Reaxys rmc unified platform_ webinar_

How do you handle data from multiple chemistry sources? Find out how Reaxys made it easier

June 2016

[email protected]

Olivier Barberan

Putting Data to Work |

Data-driven drug design and research

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• General descriptor-property relationships • Sub-structural alerts • QSAR • Matched Molecular Pair analyses • Predictive pharmacology Etc…

Cumming, J.G., Davis, A.M. et al. Nat. Rev. Drug Disc. (2013) 12, 948–962

Data Knowledge Analysis

Predictions

• Data normalization • Taxonomies • Quality control Etc…

“Those who cannot remember the past are condemned to repeat it.” George Santayana: Life of Reason, Reason in Common Sense, Scribner's, 1905, page 284

ELSEVIER

http://www.rcsb.org/pdb


Transforms Data and Knowledge to Real Research

Select the right compounds, active to right targets for right patients

Stay tuned for advanced research and competitor’s work Access data, interpret data, act on data for incisive decision


Reaxys Provides a Unified Information Portal

•Provides a single powerful interface

•Can integrate several notebook systems

•Links chemistry, structures, sourcing, citations, and full-text of articles

Reaxys API/KNIME/Pipelinepilot (Professional Services)

« Self service »

Data Integration into Reaxys (Professional Services)

In House Data Integration (RMC Flatfile)

« Discoveryconnect »

Data Integration of Reactions into Reaxys (Professional Services)

Reaxys API/KNIME/Pipelinepilot

« In silico Screening »



Reaxys API

Putting Data to Work | | 5

The current information retrieval solution at

Project chemists & modelers

Questions: • Any Information

for my target? • Any compounds

similar to my ligand

• Any new MOA for my disease?

Output: • Manual collected

customized file • Up-to-search date

data • Multi-sources

Take days to weeks

Expert searchers

Where Science Intersects with Business – Creating Business Dashboards That Combine Data from

Multiple Sources Presented at Bioit World 2015 by H. Wang,E. Gifford,

M. Clark


New Information retrieval Solution at


Design Philosophy at


Reaxys/RMC KNIME and Pipeline pilot new nodes

- 4 new KNIME nodes and Pipeline pilot components are available - Covering Bioactivities, Citations, Reactions and Substances searches - API Access is needed to use Reaxys/RMC KNIME and Pipeline piltot Nodes

Reaxys KNIME Nodes are compatible with Version 2.12 and 3.1x

- Reaxys PP Nodes are compatible with Version 9.5


Summarize Latest Information (Patents, Articles) about Cancer using Reaxys and RMC API and Pipeline pilot

Latest Patents, journal articles, compounds, reactions related to • Target • Indication • Compound

Summary of new Data for last 30 days


Compound Assays for a Given Target (p38b) using RMC API and Pipeline pilot

Lists assays, results, assay details • Enhance with clustering by structure • Include in-house information







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Reaxys API


Roche : Business Case

What - Improve efficiency of research. Synthesis planning Publication authorship Patent submission

Why - Save time and money. Short-term spend, long-term benefit

How - Reduce user interfaces and eliminate slow software. Decommission ISIS/Host, circumvent ELN search

Who - A highly functional collaboration. Arcondis Group Elsevier Information Systems GmbH F. Hoffmann-La Roche NextMove Software Ltd.


Electronic Lab

Notebooks

Data Capture, Normalization and

Integration

Reaxys Data

Unified discovery

interface for researchers

Challenge: Chemistry information is housed in separate databases, leading to inefficient research and escalating costs. Need integration of data to accelerate research and increase discoverability.

Roche Challenge


https://reaxys.roche.com

Normalized Roche-specific reaction data

Data on references and/or experiments, including PDF links

Roche-specific data is included in the Output (PDF, MS-Word etc.)

Filters on Roche-specific data fields

Up to four Roche reaction data sources are supported


• Customer Feedback

Integration of Roche in-house data

• Usability and acceptance tests by Roche showed:

• Increased productivity of researchers at Roche

• Increased discoverability of the Roche reaction content

• Reduced maintenance effort for Roche:

• Legacy systems were decommissioned

• Roche gets on-going maintenance and functionality improvements by Elsevier

• Not compromise in security

• Several advantages of going for an implementation of the flexible approach:

• Additional data sources have been added







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Reaxys API

Reaxys – Electronic Lab Notebook interoperability

The Reaxys team currently works in partnership with three major ELN providers:

PerkinElmer (formerly CambridgeSoft), Accelrys/biovia and IDBS

Run query from within

the ELN

Import reaction or

substance

Browse, navigate and

refine in Reaxys

Select and export

Example of Search In Reaxys using the Accelrys/Biovia

ELN

Results in Reaxys and output to Accelrys/Biovia ELN







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Reaxys API


Integrating E-notebook data with published data

• E-notebooks are an important mechanism to capture intellectual property

• Not optimized for data exploration

• Reaxys interface is optimized for data retrieval

• Filtering

• Cross-linking

• Search trail

• Links to source full-text

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Integration of assay data

• E-notebooks are primary data storage

• Have only internally-generated data

• Integration allows access to “all known data” with one search

• Link structures and data

• Handles target name synonyms

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Integrated Solution Search

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List of integrated sources

Sources list can include licensed databases, and multiple e-notebooks from organizational units All e-notebooks can be integrated and searched together







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Reaxys API

Ligand Based virtual Screening to Identify New T-Type Calcium

Channel blockers (Cav3.2)

Objective : Describe an In Silico Screening approach using Reaxys Medicinal Chemistry

(API and Flatfile) to identify new T-Type Calcium Channel blockers

130 Active compounds

on Cav 3.2 (affinity <0.1 µM)

Flat file

Representation & Chemical Space Molecular descriptors & Fingerprints

Virtual Screening Pharmacophoric Similarity

N

O

N

NN

O

N

N

N

"Drug-like" Filtering

1. Molecular diversity and chemical originality

2. Compounds availability

1

2

3

4

5

1) Searching for Active Compounds on Cav3.2 ( affinity < 0.1µM)

Channel in Reaxys Medicinal Chemistry Using KNIME/Pipeline pilot

2) Build a pharmacophoric similarity Model based on Active

compounds found in RMC

3) Use of the Reaxys Flat file as an External Chemical source

4) Drug like filtering of potential Cav3,2 blockers

5) Biological testing on Cav3.2

API/KNIME/Pipelinepilot

314 Hits

39 Tested

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Biological testing of the 39 Hits on Cav3.2

Electrophysiology experiments: Screening @10 µM on Cav3.2 T-Type channels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 29 30 31 32 33 34 35 36 37 38 39 0

25

50

75

100

Pe

ak

cu

rre

nt

inh

ibit

ion

(%

)

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9 compounds with a % inhibition > 75%

15 compounds with a % inhibition >50%

Compound # (@ 10µM)

Reaxys medicinal chemistry supports Ligand based virtual

Screening approaches







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Reaxys API

Target-directed and Phenotypic screening: parallel pathways to the

same goal

Phenotypic screening identifies compounds

that produce a biological response in a cell

or animal model

Target screening identifies

compounds that produce a biological

response on an isolated target

Phenotype - led vs. Target - led

However, a drawback is the need to use time-consuming genetic, chemical and/or

biophysical methods to identify the targets of compounds that are active (Target

deconvolution)

Phenotypic screening can find molecules that have more optimized drug-like

properties (such as cell penetration) than target-based screens.

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Scenario Melanoma cells predominantly occur in skin. Melanoma is less common than

other skin cancer. However, it is much more dangerous if it is not found in the

early stages. It causes the majority (75%) of deaths related to skin cancer.

Which are the targets involved in proliferation of melanoma cells lines ? And

which substances are acting on ?

Melanoma Cells

Search In RMC

Which Targets are involved in melanoma cells proliferation ?

http://en.wikipedia.org/wiki/File:Melanoma.jpg

Target Fingerprint of A 375 Melanoma cells based on Similar Compounds

996

molecules

Cell line A375

IC50 <= 1µM

3525 molecules

+In vitro Biological

results on targets

In vitro Biological

results on targets

Define activity class of

compounds

active / inactive on target

Target active

ratio calculation

3525

molecules

Chemical Similarity

Search 85%

For each compound and its given target:

Compound_Active_Ratio = (#Actives) / (#Actives +

#Inactives)

Active if Active_Ratio ≥ 0.8

Inactive if Active_Ratio ≤ 0.2

For each Target, count the number of Active and Inactive

molecules

Target_Active_Ratio = (#Actives - #Inactives) / (#Actives

+ #Inactives)

Target Fingeprint of A375 based on similar Compounds

(85% similarity)

#Molecules >= 20

Average Activity

Rate in RMC

Key Take Away

Using Reaxys API and Pipeline pilot an automated process was set up to

establish a target FingerPrint for disease specific cell lines.

Reaxys Medicinal chemistry provides High Quality data to identify

pharmacological targets involved in phenotypic screening and understand the

molecular mechanisms of action of drugs (MMOA).







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Reaxys API

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Customer problem

• In drug development, we need to screen large chemical libraries to identify compounds with desired properties

• Instead of experimentally testing all compounds, an in-silico pre-screening is usually performed

• For the in-silico pre-screening process, we need to have powerful predictive models in hand

• Such models are constructed using experimentally derived data

The Elsevier solution

• Reaxys is the most comprehensive source for experientally derived information on chemical compounds

• This information can be used to construct high-performance predictive models

• The best way to retrieve the required information from Reaxys is using the Reaxys API which can be accessed from Knime or PipelinePilot

• Elsevier provides readily usable Knime and PipelinePilot components

Your problem… and our solution

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Building a QSAR model for α-Chymotrypsin activity

Using KNIME

For training the

model, we retrieved

~ 5.000 quantitative

bioactivity data

points for “Alpha

Chymotrypsin”

extracted from RMC

using the API

With an error rate of

0.2 the model

predicts for new

compounds if they

are active or not







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Reaxys API

Health & Medicine

Reaxys rmc unified platform_ webinar_