Ignasi Belda, PhD CEO1st of February 2013 Jornada TOX®
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.
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
> 100 Research Projects in 5 years Type of Projects
> 100 Research Projects in 5 years Therapeutic AreasType of targets
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
Molecular Dynamics AllostericsPharmacophor Modeling Prof. Alejandro Pankovich, Xavier Daura Universitat Autònoma de BarcelonaBio-informatic tools
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.
Molecules used as pharmaceuticals/active ingredients 3-D structure 2-D structure Biological FunctionBiological molecules as Sugars, DNA & Proteins 3-D structure Primary sequence
Molecules with measured Cardiovascular Toxicity 3-D structure 2-D structure Cardiovascular ToxicityhERG & KCQN1 is responsible for Cardiovascular Toxicity 3-D structure
DISCOVERY PROJECTSReceptor-based Virtual Screening Determination of inhibitorsOnly receptor’s information is needed Hit to lead optimization Determines Binding Energy and Binding Design more potent ligandsConstants Kd (mM, μM and nM) Drug ReprofilingObtains Structural Data Determination of MOAHigh 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.
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.