Webinar : Predicting Pharmacology and Safety Profiles with AurPASS

Make the right decisionswith data youcan trust

Instructionsforattendees
Due to the High number of attendees:
Before starting
Use your full name for Identification
We will mute your microphone during the webinar
During the webinar
Use the chat for questions
At the End
You can ask your questions orally
We will answer them either in the webinar session or later on by email.
07/04/2011
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AurPASS

PredictingPharmacology & Safety Profiles Using AurPASS

Agenda
Aureus Sciences
Knowledge-Based Pharmacological Profiling
AurPROFILER: visualizing the Known
Filling the Gap using AurPASS to Predict the Unknown
AurPASS
PASS Predictive Algorithm
Added Value of AurSCOPE Knowledgebases
AurPASS Ion Channels & AurPASS Kinase
Live Demo: Applications for Safety Pharmacology Assessment
Discussion
07/04/2011
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AurPASS

Aureus at a Glance
Aureus is a knowledge and information management solutions provider for the Life Science industry located in Paris.
Over the past 10 years the company has developed a unique knowledge production platform which stores, indexes and organizes critical chemical and bioactivity information including experimental in vitro and in vivo protocols from the public or private literature.
Aureus employees 20 highlevelscientists (PhD and MSc) includingscientific and IT project leaders and documentation analysts
Aureus is funded by institutional investors like CDC Entreprise, AXA Provate Equity, OTC Asset Management and is part of the OSEO Excellence innovation team.
07/04/2011
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AurPASS

Aureus Approach

AurSCOPE Knowledge Databases Target Pharmacological Space
2002
2004
2005
2008
2009
Nuclear Receptor
Protease
GPCR
Kinase
Ion Channel
Publications
Analyzed
13,303 including
6,966 patents
32,588 including
15,281 patents
4,668 including
2,484 patents
4,226 including
1,060 patents
10,206 including
6,806 patents
Biological
Activities
275,044
418,202
186,047
1,243,793
670,407
98,439
354,323
146,426
Unique
Ligands
124,313
48,500
Biological
Protocols
Aureus Terminology, Glossary, Thesaurii
>70 glossaries and dictionaries, 5-10 taxonomies, >65,000 terms and synonyms, IUPHAR, SwissPROT
07/04/2011
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AurPASS

AurSCOPE Knowledge Databases ADME/DDI & TOX
hERG
ADME/DDI
Hepatotox
12,165 including
200 FDA Reviews
Publications
Analyzed
2,291 including
547 Patents
3,600 Articles (67 patents, 75 FDA Studies)
70,035
Biological
Activities
33,210
462,785
Unique
Ligands
13,887
34,872 compounds
including 4,050 metabolites
845 Drugs
Biological
Protocols
Aureus Terminology, Glossary, Thesaurii
>100 glossaries and dictionaries, 5-10 taxonomies, >65,000 terms and synonyms, IUPHAR, SwissPROT
07/04/2011
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AurPASS

Make The Right Decision With Data You Can Trust
AurPROFILER
Data analysis & Navigation
Medicinal Chemistry Space
AurQUEST
Data mining
AurPASS
Predict Biological
Activities Spectra
DDI Predict
AurPASS

AurPROFILER – Pharmacology Profiles of 74 Drugs (Big Pharma)
07/04/2011
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AurPASS

Filling the Gap Using AurPASS in silicoPredictions
07/04/2011
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Aureus
Prediction of
Activity
Spectra for
Substances
AurPASS

Predicting New Activities on Selectedbig Pharma Drugs
277 Ion Channels
86SelectedMoleculesfrom MERCK
07/04/2011
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AurPASS

Over Forty Publications with Independent Confirmation of PASS Predictions…
07/04/2011
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AurPASS

AurPASS
Training Set
SAR data extracted from individual AurSCOPE databases
Chemical & biological post-processing
Chemical Structures
Multilevel Neighborhoods of Atoms (MNA) descriptors
SAR Base
Diverse chemical structures and associated MNA descriptors
Associated qualitative biologicalactivity types
Prediction Algorithm
PASS Bayesian approach
Models Validation
LOO and L20%O validations
External dataset
07/04/2011
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AurPASS

Training Set
SAR data extracted from individual AurSCOPEknowledgebases
Chemical & biological post-processing
Chemical Structures
Multilevel Neighborhoods of Atoms (MNA) descriptors
SAR Base
Diverse chemical structures and associated MNA descriptors
Associated qualitative biologicalactivity types
Prediction Algorithm
PASS Bayesian approach
Models Validation
LOO and L20%O validations
External dataset
AurPASS
07/04/2011
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AurPASS

Added Value of Structured Data for Predictive Models
07/04/2011
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ChemicalProcessing
No Peptides
No mixtures
Inorganic and metalo-organicremoval
Charge standardization
BiologicalProcessing
Biologicalprotocols
Target hierarchy
Target species, celllinesand tissues
Biologicalactivityparameters
Ligand action on target
AurPASS

07/04/2011
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AssigningActivity Classes
AurPASSActivity Types
AurPASS

Multilevel Neighbourhoods of Atoms (MNA) descriptors are generated on the basis of the connection table of 2D molecular structures (including hydrogens)
Bond types are not specified
MNA are generated as recursively defined
Zero level MNA descriptor for each atom is the atom type itself;
The first level MNA descriptor include the atom’s zero level descriptors and zero-level descriptors of its neighboring atoms sorted lexicographically …
Encoding Chemical Structures: MNA Descriptors
07/04/2011
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MNA descriptorsdictionary
AurPASS

Encoding Chemical Structures: MNA Descriptors
07/04/2011
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MNA/2
C(C(CC-H)C(CC-C)-H(C))
C(C(CC-H)C(CN-H)-H(C))
C(C(CC-H)C(CN-H)-C(C-O-O))
C(C(CC-H)N(CC)-H(C))
C(C(CC-C)N(CC)-H(C))
N(C(CN-H)C(CN-H))
-H(C(CC-H))
-H(C(CN-H))
-H(-O(-H-C))
-C(C(CC-C)-O(-H-C)-O(-C))
-O(-H(-O)-C(C-O-O))
-O(-C(C-O-O))
MNA/0: C
MNA/1: C(CN-H)
MNA/2: C(C(CC-H)N(CC)-H(C))
AurPASS

PASS Approach for Biological Activity Prediction
07/04/2011
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According to the Bayes' theorem, the probability P(A|S) that the compound S has activity (or inactivity) A, equals to:
P(A|S) = P(S|A)•P(A)/P(S)
If the descriptors of organic compound D1, ..., Dm are independent, then:
P(S|A) = P(D1, ..., Dm|A) = ПiP(Di|A)
P(A)andP(A|Di)are calculated as sums through all compounds of the training set:
AurPASS
Filimonov D.A., Poroikov V.V. (2008). Probabilistic Approach in Virtual Screening. In: Chemoinformatics Approaches to Virtual Screening. Alexander Varnek and Alexander Tropsha, Eds. RSC Publishing.

The list of activities which are probable for a particular compound with the estimates for each activity of :
Pa: probability to be active
Pi: probability to be inactive
Pa and Pi are calculated independently: Pa + Pi  1
Pa (Pi) can be considered as the probability of the compound belonging to classes of active (inactive) compounds
PASS Approach for Biological Activity Prediction
07/04/2011
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AurPASS

AurPASS Ion Channels – Version 1.2.1
07/04/2011
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Training Set
47 938 molecules
517 Activity Types
Mean Accuracy of Prediction: 98%
Test Set
2244 molecules
113 Activity types
Mean Accuracy of Prediction: 90%
AurPASS

PredictionAccuracyusingAurPASS

How AurPASS Works?
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Ion Channels
Kinases
GPCRs
Proteases
NuclearReceptors
Off-Targets
AurPASS

AurPASS Ion Channels
07/04/2011
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Live Demo
AurPASS

AurPASS Product Line
07/04/2011
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AurPASS
Application Note and Evaluation Software available Under Request
May, 2011
AurPASS Off-Targets
AurPASS GPCR
AurPASS Nuclear Receptors

Thank you for your attention
Aureus Sciences
174, Quai de Jemmapes
75010 Paris, FRANCE
www.aureus-pharma.com
07/04/2011
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AurPASS

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Webinar : Predicting Pharmacology and Safety Profiles with AurPASS

by Aureus Sciences on Apr 07, 2011

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