1. Ignasi Belda, PhD
CEO
1st of February 2013
Jornada TOX®

2. Business lines
Intelligent 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 Areas
Type of targets

6. Determination of mechanism of action
Computer-aided Hit to Lead optimization
ADME/Tox prediction
Solving physicochemical problems
Extension of patent protection
Identification of new active compounds Drug Reprofiling
Determination of inhibitors Identification of back-ups
Identification of off-targets
Selectivity Studies

7. Molecular Dynamics Allosterics
Pharmacophor Modeling Prof. Alejandro Pankovich, Xavier Daura
Universitat Autònoma de Barcelona
Bio-informatic tools

8. PREDICTIVE
TOXICOLOGY/PHARMACOLOGY
Initiatives
Computational Toxicology Research Program (CompTox)
USA – environmental protection agency (http://www.epa.gov/heasd/edrb/comptox.html)
Predictive Toxicology
Europe – joint research center (http://ihcp.jrc.ec.europa.eu/our_labs/predictive_toxicology)
Computational toxicology at the European Commission's Joint Research Centre
Europe 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 European
Commission's 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 Function
Biological molecules as Sugars, DNA & Proteins
3-D structure
Primary sequence

10. Molecules with measured Cardiovascular Toxicity
3-D structure
2-D structure
Cardiovascular Toxicity
hERG & KCQN1 is responsible for Cardiovascular Toxicity
3-D structure

11. DISCOVERY PROJECTS
Receptor-based Virtual Screening
 Determination of inhibitors
Only receptor’s information is needed
 Hit to lead optimization
 Determines Binding Energy and Binding  Design more potent ligands
Constants Kd (mM, μM and nM)  Drug Reprofiling
Obtains Structural Data  Determination of MOA
High throughput screening
Based on Docking
Docking 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 RELATIONSHIP
Descriptors
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 MODEL
Descriptors
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
PCA
1 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
5,8
hERG & KCQN1

16. Drug Reprofiling Macromolecular Modeling
Hit to Lead
Determination of MOA
DB and Collaborative Tools Management
Hit Identification
Training on Macromolecular Modeling

17. Parc Científic de Barcelona Technologie Park Heidelberg
C/ Baldiri Reixac, 4-8 Im Neuenheimer Feld 582
08028 Barcelona 69120 Heidelberg
Spain Germany
T: +34 934 034 551 T: +49 (0) 6221 5025716
BioPark USA
Broadwater Road, Welwyn Garden City 185 Alewife Brook Parkway, #410
Hertfordshire AL7 3AX, United Kingdom Cambridge, MA 02138
T: +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