Biosignature-Based Drug Design

Joerg Kurt Wegner | 2018-10-19 | Montreal, Canada

Can Machine Learning enable a re-use of Large-Scalemultivariate data across novel Drug Design projects?

References https://goo.gl/HwEPQt

8th annual RQRM Symposium

For finding a new drug for a disease we need to rationalize at least two data challenges, actually we need to combine them for being efficient.

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Drug space and assay data Patient data

Nitril+CarbocylicAcid 3 million compounds (80% success rate)Amine+Aldehyde 2 million compounds (81% success rate)…

Billions of compound, which to test?Which data will be created for which task?

What do we know about a patient?Which data will be created for which task?

Task: Use Machine Learning to help deciding what to connect and decide which part of a “healthy vs diseased” biosignature is most informative? Then connect biosignature treatment by choosing or creating perturbations (compounds, biologicals, functional genomics).

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Patient with disease vs healthy

Imaging

Healthy Diseased

What to connect?Which data to create?

We will convince you that Biosignatures serve multiple tasks, which have multiple benefits for design decisions

• What are Biosignatures? How do they differ from ligand-and structure-base drug design?

• Machine Learning enables multiple application tasks from a single Biosignature• One data creation Multiple applications derived via data analytics• In relation to data creation, analytics is cheap

• What are Biosignature growth scenarios?

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Biosignatures are complementing other design conceptsWe use more than 100 thousands biosignatures per technology

Ligand-Based Drug Design

(Protein) Structure-Based Drug Design

Biosignature-Based Drug Design

HCI (cell line – H4) 460,000HCI (cell line – Hepg2) 724,000HCI (patient samples - PHH) 175,000L1000 (cell line – MCF7) 237,000L1000 (patient samples - PBMC) 201,000Data fusion (Factorization) 458,000Data fusion (DeepLearning) 456,000Data fusion (LargeScale) 2,798,000

Biggest public data ~20, 000

Substructure fingerprint Interaction fingerprint (Rich) Pharmacological fingerprint

compounds

Biochemical assaySingle target

Single EndpointCellular

Single Endpoint

A metastudy on imaging data showed that 80% of publications are using only one feature (very traditional single endpoint assay)

Cellular (high-content)Multivariate Biosignature

14 days on 16 compute nodes (each with 24 cores), 30 TB per 500k compound image screen

Unpublished, now possible in 45 hours

Biosignature-Based Drug Design (BBDD)?

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A Biosignature is capturing a multivariate response of a perturbed biological system or set of biological systems.

An Observation is a single marker in such a response space.

BBDD BBDD Pro BBDD Con

LBDD • Direct disease2compound mapping• Complementing design capacity• Enriching design capacity• Leveraging across projects

Multivariate analytics

SBDD No XRay needed

Applies for SBDD, too

How does BBDD differ from ligand- and structure-base drug design?

BBDD perspective under revision @JMedChem~200 references vouching for it

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Leverage insights across conditions, and technology is a condition…

Improving inhibitor potency for a single protein target might not be good enough

Observing a disease relevance by an -omics tech in one biological system might not be good enough

Believing the same data analytics process will work again might not be good enough

Traditional biosignatures have been published without consolidating commonalities of a BBDD drug design paradigm

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Assay endpoint biosignatures (#observations » 30)Hit enrichment• HTS-FP compound expansion proves more efficient than chemical structure-based methods

in terms of retrieving active compounds and diverse scaffolds.Compound MOA identification• NCI-60 cancer cell panel tubulin mitotic spindle interference• JFCR-39 cancer cell panel (1) telomerase compounds, and (2) PI3K inhibitor. • Cerep BioPrint phospholipid binders without putative target CYP51 in biosignature• BioMAPmitochondrial dysfunction, cAMP elevators, tubulin inhibitors, inducers of

endoplasmic reticulum stress, or NFκB pathway inhibitorsTranscript-omics biosignatures • A reduced gene biosignature is sufficient to categorize B-Cell Lymphoma patients after drug

treatment (Microarray Quantigene AI, cost efficiency)

We will convince you that Biosignatures serve multiple tasks, which have multiple benefits for design decisions

• What are Biosignatures? How do they differ from ligand- and structure-base drug design?

• Machine Learning enables multiple application tasks from a single Biosignature• One data creation Multiple applications

derived via data analytics• In relation to data creation, analytics is cheap

• What are Biosignature growth scenarios?

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Large-scale machine learning for translating high-dimensional biological responses into drug design decisionson –omics biosignatures

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Assay endpoint biosignatures (#observations » 30)• Cancer cell line panel (Sanger Institute) enables a

patient-centric quantitative personalized estimate for drug sensitivity, since it can estimate on novel cell lines.

Transcript-omics biosignatures • Hit rate increase of 300-fold for NR3C1, by also depleting unwanted HSP90 activity.

Our case study. Published.Phen-omics: High-content imaging biosignatures • Identify a subset of genes, including the tumor suppressor PTEN, which regulate a cell shape

in both mouse and human metastatic melanoma cells.• 50- to 250-fold hit rate increase with multiple novel Murcko scaffolds compared to previous

HTS hits. Our case study. Published. (15TB of data, four weeks on 1000 core CPU cluster)

HDAC

PLK

PI3K-mTOR

BRD

Unsupervised biosignatures and similarities are ok, and can be improved via supervision

mTOR-PI3KCDK

HSP90HDAC

HDAC

Tubulin

AR

DMSO Control

t-distributed Stochastic Neighbor Embedding

kNN, single biosignature

unsupervisedbiosignature

single vs fusedbiosignatures

unsupervisedbiosignature

Our imaging case study. Analytics is key!

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ABCD Janssen

ChEMBL

Inactiveat concetration X

Activeat concetration X

1000s of assays

millions of compounds

~100 million data points

Analytics? Artificial Intelligence?Which projects can we support?Should we create more data?

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CellProfilerMacau

CellProfilerDeepLearning

ImageDeepLearning

AUC > 0.931

AUC > 0.7218

AUC > 0.943

AUC > 0.7245

CellProfilerkNN

AUC > 0.90

AUC > 0.793

AUC > 0.9(58)ongoing

AUC > 0.7(373)ongoing

ActiveInactiveUnknown

Transfer learningof a noise reduced biosignature to other challenges

unsupervisedbiosignature

supervisedbiosignature

GR Translocation screen in H4 cells

2 h compound incubationfor NS/Mood

460K compounds

Our imaging case study. Analytics is key!

many vacuoles

no vacuolesfew vacuoles

0.3 uM

1 uM

9 uM

LO project with biosignature profiling indicates concentration dependent toxicity on top of wanted activity

Large-scale machine learning for translating high-dimensional biological responses into drug design decisionson virtual biosignatures

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• NCI-DREAM challenge46 teams, one wins!

• Screening only 10% of the screening collection 50% of hits are found.

• Our case studies. Published. Prospective applications to be submitted.

Virtual biosignatures (#observations » 30)

Single compound enrichment: Can we find more kinase inhibitors for two known reference compounds with XRay data? Yes!

HCI1:DeepLearning machine learningVirtual biosignature fusion

L1000(20k set, only)

Docking

L1000(20k set, only) inverse query

BED-

ROC

Percentage screened(single compounds)

0.5

0 10 30 50

Transcriptomics biosignature query based on MicroArray’s (a 22,000 gene transcript biosignature) after compound perturbation

Plate compound hit enrichment Can we enrich hits more consistently over 40 HTS initiatives?

biosignaturemix

tanimoto

diversityrandom

Screened non-pilot (non-seeding) plates [%]

New unique chemical clusters [%]New chemical starting points [%]

Non-Incremental screening using

plate enrichment using information from a pilot screening deck

We will convince you that Biosignatures serve multiple tasks, which have multiple benefits for design decisions

• What are Biosignatures? How do they differ from ligand-and structure-base drug design?

• Machine Learning enables multiple application tasks from a single Biosignature• One data creation Multiple applications derived via data analytics• In relation to data creation, analytics is cheap

• What are Biosignature growth scenarios?

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Combining multiple large-scale imaging biosignatures is improving scientific accuracy.

20Our case study. Published.

We expect that biosignatures keep growing exponentially

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BBDD perspective under revision @JMedChem

We expect that biosignatures keep growing exponentially

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Technology advancement (<cost)Machine learning

BBDD enables to

Unique concept for BBDD

• employ a gene signature compound search, which is a BBDD unique design concept not existing for LBDD and SBDD.

• an imaging single cell population analytics being a BBDD unique design concept.

Exceeds compare to LBDD and SBDD

• exceeded a hit expansion compared to a LBDD (or SBDD) leading to a higher number of hits and to a higher number of novel scaffolds.

• exceed the performance of individual data sources, like 2D or 3D drug descriptors or transcriptomics microarrays

• estimate a drug sensitivity prediction for novel cell lines. 23

We have convinced you that BBDD is complementing or enriching as design concept, andthat multivariate data analytics skills are key.

Questions?

Referenceshttps://goo.gl/HwEPQt

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Backup – Just in case the discussion requires it

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26Single compound enrichment.A biosignature expansion yields 6-80 fold enrichments in novel and diverse hits

Onco1 IDV1 IDV2 CNS2(primary, only)

Biosignature fusionMacau fusion HCI:H4max(fusion1,fusion2)HCI:A549 clustering + fusion1 RF fusion on L1000max(fusion1,fusion5)random

CNS1

11(127)

3(94)

0(21)

2(50)

6(97)

Novel Murcko scaffold, duplicate+(Novel singleton scaffolds)

Novel diverse chemical hits

(Chemical) analogs

Introduction – Applications - Conclusion

Multi-task learningexpands the applicability domain of models

overlapping chem space⇒ mutual assay information

assay X

assay Y

predict activity in Ybased on observations in X

predict activity in Xbased on observations in Y unimpressive at small

scale,awesome at massive scale

A model built in isolation needs 2 x more datapoints than one built together with other models to reach same performance

All our methods scale to all our data set sizes.

Multi-task learningtends to win from single-task (55% of cases); it never loses

A DeepLearning imaging fusion biosignature on correlating columns with activity and toxic endpoints is able to separate toxic from non-toxic compounds

Chemical structure mapping DeepLearning imaging fusion biosignature mapping

Size: target activityColor: toxicity

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Why convolutional deep learning for images?

Convolutional DNN re-arrange the input allowing to capture spatial relations.

31 DeepTox

https://doi.org/10.3389/fenvs.2015.00080

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DeepTox

How does BBDD differ from ligand- and structure-base drug design?

BBDD perspective under revision @JMedChem~200 references vouching for it

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Leverage insights across conditions, and technology is a condition…

All listed use-cases in the original articles show that BBDD outperforms or complements LBDD or SBDD paradigms