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  • 1. Ignasi Belda, PhD CEO1st of February 2013 Jornada TOX®
  • 2. Business linesIntelligent Discovery We carry out computational chemistry projects using our self- developed and third party technologies for drug discovery, cosmetics and nutraceuticals.Intelligent Software We offer advanced software development solutions for companies and institutions working in life sciences.Intelligent Knowledge We commercialize third party software application for knowledge management focusing on life sciences.
  • 3. Offices: Clients: Markets: • Pharmaceutical companies • Europe Barcelona Science Park Spain • Biotech companies • USA • Life Sciences institutions: • South America: Mexico, Brazil Hospitals, Universities, • Asia: Korea Technologie Park Heidelberg Technological Transfer Offices Germany Collaborations: Synthesis and Medicinal Chemistry Software Partners BioPark Hertfordshire United Kingdom 185 Alewife Brook Parkway Cambridge, MA USA
  • 4. > 100 Research Projects in 5 years Type of Projects
  • 5. > 100 Research Projects in 5 years Therapeutic AreasType of targets
  • 6. Determination of mechanism of action Computer-aided Hit to Lead optimization ADME/Tox prediction Solving physicochemical problems Extension of patent protectionIdentification of new active compounds Drug ReprofilingDetermination of inhibitors Identification of back-upsIdentification of off-targetsSelectivity Studies
  • 7. Molecular Dynamics AllostericsPharmacophor Modeling Prof. Alejandro Pankovich, Xavier Daura Universitat Autònoma de BarcelonaBio-informatic tools
  • 8. PREDICTIVE TOXICOLOGY/PHARMACOLOGYInitiativesComputational Toxicology Research Program (CompTox)USA – environmental protection agency (http://www.epa.gov/heasd/edrb/comptox.html)Predictive ToxicologyEurope – joint research center (http://ihcp.jrc.ec.europa.eu/our_labs/predictive_toxicology)Computational toxicology at the European Commissions Joint Research CentreEurope Union The methods and tools of computational toxicology form an essential and integrating pillar in the new paradigm of predictive toxicology, which seeks to develop more efficient and effective means of assessing chemical toxicity, while also reducing animal testing.**Mostrag-Szlichtyng A., Zaldivar Comenges JM, Worth AP. Computational toxicology at the EuropeanCommissions Joint Research Centre (2010) Expert Opin Drug Metab Toxicol, 6(7), 785-92.
  • 9. Molecules used as pharmaceuticals/active ingredients 3-D structure 2-D structure Biological FunctionBiological molecules as Sugars, DNA & Proteins 3-D structure Primary sequence
  • 10. Molecules with measured Cardiovascular Toxicity 3-D structure 2-D structure Cardiovascular ToxicityhERG & KCQN1 is responsible for Cardiovascular Toxicity 3-D structure
  • 11. DISCOVERY PROJECTSReceptor-based Virtual Screening  Determination of inhibitorsOnly receptor’s information is needed  Hit to lead optimization Determines Binding Energy and Binding  Design more potent ligandsConstants Kd (mM, μM and nM)  Drug ReprofilingObtains Structural Data  Determination of MOAHigh throughput screeningBased on DockingDocking algorithms based on Vina1 & Autodock 4.22 Binding Energies & Binding Modes Biological Target + Molecules Receptor Active  -13kcal/mol  Expected binding mode HMG-CoA Reductase  -6kcal/mol Inactive  Other binding mode 1 O Trott, AJ Olson J Comput Chem. 2010, 31, 455–461. 2 G Morris, D Goodsell, R Halliday, R Huey, W Hart, R Belew, A Olson J Comput Chem. 1998, 19, 1639–62.
  • 12. QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPDescriptors MW 423 358 284 … hERG 3,1 6,7 4,3 … RotBond 7 3 0 … PSA 110 60 160 … …. … … … … Mathematical tools PLS Pred. Func. = w1Des1 + w2Des2 + … Model hERG 6.0
  • 13. VALIDITY OF QSAR MODELDescriptors MW 423 358 284 … IC50 1 16 4 … RotBond 7 3 0 … PSA 110 60 160 … …. … … … … Mathematical tools PLS IC50(pred) Model IC50(pred)
  • 14. Based on different descriptors & algorithms Descriptors: Property based MW 423 358 284 … RotBond 7 3 0 … PSA 110 60 160 … …. … … … … Circular fingerprints (ECFP2, Molprint2D3) Fragments (Lingo4) 2D molecular fields (GRIND1) Mathematical tools: PLS SVM Bayesian PCA1 M Pastor, G Cruciani, I McLay, S Pickett, S Clementi J. Med. Chem. 2000, 43, 3233-43.2 D Rogers, M Hahn J. Chem. Inf. Model. 2010, 50, 742-54.3 A Bender, HY Mussa, RC Glen J. Chem. Inf. Comput. Sci. 2003, 44, 170-88.4 D Vidal, M Thormann, M Pons J. Chem. Inf. Model. 2005, 45, 386-93.
  • 15. 6,0 6,7 4,3 3,1 4,5 5,8 5,8hERG & KCQN1
  • 16. Drug Reprofiling Macromolecular Modeling Hit to LeadDetermination of MOA DB and Collaborative Tools ManagementHit Identification Training on Macromolecular Modeling
  • 17. Parc Científic de Barcelona Technologie Park HeidelbergC/ Baldiri Reixac, 4-8 Im Neuenheimer Feld 58208028 Barcelona 69120 HeidelbergSpain GermanyT: +34 934 034 551 T: +49 (0) 6221 5025716BioPark USABroadwater Road, Welwyn Garden City 185 Alewife Brook Parkway, #410Hertfordshire AL7 3AX, United Kingdom Cambridge, MA 02138T: +44 (0) 1707 356100 Sales & Business Development Department Jascha Blobel, PhD jblobel@intelligentpharma.com Anna Serra, PhD aserra@intelligentpharma.com Irene Meliciani, PhD imeliciani@intelligentpharma.com www.intelligentpharma.com