Molecular Clinical Safety Intelligence: Tools to detect risk (or at least illuminate blind

spots) earlier in drug discovery.

Traditional Development(shown in gray)

Drug Discovery Human Safety Experience

Medicines are developed for safe and effective human use

What is Molecular Clinical Safety Intelligence?

Traditional Development(shown in gray)

Drug Discovery Human Safety Experience

Medicines are developed for safe and effective human use

Continual input of Human Safety Information

MCSI System integrates Chemistry, Pharmacology, and Human Safety Knowledge

Scientists use MCSI system info to develop safer medicines

MCSI SystemCloses the Knowledge

Gap

What is Molecular Clinical Safety Intelligence?

APPLICATION FRAMEWORK

Data mappedto chemicalstructures

MCSI DATA WAREHOUSE

What is Molecular Clinical Safety Intelligence? (MCSI)

ChemAxon Chemistry Drawing Tool: Marvin Sketch

ChemAxon Chemistry Data Cartridge: JChem

Visual iloData Visualization

Statistical Modeling: Recursive Partitioning, Random Forests

Support for downloading data for use in Excel, Search Iris, Spotfire, Word (RTF)

Chemical structures & computedproperties for 70K (mostly) external compounds

FDA / WHOQuantitative Adverse Event Signal Scores on marketed

Rx

GSK: In-house Compound

Profiling Data on Marketed Drugs

University of Washington

metabolism & drug-interaction data

GVK Bio Lit.: Clinical, Pharmacological, & animal

and mechanism-basedtoxicity data, metabolite

structures

Data Sources• Adverse Events

– Lincoln Technologies (Phase Forward)– Quantitative signal scores for adverse events from FDA AERS & WHO Vigibase

• Data/Information Curated From Literature – GVK Bio – marketed drugs and clinical candidates (DD and CCD)– GVK Bio – Mechanism-Based Toxicity (MBT)– GSK – literature data on marketed compounds (“Top 2500”)

• Drug Metabolism/Drug-Drug Interactions– University of Washington – Drug Interaction Database (DIDB)– David Flockhart – P450 and drug interactions– Edmund Hayes – P450 and drug interactions

• Target Activity Profiles– GSK – compound profiling data on marketed compounds (CPDP)

• Chemical Properties– GSK – Adamantis computed properties

Capturing the human safety experience of marketed drugs

doing more,feeling better,living longer

Drug & Adverse event

• Adverse events (AEs) are reported to the FDA & WHO and recorded in the Adverse Event Reporting System (AERS; FDA) & Vigibase (WHO)– As a drug & AE pair– Millions of reports gathered over decades

• These data are used by FDA/WHO & Pharmacovigilance groups in the pharma to monitor the safety of drugs on the market– “Disproportionality Analysis”: a statistical method that

quantitates the association between Drug & AE– The resulting Emperical Bayes Geometric Mean (EBGM)

value is ~fold increase in occurrence of Drug-AE pair over “expected” rate

Typical workflow using MCSI

Query MCSI with structures of interest using chemical

substructure & similarity

MCSIMCSI  

  

  

  

  

  

  

  

structurecompound

Q: What do we know about these compounds?

     

     

     

     

     

     

     

     

AE2AE1bindingstructurecompoundDrill down into the data to

learn about the safety, pharmacology, metabolism of these similar molecules

Adverse EventsFunctional Assays

DMPK

Identify similar moleculesN

N

O

N

Novel Compound

MCSIMCSI

Q: Are there any compounds similar to mine that have had

safety issues?

Progression of a Series from an HTSProlyl Hydroxylase Inhibitors (Oncology CEDD)

• Progression of hit in Prolyl Hydroxylase Inhibitors Program discussed in Oncology Chem Exec (Aug 2007)

• A hit from screening, GSK147098A, showed good activity as a PH inhibitor

• SAR showed that thionocarbonyl function (thiourea) was key for cmpd activity

• A question arose regarding toxicity of thionocarbonyl/thiourea functional groups

• A search of MCSI was conducted:– Are there any marketed drugs containing thioureas?

– If there are, what is their Tox profile

NH

O

NH

N

OMe

S

OCl

OMe

OMe

OMe

NH

NSNH

O

N

Cl

S

O

GSK1992771AGSK147098A

Currently 4 std quick reports

Query by chemical similarity or substructure

Query target in GSK profiling

dataQuery by

adverse event in FDA AERS

H2N NH2

S H2N NH2

S

The list of compounds retrieved or the report output itself can be saved within the system (privately or

“published”), or exported

The list of compounds retrieved or the report output itself can be saved within the system (privately or

“published”), or exported

Safety Summary Report for Thioureas (cnt’d)

Propylthiouracil: Similarity with PH Hits

NH

O

NH

N

OMe

S

OCl

OMe

OMe

OMe

NH

NSNH

O

N

Cl

S

O

GSK1992771AGSK147098A

• Prolyl Hydroxylase Lead Compounds:

NH

NH

S

O• Propylthiouracil:

Compound Details show all of the available data on one compound

Compound Details show all of the available data on one compound

Propylthiouracil Metabolites• Metabolite section of Propylthiouracyl shows the toxic reactive

metabolites associated with thioureas

Propylthiouracil Toxicity Mechanism• Toxicity Mechanism section reveals key information for thiourea

toxicity

Signal Scores capture the EBGM values for adverse events from

FDA & WHO databases

(EBGM >3 is generally considered clinically relevant)

Signal Scores capture the EBGM values for adverse events from

FDA & WHO databases

(EBGM >3 is generally considered clinically relevant)

Propylthiouracil Adverse Events

Benefit and Application• MCSI can be used at almost any point in the discovery

process

prior to molecule selection!– Tool compound identification– Fragment / HTS triage– Lead optimisation / scaffold hopping techniques– Due diligence processes / in licensing...

• Enables assessment of potential issues for compound or series to be weighed with other considerations

• Might be also be helpful in understanding observed toxicity, adding more context

The MCSI Community• Led by James Bailey, Dana Vanderwall, Nancy Yuen• ~75 User-Members within R&D

– DPU Chemistry, Biology, DMPK– Computational Chemistry, MDR– Global Clinical Safety & Pharmacovigilance – Scinovo (preclinical externalization group)

• Members serve as a resource to drug discovery/development programs

• Always open to new members: email: nancy.a.yuen@gsk.com

Other types of questions supported

• The data can be used look for new hypothesis in relationships between human adverse events & chemical or biological characteristics of drugs

• Marketed drugs profiled in assays for >500 targets– >54K pXC50 determinations on 1336 drugs @ 545 targets– Via standard SS-> FC hit progression (CCP, 2 later profiling exercises)

• Molecular target activities can be compared with quantitative AEs (EBGM)– Look for either increase or decrease associated with a target

Comparing the adverse event data with the measured target activity profiles

hERG IonWorks pIC50 AE

“S

co

re”:

QT

pro

lon

ga

tio

n

Generic_Name AE N EBGM EB05 EB95

Bepridil Electrocardiogram QT prolonged 41 86.4 66.3 111

Halofantrine Electrocardiogram QT prolonged 26 82.7 59.1 113.2

Probucol Electrocardiogram QT prolonged 48 74.5 58.4 94

Levacetylmethadol Electrocardiogram QT prolonged 15 63.6 40.6 96

Doxapram Electrocardiogram QT prolonged 18 62.8 41.8 91.5

Cisapride Electrocardiogram QT prolonged 862 41.8 39.5 44.2

Pimozide Electrocardiogram QT prolonged 34 40.1 30 52.8

Astemizole Electrocardiogram QT prolonged 73 37.4 30.8 45.2

Sotalol Electrocardiogram QT prolonged 135 33.7 29.2 38.8

Dofetilide Electrocardiogram QT prolonged 265 33.3 30.1 36.8

Nilotinib Electrocardiogram QT prolonged 8 31.5 16.1 56.2

Ibutilide Electrocardiogram QT prolonged 15 30.5 19.4 46

Terfenadine Electrocardiogram QT prolonged 151 26.9 23.5 30.8

Disopyramide Electrocardiogram QT prolonged 43 26.3 20.3 33.6

Quinidine Electrocardiogram QT prolonged 48 23.5 18.4 29.7

Ziprasidone Electrocardiogram QT prolonged 279 21.3 19.3 23.5

Papaverine Electrocardiogram QT prolonged 6 20.2 4.2 48.5

Pentamidine Electrocardiogram QT prolonged 21 17.6 11.8 25.2

Erythromycin Electrocardiogram QT prolonged 147 16.7 14.5 19

Take one well understood example:hERG inhibition & QT prolongation

Marketed Drugs

Adverse events

Potency/efficacy in target assay

Drug Name Compound Number Target Name Result Type ModifierpXC50

Roflumilast GW775679 Phosphodiesterase 4A, cAMP-specific (phosphodiesterase E2 dunce homolog, Drosophila)pIC50 = 10.6

Roflumilast GW775679 Phosphodiesterase 4D, cAMP-specific (phosphodiesterase E3 dunce homolog, Drosophila)pIC50 = 10.5

Isamoltane SB-276578 Beta 2 adrenergic receptor FUNC_pKI = 10.5

Clobetasol CCI4725 Nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor)pIC50 = 10.5

Benzyl Benzoate GR176931 Dopamine receptor D2 pEC50 = 10.5

Clobetasol CCI4725 Progesterone receptor pEC50 = 10.4

Alfuzosin GI185141 Alpha-1B adrenergic receptor pIC50 > 10.4

Roflumilast GW775679 Phosphodiesterase 4B, cAMP-specific pIC50 = 10.4

Trimipramine SKF-12569 Histamine receptor H1 pIC50 = 10.4

Budesonide GR160288 Nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor)pIC50 = 10.3

Alprenolol SKF-102935 Beta 2 adrenergic receptor FUNC_pKI = 10.2

Medroxyprogesterone acetateCCI175 Progesterone receptor pEC50 = 10.1

Dinoprostone GR33280 Prostaglandin E receptor 3 (subtype EP3) pKi = 10.1

Levonorgestrel CCI8875 Progesterone receptor pEC50 = 10.1

Mometasone GR226838 Nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor)pEC50 = 10.1

Iloperidone SB-729825 5-hydroxytryptamine (serotonin) receptor 2A pEC50 = 10.0

Mometasone GR226838 Nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor)pEC50 = 10.0

Niacin CCI11714 High affinity nicotinic acid receptor HM74A pIC50 > 10.0

Estradiol CCI100 Estrogen receptor 1 pEC50 = 10.0

Fludrocortisone CCI22434 Mineralocorticoid Receptor pEC50 = 10.0

Ipratropium SB-452594 Cholinergic receptor, muscarinic 4 FUNC_pKI > 10.0

Tegaserod GSK256562 5-hydroxytryptamine (serotonin) receptor 4 pEC50 = 10.0

Formoterol GW577790 Beta 2 adrenergic receptor pEC50 = 9.9

Paroxetine BRL-29060 Solute carrier family 6 (neurotransmitter transporter, serotonin), member 4pIC50 = 9.9

Tiotropium GW696257 Cholinergic receptor, muscarinic 1 FUNC_pKI = 9.9

Raloxifene GI183990 Estrogen receptor 1 pIC50 = 9.9

Tiotropium GW696257 Cholinergic receptor, muscarinic 2 FUNC_pKI = 9.8

Estradiol CCI100 Estrogen receptor 2 (ER beta) pEC50 = 9.8

Norethisterone acetate CCI169 Progesterone receptor pEC50 = 9.8

Pantothenic Acid AH15048 Tachykinin receptor 2 pIC50 > 9.8

Pantothenic Acid AH15048 Tachykinin receptor 3 pIC50 > 9.8

Mupirocin BRL-4910 Tachykinin receptor 3 pIC50 > 9.8

Zafirlukast GR138714 Tachykinin receptor 2 pIC50 > 9.8

Zafirlukast GR138714 Tachykinin receptor 3 pIC50 > 9.8

Tropisetron GR58760 5-hydroxytryptamine (serotonin) receptor 3A pIC50 > 9.8

Generic_Name PT N EBGM EB05 EB95

Sevoflurane Hyperthermia malignant 69 96.9 79.1 117.7

Benztropine Anticholinergic syndrome 36 96.8 72.9 126.6

Quinine Cinchonism 6 96.7 45.9 184.7

Cilostazol Coronary revascularisation 6 96.5 45.8 184.2

Spironolactone Hepatoblastoma 8 96.2 51 168.5

Arsenic Trioxide Acute promyelocytic leukaemia 13 96.1 59.1 149.4

Tamoxifen Endometrial atrophy 20 95.2 64.7 136.1

Zolpidem Sleep-related eating disorder 10 93.9 53.6 155.1

Sotalol Cardioactive drug level decreased 7 93.8 47.4 170.8

Ciprofloxacin Factor V inhibition 9 93.4 51.6 158.5

Amiodarone Hyperglobulinaemia 29 93.2 67.9 125.6

Simvastatin Glycogen storage disease type V 10 93.1 53.1 153.8

Theophylline Hepatoblastoma 10 92.2 52.7 152.4

Ritonavir Hypercorticoidism 10 91.4 52.2 151.1

Cytarabine Idiopathic pneumonia syndrome 9 91.1 50.3 154.5

Furosemide Adrenogenital syndrome 10 91 51.9 150.3

HydrochlorothiazideAnti-SS-A antibody positive 6 91 43.2 173.8

Cytarabine Leukostasis 10 90.7 51.8 149.9

Lamivudine And ZidovudineAlpha 1 foetoprotein abnormal 22 90.2 62.5 126.8

Stavudine Lipoatrophy 94 90 75.7 106.4

Dobutamine Myocardial rupture 7 89.9 45.4 163.6

Phenytoin Purple glove syndrome 7 89.8 45.4 163.5

Bepridil Torsade de pointes 29 89.2 64.9 120.1

Zolpidem Sleep talking 70 88.4 72.3 107.2

Zoledronic Acid Life expectancy shortened 55 88.2 70.2 109.6

Dobutamine Eosinophilic myocarditis 7 88 44.4 160.1

Lidocaine Anaesthetic complication 130 87.8 75.8 101.2

MethylprednisoloneIdiopathic pneumonia syndrome 10 87.7 50.1 144.9

Sevoflurane Anaesthetic complication cardiac 8 87.4 46.4 153.1

Pantoprazole Nikolsky's sign 15 87.3 55.7 131.8

Pentamidine Pneumocystis jiroveci pneumonia 14 87.1 54.6 133.2

Didanosine Mitochondrial toxicity 30 86.6 63.4 116.1

Disopyramide Hepatic atrophy 6 86.6 41.1 165.3

Bepridil Electrocardiogram QT prolonged 41 86.4 66.3 111

Nicardipine Foetal arrhythmia 7 86.3 43.6 157.1

Now compare all target activities to all AE scores

All measured pXC50 data All AE “Scores”

X

= potential new associations between activity at target(s) & adverse event

Target Activity and AE AssociationsTarget (Result Type) Adverse Event (PT) Correlation Count Signif Prob

N Rows where both 'active'

Histamine receptor H1 (pIC50) Sedation 0.61086584 140 1.11E-15 25Cholinergic receptor, muscarinic 1 (pIC50) Urinary retention 0.69260995 84 2.91E-13 21Dopamine receptor D2 (pIC50) Extrapyramidal disorder 0.74800709 63 1.85E-12 24Alpha-1A adrenergic receptor (pIC50) Urinary incontinence 0.65741672 90 1.95E-12 9Alpha-1B adrenergic receptor (pIC50) Urinary incontinence 0.63342427 89 2.71E-11 8Dopamine receptor D3 (pIC50) Extrapyramidal disorder 0.71076876 63 6.75E-11 24Dopamine receptor D2 (pIC50) Hypertonia 0.67525776 71 1.06E-10 9Cholinergic receptor, muscarinic 2 (pIC50) Urinary retention 0.63018186 84 1.34E-10 165-hydroxytryptamine (serotonin) receptor 2A (pIC50) Extrapyramidal disorder 0.67590426 65 6.50E-10 24Dopamine receptor D3 (pIC50) Sedation 0.5542345 106 7.12E-10 22Dopamine receptor D2 (pIC50) Agitation 0.56164302 101 9.94E-10 14Histamine receptor H1 (pIC50) Urinary retention 0.56873519 91 4.07E-09 20Dopamine receptor D3 (pIC50) Restlessness 0.70065497 50 1.47E-08 19Cholinergic receptor, muscarinic 3 (pIC50) Urinary retention 0.62321782 67 1.78E-08 17Alpha-1B adrenergic receptor (pIC50) Extrapyramidal disorder 0.5976482 72 2.98E-08 22Beta 2 adrenergic receptor (pEC50) Asthma 0.68117854 50 5.20E-08 85-hydroxytryptamine (serotonin) receptor 2A (pIC50) Hypertonia 0.5619444 80 5.84E-08 10Cholinergic receptor, muscarinic 3 (pIC50) Dry mouth 0.55097494 82 8.16E-08 16Dopamine receptor D3 (pIC50) Hypertonia 0.59187564 69 8.50E-08 9Alpha-1A adrenergic receptor (pIC50) Extrapyramidal disorder 0.5795863 72 9.58E-08 22Alpha-1B adrenergic receptor (pIC50) Restlessness 0.59223105 65 2.03E-07 16Dopamine receptor D2 (pIC50) Salivary hypersecretion 0.75004127 35 2.14E-07 10Alpha-1A adrenergic receptor (pIC50) Neuroleptic malignant syndrome 0.6646627 47 3.49E-07 28Dopamine receptor D2 (pIC50) Neuroleptic malignant syndrome 0.69384601 42 3.49E-07 26Prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase) (pIC50)Gastrointestinal haemorrhage 0.77906934 30 3.95E-07 9Dopamine receptor D2 (pIC50) Musculoskeletal stiffness 0.61886797 55 4.75E-07 8Dopamine receptor D2 (pIC50) Restlessness 0.64129668 50 5.23E-07 15Dopamine receptor D2 (pIC50) Muscle twitching 0.59476829 60 5.41E-07 8Alpha-1B adrenergic receptor (pIC50) Neuroleptic malignant syndrome 0.66173835 46 5.52E-07 27Dopamine receptor D3 (pIC50) Neuroleptic malignant syndrome 0.68162561 42 6.71E-07 29Dopamine receptor D2 (pIC50) Dystonia 0.65154166 47 7.05E-07 19Dopamine receptor D3 (pIC50) Dyskinesia 0.57304188 64 7.48E-07 21Alpha-1A adrenergic receptor (pIC50) Restlessness 0.56530148 66 7.60E-07 17Dopamine receptor D3 (pIC50) Salivary hypersecretion 0.72541538 35 8.23E-07 10Solute carrier family 6 (neurotransmitter transporter, serotonin), member 4 (pIC50)Agitation 0.66380637 44 9.03E-07 9Cholinergic receptor, muscarinic 1 (pIC50) Mydriasis 0.57394402 62 1.08E-06 17Dopamine receptor D2 (pIC50) Tardive dyskinesia 0.74547932 31 1.50E-06 19Dopamine receptor D2 (pIC50) Galactorrhoea 0.68478761 39 1.52E-06 15Dopamine receptor D3 (pIC50) Musculoskeletal stiffness 0.59349315 53 2.82E-06 8Cholinergic receptor, muscarinic 3 (pIC50) Mydriasis 0.56298592 60 2.83E-06 13Histamine receptor H1 (pIC50) Pneumonia aspiration 0.60646386 50 3.04E-06 11Cholinergic receptor, muscarinic 1 (pIC50) Pneumonia aspiration 0.63613618 43 4.55E-06 9Alpha-1B adrenergic receptor (pIC50) Pneumonia aspiration 0.61170904 46 6.29E-06 13Dopamine receptor D3 (pIC50) Dystonia 0.6028081 47 7.33E-06 21Dopamine receptor D4 (pIC50) Dyskinesia 0.60577511 46 8.16E-06 15Dopamine receptor D3 (pIC50) Muscle rigidity 0.62694625 42 8.89E-06 27

Tardive DyskinesiaNH

N

O

O

N

NHO

Cl

NH2

O

HO

O

NN

N

S

Reglan

Cl NH

O

N

NN

S

HO

N

N

N

S

Cl

N

N

O

N

NO

FNN

N

O

Cl

NN

N

Cl

NH

antipsychotics

Molindrone

Other liabilities well represented by assays in our portfolio: Histamine H1

Allegra

O OH

OH

N

OH

Comtrex/Contact

N

Cl

N

Claritin

O

O

NCl

N

Sleep-eze

N

O

Astelin

N

N

N

Cl

O

antihistamines

Acknowledgements

Nancy Yuen, James Bailey (co-leads) Juan Luengo Giovanni Vitulli James Bailey

Subhas Chakravorty Sunny Hung Francoise Gellibert

John Walsh Dennis Lee David Livermore

Simon Semus Stephen Pickett Ricardo Macarron

Mui Cheung Rick Cousins Gordon Dear

Jakob Busch-Petersen Robin Cregan Graham Simpson

Ian Churcher Laurie Bayon Katherine Widdowson

Deanna Frey Ceara Rea Chris N. Johnson

Darren Green Dave Morris

Phase Forward-/Lincoln Technologies: Mohammad Al-Ansari, David Fram

Supplementary information

Data Sources• Adverse Events

– Lincoln Technologies (Phase Forward)– Quantitative signal scores for adverse events from FDA AERS & WHO Vigibase

• Data Curated From Literature – GVK Bio – marketed drugs and clinical candidates (DD and CCD)– GVK Bio – Mechanism-Based Toxicity (MBT)– GSK – literature data on marketed compounds (“Top 2500”)

• Drug Metabolism/Drug-Drug Interactions– University of Washington – Drug Interaction Database (DIDB)– David Flockhart – P450 and drug interactions– Edmund Hayes – P450 and drug interactions

• Target Activity Profiles– GSK – compound profiling data on marketed compounds

• From CCP & additional bespoke profiling; total ~1500 drugs @580 targets

• Chemical Properties– GSK – Adamantis computed properties– GSK - Structural Alerts for TOX and CYP

GVK BioMarketed Drugs/Clinical Candidates/Mechanism

Based Toxicity • Result of large abstracting/curating activity by discovery-stage

CRO based in India• Raw data sources include patents, journals, drug information

sheets, reference books • Two of GVK’s databases relate specifically to compounds that

have proceeded through clinical development:– DD (Drug Database) – 2,475 marketed drugs– CCD (Clinical Candidates Database) – 8,756 clinical candidates

• Integration process:– Compounds integrated across all data sources, original source of compound

retained – Combination products eliminated (e.g., avandamet-which contains

rosiglitazone, metformin)– Original SMILES strings from GVK converted to unique representation

including de-salting and normalization– Assigned MCSP compound ID’s (CMPD-nnnnn) based on the original GVK

ID’s

GVK Data Content Marketed Drugs / Clinical Candidates

• 2D chemical structures and related properties• Pharmacokinetics• Binding data • Functional data• Metabolites • Cytochrome P450 interactions • Drug interactions• Clinical studies• Physical properties, mostly solubility• References

GVK Bio Mechanism-Based Toxicity (MBT)• Broad set of clinically relevant compounds (drugs, pre-

clinical and clinical candidates, drug-like substances, environmentally toxic substances, etc.)

• 13,000 compounds• Includes:

– General toxicities (cytotoxic, carcinogenic, mutagenic, etc.)– Organ-specific toxicities with organ and reaction term– Mechanistic terms (reactive metabolite, DNA damage, etc.)– Quantitative values for various toxic endpoints (LD50, % Cell Death,

etc.)– Free-text description of mechanism– Metabolite structures

• Integrated primarily for use in drill-down and reporting• Overall data mart constructed by adding non-duplicative

compounds from MBT to those resulting from combining DD and CCD (total 22,332)

University of WashingtonDrug Interaction Database (DIDB)

• In vitro and in vivo information on drug interactions from over 4,000 publications

• Integrated:– In vitro enzyme inhibition results

• Raw data at the level of an individual “precipitant” modifying the metabolism of a given “probe” drug

• Converted semi-quantitative (“30+/-8 umol”) values to numbers with standard units; re-expressed as negative logarithm of concentration in micromoles

– In vitro substrate (metabolism) information• Both inhibition and substrate results are available for feature

creation in modeling

P450 and Drug Interactions

• Merged and combined substrate information from 4 sources– University of Washington (preceding slide)– Flockhart (medicine.iupui.edu/flockhart/)– Hayes (www.edhayes.com/CYP450-1.html)– “Top 2500”

• Combined results available for feature creation in modeling

Functional Capabilities• Query

– Search data mart creating saved compound list– Criteria based on structure matching (substructure similarity) or

characteristics (chemical, biological, pre-clinical and clinical safety)

• Reporting– Present in tabular form a variety of information retrieved for a

compound list– Can be combined with query in one step via “Quick Report”

• Modeling– Build models based on recursive partitioning and random forest

techniques to capture relationships between predictors and safety outcomes

• Visualization– Visualize data and models using Visual i | o, Spotfire, Reaper

Data were clustered in 2 dimensions (using hierarchical clustering); targets & compounds are both clustered based on their data profiles.

ASSAYS

Focus on specific region of the heat map in the previous slide containing primarily the anti-histamines.

The profile of the tranquilizer Azacyclonol aligns with the anti-histamines, largely due to the histamine receptor activity of this compound.

Focus on specific region of the heat map in the previous slide containing primarily the anti-histamines.

The profile of the tranquilizer Azacyclonol aligns with the anti-histamines, largely due to the histamine receptor activity of this compound.

OH

NH

N

N

O OH

OH

N

OH

OHO

O

N

N

Cl

N

Cl

N

Profiles of anti-psychotic drugs, grouped by therapeutic class, to enable study of the data profiles in the class

A

T

A = AtypicalT = Typical

The compounds which have a weight gain side effect exhibit the pattern of muscarinic activity.

Profiles of anti-psychotic drugs, grouped by therapeutic class, to enable study of the data profiles in the class

A

T

A = AtypicalT = Typical

NN

N

Cl

NHNN

N

NH

S

HO

O

NN

N

S

Literature evidence that links some anti-psychotic drugs with metabolic disturbances

Interpretation of MGPS Parameters

The AERS database

• AERS (Adverse Event Reporting System) is the database that supports FDA's post-marketing safety surveillance.

• Contains adverse event (AE) reports for all drugs and biologics marketed in the US.

• 35 years of data (worldwide) • >3 million reports• ~2000 products

• Passive surveillance tool based on voluntary reporting– No denominator data– Can’t measure absolute incidence or prevalence– May provide information about relative reporting

• Facilitates identification and characterization of rare AE’s

• Detects reporting frequencies that are “higher than expected” in large adverse event databases

• Stratified for factors including age, gender, year of report to mitigate confounding factors

• A high disproportionality score does not necessarily indicate a high probability of a CAUSAL association between the drug and event, or high INCIDENCE.

• Should be interpreted as hypotheses regarding potential causal associations between drugs and events, signals always medically validated

• High signal scores are not necessarily due to causality…many other possible explanations

– background disease in the population using the drug of interest– Publicity / litigation about the possible adverse drug reaction – concurrent medication(s) often used with drug of interest– infrequently used MedDRA term

Disproportionality Analysis

Assessing the frequency of specific drug-adverse event combinations against the background of all other drugs

& events

Drug X All other Drugs

Event of interest

All other Events

A

B

C

D

Is A > C ?? A+B C+D

GSK uses the same empirical Bayesian

methodology as used by FDA that estimates relative

reporting rates using “EBGM”.

Definitions• MGPS: Multi-item Gamma Poisson Shrinker;

– Statistical method used to estimate disproportionality in the reported vs. expected AEs reported for a drug

• EBGM: fr. MGPS, empirical Bayes geometric mean; – Mean fold change in frequency relative to no association

between drug and event• EB05/EB95, 95% confidence intervals;

– 95% confidence that value is not less than EB05, not greater than EB95

• >1 if there is an association between drug & event• >3 is generally considered medically significant

Interpretation of MGPS Parameters

Wonderex - Rash (16 reports in the database)• EBGM: 3.0 EB05: 1.8 EB95: 4.3

Interpretation

• Wonderex-rash combination is reported at 3-fold greater frequency than if there were no association between Wonderex and rash

• 95% confidence that the true relative reporting rate is at least 1.8

• 95% confidence that the true relative reporting rate does not exceed 4.3

Are there examples of an adverse event with a known target association?

• ..and does the EBGM compare reasonably to the target activity?

Integrating CCP and AERS data shows the potential for bridging human safety and pre-clinical information: comparison of pIC50 in dofetelide binding assay

and score for Prolonged QT in post marketing adverse events data

High QT, low hERG-High exposure (interactions)-non-HERG channel activity

Carries black box, or removed from market for QT

High hERG, low QT-low bioavailability, topicals