Webinar : Predicting Pharmacology and Safety Profiles with AurPASS

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Webinar by Aureus Sciences : "Predicting Pharmacology and Safety Profiles with AurPASS"

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Webinar : Predicting Pharmacology and Safety Profiles with AurPASS

  1. 1. Make the right decisionswith data youcan trust<br />
  2. 2. Instructionsforattendees<br />Due to the High number of attendees: <br />Before starting <br />Use your full name for Identification<br />We will mute your microphone during the webinar<br />During the webinar<br />Use the chat for questions<br />At the End <br />You can ask your questions orally<br />We will answer them either in the webinar session or later on by email.<br />07/04/2011<br />2<br />AurPASS<br />
  3. 3. PredictingPharmacology & Safety Profiles Using AurPASS<br />
  4. 4. Agenda<br />Aureus Sciences<br />Knowledge-Based Pharmacological Profiling<br />AurPROFILER: visualizing the Known <br />Filling the Gap using AurPASS to Predict the Unknown<br />AurPASS<br />PASS Predictive Algorithm<br />Added Value of AurSCOPE Knowledgebases<br />AurPASS Ion Channels & AurPASS Kinase<br />Live Demo: Applications for Safety Pharmacology Assessment<br />Discussion<br />07/04/2011<br />4<br />AurPASS<br />
  5. 5. Aureus at a Glance<br />Aureus is a knowledge and information management solutions provider for the Life Science industry located in Paris. <br />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.<br />Aureus employees 20 highlevelscientists (PhD and MSc) includingscientific and IT project leaders and documentation analysts<br />Aureus is funded by institutional investors like CDC Entreprise, AXA Provate Equity, OTC Asset Management and is part of the OSEO Excellence innovation team.<br />07/04/2011<br />5<br />AurPASS<br />
  6. 6. Aureus Approach<br />
  7. 7. AurSCOPE Knowledge Databases Target Pharmacological Space<br />2002<br />2004<br />2005<br />2008<br />2009<br />Nuclear Receptor<br />Protease<br />GPCR<br />Kinase<br />Ion Channel<br />Publications<br />Analyzed<br /> 13,303 including <br /> 6,966 patents<br />32,588 including <br />15,281 patents<br />4,668 including<br />2,484 patents<br />4,226 including<br />1,060 patents<br />10,206 including <br />6,806 patents<br />Biological<br />Activities<br />275,044<br />418,202<br />186,047<br />1,243,793<br />670,407<br />98,439<br />354,323<br />146,426<br />Unique <br />Ligands<br />124,313<br />48,500<br />Biological<br />Protocols<br />Aureus Terminology, Glossary, Thesaurii<br />>70 glossaries and dictionaries, 5-10 taxonomies, >65,000 terms and synonyms, IUPHAR, SwissPROT<br />07/04/2011<br />7<br />AurPASS<br />
  8. 8. AurSCOPE Knowledge Databases ADME/DDI & TOX<br />hERG<br />ADME/DDI<br />Hepatotox<br />12,165 including<br />200 FDA Reviews<br />Publications<br />Analyzed<br />2,291 including <br />547 Patents<br />3,600 Articles (67 patents, 75 FDA Studies)<br />70,035<br />Biological<br />Activities<br />33,210<br />462,785<br />Unique <br />Ligands<br />13,887<br />34,872 compounds <br />including 4,050 metabolites<br />845 Drugs<br />Biological<br />Protocols<br />Aureus Terminology, Glossary, Thesaurii<br />>100 glossaries and dictionaries, 5-10 taxonomies, >65,000 terms and synonyms, IUPHAR, SwissPROT<br />07/04/2011<br />8<br />AurPASS<br />
  9. 9. Make The Right Decision With Data You Can Trust<br />AurPROFILER<br />Data analysis & Navigation<br />Medicinal Chemistry Space<br />AurQUEST<br />Data mining<br />AurPASS<br />Predict Biological <br />Activities Spectra<br />DDI Predict<br />AurPASS<br />
  10. 10. AurPROFILER – Pharmacology Profiles of 74 Drugs (Big Pharma)<br />07/04/2011<br />10<br />AurPASS<br />
  11. 11. Filling the Gap Using AurPASS in silicoPredictions<br />07/04/2011<br />11<br />Aureus<br />Prediction of<br />Activity<br />Spectra for<br />Substances<br />AurPASS<br />
  12. 12. Predicting New Activities on Selectedbig Pharma Drugs<br />277 Ion Channels<br />86SelectedMoleculesfrom MERCK<br />07/04/2011<br />12<br />AurPASS<br />
  13. 13. Over Forty Publications with Independent Confirmation of PASS Predictions…<br />07/04/2011<br />13<br />AurPASS<br />
  14. 14. AurPASS<br />Training Set<br />SAR data extracted from individual AurSCOPE databases<br />Chemical & biological post-processing<br />Chemical Structures<br />Multilevel Neighborhoods of Atoms (MNA) descriptors<br />SAR Base<br />Diverse chemical structures and associated MNA descriptors<br />Associated qualitative biologicalactivity types<br />Prediction Algorithm<br />PASS Bayesian approach<br />Models Validation<br />LOO and L20%O validations<br />External dataset<br />07/04/2011<br />14<br />AurPASS<br />
  15. 15. Training Set<br />SAR data extracted from individual AurSCOPEknowledgebases<br />Chemical & biological post-processing<br />Chemical Structures<br />Multilevel Neighborhoods of Atoms (MNA) descriptors<br />SAR Base<br />Diverse chemical structures and associated MNA descriptors<br />Associated qualitative biologicalactivity types<br />Prediction Algorithm<br />PASS Bayesian approach<br />Models Validation<br />LOO and L20%O validations<br />External dataset<br />AurPASS<br />07/04/2011<br />15<br />AurPASS<br />
  16. 16. Added Value of Structured Data for Predictive Models<br />07/04/2011<br />16<br />ChemicalProcessing<br /><ul><li> No Peptides
  17. 17. No mixtures
  18. 18. Inorganic and metalo-organicremoval
  19. 19. Charge standardization</li></ul>BiologicalProcessing<br /><ul><li>Biologicalprotocols
  20. 20. Target hierarchy
  21. 21. Target species, celllinesand tissues
  22. 22. Biologicalactivityparameters
  23. 23. Ligand action on target</li></ul>AurPASS<br />
  24. 24. 07/04/2011<br />17<br />AssigningActivity Classes<br />AurPASSActivity Types<br />AurPASS<br />
  25. 25. Multilevel Neighbourhoods of Atoms (MNA) descriptors are generated on the basis of the connection table of 2D molecular structures (including hydrogens)<br />Bond types are not specified<br />MNA are generated as recursively defined <br /><ul><li>Zero level MNA descriptor for each atom is the atom type itself;
  26. 26. The first level MNA descriptor include the atom’s zero level descriptors and zero-level descriptors of its neighboring atoms sorted lexicographically … </li></ul> Encoding Chemical Structures: MNA Descriptors<br />07/04/2011<br />18<br />MNA descriptorsdictionary<br />AurPASS<br />
  27. 27. Encoding Chemical Structures: MNA Descriptors<br />07/04/2011<br />19<br /> MNA/2<br />C(C(CC-H)C(CC-C)-H(C))<br />C(C(CC-H)C(CN-H)-H(C))<br />C(C(CC-H)C(CN-H)-C(C-O-O))<br />C(C(CC-H)N(CC)-H(C))<br />C(C(CC-C)N(CC)-H(C))<br />N(C(CN-H)C(CN-H))<br />-H(C(CC-H))<br />-H(C(CN-H))<br />-H(-O(-H-C))<br />-C(C(CC-C)-O(-H-C)-O(-C))<br />-O(-H(-O)-C(C-O-O))<br />-O(-C(C-O-O))<br />MNA/0: C<br />MNA/1: C(CN-H)<br />MNA/2: C(C(CC-H)N(CC)-H(C))<br />AurPASS<br />
  28. 28. PASS Approach for Biological Activity Prediction<br />07/04/2011<br />20<br />According to the Bayes' theorem, the probability P(A|S) that the compound S has activity (or inactivity) A, equals to:<br />P(A|S) = P(S|A)•P(A)/P(S)<br />If the descriptors of organic compound D1, ..., Dm are independent, then:<br />P(S|A) = P(D1, ..., Dm|A) = ПiP(Di|A)<br />P(A)andP(A|Di)are calculated as sums through all compounds of the training set:<br />AurPASS<br />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.<br />
  29. 29. The list of activities which are probable for a particular compound with the estimates for each activity of :<br />Pa: probability to be active<br />Pi: probability to be inactive<br />Pa and Pi are calculated independently: Pa + Pi  1<br />Pa (Pi) can be considered as the probability of the compound belonging to classes of active (inactive) compounds<br /> PASS Approach for Biological Activity Prediction<br />07/04/2011<br />21<br />AurPASS<br />
  30. 30. AurPASS Ion Channels – Version 1.2.1<br />07/04/2011<br />22<br />Training Set<br /> 47 938 molecules<br /> 517 Activity Types <br />Mean Accuracy of Prediction: 98% <br />Test Set<br /> 2244 molecules<br /> 113 Activity types <br />Mean Accuracy of Prediction: 90%<br />AurPASS<br />
  31. 31. PredictionAccuracyusingAurPASS<br />
  32. 32. How AurPASS Works?<br />07/04/2011<br />24<br />Ion Channels<br />Kinases<br />GPCRs<br />Proteases<br />NuclearReceptors<br />Off-Targets<br />AurPASS<br />
  33. 33. AurPASS Ion Channels<br />07/04/2011<br />25<br />Live Demo<br />AurPASS<br />
  34. 34. AurPASS Product Line<br />07/04/2011<br />26<br />AurPASS<br />Application Note and Evaluation Software available Under Request<br /> May, 2011<br />AurPASS Off-Targets<br />AurPASS GPCR<br />AurPASS Nuclear Receptors<br />
  35. 35. Thank you for your attention<br />Aureus Sciences<br />174, Quai de Jemmapes <br />75010 Paris, FRANCE<br />www.aureus-pharma.com<br />07/04/2011<br />27<br />AurPASS<br />

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