Extending the small molecule similarity principle to all ... · Biological processes Interactome...

Patrick Aloyhttps://sbnb.irbbarcelona.org

Challenges within and between Omics data integrationNovember 19, 2018

Extending the small molecule similarity principle to all levels of biology

The ‘omics’ revolution is not yielding better drugs

�2Organizing bioactivity data

— High attrition rates in pre-clinical and clinical stages of drug discovery

— More investment did not translate into more drugs approved

— Expensive experiments are needed to elucidate the bioactivity of small molecules

— Data are sparse and dirty

— Chemistry and biology are disconnected

Dise

ase

-gene c

orre

latio

n

Disease

models P

harm

acokinetic

s

Pharm

acodyn

am

ics

DIS

EA

SE

TH

ER

AP

YM

o le c ule

s

Co

mp

lexity

2k molecules

1M m

olecules

Bioinformatics is heaven (cheminformatics is hell)

�3

— Proprietary data— Monolithic entities— No architecture— Structure ¿? Function— Optimized by people— 80M commercial molecules— Chaotic databases

— Public data— Building blocks (20 aa)— Domain architecture— Sequence - Structure - Function— Optimized by evolution— 20k proteins in Human— Beautiful databases

— Small molecules— Proteins

Organizing bioactivity data

The Chemical Checker

�4

— 1M bioactive molecules— 25 data types, from

chemistry to the clinics— Major small molecule

databases are integrated— State-of-the-art machine

learning— Possibly applicable to

“any” molecule (i.e. 80M)— Automated and flexible

3D

fingerprints Scaffolds

Structural

keys

Physico-

chemistry

Metabolic

genes Crystals

Small mol.

roles

Small mol.

pathways

Signaling

pathways

Biological

processes Interactome

Gene

expression

Cancer

cell lines

Chemical

genetics Morphology

Cell

bioassays

Therapeutic

areas Indications Side effects

Diseases and

toxicology

Drug-drug

interactions

Chemistry

Targets

Networks

Cells

Clinics

2D

fingerprints

Binding

Mechanism

of action

HTS

bioassays


1M

500k

300k

10k

3k

Chemistry

�5

— What is the 2D structure of the molecule?

— And its 3D structure?— What scaffolds

(chemotypes, synthetic families)?

— Size, molecular weight, charge, lipophilicity, drug-likeness…

3D


Structural

keys

Physico-

chemistry

Metabolic

genes Crystals

Small mol.

roles

Small mol.

pathways

Signaling

pathways

Biological


Gene

expression

Cancer

cell lines

Chemical

genetics Morphology

Cell

bioassays

Therapeutic


Diseases and

toxicology

Drug-drug

interactions

Chemistry

Targets

Networks

Cells

Clinics

2D

fingerprints

Binding

Mechanism

of action

HTS

bioassays


— What the taxonomist does

— What the computer scientist does

— What the organic chemist does

— What the physicist does

Communicating chemistry to the computer


Targets

�7

— Mode of action (when available), inhibition/activation

— Drug metabolizing enzymes, transporters and carriers

— Crystalized small molecules in the PDB, structural family of receptors

— Binding assays in the literature

— HTS (chemogenomics) binding and functional assays

3D


Structural

keys

Physico-

chemistry

Metabolic

genes Crystals

Small mol.

roles

Small mol.

pathways

Signaling

pathways

Biological


Gene

expression

Cancer

cell lines

Chemical

genetics Morphology

Cell

bioassays

Therapeutic


Diseases and

toxicology

Drug-drug

interactions

Chemistry

Targets

Networks

Cells

Clinics

2D

fingerprints

Binding

Mechanism

of action

HTS

bioassays


Networks

�8

— Popular bioactivity ontology

— Metabolic pathways (metabolites + drugs)

— Signaling cascades of the targets

— Biological processes of the targets

— Neighbors of the targets in large biological networks (interactomes)

— (Tissues where the targets are expressed)

3D


Structural

keys

Physico-

chemistry

Metabolic

genes Crystals

Small mol.

roles

Small mol.

pathways

Signaling

pathways

Biological


Gene

expression

Cancer

cell lines

Chemical

genetics Morphology

Cell

bioassays

Therapeutic


Diseases and

toxicology

Drug-drug

interactions

Chemistry

Targets

Networks

Cells

Clinics

2D

fingerprints

Binding

Mechanism

of action

HTS

bioassays


Cells

�9

— Transcriptional response in cell lines (LINCS)

— Growth inhibition in cancer cell lines (NCI60)

— Growth inhibition in a panel of yeast mutants (equivalent to genetic interactions)

— Changes in morphology, measured with a cell-painting assay

3D


Structural

keys

Physico-

chemistry

Metabolic

genes Crystals

Small mol.

roles

Small mol.

pathways

Signaling

pathways

Biological


Gene

expression

Cancer

cell lines

Chemical

genetics Morphology

Cell

bioassays

Therapeutic


Diseases and

toxicology

Drug-drug

interactions

Chemistry

Targets

Networks

Cells

Clinics

2D

fingerprints

Binding

Mechanism

of action

HTS

bioassays


Clinics

�10

— Therapeutic areas (ATC codes)

— Indications (disease terms)

— Side effects in drug package labels

— Drug-drug interactions— (Pharmacogenomics)— (General toxicology)

3D


Structural

keys

Physico-

chemistry

Metabolic

genes Crystals

Small mol.

roles

Small mol.

pathways

Signaling

pathways

Biological


Gene

expression

Cancer

cell lines

Chemical

genetics Morphology

Cell

bioassays

Therapeutic


Diseases and

toxicology

Drug-drug

interactions

Chemistry

Targets

Networks

Cells

Clinics

2D

fingerprints

Binding

Mechanism

of action

HTS

bioassays


A few (ahem) straight applications to biopharma…


Characterization of chemical collections

�12

— A front-end to rapidly characterize chemicals

— Global view of the chemical and biological space

— Popularity, singularity and mappability scores help contextualize compounds



Proparacaine Propantheline

Scaffolds

3D fingerprints

Mechs. of action

Mechs. of action

Pathways

Gene expression

Cancer cell lines

Therap. areas

Indications

Side effects

1

1

1

1

1

1

2

2

2

2

22

3

3

3

3

33

4

4

4

4

4

4

5

5 5

55

5

6

6

6

6

6

6

Erlotinib Vandetanib Gefitinib Ponatinib Semaxanib Dasatinib

1 2 3 4Rimantadine Tetracaine

1

1

1

11

2

2

2

2

23

3

3

3

34

44

4

4

Complex queries to compound libraries— Similar MoA/indication, diverse chemistry and pharmacogenomics

— Different therapeutic areas, similar gene expression and side effects

Proparacaine Propantheline

Scaffolds

3D fingerprints

Mechs. of action

Mechs. of action

Pathways

Gene expression

Cancer cell lines

Therap. areas

Indications

Side effects

1

1

1

1

1

1

2

2

2

2

22

3

3

3

3

33

4

4

4

4

4

4

5

5 5

55

5

6

6

6

6

6

6

Erlotinib Vandetanib Gefitinib Ponatinib Semaxanib Dasatinib

1 2 3 4Rimantadine Tetracaine

1

1

1

11

2

2

2

2

23

3

3

3

34

44

4

4

Large-scale target prediction

�14

— Based on chemical and biological similarities

Target 1 > Target 2 > Target 3

Ranked list of similar molecules in different chemical and biological spaces


ChemistryBinding

NetworksCells

Clinics

Based onchemistry

Addingphenotypicdata

Addingchemogenomicsdata

BromperidolBenperidol

Spiperone

F

N

O

Chemical sim.

DRD2Prediction

(2018)

Knowledge(bef. 2016) Tacedinaline

HDAC1

HDAC2

HDAC5 HDAC6

HDAC8

Cross-pharmacology

AC1061JE Nortryptiline HRH1Yeast chemical genetics

Chemical prediction Chemogenomic prediction

Phenotypic prediction

Clinical trial toxicity failures

�15

— Flexible knowledge-based clinical predictors


Black-box predictor Based on toxicity panels Based on liable targets

Clinical trial toxicity failures

�16

— Flexible knowledge-based clinical predictors


Black-box predictor Based on toxicity panels Based on liable targets

BIA10-2474

VX-745

Tozadenant

CH3

N

NN

N+

O

O-

Cl Cl

F

F

NN

N

O

S

CH3

CH3

NN

N

NOHOH

O

O

S

Concluding remarks (wrap-up)


Drug discovery is highly inefficient and secretive, and current computational tools are insufficient and isolated

Miscellanea of Nat Rev Drug Discov articles

Drug discovery pipeline

The accumulation of omics data is not yielding better drugs

Duran-Frigola et al, Curr Opinion Syst Biol (2018)

Computational tools are focused on curating and organizing the data

The Omics revolution

Structural Bioinformatics & Network Biology group


Miquel Duran-Frigola Carles Pons Eduard Pauls Sergi Bayod Francesco Sirci Martino Bertoni Lídia Mateo Csaba Fehér Adrià Fernández-Torras Víctor Alcalde Oriol Guitart

We are looking for postdocs !!

Patrick Aloyhttps://sbnb.irbbarcelona.org

Challenges within and between Omics data integrationNovember 19, 2018

Extending the small molecule similarity principle to all levels of biology

Extending the small molecule similarity principle to all ... · Biological processes Interactome...

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