Discovering drugs (I. Belda)

Ignasi Belda, PhD CEO

1st of February 2013

Jornada TOX®

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.

Collaborations:

Synthesis and Medicinal Chemistry Software Partners

Barcelona Science Park Spain

Technologie Park Heidelberg Germany

Offices: Clients:

• Pharmaceutical companies

• Biotech companies

• Life Sciences institutions:

Hospitals, Universities,

Technological Transfer Offices

BioPark Hertfordshire United Kingdom

185 Alewife Brook Parkway Cambridge, MA USA

Markets:

• Europe

• USA

• South America: Mexico, Brazil

• Asia: Korea

Type of Projects

> 100 Research Projects in 5 years

Type of targets

> 100 Research Projects in 5 years

Therapeutic Areas

Identification of new active compounds Determination of inhibitors Identification of off-targets Selectivity Studies

Drug Reprofiling Identification of back-ups

Determination of mechanism of action

Computer-aided Hit to Lead optimization ADME/Tox prediction Solving physicochemical problems

Extension of patent protection

Allosterics Prof. Alejandro Pankovich, Xavier Daura Universitat Autònoma de Barcelona

Molecular Dynamics Pharmacophor Modeling Bio-informatic tools

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.

Biological molecules as Sugars, DNA & Proteins

Molecules used as pharmaceuticals/active ingredients

3-D structure Primary sequence

3-D structure 2-D structure

Biological Function

hERG & KCQN1 is responsible for Cardiovascular Toxicity

Molecules with measured Cardiovascular Toxicity

3-D structure

3-D structure 2-D structure

Cardiovascular Toxicity

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.

Receptor-based Virtual Screening

Only receptor’s information is needed

Determines Binding Energy and Binding Constants Kd (mM, μM and nM)

Obtains Structural Data

High throughput screening

Based on Docking Docking algorithms based on Vina1 & Autodock 4.22

Binding Energies & Binding Modes

Determination of inhibitors Hit to lead optimization Design more potent ligands Drug Reprofiling Determination of MOA

DISCOVERY PROJECTS

Biological Target Receptor

+ Molecules

HMG-CoA Reductase

Active

Inactive

-13kcal/mol Expected binding mode -6kcal/mol Other binding mode

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP

423 358 284 …

7 3 0 …

110 60 160 …

…. … … … …

Descriptors

MW

RotBond

PSA

Mathematical tools

PLS

hERG 3,1 6,7 4,3 …

Model

hERG 6.0

Pred. Func. = w1Des1 + w2Des2 + …

VALIDITY OF QSAR MODEL

423 358 284 …

7 3 0 …

110 60 160 …

…. … … … …

Descriptors

MW

RotBond

PSA

Mathematical tools

PLS

IC50 1 16 4 …

Model

IC

50(p

red

)

IC50(pred)

Based on different descriptors & algorithms Descriptors: Property based Circular fingerprints (ECFP2, Molprint2D3) Fragments (Lingo4) 2D molecular fields (GRIND1) Mathematical tools: PLS SVM Bayesian PCA

423 358 284 …

7 3 0 …

110 60 160 …

…. … … … …

MW

RotBond

PSA

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.

3,1

5,8

6,7

4,5

4,3

5,8

hERG & KCQN1

6,0

Drug Reprofiling Macromolecular Modeling

Determination of MOA

Hit to Lead

Hit Identification

DB and Collaborative Tools Management

Training on Macromolecular Modeling

Parc Científic de Barcelona C/ Baldiri Reixac, 4-8 08028 Barcelona Spain T: +34 934 034 551

Sales & Business Development Department Jascha Blobel, PhD jblobel@intelligentpharma.com Anna Serra, PhD aserra@intelligentpharma.com Irene Meliciani, PhD imeliciani@intelligentpharma.com

Technologie Park Heidelberg Im Neuenheimer Feld 582 69120 Heidelberg Germany T: +49 (0) 6221 5025716

www.intelligentpharma.com

BioPark Broadwater Road, Welwyn Garden City Hertfordshire AL7 3AX, United Kingdom T: +44 (0) 1707 356100

USA 185 Alewife Brook Parkway, #410 Cambridge, MA 02138