Profiling potential drug-induced hepatotoxicity through ... potential... · IVTS 2017, 23-24...
Transcript of Profiling potential drug-induced hepatotoxicity through ... potential... · IVTS 2017, 23-24...
Profiling potential drug-induced hepatotoxicity through modelling molecular initiating events (MIEs)
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?
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• 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
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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• 462 medicinal products withdrawn from the market (1953-2013) [Onakpoya et al.]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%
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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
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Toxicity testing
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• 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
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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
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• 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• …
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DrugDrug
Clearance
Adaptive response
Metabolism
Clearance
Mitochondrial impairmentnecrosis, steatosis
Reactive metabolitesnecrosis
Inhibition of billiary effluxcholestasis
Immune-mediated
Lysosomal impairment
AOP – Adverse Outcome Pathway
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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
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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
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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
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AOP for liver fibrosis• Adopted from Landesmann et al.
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AOP for cholestasis• Adopted from Vinken et al.
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Identified liver toxicity MIEs (subset)
• SEURAT-1 liver gold reference compounds (Jennings et al.)
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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
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MIEs data compilation
• Data sets combined resulting in a data set containing unique compound (>8000)
MIEs data
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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)
629 membrane vesicles
3H-taurocholate Room temp 15-20 4mM 10 conc 0-133µΜ
100μM
Thompson et al. (2012)
36 membrane vesicles
7β-[(4-Nitro-2,1,3-benzoxadiazol-7-yl)amino] taurocholate (NBD-taurocholate)
37 5 5mM 0−1000 μM 500μM
Pedersen et al. (2013)
250 membrane vesicles
3H-taurocholate 37 10 4mM 50 uM 27.5%inhibition
Dawson et al. (2012)
85 membrane vesicles
3H-taurocholate 37 5 5mM ? 300uM (recommended by authors);used 100uM
Aleo et al. (2014)
72 membrane vesicles
3H-taurocholate 37 5 4mM 100uM highest
used 100uM
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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
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Model development
HBD
List of descriptors most predictive: positive and negative
Descriptor generation
Selection
Model building
Prediction (training/test set)
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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
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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
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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)
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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)
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BSEP modelling
• BSEP – individual model implementation
Applicability Domain(Y/N)
Data set
Prediction(positive/negative)
RF Model
Based on numberof trees
Confidence
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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)
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Hepatotoxicity MIEs modelling
• Machine Learning (Random Forest) models based on RDKit molecular descriptors
• BSEP inhibition• Mitochondrial toxicity• Nuclear receptor agonism
• AHR, GR, ER, FXR, PPRA, RXR, LXR
• Patterns (expert-derived)
• MRP2 inhibition• MRP4 inhibition• Reactive metabolites (Kalgultkar alerts)
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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)
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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)
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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
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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
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Hepatotoxicity profiler (KNIME)
Liver toxicity potentialBSEP model
Mitotox model
Nuclear receptor binding
models
MRP2
Reactive metabolites
….
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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
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Results• In vivo human hepatotoxicity dataset – prediction by profiler
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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
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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
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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
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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.
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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
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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
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Current work (hepatoxicity profiler version 2)
• Adopted from Hanser et al.
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Applicability domain
Descriptor range
Convex hull (descriptors)
Organic (no inorganic or
organometallic, no proteins)
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Reliability domain (RD)• Distance to data points (k-nearest neighbours)
• Density of information (based on descriptor distribution)
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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)
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Decidability domain (DD)
• Random forest (RF) – decidability on agreement between predictions between trees in RF model
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Conformal predictions
• Mondrian conformal prediction – classification settings (Norinder et al.)
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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
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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)
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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
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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
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Thank you!
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References (1)
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• 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)
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• 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
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.
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