Hypothesis Fusion to Improve the Odds of Successful Drug

Repurposing

Alexander Tropsha, Charles Schmitt, Eugene Muratov

UNC-Chapel HillWeifan Zheng, NCCU

Nabarun Dasgupta, Epidemico

Information resources for bioactive chemicals are abundant and growing

FDA approved labels for marketed drugs

Over 18 million citations from MEDLINE and other life science

journals for biomedical articles back to the 1950s

Reflects the scientific conclusion reached by the Committee for

Medicinal Products for Human Use (CHMP) at the end of the centralised

evaluation process.

European Public Assessment Reports (EPAR)

The DrugBank database combines detailed drug data (8200+ drug entries) with comprehensive drug target

information

The FDA Safety Information and Adverse Event Reporting

Program

FDA data for five liver enzyme endpoints for

Drug interactions with cytochrome P450 isoforms

Cytochrome P450 Drug Interaction Table

FDA Orange Book ofApproved Drug Products

“Potential Safety Issue” data“Drug Interactions” table FDA New Drug

Application documents

eMC provides electronic Summaries of Product

Characteristics

Compound Assay data for proteins and cytotoxicity

IntegratedChemical-

BioactivityData

Modified from a slide provided by Julie Barnes, Biowisdom

Data Science and Data Cycle

Predictive data models & toolsExperimental Design

Data Analysis

and Modeling

Structured Data

Repository

Data collection, curation, integration, and

structuring (ontology).Literature data

Electronic Databases:

Text MiningLab collections

Disease

ExperimentalValidation

4

Effect

Unstructured test:FacebookTwitterOther Social Media

Decision support

Data reproducibility and data curation are critical, otherwise:

BD2K = Bogus Data to Knonsense

5

Data set curation workflows: Trust but Verify!

Fourches D. et al. J. Chem. Inf. Model., 2010, 50, 1189

Fourches D. et al. Nat. Chem. Bio. 2015, 11, 535

Disease gene

signatures

Disease related

genes or proteins

Text/database mining Network mining

PubMed/Chemotext

CTD

HMDB

Disease related

proteins

cmapChemoText

New hypothesis about connectivity between chemicals and diseases

Binding data

Target related ligands

Functional data

QSAR

Predictive models

Database mining

Structural hypothesis“putative drug candidates”

Hypothesesfusion

New testable hypotheses with higher confidence

Disease-TargetAssociation

Hajjo et al, Chemocentric Informatics Approach to Drug DiscoveryJ Med Chem. 2012, 55(12):5704-19

QSAR modeling and Virtual Screening: Hit identification in external libraries

~106 – 109

molecules

VIRTUAL SCREENING

CHEMICALSTRUCTURES

CHEMICALDESCRIPTORS

PROPERTY/ACTIVITY

PREDICTIVEQSAR MODELS

INACTIVES (confirmed inactives)

QSARMAGIC

HITS (confirmed

actives)

CHEMICAL DATABASE

5-HT6 receptor QSAR models & QSAR-based VS

5-HT6predictor

300 VS Hits“Actives”

59 K cps.

Model statistics

94 Inactives Ki ≥ 10 µM

196 cps.

102 Actives Ki < 10 µM

Dataset Virtual screening

Source: PDSP Ki-DB

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Model

CC

Rev

s kNN-Dragon Model

kNN-Dragon Random

CBA-SG Model

CBA-SG Random

8

The connectivity map

Step3 : list of correlated compounds

Step2: query the cmap

Database

Step1: upload signature

Output

High correlation

Low correlation

Null

Biological state 1

ControlSignature

Input

Lamb, J. et al. Science, 313, 1929-1935 (2006)Lamb, J. Nature 7, 54-60 (2007)

Querying the cmap

S1: Hata, R. et al., Biochem. Biophys. Res. Commun 284, 310 (2001).S2: Ricciarelli, R. et al., IUBMB Life 56, 349 (2004).

10

cmap

1.00

0.00

0.00

-1.00

cmap SCORE

Upload signature Query the cmap List of compounds

(S1) (S2)

Alzheimer’s disease gene signatures

97 COMMON HITS with S1106 COMMON HITS with S2

Chemocentric Informatics

QSARFILTER

Furtherselection

34 Higher Confidence Hits

CONSENSUSHYPOTHESES

300 5-HT6Active HITS

WDIDATABASE

73 COMMON HITS with S1 & S2

cmapFILTER

cmapDATABASE

881 instances with S1861 instances with S2

59 Kcompounds

6.1 K Individual instances

AntipsychoticsAntidepressantsCalcium Channel BlockersSelective Estrogen Receptor

Modulators (SERMs)

Exploring PubMed as one of the largest Chemical Biology Databases: the ChemoText Project

•2008 Medline baseline: 16,880,015 records •6,635,344 records had subject chemicals

9,360,330 relationships

5,395,144relationships

20,466,335relationships

SubjectChemical134,184 distinct

Diseases4,865 distinct

Proteins61,329 distinct

Drug Effects7,761 distinct

9,088,747relationships

13,157,701relationships

http://chemotext.mml.unc.edu/

Baker, N. Hemminger, B.J Biomed Inform. 2010 Aug;43(4):510-9

Swanson’s ABC approach to drug discovery via text mining*

AChemicals

C Disease

B Intermediate

Terms Relationships established through co-occurrence of terms

Migraine

VasodilationSpreading cortical depressionPlatelet aggregation

Magnesium

Relationships established through co-occurrence of terms

Deduced relationship

*Swanson DR. Medical literature as a potential source of new knowledge. Bull Med Libr Assoc 1990;78(1):29–37

ABC Method as applied to discern chemical-target-disease associations (using Chemotext)

CDisease

BProtein

AChemical

http://chemotext.mml.unc.edu/

Raloxifene identified as a 5-HT6 receptor ligand and potential treatment for the Alzheimer’s disease Raloxifene binds to 5-HT6

receptor with a Ki= 750 nM.*

Raloxifene given at a dose of 120 mg/day led to reduced risk of cognitive impairment in post-menopausal women.Yaffe, K. et al., Am J Psychiatry, 2005, 162, 683–690.

Adjunctive raloxifene treatment improves attention and memory in men and women with schizophrenia.Weickert TW, et al Mol Psychiatry. 201520, 685-94

Raloxifene

Chlorpromazine

Competition binding at 5-HT6 receptors forraloxifene (yellow triangle) andchlorpromazine (square) versus [3H] LSD.Tested by our collaborators at PDSP.

*Hajjo et al, Chemocentric Informatics Approach toDrug Discovery. J Med Chem. 2012, 55(12):5704-19

Social Media

Etc.

Anal

ysis

Cancer-Related Assertions

Aim 1: From Man

Curated Database of Assertions

Hypothesis generation

Aim 2: To Molecule

Hypothesis confirmation

Curated Cancer-Related Bioassay Database

PrimaryHits

Virtual screening platform

Aim 3: To Man

Hypothesis enrichment

Disease Effect

Drug-Target-Disease Database

Candidates for Repurposing

ElectronicMedical Records

On-line Databases

Etc.

Expe

rimen

tal v

alid

atio

n in

-vitr

o an

d in

-viv

o

Chemotext

NIH 1U01CA207160-01. Drug repurposing: From Man to Molecules to Man