds (Jennings et al.)


Hepatotoxicity profiler

Hepatotox MIE 1

Sufficient data Build models

yesno

Models based on molecular descriptors, or expert patterns (where appropriate)MIE 2 - not used

.......

New data?

New data identifiedMembers data provided..

assay1a, assay 1b

assay2a assay3a, assay 3b, assay3d

assay n, ..

Hepatotox MIE 2 Hepatotox MIE 3 Hepatotox MIE n

yes

Model MIE 1 .......Model MIE 3 Model MIE n

Hepatotoxicity prediction


MIEs data compilation

• Data sets combined resulting in a data set containing unique compound (>8000)

MIEs data


BSEP inhibition: comparison of assay conditionData set No of

compounds in DS

System substrate Temperature, C Time, min

ATP Concentrations tested

Pos/neg cut off (IC50)

Warner et al. (2012)

610 membrane vesicles

3H-taurocholate 37 5 5mM 10, 30, 100, 250, 500, 1000µΜ

300μM

Morgan et al. (2010)


3H-taurocholate Room temp 15-20 4mM 10 conc 0-133µΜ

100μM

Thompson et al. (2012)


7β-[(4-Nitro-2,1,3-benzoxadiazol-7-yl)amino] taurocholate (NBD-taurocholate)

37 5 5mM 0−1000 μM 500μM

Pedersen et al. (2013)


3H-taurocholate 37 10 4mM 50 uM 27.5%inhibition

Dawson et al. (2012)


3H-taurocholate 37 5 5mM ? 300uM (recommended by authors);used 100uM

Aleo et al. (2014)


3H-taurocholate 37 5 4mM 100uM highest

used 100uM


Heterogeneous data

• Data for troglitazone showing the heterogeneity of the data collected

Reference Data Data value Troglitazone -example

ChemBL increase in dihydrofluorescein (1uM) intracellular accumulation in SK-E2 cell (expressing hBSEP)

continuous, IC50 66.4

Thompson et al. (2012)

BSEP signal Y (positive)/ N (negative)

Y

Pedersen et al. (2013)

human BSEP inhibitor/weak inhibitor/non-inhibitor

inhibitor

Dawson et al. (2012)

human BSEP activity, TA transport assay in Sf9 cells

continuous, IC50 uM 2.7uM

Aleo et al. (2014) human BSEP activity, the transport of [3H]taurocholic acid in SB-BSEP-Sf9-VT vesicles

continuous, IC50 uM 5.9 uM


Model development

HBD

List of descriptors most predictive: positive and negative

Descriptor generation

Selection

Model building

Prediction (training/test set)


Dataset

ModelValidation

Considerations for confidence for in silico predictions

in vivohepatotoxicity

in vitro assay

in silico model

• correlation of in vitroassay with in vivo outcome

• in vitro assay data used to build in silico model

• prediction of in vivo toxicity by in silico model

• prediction of in vitrodata (data used for modelling) by in silicomodel


Modelling

• Techniques used• Applying published models: physchem (clogP, MW) [Warner et al.]• Machine learning methods: k-nearest neighbour (kNN), decision tree (DT), random forest

(RF), internal tools• Descriptors: generated by RDKit, pharmacophore, structural fragments (internal)• Expert-derived patterns

• Applicability domain (defining in and out of domain – what compounds the model can be used for)

• Confidence in the in silico predictions


Hepatotoxicity MIEs modelling

• Machine Learning (Random Forest) models based on RDKit molecular descriptors

• BSEP inhibition• Mitochondrial toxicity• Nuclear receptor agonism

• AHR, GR, ER, FXR, PPAR, RXR, LXR

• Patterns (expert-derived)

• MRP2 inhibition• MRP4 inhibition• Reactive metabolites (Kalgultkar alerts)


BSEP modelling

• BSEP – individual model implementation

Applicability Domain(Y/N)

Data set

Prediction(positive/negative)

RF Model

Based on numberof trees

Confidence


BSEP modelling (cont.)• BSEP – combination of models

Model 1 (Training

set 1)

Model 2 (Training

set 2)

Model 3 (Training

set 3)

- Combination of predictions from 3 models- In vitro/in vivo correlation (BSEP inhibition/ liver

toxicity)





• AHR, GR, ER, FXR, PPRA, RXR, LXR




Pattern approach for MIEs modelling• MRP2 and MR4 inhibition

• Single dataset for each MIE

• Unbalanced dataset - a small proportion of compounds with positive call

• Machine learning approach unsuccessful

• Expert-derived patterns (based on visual analysis)

• Predictivity figures of in silico model of in vitro assay data

• MRP2 (12 patterns) – sensitivity 0.266, specificity 0.985

• MPR4 (13 patterns) – sensitivity 0.273, specificity 0.994

Patterns (structural alets)


Hepatotoxicity MIEs modelling• Machine Learning (Random Forest) models based on RDKit molecular descriptors






Reactivity modelling

• Chemical features are known to be bioactivated to reactive metabolites [Stepan et al.]• 56 alerts (patterns generated based on “alert” description in paper)

• Presence of “reactivity” feature is not equal to metabolic activation• WhichCYP (KNIME node) - predicts binding to CYP isoforms: 1A2, 2C9,

2C19, 2D6 and 3A4• No binding – no activation – no reactive metabolite


Reactivity modelling (cont.)

Kalgutkar alerts

• Patterns (known structural features) Yes/No

Yes WhichCYP

• Binding to CYP isoforms (1A2, 2C9, 2C19, 2D6 and 3A4) Yes/No

Yes Formation of reactive metabolites

• Presence of structural features

• Binding to CYP

Yes In vivo /in vitro correlation

• Based on correlation b/n data on bio-activiation in vitro /in vivoliver toxicity


Hepatotoxicity profiler (KNIME)

Liver toxicity potentialBSEP model

Mitotox model

Nuclear receptor binding

models

MRP2

Reactive metabolites

….


In Vivo Human Hepatotoxicity Dataset• 7 datasets from the literature

• Structures: retrieved and standardised

• Human in vivo annotations

• Annotations converted into a binary classification: Positive/Negative

• A dataset (unique compounds):

899 – positive, 649 - negativeIn Vivo Human Hepatotoxicity Dataset


Results• In vivo human hepatotoxicity dataset – prediction by profiler

0.61

0.86

0.25

0.62

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Accuracy Sensitivity Specificity PPV

Total TP FP TN FN Equivocal Out of Domain

1537 771 482 159 125 0 11IVTS 2017, 23-24 November 36

MIP DILI training compounds

• Predictions generated by profiler for 14 MIP DILI training compounds

Training set call

In silico prediction

Confidence (in silico)

BSEP inhibition Mitotox AHR ER FXR GR PPAR PXR RAR_RXR LXR MRP2 MRP4 Reactivity

amiodarone Positive Positive 0.833 0.785 0.735 0.605 -0.32294 0.667 0.558 0.703 0.833 -0.68 0.824 0.639

bosentan Positive Positive 0.785 0.785 0.735 -0.605 -0.61 0.5336 0.558 0.703 0.043842 -0.68 0.639

buspirone Negative Negative 0.007876 -0.01138 -0.735 -0.605 -0.61 -0.14822 -0.28469 -0.703 -0.22718 -0.68

diclofenac Positive Positive 0.735 0.624432 0.735 0.605 0.61 0.667 -0.558 0.043938 -0.1356 0.68 0.639

entacapone Negative Positive 0.453971 0.112143 0.453971 0.191053 -0.52286 -0.667 -0.558 -0.703 -0.47795 -0.68

fialuridine Positive Negative 0.286579 -0.785 -0.735 -0.41395 -0.61 -0.667 -0.558 -0.703 -0.833 -0.68

metformin Negative Negative 0.343385 -0.785 -0.735 -0.605 -0.61 -0.667 -0.558 -0.703 -0.68

nefazodone Positive Positive 0.833 0.785 0.735 0.605 -0.50833 0.354787 0.558 0.502143 0.833 0.182439 0.639

paracetamol Positive Positive 0.605 -0.785 -0.735 0.605 -0.61 -0.667 -0.558 -0.703 -0.833 -0.68

perhexiline Positive Positive 0.516064 0.020658 0.516064 -0.605 -0.61 -0.667 -0.558 -0.703 -0.54326 -0.68

pioglitazone Negative Positive 0.833 0.274277 0.42 0.605 -0.61 0.667 0.558 0.293982 0.833 0.68 0.25878 0.824 0.639

tolcapone Positive Positive 0.68 -0.05888 0.568585 0.605 0.122 -0.667 -0.558 -0.4218 -0.25453 0.68 0.639

troglitazone Positive Positive 0.833 0.785 0.519878 -0.605 -0.61 0.667 0.558 0.703 0.833 0.68 0.4767 0.824 0.639

ximelagatran Positive Positive 0.833 -0.70237 -0.735 -0.605 -0.61 0.667 0.558 -0.703 0.833 -0.68


MIP DILI training compounds

• 14 compounds:

• 9 true positives• 2 true negatives• 1 false negative• 2 false positives

Training set call In silico predictionamiodarone Positive Positive

bosentan Positive Positivebuspirone Negative Negativediclofenac Positive Positive

entacapone Negative Positivefialuridine Positive Negativemetformin Negative Negative

nefazodone Positive Positiveparacetamol Positive Positiveperhexiline Positive Positivepioglitazone Negative Positivetolcapone Positive Positive

troglitazone Positive Positiveximelagatran Positive Positive


True negatives

• Buspirone and metformin

Training set call In silico prediction

buspirone Negative Negative

metformin Negative Negative

BSEP inhibition Mitotox AHR ER FXR GR PPAR PXR RAR_RXR LXR MRP2 MRP4 Reactivity

buspirone -0.01138 -0.735 -0.605 -0.61 -0.14822 -0.28469 -0.703 -0.22718 -0.68

metformin -0.785 -0.735 -0.605 -0.61 -0.667 -0.558 -0.703 -0.68


True positive

• Amiodaroneamiodarone

Training set call

In silicoprediction

Call Positive PositiveConfidence (in silico) 0.83BSEP inhibition 1 0.79Mitotox 1 0.74AHR 0.61ER -0.32FXR 0.67GR 0.56PPAR 0.70PXR 0.83RAR_RXR -0.68LXRMRP2 0.82MRP4Reactivity 1 0.64

PXR – no data in ChemBL or PubMed

Inhibition of human MRP2 - J. Med. Chem., 2008, 51, 11, 3275.


False negative

• FialuridineTraining set call

In silico prediction

fialuridine Positive Negative

Confidence (in silico) 0.29BSEP inhibition -0.79Mitotox -0.74AHR -0.41ER -0.61FXR -0.67GR -0.56PPAR -0.70PXR -0.83RAR_RXR -0.68LXRMRP2MRP4Reactivity


Summary (hepatotox profiler version 1)• List of MIEs created and dataset compiled

• Individual MIEs modelled and incorporated into the profiler

• In vivo human hepatotoxicity dataset created

• Profiler tested with in vivo human hepatotoxicity dataset

• High sensitivity (86%) indicates that majority of potential mechanisms for liver damage are covered

• Low specificity – requires further investigation in model(s) applicability

• 9 out of 10 positive compounds from MIP DILI training set predicted as positive by in silicomodel


Current work (hepatoxicity profiler version 2)

• Adopted from Hanser et al.


Applicability domain

Descriptor range

Convex hull (descriptors)

Organic (no inorganic or

organometallic, no proteins)


Reliability domain (RD)• Distance to data points (k-nearest neighbours)

• Density of information (based on descriptor distribution)


Data distribution (examples)

• Variance in data distribution (based on PCA – principal component analysis)

• BSEP inhibition Warner et al. dataset (a)• PXR PubChem dataset (b)

a) b)


Decidability domain (DD)

• Random forest (RF) – decidability on agreement between predictions between trees in RF model


Conformal predictions

• Mondrian conformal prediction – classification settings (Norinder et al.)


Conformal predictions• Probabilities for class A and B (based on calibration test) – look up list

• Query compound probabilities for corresponding classes (RF model)

• p-value for each class recalculated based on the position in the calibration list

• Classes p-values to be used to define confidence level


False positive reduction: RD + DD • Ultimate aim for using reliability domain (RD) and decidability domain (DD) with conformal prediction to

reduce number of false positive predictions

• outside of reliability domain (insufficient information to make a decision)

• outside of decidability domain (no consensus on outcome - based on RF)


Future plans

• Identify further MIEs – for those compounds that are currently are false negatives

• Expansion of chemical space covered by MIE models by searching for /identifying new

sources of data

• Further testing of the profiler with proprietary datasets



Liver toxicity MIE data:• BSEP inhibition• Hepatobiliary transporters • Mitochondrial dysfunction• Reactive metabolites• Nuclear receptor agonism• …

Hepatotoxicity Profiler ++

Acknowledgements

• Sebastien Guesne

• Richard Williams

• Thierry Hanser

• Jonathan Vessey

• Sam Webb

• Mukesh Patel

• Alex Cayley

• Carol Marchant


Thank you!


References (1)


• Aleo MD, et al. Hepatology. 2014, 60(3): 1015-1022, doi: 10.1002/hep.27206

• Chen M, et al. Drug Discovery today 2016, 21, 648-653, doi: 10.1016/j.drudis.2016.02.015

• Dawson S, et al. Drug Metab Dispos. 2012, 40(1): 130-138, doi: 10.1124/dmd.111.040758

• Greene N, et al. Chem Res Toxicol 2010, 23, 1215-1222, doi: 10.1021/tx1000865

• Hanser T, et al. Journal SAR and QSAR in Environmental Research. 2016, 27, issue 11, doi: 10.1080/1062936X.2016.1250229

• http://emedicine.medscape.com/article/169814

• Jennings P, et al. Arch Toxicol. 2014; 88: 2099-2133, doi: 10.1007/s00204-014-1410-8

• Landesmann B, et al. JRC scientific and policy report, 2012, doi: 10.2788/71112

• Morgan RE, et al. Toxicol Sci. 2010, 118(2): 485-500, doi: 10.1093/toxsci/kfq269

• Mulliner D, et al. Chem Res Toxicol, 2016, 29, 757-767, doi: 0.1021/acs.chemrestox.5b00465

• Norinder U, Carlsson L, Boyer S, et. al. J Chem Inf Model, 2014, 54 (6), 1596–1603, doi: 10.1021/ci5001168

References (2)


• Onakpoya IJ, et. al. BMC Med. 2016, 14: 10, doi: 10.1186/s12916-016-0553-2

• Pedersen JM, et al. J Med Chem. 2008, 51(11): 3275-3287, doi: 10.1021/jm7015683

• Sakatis M, et al. Chem Res Toxicol 2012, 25, 2067-2082, doi: 10.1021/tx300075j

• Schadt S, et al. Toxicol In Vitro, 2015, 30, 429-437, doi: 10.1016/j.tiv.2015.09.019

• Soldatow VY, et. al. Toxicol Res (Camb). 2013, 2(1): 23–39, doi: 10.1039/C2TX20051A

• Stepan AF, et al. Chem Res in Toxicol, 2011, 24, 1345-1410, doi: 10.1021/tx200168d

• Thompson RA, et al. Chem Res Toxicol. 2012, 25(8): 1616-1632, doi: 10.1021/tx300091x

• Vinken M, Landesmann B, Goumenou M, et al. Toxicol Sci. 2013, 136, 97-106, doi: 10.1093/toxsci/kft177

• Warner DJ, et al. Drug Metab Dispos. 2012, 40(12): 2332-2341, doi: 10.1124/dmd.112.047068

• Xu JJ, et al. Toxicol Sci 2008, 105, 97-105, doi: 10.1093/toxsci/kfn109

• Zhu X, et al. Toxicology 2014, 321, 62-72, doi: 10.1016/j.tox.2014.03.009

Lhasa Limited

Granary Wharf House, 2 Canal Wharf

Leeds, LS11 5PS

Registered Charity (290866)

Company Registration Number 01765239

+44(0)113 394 6020

[email protected]

www.lhasalimited.org

Work in progress disclaimerThis document is intended to outline our general product direction and is for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon. The development, release, and timing of any features or functionality described for Lhasa Limited’s products remains at the sole discretion of Lhasa Limited.


profiling potential drug-induced hepatotoxicity through ... potential... · ivts 2017, 23-24...

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